Targeting AI

How humans and AI can co-exist productively in the workplace

Tue, 02 Jun 2026 08:00:00 -0300

The future of work is humans and AI collaborating. Despite developments in which technology companies blame AI for the decision to lay off large numbers of workers, Nikhil Krishnan of C3 AI says there is still a need for a human in the loop. On the latest Targeting AI podcast from AI Business, Krishnan said the future of work will involve a pyramid-type system. At the bottom, the AI will automate certain processes, but at the middle and top levels, there should be collaboration between the human and AI.

Featuring: Nikhil Krishnan, CTO at C3 AI

In this episode, we discuss:

The pyramid structure of the future of work
The need for the human in the loop
The industry that is currently not seeing AI replacing humans
C3 AI’s differentiation from hyperscalers and competitors
The AI boom versus bubble debate
The importance of operational efficiency in any economic environment

To learn more about C3 AI and the future of work, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

C3 AI Launches C3 Code for Businesses Seeking Domain Expertise

Phenom’s Acquisition: AI, Automation and the Future of Work

Fourth Industrial Revolution: How AI Agents Are Transforming the Future of Work

Real-Time Intelligence in the Age of Generative AI: Insights from Dataminr

Tue, 19 May 2026 08:00:00 -0300

For many organizations, the advent of AI has necessitated a transition from a human-centric to an intelligence- or even agentic-based approach. Real-time event, threat and risk vendor Dataminr began its transition to intelligent automation in 2018, marking the start of its AI journey. Since then, the vendor has transitioned to a more agentic and generative AI approach. In this interview, Joel Tetreault of Dataminr discusses the evolution of real time intelligence platforms, the integration of generative AI and agentic AI, and the importance of data strategy in AI development.

Featuring: Joel Tetreault, chief AI officer, Dataminr

In this episode, we discuss the:

Evolution of Dataminr’s platform pre- and post-generative AI
Integration of GPT and modern models in real-time data processing
Role of data strategy and domain-specific models in AI effectiveness
Use of multimodal AI for security and threat detection
Impact of agentic AI and future trends in cybersecurity

To learn more about AI search, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Top 10 Cybersecurity predictions for 2026

How to enhance OSINT investigations using AI

Company Using AI to Strengthen Cybersecurity Raises $50M

LinkedIn’s Change to its Search Engine Could Affect Your Job Search

Tue, 05 May 2026 08:00:00 -0300

Most people looking for a job usually spend hours scouring job search engines and LinkedIn. However, the professional network has changed the way its search engine works, shifting from a keyword-based, taxonomy-driven system to an AI-powered semantic search that understands natural language. In this podcast episode, Caleb Johnson of LinkedIn dives into how LinkedIn uses AI and large language models (LLMs) to revolutionize job search, improve search relevance, and ensure data privacy.

Featuring: Caleb Johnson, principal staff software engineer

In this episode, we cover:

AI-powered job search and semantic understanding
Use of LLMs and transformer architecture
Bias mitigation and fairness in AI systems
Data privacy and compliance in AI applications
Future directions: voice, visual search, and interactive AI

To learn more about AI search, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

AI PCs and chips: Their role in the enterprise

Tue, 21 Apr 2026 08:00:00 -0300

With the rise of generative AI and agentic AI, there has also been a push for AI PCs within the enterprise. Companies like Lenovo and Microsoft are providing enterprises with devices that help create these devices. However, there is no AI PC without AI chips. In this podcast, Michael Nordquist of chipmaker AMD discusses the evolution of AI chips and AMD's role in the rapidly changing AI landscape. He highlights the features of AI PCs, the impact of AI on enterprise efficiency, and AMD's strategy against competitors such as Nvidia.

Featuring: Michael Nordquist, corporate VP of product marketing, AMD

In this episode, we cover:

AMD's position as a key player in AI technology.
How AI PCs integrate NPUs for enhanced performance.
The need for vendors to focus on security when developing AI PCs.
How adoption of AI PCs is influenced by perceived value.
The future will see a blend of personal and enterprise AI agents.

To learn more about AI PCs, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

The Agentic AI Foundation and Linux Foundation on agentic AI

Tue, 07 Apr 2026 08:03:00 -0300

Agentic AI has grown rapidly in the past two years, and with that growth comes different tools that help agents work. Among those tools is the Model Context Protocol from Anthropic. In this episode of the Targeting AI podcast from AI Business, Jim Zemlin and Mazin Gilbert dive into the importance of agentic AI, the relevance of MCP, the nuances of openness in AI, and the responsibilities surrounding AI security and ethics. The conversation also touches on the future of personal agents and the evolving role of developers in the AI landscape, with the popularity of OpenClaw. This episode was recorded on-site in New York City last week at the MCP Developer Summit presented by the Agentic AI Foundation.

Featuring: Jim Zemlin, CEO of the Linux Foundation, Mazin Gilbert, executive director of the Agentic AI Foundation

In this episode, we cover how:

Agentic AI is crucial for driving information exchange and financial transactions.
Standardization is necessary as we move from experimentation to production in AI.
The Linux Foundation provides a neutral space for collaboration among tech companies.
Openness in AI includes varying degrees of access to data and models.
Ethical AI usage is a priority for the AI industry to prevent bias.
Developers' roles are shifting from coding to system architecture and security.
The future of AI will involve both open and closed data.

To learn more about Agentic AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

AI Co-Workers and the Future of Work

Tue, 24 Mar 2026 14:27:01 -0300

The future of work is continuing to change with AI, and many agree that AI co-workers are becoming part of everyday work. However, many enterprises still find it challenging to understand the various use cases for AI, the role AI can play in enhancing productivity, and the need to approach AI implementation thoughtfully, focusing on real problems rather than succumbing to FOMO. In this conversation on the Targeting AI podcast from AI Business, HP's Faisal Masud shares insights on the future of work and HP's commitment to integrating AI into its offerings.

Featuring: Faisal Masud, President, digital & lifecycle services, HP

In this episode, we cover how:

Consumers are more advanced in using AI than enterprises.
AI at the edge enhances privacy and security.
Enterprises need to understand specific use cases for AI.
How HP approaches its differentiation strategy.
ROI in AI projects should consider productivity and cost reduction.
AI should augment human capabilities, not replace them.
The future of work will involve AI as a co-worker.

To learn more about AI adoption, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Understanding Human Impact and Safety in AI

Tue, 10 Mar 2026 15:26:10 -0300

In a special episode of the Targeting AI podcast from AI Business, host Esther Shittu interviews Christopher Campbell of Lenovo about the challenges and considerations surrounding AI governance, emphasizing the importance of human impact, safety, and accountability. They explore the evolving perspectives on bias and hallucinations in AI, the role of hardware in AI development, and the implications of personal AI agents. The discussion highlights the importance of selecting the right AI partners, maintaining governance in hybrid AI environments, and addressing the complexities of shadow AI and AI governance sovereignty. The episode concludes with advice for organizations on effectively adopting AI governance practices. The podcast was recorded on-site at the Gartner Data & Analytics Summit in Orlando.

Featuring: Christopher Campbell, director of AI governance and global products and services security leader at Lenovo

In this episode, we cover how:

The human impact and safety of AI are paramount.
Trust in AI systems is essential for their success.
Bias and hallucination perspectives have matured over time.
Accountability in AI governance lies with leadership.
Choosing AI partners with aligned philosophies is crucial.
Governance standards apply equally to local and cloud models.
Shadow AI presents a complex challenge for organizations.
Sovereignty in AI gives regions more control over their data.
Understanding technology is key to effective AI adoption.
There is no one-size-fits-all approach to AI governance.

To learn more about AI governance, safety and sovereignty, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

How Capital One is building an AI-ready data ecosystem with creative talent

Mon, 09 Mar 2026 19:17:45 -0300

In this interview on the Targeting AI podcast from AI Business, Amy Lenander of financial services giant Capital One discusses the critical role of talent in building AI-ready data ecosystems. She explores how organizations can cultivate the right skills, develop foundational data platforms and use AI to drive business value. The interview was recorded on-site at the Gartner Data & Analytics Summit 2026 in Orlando.

Featuring Amy Lenander, chief data officer, Capital One

In this episode, we cover how:

Talent agility outweighs technical experience in AI success.
Organizations that develop learning agility and curiosity foster talent capable of navigating rapidly evolving AI landscapes.
Instead of hiring for a specific toolset, focus on candidates who demonstrate rapid learning, problem-solving, and collaboration—traits that enable mastery of new AI methods as they emerge.
Building a unified data ecosystem creates a competitive moat.
A well-designed data ecosystem, prioritized over immediate AI application, provides a robust foundation that supports all future data and AI initiatives.
Investing in governance, data trustworthiness, and accessibility shields organizations from fragmentation, enabling scalable innovation regardless of future technological shifts.
AI adoption is a cultural shift, not just a technology implementation.
Domain-specific data products enhance AI interpretability and trust.
Specialized data teams responsible for understanding business nuances ensure AI systems interpret data context correctly for strategic use.

To learn more about generative and agentic AI and AI-ready data ecosystems, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Navigating the generative AI landscape with two MIT profs

Tue, 03 Mar 2026 08:00:00 -0400

In this episode of the Targeting AI podcast from AI Business, hosts Shaun Sutner and Esther Shittu engage with Abel Sanchez and John Williams from MIT to discuss the evolving landscape of generative AI. The conversation covers the motivation behind their initiative, Gen AI Global, the dynamics of their professional relationship, and the societal implications of AI technologies. They explore concepts such as "vibe living," the energy demands of AI, and contrasting perspectives on AI's future, including the debate between optimists and skeptics. The episode concludes with a discussion on the sustainability of the AI boom and the importance of human involvement in an increasingly automated world.

Featuring: Abel Sanchez, a research scientist and executive director of MIT's Geospatial Data Center; and John Williams, professor of civil and environmental engineering at MIT and director of the Geospatial Data Center and Intelligent Engineering Systems laboratory at MIT.

In this episode, we cover how:

Learning is social; community enhances educational outcomes.

Generative AI is rapidly changing industries and education.

AI's impact on society is both exciting and concerning.

The relationship between Abel and John is built on trust and differing perspectives.

Generative AI can empower non-experts to achieve expert-level results.

Energy consumption for AI is a growing concern.

The future of AI models may involve new architectures beyond transformers.

Human intuition and emotion remain valuable in AI applications.

The AI boom is characterized by rapid adoption and innovation.

Organizations must adapt to integrate AI effectively.

To learn more about generative AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Gen AI Global

How much energy do data centers consume?

Debate Rages Over AI Bubble vs. Boom

Coinbase, crypto, blockchain and the outlook for digital payments

Tue, 17 Feb 2026 10:50:23 -0400

AI is changing digital payments, and Coinbase is trying to lead that change. Last year, the cryptocurrency exchange provider partnered with Cloudflare, AWS, Anthropic and others to create the x402 protocol, a standard that enables AI agents to make transactions online. In this conversation, Coinbase’s Dan Kim talks with Targeting AI hosts Esther Shittu and Shaun Sutner AI about how generative AI is critical in creating a new class of AI agents that can autonomously engage in trading and transactions.

Featuring: Dan Kim, vice president, head of digital asset listings & services at Coinbase

In this episode, we cover:

Coinbase's mission is economic freedom through cryptocurrency and blockchain.
AI is transforming software to be more intelligent and adaptive.
The X402 Foundation aims to standardize how payments are processed over the internet.
AI agents are becoming a new class of customers in the trading space.
Stablecoins are crucial for secure transactions between AI agents.

To learn more about generative and agentic AI and RPA, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

The Future of AI in Process Intelligence

Tue, 03 Feb 2026 08:15:00 -0400

In this episode of the Targeting AI podcast from AI Business, Manuel Haug, of Germany-based process mining vendor Celonis, discusses the intricacies of process mining and its integration with AI technologies. He explains how Celonis differentiates itself in the market, the evolution of its strategy in light of generative AI, and the practical applications of AI agents in various industries. Haug emphasizes the importance of operationalizing process mining findings and preparing for the future of work as the workforce ages. He also touches on the complementary nature of AI and traditional automation methods, such as RPA, and the need to capture organizational knowledge before it is lost.

Featuring: Manuel Haug, field CTO of Celonis

In this episode, we cover how:

Process mining connects to various IT systems to analyze business processes.
AI can improve and automate manual processes in companies.
AI agents can assist human teams in decision-making.
Operationalizing findings from process mining is crucial for improvement.
The aging workforce necessitates capturing knowledge effectively.
RPA and AI can coexist and complement each other in automation.
Understanding processes is foundational for effective AI implementation.
AI technology is becoming more reliable and powerful.
The future of work will involve a blend of AI and human oversight.

To learn more about generative and agentic AI and RPA, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

From Predictive to Agentic: The Future of AI in Sales

Tue, 20 Jan 2026 15:22:35 -0400

If most sales representatives spend nearly a quarter of their time on administrative tasks, they are losing opportunities to generate revenue and be productive in sales. This is why Eilon Reshef of AI sales platform vendor Gong sees AI technology as a supportive co-worker that can offload menial admin tasks from sales agents so they can focus on their new jobs. He shares insights into Gong's mission to enhance sales team productivity and the importance of data in AI applications.

Featuring: Eilon Reshef, co-founder and chief product officer, Gong

In this episode, we cover how:

AI's effectiveness is heavily dependent on the quality of data.
"Gong" symbolizes success in sales.
Agentic AI is about automating complex tasks intelligently.
Sales roles are evolving, not disappearing, due to AI.
The future of sales will involve more AI-driven insights.

To learn more about generative AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Google AI exec says data is the next phase of generative AI

Tue, 06 Jan 2026 08:15:00 -0400

At the start of the mass popularity phase of generative AI, large language models were the star of the show. Vendors released bigger and newer models. However, the conversation has recently shifted from considering big or small models to a deep focus on data. In this episode of the Targeting AI podcast from AI Business, Yasmeen Ahmad, of Google Cloud, discusses the transformative effect of generative AI on the data landscape. She emphasizes the importance of treating data as a product, the shift toward multimodal data, and the role of AI agents in enhancing data management and decision-making processes.

Featuring: Yasmeen Ahmad, managing director of product management for data and AI Cloud, Google Cloud

In this episode, we cover how:

The era of multimodal data is upon us, integrating various data types.
Agentic AI enhances the understanding of unstructured data.
Databases must evolve into cognitive reasoning engines for AI.
Gemini Enterprise provides a unified platform for AI and data.
Data security and responsibility are critical in AI deployment.

To learn more about the role data plays in generative AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Vertical AI Systems and Open Source Flexibility

Tue, 16 Dec 2025 08:00:00 -0400

Generative AI and agentic AI tools are only as good as the problem that they are used to solve. In some cases, using generic AI tools can help with non-specific issues. However, Raj Shukla, of enterprise AI platform vendor Symphony AI, says the future of AI technology will focus on vertical applications and open models. In this Targeting AI episode from AI Business, he emphasizes that open source models provide flexibility and the ability to fine-tune for specific use cases.

Featuring: Raj Shukla, CTO, Symphony AI

In this episode, we cover:

Symphony's AI mission of bringing AI technology to legacy industries that may struggle with adoption.
A vertical approach combines predictive, generative and agentic AI to address specific challenges.
The move in vertical areas from a traditional rule-based approach to a more dynamic, non-deterministic tool.
AI applications in these verticals can significantly improve operational efficiencies and strategic decision-making.

To learn more about vertical AI applications, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Small Language Models Gaining Ground at Enterprises

Vertical AI agents explained: The future of enterprise tech

AI21 releases open source tiny language model

The Impact of the "One Rule" AI Executive Order

Mon, 15 Dec 2025 14:20:45 -0400

President Donald Trump signed an executive order last week that looks to override AI state laws in favor of a national policy. Titled "Ensuring a National Policy Framework for Artificial Intelligence," it directs the Department of Justice to establish an AI Litigation Task Force and challenge "cumbersome" state laws. It also asks the Secretary of Commerce to consider withholding federal funds from states found to have restrictive AI laws. In this podcast, Michael Bennett discusses what the EO means for states like New York and California, which already have established laws in place, and how they might respond.

Featuring: Michael Bennett, Associate Vice Chancellor for Data Science and Artificial Intelligence Strategy, University of Illinois Chicago

In this episode, we cover how:

The EO aims to prevent conflicting state laws on AI.

States with existing AI regulations are likely prepared to resist the EO.

The U.S. has a more laissez-faire approach to AI regulation compared with the EU and China.

The order could lead to significant political battles leading up to the midterm elections.

The effectiveness of minimal regulation in winning the AI race is uncertain.

To learn more about AI regulations, check out AI Business, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Navigating Big Tech’s Influence on the AI Regulatory Landscape in 2025

Big Tech Firms Ask for AI Regulation but Quietly Hedge Their Bets

US State Attorneys General Demand Greater AI Safety From Tech Giants

Securing autonomous enterprise agents in the age of generative AI

Wed, 10 Dec 2025 18:13:39 -0400

In this episode of the Targeting AI podcast from AI Business, host Esther Shittu interviews Oren Michels, of 2024 startup Barndoor.ai, an AI data and access management vendor, about how to effectively secure enterprise agentic and generative AI systems. The approach is different from traditional cybersecurity paradigms designed to prevent outside intruders from doing harm within an organization's IT system, according to Michels. With agents, security procedures need to focus on the agents themselves to ensure they are performing as their human counterparts intend. The podcast was recorded at the AI Summit conference in New York City on Dec. 10.

Featuring Oren Michels, founder and CEO of Barndoor.ai

In this episode, we cover:

How enterprises can secure agentic and generative AI systems.
What mistakes businesses make that make them vulnerable to security threats to AI systems.
Some of the biggest security threats to large-scale business users of generative and agentic AI technology.
How to use the Model Context Protocol standard with cybersecurity measures to protect and govern AI agents.

To learn more about security for generative and agentic AI systems, check out AI Business, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Breaking news: AWS moves at re:Invent make the cloud giant an AI player

Tue, 02 Dec 2025 17:20:46 -0400

In this special news analysis edition of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutter interview R "Ray" Wang of Constellation Research, with Wang live from the AWS re:Invent 2025 conference in Las Vegas. Wang says AWS's new frontier AI agents represent a major step in the development of agentic AI, and other AI vendors are likely to follow AWS's lead. He also notes that AWS's new Trainium AI chips position AWS to be less reliant on AI chips from Nvidia, though the AI hardware giant continues to be a major chip provider to AWS. Wang also notes that AWS's new "AI factories" are crucial for the growing sovereign AI movement, as countries and regions worldwide are establishing their own AI industries and are less dependent on the U.S. and China.

Featuring R "Ray" Wang, founder and analyst at Constellation Research

In this episode, we cover how:

The demand for AI chips is growing rapidly.
AWS's Trainium AI chips offer cost-effective options for developers.
Pre-built models are essential for speeding up development.
AWS is focusing on providing choices for developers.
The integration of AI into existing systems is crucial for businesses.
AWS is catching up in AI capabilities compared to competitors.
The importance of governance and security in AI deployment.
Startups are increasingly building on AWS infrastructure.
The future of AI will involve multi-agent systems across platforms.

To learn more about AWS, generative AI, agentic AI and sovereign AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Generative AI and Diversity: What WLDA is Doing About It

Tue, 02 Dec 2025 08:00:00 -0400

It’s no secret that generative AI has led to exponential growth in AI technology. However, one area continues to seem to be lacking. Years ago, Asha Saxena, of the World Leaders in Data and AI (WLDA) organization, attempted to shift the landscape by creating an organization that emphasizes the importance of diversity in AI and the ethical challenges organizations face when implementing AI systems. In this Targeting AI podcast from AI Business, she emphasizes the need for women to have a bigger role in the AI community and the role of men as allies in this mission.

Featuring: Asha Saxena, CEO of World Leaders in Data and AI

In this episode, we cover how:

Diversity is essential for innovation and excellence.
Men must be included in the conversation about women in leadership.
AI can help detect and rectify bias in data.
Organizations face challenges in obtaining diverse data sets.
Lifelong learning is crucial in the rapidly evolving AI landscape.
Personalization in AI applications is a significant trend.

To learn more about generative AI and diversity, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

Alibaba.com B2B marketplace buys and sells with AI

Tue, 18 Nov 2025 08:15:00 -0400

In this episode of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutner interview Justin Liu of B2B platform Alibaba.com.,

discussing his extensive experience in e-commerce and the evolution of B2B sourcing in the age of AI. Liu shares insights on the complexities of B2B transactions, the innovative AI tools being implemented to enhance buyer and seller experiences, and the rapid adoption of these technologies by small businesses. He also highlights the importance of supplier verification and security in B2B commerce, and how AI is transforming traditional roles in the industry. The conversation concludes with a look at Alibaba's global expansion efforts and the future of AI in the e-commerce sector.

Featuring: Just Liu, general manager, Alibaba.com U.S

In this episode, we cover how:

B2B sourcing is more complex than B2C transactions.
AI can simplify the tedious processes in B2B sourcing.
Alibaba.com focuses on helping buyers and sellers with AI.
AI adoption is growing rapidly among professional buyers.
AI enhances supplier verification and transaction security.
AI is transforming traditional sales roles in B2B.
AI helps lower the entry barrier for small businesses.

To learn more about generative AI and agentic AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Changing enterprises' misconceptions about AI

Tue, 04 Nov 2025 08:15:00 -0400

Some think AI is just a trend and that we are on the verge of a bubble. That is not the case for Arun Subramaniyan, of Articul8. This enterprise AI vendor offers customers a platform for developing and deploying customized generative AI applications. In this Targeting AI podcast, Subramaniyan discusses some of the misconceptions enterprises have about implementing AI technology and the significance of measuring ROI.

Featuring: Arun Subramaniyan, CEO and founder of Articul8

In this episode, we cover how:

AI is a necessity for solving complex problems, not just a trend.
Enterprises struggle with data synthesis and knowledge discovery.
Customer data remains secure within its environment.
Open source is crucial for the evolution of AI technology
Many enterprises misunderstand the complexities of AI implementation.

To learn more about generative AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Breaking news: What Nvidia's $5 trillion milestone means for AI

Wed, 29 Oct 2025 17:17:46 -0300

In this special news analysis edition of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutner discuss Nvidia's historic achievement on Oct. 29 of becoming the first company to reach a $5 trillion market valuation with R "Ray" Wang of Constellation Research. The conversation explores the implications of this milestone for enterprise AI technology, the current AI boom, and the potential for a bubble in the market. They also touch on Nvidia's market position and the concerns surrounding monopoly in the context of the ongoing U.S.-China AI war.

Featuring R "Ray" Wang, founder and analyst at Constellation Research

In this episode, we cover how:

Nvidia's valuation reflects the growing importance of AI technology.
The AI market is expected to continue expanding significantly.
There is a potential for an AI bubble if job creation does not keep pace with AI advancements.
Entrepreneurship in AI is thriving, with small companies achieving significant revenue.
The emergence of AI exponentials is disrupting traditional business models.
Nvidia's dominance is partly due to geopolitical factors, particularly the U.S.-China AI war.
Concerns about monopolistic practices exist but are complicated by the competitive landscape.
The future of AI jobs remains uncertain as automation replaces traditional roles.

To learn more about Nvidia, generative AI and agentic AI, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Breaking News: Impact of the AWS Outage on AI Applications

Tue, 21 Oct 2025 15:38:13 -0300

In this breaking news analysis episode of the Targeting AI podcast from Informa TechTarget's AI Business, Esther Shittu and Shaun Sutner discuss the recent AWS outage that disrupted numerous websites and services, including AI applications such as widely used generative AI models from OpenAI and Anthropic. Tech analyst David Nicholson provides insights into the causes of the outage, emphasizing the importance of multi-site redundancy for enterprises relying on cloud services. The discussion also touches on the implications for AI applications and the need for businesses to consider redundancy options to prevent future disruptions.

Featuring: David Nicholson, analyst, The Futurum Group

In this episode, we cover how:

AWS experienced a major outage due to DNS problems.
The outage affected several large language models.
Multi-site redundancy is a way to prevent future disruptions.
Enterprises need to invest in redundancy for cloud services.
AI applications are not the cause of outages but are affected by them.
Cloud services have become more resilient over time.
Companies must be proactive in ensuring service continuity.
The cost of redundancy can be high, but it is necessary.
Smaller cloud providers may not offer the same level of resilience.

To learn more about generative AI, agentic AI and AI cloud services, check out AI Business from Informa TechTarget.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Streaming data and generative AI: Confluent's approach

Tue, 21 Oct 2025 08:00:00 -0300

In this episode of the Targeting AI podcast from AI Business, Shaun Sutner and Esther Shittu interview Sean Falconer of streaming data platform vendor Confluent. They discuss Confluent's AI strategy, the importance of real-time data management, and the integration of generative AI and multi-agent systems into business processes. Falconer emphasizes the need for high-quality data and the advantages of open source technologies like Apache Kafka and Flink. The conversation also touches on the challenges of implementing AI systems and the future direction of AI technology at Confluent.

Featuring: Sean Falconer, senior director of AI Strategy at Confluent.

In today's episode, we cover how:

Confluent focuses on real-time data processing and management.
Generative AI requires fresh, relevant data to be effective.
Data quality should be enforced at the source, not downstream.
Multi-agent systems can operate continuously and autonomously.
Confluent partners with major AI model providers for integration.
Reliability and testing are critical challenges in AI development.
The future of AI at Confluent includes building support for ambient agent experiences.

To learn more about AI, open source and agentic systems AI, check out AI Business.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

The Future of Patient Care: AI-First Approaches with Salesforce Agentforce

Tue, 14 Oct 2025 08:00:00 -0300

In this episode of the Targeting AI podcast from AI Business, hosts Esther Shittu and Shaun Sutner discuss the role of AI in healthcare with Madhav Thattai of Salesforce and John Oberg of Precina Health. They explore the concept of being AI-first, the integration of AI in patient care, and the impact of agentic systems on healthcare outcomes. The conversation highlights how AI can enhance clinical practices, improve patient interactions, and streamline business processes, ultimately leading to better health outcomes and operational efficiency. In this episode, the conversation revolves around the transformative role of AI in healthcare, particularly focusing on patient experience, the integration of Salesforce Health Cloud, and the balance between AI automation and human clinical judgment. The speakers discuss the supportive role of AI in clinical decisions, innovative applications in mental health, and the importance of trust and ROI in AI deployments. They emphasize the need for clear KPIs and the potential for AI to unlock efficiencies in healthcare delivery.

Featuring: Madhav Thattai, SVP & COO of Agentforce product management at Salesforce, and John Oberg, founder and CEO of Precina Health

In this episode, we cover how:

AI is used extensively in healthcare to enhance patient-provider interactions.
Being AI-first can lead to improved clinical and financial outcomes.
Salesforce's agentic technology is being used for customer support and marketing.
AI can automate routine tasks, allowing healthcare providers to focus on patient care.
The integration of AI in diabetes management has shown significant success.
AI can personalize patient care through meal planning and recipe suggestions.
The future of healthcare involves a collaborative approach between technology and human providers. AI is not the focus; it's a catalyst for patient experience.
AI supports clinicians without replacing their judgment.

To learn more about agentic AI and generative AI, check out AI Business.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Salesforce advances its agentic AI platform with Agentforce 360

Mon, 13 Oct 2025 08:00:00 -0300

In this breaking news analysis episode of the Targeting AI podcast from Informa TechTarget's AI Business, hosts Esther Shittu and Shaun Sutner discuss the latest innovations in agentic AI technology unveiled at Salesforce's Dreamforce conference in October with guest Madhav Thattai of CRM and CX giant Salesforce. The conversation covers the new Agentforce 360 platform, including hybrid reasoning, enhanced control and context for agents, and the importance of the user experience and data privacy. Thattai emphasizes the need for a balance between creativity and control in enterprise AI applications.

Featuring: Madhav Thattai, SVP and COO of Agentforce product management at Salesforce

In today's episode, we cover how:

Hybrid reasoning combines LLMs with structured processes.
Control and context are essential for agent functionality.
UX features are being enhanced for agents.
Data privacy is important to Salesforce.
AI agents must respect user permissions and access.
Salesforce aims to democratize agent development.
Context indexing improves agent accuracy.

To learn more about agentic AI, generative AI and Salesforce, check out AI Business.

To watch videos of our podcasts, subscribe to our YouTube channel, @EyeonTech.

UiPath moves from RPA to agentic AI

Tue, 07 Oct 2025 08:00:00 -0300

In this podcast, Mark Geene of robotic process automation (RPA) vendor UiPath discusses the evolution of RPA and the emergence of agentic AI. He explains how these technologies are transforming business processes, the importance of governance and compliance, and the future of work with digital workers. Geene also highlights the role of data in enabling effective AI agents and shares insights on the competitive landscape of RPA vendors. The discussion concludes with predictions about the future of AI in business.

In the episode, we cover how:

RPA automates repetitive tasks and is limited to deterministic workflows
Agentic AI combines deterministic and ad hoc processes for greater flexibility
Governance and compliance are critical for successful automation
Orchestration allows for effective collaboration between agents, robots, and humans
Data is essential for providing context to AI agents
Narrowly scoped agents can operate with more autonomy
The future of work will see agents supervising business processes

Featuring: Mark Greene, senior vice president and general manager of AI products and platform at UiPath

To learn more about agentic AI, RPA and generative AI, check out AI Business from Informa TechTarget.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

CoreWeave signs $14.2 billion deal with Meta Platforms

Tue, 30 Sep 2025 14:23:17 -0300

In this special breaking news analysis edition of the Targeting AI podcast from AI Business, hosts Shaun Sutner and Esther Shittu dive into the latest developments in the AI industry with Torsten Volk, an analyst with Omdia. Both AI Business and Omdia are owned by Informa TechTarget. This episode covers AI cloud computing vendor CoreWeave's groundbreaking $14 billion AI compute deal with Meta Platforms, exploring its implications for enterprise AI and the future of data center services. Join us as we unravel the complexities of AI infrastructure, the race for GPU power, and the strategic moves shaping the tech landscape. Don't miss this discussion on the forces driving innovation and competition in AI.

Takeaways:

CoreWeave's partnership with Meta underscores the growing need for specialized AI infrastructure.
Efficient GPU utilization is crucial for AI companies to maintain competitiveness.
The AI sector is rapidly evolving, with significant investments in infrastructure and talent.
Meta's strategy involves collaborating with various vendors to enhance its AI capabilities.
The deal may signal the emergence of a new sector within the AI industry, focusing on data center services.

Capital One takes on GenAI in a big way

Tue, 23 Sep 2025 08:00:00 -0300

As one of the biggest financial institutions in the U.S., Capital One isn’t running away from generative AI and agentic AI. Instead, the $490 billion company is using the technology to enhance both internal operations and customer experience. In this Targeting AI episode, the chief scientist and executive vice president at Capital One discusses some of the challenges and opportunities the financial giant is facing in customizing LLMs, and how the company continues to prioritize risk management and safety.

Featuring: Prem Natarajan, executive vice president, head of enterprise AI and chief scientist, Capital One

In today's episode, we cover:

Capital One's enterprise AI strategy is focused on creating customizable platforms using open source or open weight models
Capital One uses its proprietary data to customize AI models
The company uses GenAI and agentic AI for internal operations, such as with agent assist tools for customer service and customer-facing experiences like chat concierge
The enterprise has a focus on long-term transformation and not short-term ROI

To learn more about AI, open source and agentic AI, check out AI Business and SearchEnterpriseAI from Informa TechTarget.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

IBM, agentic AI and the future of enterprise tools

Tue, 09 Sep 2025 07:00:00 -0300

As a legacy organization, IBM has long been a champion for open source, especially in the age of GenAI. In this episode of Targeting AI from Informa TechTarget, Bruno Aziza, vice president of data, AI and analytics at IBM, discusses how the vendor has had to rebrand and shift in the age of GenAI and agentic AI. Aziza shares insights on talent challenges, IBM's data strategy with Watson X, and the significance of customer-centric AI solutions.

Featuring: Bruno Aziza, vice president of data, AI and analytics at IBM

In today’s episode, we cover how:

The shift to agentic AI is crucial for modern enterprises.

Open source plays a vital role in AI development.

IBM focuses on enterprise AI, rather than consumer-facing solutions.

Talent scarcity is a significant challenge in AI innovation.

99% of enterprise data remains untouched by AI.

To learn more about AI, open source, agentic AI, check out SearchEnterpriseAI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

IBM customers assess the performance of AI agents

IBM to buy open source data platform and AI vendor DataStax

IBM targets agentic AI orchestration

How Black Girls Code is Transforming Tech Diversity

Tue, 26 Aug 2025 07:00:00 -0300

When it comes to diversity, AI systems often fail. In this episode of the Targeting AI podcast, Christina Mancini, CEO of Black Girls Code, discusses the importance of inclusion in tech, the strategies Black Girls Code employs to empower girls of color, and the need for ethical considerations in AI education. Christina emphasizes the role of communities of color as creators in AI and the necessity for equitable development in technology. She also outlines the future goals of Black Girls Code and how organizations can support their mission.

Featuring: Christina Mancini, CEO of Black Girls Code

In today’s episode, we cover:

Communities of color are often super users of technology but need to be creators too.
AI technologies must be built by diverse teams to avoid bias.
Organizations should pause to consider the impact of their products on all communities.
Collaboration with tech partners is essential for achieving their mission.

To learn more about AI, bias and diversity check out SearchEnterpriseAI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

The future of AI chips: Cerebras’ unique strategy

Tue, 12 Aug 2025 07:00:00 -0300

In a market dominated by Nvidia H100 GPUs, Cerebras Systems seeks to develop the world's largest AI chip, the Wafer Scale Engine. In this episode of Targeting AI from Informa TechTarget, James Wang, the vendor's director of product marketing, discusses the importance of the inference market for AI technology and how the company's strategic partnerships are essential for growth. He elaborates on the evolving landscape of AI, including the significance of agentic AI and touches on Cerebras' future direction as a cloud and API company.

Featuring: James Wang, director of product marketing at Cerebras Systems

In this episode, we cover how:

Cerebras approaches competing against Nvidia.
Cerebras approaches differentiating itself from other AI inference vendors
The company is evolving into a cloud and API product company to meet market demands.
Agentic AI represents a new frontier in AI applications, enabling complex tasks through multiple requests.

To learn more about AI and Cerebras Systems and other hardware news, check out SearchEnterpriseAI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Not yet three years old, GenAI has made a dramatic impact on society, law and art

Tue, 29 Jul 2025 08:00:00 -0300

In this two-year anniversary episode of Targeting AI from Informa TechTarget, Michael Bennett discusses the rapid evolution of generative AI technology, and its implications for society, legal frameworks and creative industries. He highlights the public's growing awareness and understanding of AI, the legal challenges surrounding copyright and fair use, and the moral questions that arise from the use of AI in creative fields.

Featuring: Michael Bennett, associate vice chancellor for data science and artificial intelligence strategy at University of Illinois Chicago

In this episode, we cover:

The public's awareness of AI technology has significantly increased since the release of ChatGPT.

Legal challenges surrounding generative AI focus on copyright and fair use, creating uncertainty for the industry.

The disparity in AI infrastructure may lead to unequal benefits and negative consequences globally.

The future of AI, including AGI (artificial general intelligence), is uncertain and requires careful consideration.

To learn more about AI and the other regulation and governance news, check out SearchEnterpriseAI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

AI regulation: What businesses need to know in 2025

XAI releases Grok 4 amid furor over antisemitic comments

Anthropic’s early lawsuit win pushes courts forward on fair use

News Now: Trump's AI plan and its impact on tech industry and users

Thu, 24 Jul 2025 17:11:41 -0300

In this special news edition of Targeting AI from Informa TechTarget, government reporter Makenzie Holland discusses President Trump's AI action plan and executive orders aimed at promoting AI development and ensuring U.S. dominance in the AI race.

Featuring: Makenzie Holland, Informa TechTarget senior news writer

In today’s episode, senior new director Shaun Sutner and AI news writer Esther Shittu cover these topics:

President Trump’s new executive order and its intent
How the executive orders differ from the president’s previous orders and President Biden’s 2023 executive order
Woke AI and political bias concerns

To learn more about AI and the other regulation and governance news, check out SearchEnterprise AI and SearchCIO

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Database vendor MongoDB embraces GenAI

Tue, 15 Jul 2025 08:00:00 -0300

In this episode of the Targeting AI podcast from Informa TechTarget, Ben Flast, of NoSQL database vendor MongoDB, discusses the company's rapid integration of generative AI technologies, including vector search and real-time updates through Atlas Stream Processing. He emphasizes the importance of community engagement and the role of agentic AI in enhancing developer productivity. The conversation also explores the differences between open source and proprietary models, the impact of model sizes on performance, and MongoDB's approach to AI governance. Flast shares customer applications that highlight the transformative potential of AI in various industries and concludes with insights into future innovations at MongoDB.

Featuring: Ben Flast, director of product management at MongoDB

In today's episode, we cover these topics:

Vector search enhances the capabilities of GenAI applications.
Agentic AI represents a new application pattern for AI capabilities.
Model size affects performance and cost for developers.

To learn more about AI and the importance of GenAI in database platforms, check out SearchEnterprise AI and SearchDataManagement.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Diverse data, ethical use key to responsible AI engineering

Tue, 01 Jul 2025 08:00:00 -0300

The lack of diversity in AI systems has been an issue since the birth of the technology. In this episode of the Targeting AI podcast from Informa TechTarget, Karen Panetta discusses the importance of diversity in tech, and the ethical implications of AI. She emphasizes the need for inclusive design in engineering and AI systems, the role of digital twins in education, and the challenges of AI bias.

Featuring: Karen Panetta, an IEEE fellow and dean of graduate engineering education at Tufts University

In today's episode, we cover these topics:

AI should focus on solving real-world problems rather than being applied indiscriminately.
Ethical AI must prioritize the principle of “do no harm” to individuals and communities.
AI bias can lead to significant real-world consequences, especially in healthcare and hiring.

and more.

To learn more about AI and the importance of diversity in AI systems, check out SearchEnterprise AI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Tech pioneer Alan Trefler of Pegasystems: GenAI added creativity, speed and scale to workflows

Tue, 17 Jun 2025 08:00:00 -0300

In this episode of the Targeting AI podcast, Shaun Sutner and Esther Ajao interview Alan Trefler, founder and CEO of Pegasystems, discussing the evolution of AI technology, particularly generative AI, and its integration into business processes. Trefler shares insights on the differences between design time and runtime applications of AI, the importance of workflow engines, and the challenges of AI safety and reliability. He emphasizes the need for collaboration between AI and human expertise, and outlines Pegasystems' roadmap for effectively using AI in business process automation and legacy transformation.

Featuring: Alan Trefler, founder and CEO, Pegasystems.

In today's episode, we cover how:

GenAI has significantly advanced Pegasystems' offerings.
GenAI coaches differ from traditional generative AI assistants by focusing on design time.
Design time is crucial for ensuring reliable AI outcomes in business settings.
GenAI can enhance business process automation by streamlining workflows.

References:

To learn more about AI and Pegasystems, check out Informa TechTarget news sites, including SearchCustomerExperience and SeachEnterpriseAI

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

Exploring Nvidia’s approach to AI factories and AI supercomputers

Tue, 03 Jun 2025 08:00:00 -0300

Nvidia's hardware strategies are powering AI technologies. Recently, networking has become the critical backbone of modern AI systems. In today’s episode, Kevin Deierling provides practical insights for enterprises looking to implement AI technologies effectively. Deierling contrasts traditional data centers with the emerging concept of AI factories, revealing how these specialized environments are reshaping enterprise computing.

Featuring: Kevin Deierling, senior vice president of networking, Nvidia.

In today's episode, we cover:

Nvidia hardware and software approach
AI factories and data centers
Agentic AI and the shift toward complex reasoning

and more.

To learn more about AI and Nvidia, check out SearchEnterprise AI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Using GenAI and AI agents as key assets in work management

Tue, 20 May 2025 08:00:00 -0300

Generative AI has led to many fears about the workforce. However, for work management platform vendor Asana, GenAI and agentic AI can be effective tools in the workforce. Instead of replacing humans, AI technology can work alongside humans. Despite the potential for collaboration, not all tasks require the use of AI technology.

Featuring: Saket Srivastava, CIO of work management platform, Asana.

In today's episode, we cover:

The collaboration between AI technology and humans
Employees need training and support in AI
How GenAI can significantly improve project management tasks

and more.

To learn more about AI and Asana, check out SearchEnterpriseAI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Evolution of Grammarly AI and the future of work

Mon, 05 May 2025 05:00:00 -0300

As an AI writing assistant, Grammarly has used AI technology from its inception. The popularity of large language models has led to a shift in which the writing assistant vendor moved from natural language processing to including large language models to help enterprise employees improve their writing as they work. This has led Grammarly to see a possibility in the part it can play in transforming the future of work.

Featuring: Luke Behnke, head of Enterprise Product at Grammarly, an AI-powered assistant writing platform.

In today’s episode, we cover:

Grammarly’s AI evolution
Agentic AI and the future of work
AI technology as an assistant and not a replacement for work

and more.

To learn more about AI and Grammarly, check out SearchEnterprise AI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Responsible AI and the need for AI safety standards

Tue, 22 Apr 2025 05:00:00 -0300

A key truth about AI is that regulation has long lagged innovation. However, this has not removed the responsibility of enterprises to deploy AI systems responsibly or for AI vendors to create responsible systems. What are the key metrics to understanding a safe AI system?

Featuring: Stuart Battersby, CTO at Chatterbox Labs, vendor of a quantitative AI risk metrics platform, and Danny Coleman, CEO at Chatterbox.

In today’s episode, we cover:

The difference between AI safety and responsible AI
The need for standards in AI safety
The future of AI safety in Enterprises

and more.

To learn more about responsible AI, check out SearchEnterprise AI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

Resilient AI: Siemens' journey into industrial AI and generative technologies

Tue, 08 Apr 2025 05:00:00 -0300

Industrial AI is less familiar than consumer AI, but represents a critical and growing sector within AI’s influence. What unique AI applications are surfacing in this area?

Featuring: Olympia Brikis, director of Industrial AI research at Siemens

In today’s episode, we’ll cover…

Understanding Industrial AI and its distinctions from consumer AI
AI and, specifically, generative AI adoption at Siemens
The role of digital twins in testing AI recommendations

and more.

To learn more about AI in healthcare, check out Search Enterprise AI.

To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech.

References:

AWS developing high-performing autonomous AI agents

Tue, 25 Mar 2025 05:00:00 -0300

Traditional, generative, agentic—in the past couple of decades, AI metamorphosed into an indisposable tool for enterprises wanting to streamline their processes and improve their impact. In this episode, we dive into the different types of AI, best practices for implementation, and the challenges faced in the industry.

Featuring: Deepak Singh, Vice President at AWS

In today’s episode, we’ll cover…

The difference between traditional AI, generative AI, and agentic AI
The role of agentic AI in software development
Best practices for implementing agentic AI

and more!

To learn more about agentic AI, check out Search Enterprise AI.

To watch the video version our podcast, subscribe to our YouTube channel, @EyeOnTech.

References:

How the legal profession can benefit from AI technology

Tue, 11 Mar 2025 08:00:00 -0300

In the couple of years since the popularization of ChatGPT, generative AI technology has quickly taken hold in the legal profession.

It has backfired in some cases, such as when an attorney filed a legal brief written with ChatGPT's help and the AI platform hallucinated some of the cases in the brief. That case and others have led some law firms to block general access to AI tools. Most recently, Hill Dickinson, a law firm in the U.K., asked its staff not to use generative AI tools like ChatGPT.

Many law firms are using generative AI tools, and some even market their own AI systems. AI vendors are also partnering with law firms and companies in the legal profession. In February, LexisNexis and OpenAI agreed to integrate OpenAI's large language models across its products.

The success, and uncertainty, surrounding AI tools in the legal profession led James M. Cooper and Kashyap Kompella to write the book A Short and Happy Guide for Artificial Intelligence for Lawyers. Cooper is a law professor at California Western School of Law, while Kompella is CEO of AI analyst firm RPA2AI Research.

In the book, Cooper and Kompella explore how lawyers can understand and use AI technology.

"We saw an urgent need to upskill lawyers on AI," Kompella said on the latest episode of Informa TechTarget's Targeting AI podcast. "How do you move AI ethics and responsible AI into practice? You have to move them through lawyers. Lawyers are a big part of that equation."

Kompella and Cooper argue that while numerous books for lawyers about AI exist, few focus on using the technology ethically.

The authors also argue that while the legal profession has traditionally been slow to adopt new technologies, it can benefit from AI for several reasons. For example, AI technology can provide access to legal services for those in underserved areas like rural communities in the United States, Cooper said.

"AI can be a game changer in terms of provision of legal services," he said.

However, providing more education is the key to helping legal professionals understand AI technology.

"The law school curriculum is not teaching AI or any technologies to the students, so there is a huge skill gap," Kompella said.

Cooper added, "The skill sets of prompt engineering, of knowing how to use these AI tools and the dangers that come with them, should be rote in law schools now right from the first year. Those law schools around the world that embrace this idea are future-proofing their students. They're not going to have to play catch up."

Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series

Good data strategy is needed for GenAI

Tue, 25 Feb 2025 08:00:00 -0400

Without a good data strategy, generative AI becomes unusable technology for enterprises.

This was true when ChatGPT started becoming popular, and it is even more accurate years later.

The most recent example is the AI Chinese startup DeepSeek. While most AI cloud providers like Google, AWS and Microsoft now offer the DeepSeek-R1 reasoning model, many AI experts believe that enterprises might be hesitant to use it due to the data it was trained on.

Despite DeepSeek's R1's innovation, it all comes down to the foundation, said Michelle Bonat, chief AI officer at AI Squared, an AI and data integration platform.

"As GenAI expands and expands ... the fundamentals are the fundamentals," Bonat said on the latest episode of Informa TechTarget's Targeting AI podcast.

She added that while many organizations may have started with GenAI by just putting up a chatbot, many have found that if they do not have good quality data, they might have to pause their GenAI initiatives.

The reason is that the nature of generative AI systems is to produce responses. Therefore, if they do not have good-quality data, they tend to hallucinate.

Thus, Bonat said the growth in GenAI initiatives across organizations has also led to an increase in conversation around data strategy, data quality and data cleanliness.

"They're very much connected," she said. "GenAI has become important in the conversation that connects with data strategy, data quality, data cleanliness and also, ultimately, in responsible AI and governance within the organization."

She added that enterprises should pay attention to data and responsible AI because it benefits their businesses.

"It's a competitive advantage to have responsible AI," she continued. "Customers want AI systems they can trust. ... Being transparent and having responsible AI helps increase your brand reputation."

Multilingual LLM revolves around synthetic data

Tue, 11 Feb 2025 08:00:00 -0400

While some vendors are working to ensure large language models become better at reasoning, other AI vendors are making them compatible in multiple languages.

Writer is a provider of a full-stack generative AI platform for enterprises.

While the vendor provides a generative AI platform that enterprises can use to build generative AI capabilities into their workflows, it also offers a family of LLMs: Palmyra. The models support text generation and translation in numerous languages, including Spanish, French, Hindi and Russian.

"Multilingual training data and models that can be as good in dozens of other languages as they are in English is something everybody should strive for," said Writer cofounder and CEO May Habib on a recent episode of Informa TechTarget's Targeting AI Podcast. Writer also uses large volumes of synthetic data to help build legal confidence in generative AI technology, Habib said.

Writer also publishes data on how its models score for bias and toxicity.

"We really want to make sure that we are compliant with folks' ESG [equity, sustainability and governance] guardrails and guidelines," Habib said.

Writer recently raised $200 million in series C funding, bringing its valuation to $1.9 billion.

Esther Shittu is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

Cisco generative AI strategy hinges on CX and agents

Tue, 28 Jan 2025 08:00:00 -0400

The contact center world is a difficult place, packed with frustration and stress.

Digital communications giant Cisco sees its mission as easing that experience for human contact center workers and the customers they deal with every day.

For that undertaking, the vendor has seized on generative AI and agentic AI as the vehicles to both automate and augment the work of humans, in essence, smartening up the traditional chatbots that have long helped companies interact with their customers.

"We're to see a lot more of what I call event-based communication, proactive communication outbound that we do particularly well, powered by AI," said Jay Patel, senior vice president and general manager for customer experience at Cisco Webex, on the Targeting AI podcast from Informa TechTarget. "And then the response path to that is we think there will be AI agents involved in some of the more simple use cases.

"For example, if you haven't paid a bill, they can obviously call you in the outbound call center, but probably a better way of doing it is probably to send you a message with a link to then basically make the payment," Patel continued.

Like many other big tech vendors, Cisco deploys large language models (LLMs) from a variety of specialist vendors, including OpenAI and Microsoft. It also uses open models from independent generative AI vendor Mistral, as well as its own AI technology developed in-house or acquired by acquisition.

"Fundamentally, what we are looking at is the idea of an AI engine for each use case, and within the AI engine you would have a particular LLM," Patel said.

Among the generative AI-powered tools Cisco has assembled are Webex AI Assistant and Agent Wellness, to tend to the psyches of busy contact center human workers.

"Customers call very frustrated; they may shout at somebody. And then if you've had a difficult call, the agent wellness feature will mean that the supervisor knows that this set of agents has had a set of difficult calls," Patel said. "Maybe they're the ones who need a break now. So, there are ways of improving employee experience inside the contact center that we think we can … use AI for."

Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 35 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.

Box content management generative AI route is model-agnostic

Tue, 14 Jan 2025 08:00:00 -0400

Box has been in the AI game for a long time.

But when generative AI mushroomed into a transformative force in the tech world, the cloud content management vendor opted to turn to specialists in the new and fast-growing technology to power the arsenal of tools in its platform.

"We've been doing AI for many years. But the really cool thing that happened … AI got to the point where the generative AI models understood content," said Ben Kus, CTO at Box, on the Targeting AI podcast from Informa TechTarget. "For us, this whole generative AI revolution has been this great gift to everybody who deals with content. It's almost like having a very dedicated, very intelligent person who stands next to you, ready to do what you want."

When generative AI exploded with OpenAI's release of ChatGPT in November 2022, Box turned to OpenAI for its first batch of generative AI tools. Box CEO Aaron Levie had known OpenAI CEO and co-founder Sam Altman for many years.

However, when a passel of other independent generative AI vendors sprang up and the tech giants started releasing their own powerful large language models (LLMs) and multimodal models, Box decided to broaden its generative AI palette.

"Azure and OpenAI are partners of ours and we think they have great models, but we are not at all dedicated to any one model," Kus said. "In fact, at Box, one of our goals is to provide you with all of the major models that you might want."

These include generative AI models from Google, IBM, Anthropic and Amazon.

One example of how Box uses an outside model is Anthropic's 3.5 Sonnet LLM, which Kus called "one of the best models out there right now."

One application is at a financial firm that deals with long bond offerings. The company needs to analyze many of these complex financial vehicles to evaluate which bonds in which it wants to invest.

"They use [the model] to extract key info. It takes the [job] of looking through these bonds. From hours or days to … hopefully, minutes," Kus said. "If the model is very good, it can give you very good answers. If it's not as smart, then it can be off a little bit. So, this particular company really wants to have the best models so they can get the best sort of use of this kind of AI."

Shaun Sutner is senior news director for Informa TechTarget's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Shittu is an Informa TechTarget news writer and podcast host covering artificial intelligence software and systems.

Autonomous AI agents on the rise

Thu, 02 Jan 2025 16:40:18 -0400

This is the year of AI agents.

The last few months of 2024 brought much talk about and expectations for AI agents that can operate autonomously and semi-autonomously. Many vendors have capitalized on the enthusiasm to introduce new agentic products: Salesforce came out with Agentforce, and Microsoft introduced Copilot agents.

With 2025 here, questions about whether the momentum on agents will continue. Some see the agentic hype, and real progress, persisting this year.

Craig Le Clair, a Forrester Research analyst and author of the soon-to-be-published book Random Acts of Automation, is among those who think AI agents will continue to gain momentum in the new year.

"It's the biggest change toward AGI [artificial general intelligence] that I've seen," Le Clair said on the latest episode of Informa TechTarget's Targeting AI podcast, referring to the concept of AI that is as smart or smarter than human intelligence.

Enterprises will likely adjust the ways they use applications that use AI agents as copilots to augment humans, because many of those applications are not profitable, he said. However, AI agents will be the driving force in helping enterprises build platforms that use generative AI technology to spur business value, he said.

"When you really start to turn piles of data into conversations with people ... that's the opportunity for this," Le Clair said. "For an employee to have a conversation with standard operating procedures to get advice on what to do, or for standard operating procedures to be taken out of that PDF repository and actually put into a prompt and generate tasks that are then followed by an agent to get something done -- the potential is really there."

As with all new technology, AI agents involve a trust issue. Enterprises still do not trust the technology to be fully autonomous and perform tasks from start to finish all on its own, Le Clair said.

However, organizations can rely on AI agents to perform part of the work with the assistance of a human in the loop.

With the speed of the technology's maturation, progress toward fully autonomous agents by 2028 is likely, Le Clair predicted.

Moveworks uses AI to grow its employee automation platform

Tue, 10 Dec 2024 08:00:00 -0400

The AI application startup, which was founded in 2016 and was valued at more than $2.1 billion in 2021, uses a reasoning engine to help employees search for information across the enterprise.

Since its inception, a key ingredient in the company's success has been AI and generative AI technology.

"We were the first company after Google to deploy BERT in production," said co-founder and president Varun Singh on the latest episode of Informa TechTarget's Targeting AI podcast.

BERT was Google's first model with bidirectional encoding that enabled computers to understand large text spans. It was pretrained, so Moveworks did not have to train it from the ground up. It also did not require a lot of data.

After using BERT to train its automation platform, Moveworks started using GPT-2 from OpenAI in 2020. This is two years before the mass popularization of the generative AI vendor's ChatGPT chatbot, mostly to generate synthetic data.

Singh added that he and his team had failed to realize right away that the model could also be used for reasoning tasks.

"It's not so much a mistake that was made or not, but it was just sort of as technology evolved, the moment a paradigm shift actually comes into full focus, you look back and you're like, 'We could have done that sooner because we had access to the models, but we didn't see how powerful they could be,'" he said.

Since the shift, Moveworks has evolved from a platform with a reasoning engine to a platform for building AI agents.

On Oct. 1, Moveworks launched Agentic Automation as part of its Creator Studio offering. The system enables developers to build AI agents.

Throughout the evolution of its business, Moveworks has differentiated itself with its use of AI technology, Singh said.

"Without AI, there's nothing Moveworks has to offer to the world," he said. "There's only value from Moveworks because of AI."

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

Oracle generative AI approach based on Cohere, Meta models

Tue, 26 Nov 2024 08:00:00 -0400

When generative AI became the next big thing in tech, enterprise software giant Oracle bet heavily on a startup to provide it with foundation and large language models rather than scramble to develop its own.

That then-fledgling company was Cohere. Founded in 2019, the generative AI vendor raised $270 million in a Series C round, and its investors included Oracle, Nvidia, Salesforce Ventures, and some private equity firms. In July, Cohere raised another $500 million and reached a market valuation of $5.5 billion.

Cohere's open generative AI technology is now infused in many of Oracle's databases, a fixture among large enterprises. The tech giant has also tapped Cohere's powerful and scalable Control-R model for Oracle's popular vertical market applications, including those for finance, supply chain and human capital management.

But while Oracle has put Cohere at the center of its generative AI and agentic AI strategy, the tech giant is also working closely with Meta.

The social media colossus has gained a foothold in the enterprise AI market with its Llama family of open foundation models. Oracle is customizing Llama for its Oracle Cloud Infrastructure platform, along with Cohere's models.

"We have made a decision to really partner deeply around the foundation models," said Greg Pavlik, executive vice president, AI and data management services at Oracle Cloud Infrastructure, on the Targeting AI podcast from TechTarget Editorial.

"What we're looking for are companies that are experienced with creating high-quality generative AI models," he continued. "But more importantly … companies that are interested in enterprise and specifically business solutions."

Pavlik said Oracle values the open architecture of the models from both Cohere and Meta, which makes it easier for Oracle to customize and fine-tune them for enterprise applications.

"The advantage really of having a deep partnership is that we're able to sit down with the foundation model providers and look at the evolution of the models themselves, because they're not really static," he said. "A company will create a model and then they'll continually retrain it.

"We see our role as to come in and proxy for the enterprise user, proxy for a number of verticals," Pavlik continued. "And then try to move the state of the art in the technology base closer and closer to the kinds of patterns and the kinds of scenarios that are important for enterprise users."

Oracle also uses generative AI technology from other vendors and enables its customers to use other third-party models, he noted.

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering AI software and systems. Together, they host the Targeting AI podcast.

Creating a clean generative AI data set with Getty Images

Tue, 12 Nov 2024 12:07:44 -0400

At the beginning of the wave of generative AI hype, many feared that generative models would replace the jobs of creatives like artists and photographers.

With generative AI models such as Dall-E and Midjourney seemingly creating unique works of art and images, some artists found themselves at a disadvantage. Some say the generative systems took their artwork, copied it and used it to produce their own images. In some cases, the generative systems allegedly outright stole the creative work.

Two years later, artists have to some extent been reassured by the support of stock vendors like Getty Images.

Instead of trailing behind generative AI tools such as Stable Diffusion, Getty created its own image-generating tool: Generative AI by Getty Images.

Compared with other image generators, Getty has taken great lengths to restrict its model through the data set. The stock photography company maintains what it calls a clean data set.

"A clean data set is really a training data set that a model is trained on that can lead to a commercially safe or responsible model," said Andrea Gagliano, senior director of AI and machine learning at Getty Images, on the latest episode of TechTarget Editorial's Targeting AI podcast.

Getty's clean data set does not contain brands or intellectual property products, Gagliano said. The model's data set also does not include images of well-known people or likenesses of celebrities like Taylor Swift or presidential candidates.

"We have taken the very cautious approach where our generator will not generate any known person or any celebrity," Gagliano said.

"It will not generate Donald Trump," she said, referring to the President-elect. "And it will not generate Kamala Harris," referring to the vice president and former presidential candidate.

"It has never seen a picture of Donald Trump," she continued. "The model has never seen a picture of Kamala Harris."

Gagliano added that removing this possibility also guards against those who want to misuse the technology to create deepfakes. Therefore, any generated output is labeled synthetic or AI-generated.

"We don't want any situation where we start to undermine the value of a real image," Gagliano said.

Finally, the data set that Getty uses produces images with licenses on them, ensuring that creators get compensated. Thus, a portion of every dollar made by Generative AI by Getty Images is given to the creator who contributed to the data set.

"The reason for that is the more unique imagery that we bring into the training data set, the more additive it is," Gagliano said.

Getty updated its generative AI tools Tuesday. The new capabilities include Product Placement, which lets users upload their own product images and generate backgrounds, and Reference Image, which enables users to upload sample images to guide the color and composition of the AI-generated output.

AI industry could see regulation rollback under Trump

Thu, 07 Nov 2024 17:42:19 -0400

President-elect Donald Trump during his election campaign offered clues about how his administration would handle the fast-growing AI sector.

One thing is clear: AI, to the extent that it is regulated, is headed for deregulation.

"It's likely going to mean less regulation for the AI industry," said Makenzie Holland, senior news writer at TechTarget Editorial covering tech regulation and compliance, on the Targeting AI podcast. "Being against regulation and [for] deregulation is a huge theme across his platform."

Trump views rules and regulations on business as costly and burdensome, Holland noted. The former president and longtime businessman's outlook presumably includes independent AI vendors and the tech giants that also develop and sell the powerful generative AI models that have swept the tech world.

President Joe Biden's wide-ranging executive order on AI has been the strongest articulation of how the federal government views AI policy. However, it's unclear which elements of the Democratic president's plan Trump will scrap and which he'll keep. Trump established the National Artificial Intelligence Initiative Office at the end of his first term as president in 2021.

David Nicholson, chief technology advisor at Futurum Group, said on the podcast that Trump will likely retain some aspects of the executive order with bipartisan support. Among these is the federal government's recognition that it should guide and promote AI technology.

"[Trump will] definitely not scrap it wholesale," Nicholson said. "There's something behind a lot of those concerns ... and pretty bipartisan concern that AI is a genie that we only want to let out of the bottle, if possible, very carefully."

Holland, however, doesn't expect many regulatory proposals in Biden's executive order to survive the next Trump presidency. Trump is also likely to dramatically de-emphasize the AI safety concerns and regulatory proposals that feature prominently in Biden's executive order, she said.

Meanwhile, concerning Elon Musk -- a major Trump backer and owner of the social media platform X, formerly Twitter, and generative AI vendor xAI -- the issue is complicated, Nicholson said.

Musk has been a trenchant critic of xAI competitor OpenAI, alleging in a lawsuit that the rival vendor abandoned its commitment to openness in AI technology. However, Nicholson noted that Musk's definition of transparency in training large language models is unorthodox, insisting that models be "honest" and not contain political bias.

"Having the ear of the president and the administration, I think he could be meaningful in that regard," Nicholson said. "[Musk] is going to be the loudest voice in the room when it comes to a lot of this stuff."

While Trump is expected to try to reverse or ignore much of Biden's agenda, one major piece of bipartisan legislation passed during Biden's tenure, the CHIPS and Science Act of 2022, is likely to survive because it emphasizes reviving manufacturing and technology development in the U.S., Nicholson said.

But the Federal Trade Commission's and Department of Justice's active stances on AI rulemaking and big tech regulation -- the DOJ successfully sued Google for monopolizing the search engine business -- are ripe for a Trump rollback.

"The FTC is likely to face a shake-up, as far as Lina Khan's job probably is on the line," Holland said, referring to the activist FTC chair, who has vigorously pursued a number of big tech vendors.

"Trump's entire platform is about deregulation and being against regulation. That's automatically going to impact these enforcement agencies, which, in some capacity, can make their own rules," Holland said.

In the absence of meaningful federal regulation of AI, the U.S. is moving toward a state-by-state regulatory patchwork.

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.

Exploring the role of AI in beauty and haircare

Tue, 29 Oct 2024 08:00:00 -0300

When Candace Mitchell was young, she discovered a love for computers and haircare. Her interest in technology led her to study coding in high school, leading her to build websites.

Meanwhile, she also considered going to cosmetology school.

She found a middle ground in beauty technology, later becoming co-founder and CEO of Myavana, a Black-owned beauty technology vendor. Myavana uses AI technology to analyze hair strands and make haircare recommendations.

Myavana started with a hair analysis kit; the startup's technology uses machine learning to identify and analyze the different unique combinations in people's hair.

"Our research shows us that there are actually 972 unique combinations of hair profiles," Mitchell said on the latest episode of the Targeting AI podcast. "Using machine learning is how we can automate the process of the analysis and generate those product recommendations."

While Myavana works with consumers, it found that its data on hair is also valuable to enterprises interested in the haircare business.

"When you come to Myavana, you can target consumers based on their hair goals and hair challenges," Mitchell said. "That's the cool thing with AI -- it has uncovered new data that is helpful for businesses and how to target consumers. And again, just making it personalized."

Myavana recently raised $5.9 million in seed round funding.

While the vendor developed proprietary technology, it runs its model on AWS. It also built a conversational AI chatbot with Google.

Closing the gap between open source and closed AI models

Tue, 15 Oct 2024 08:00:00 -0300

Open source AI models are closing the gap in the debate between open and closed models.

Since the introduction of Meta Llama generative AI models in February 2023, more enterprises have started to run their AI applications on open source models.

Cloud providers like Google have also noticed this shift and have accommodated enterprises by introducing models from open source vendors such as Mistral AI and Meta. At the same time, proprietary closed source generative AI models from OpenAI, Anthropic and others continue to attract widespread enterprise interest.

But the growing popularity of open source and open models has also made way for AI vendors like Together AI that support enterprises using open source models. Together AI runs its own private cloud and provides model fine-tuning and deployment managed services. It also contributes to open source research models and databases.

"We do believe that the future includes open source AI," said Jamie De Guerre, senior vice president of product at Together AI, on the latest episode of TechTarget's Targeting AI podcast.

"We think that in the future there will be organizations that do that on top of a closed source model," De Guerre added. "However, there's also going to be a significant number of organizations in the future that deploy their applications on top of an open source model."

Enterprises use and fine-tune open source models for concrete reasons, according to De Guerre.

For one, open models offer more privacy controls in their infrastructure, he said. Enterprises also have more flexibility. When organizations customize open source models, the resulting model is something they own.

"If you think of organizations making a significant investment in generative AI, we think that most of them will want to own their destiny," he said. "They'll want to own that future."

Enterprises can also choose where to deploy their fine-tuned models.

However, there are levels involved in what is fully open source and what is just an open model, De Guerre said.

Open models refers to models from vendors that do not include the training data or the training code used to build the model, but only the weights used.

"It still provides a lot of value because organizations can download it in their organization, deeply fine-tune it and own any resulting kind of fine-tuned version," De Guerre said. "But the models that go even further to release the training source code, as well as the training data used, really help the open community grow and help the open research around generative AI continue to innovate."

Enterprise adoption of generative AI is accelerating

Tue, 01 Oct 2024 08:00:00 -0300

Nearly two years after the mass consumerization of generative AI with the introduction of ChatGPT, the technology is now moving from experimentation to implementation.

A recent survey by TechTarget's Enterprise Strategy Group found that generative AI adoption is growing. The analyst firm surveyed 832 professionals worldwide and found that adoption has increased in the last year.

"We're in the acceleration phase," said Mark Beccue, an analyst at Enterprise Strategy Group and an author of the survey report, on the Targeting AI podcast.

Organizations are using generative AI in areas such as software development, research, IT operations and customer service, according to the survey.

However, there isn't a particular use case that is a top priority. Organizations are focusing on several applications of generative AI and still face some challenges when trying to adopt generative AI technology.

One is a need for more infrastructure, Beccue said.

"They feel that the changes are needed to support infrastructure before they can proceed with GenAI," he said.

This might include adding platforms for enterprise generative AI projects or more development tools, he added.

"It's really everything that gets you to being able to build an app," Beccue continued.

Organizations also don't have consensus about what kind of AI model is best for their needs: open or closed source.

"It's probably both," Beccue said. "People are thinking about how to use these things and they're understanding that not one model fits everything that they need. So, they're looking through to see what works for them in certain instances."

The enterprises that have found quick success with generative AI are ones that invested in AI years before it was popularized by OpenAI's ChatGPT, Beccue said.

He said these are companies like Adobe, ServiceNow -- which, for example, used machine learning, natural language understanding, process automation and AIOps since at least 2017 -- and Zoom.

"They did it in a way where they said, 'We think there is potential here for this to help us do what we do better,'" he said. "That was their driver."

This was what made them ready when generative AI hit the market.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Together, they host the Targeting AI podcast series.

Google head of product on generative AI strategy

Mon, 16 Sep 2024 08:00:00 -0300

As one of the top cloud providers, Google Cloud also stands at the forefront of the generative AI market.

Over the past two years, Google has been enmeshed in a push and pull with its chief competitors -- AWS, Microsoft and OpenAI -- in the race to dominate generative AI.

Google has introduced a slate of new generative AI products in the past year, including its main proprietary large language model (LLM), Gemini and the Vertex AI Model Garden. Last week, it also debuted Audio Overview, which turns documents into audio discussions.

The tech giant has also faced criticism that it might be falling behind on generative AI challenges such as the malfunctioning of its initial image generator.

Part of Google's strategy with generative AI is not only providing the technology through its own LLMs and those of many other vendors in the Model Garden, but also constantly advancing generative AI, said Warren Barkley, head of product at Google for Vertex AI, GenAI and machine learning, on the Targeting AI podcast from TechTarget Editorial.

"A lot of what we did in the early days, and we continue to do now is … make it easy for people to go to the next generation and continue to move forward," Barkley said. "The models that we built 18 months ago are a shadow of the things that we have today. And so, making sure that you have ways for people to upgrade and continue to get that innovation is a big part of some of the things that we had to change."

Google is also focused on helping customers choose the right models for their particular applications.

The Model Garden offers more than 100 closed and open models.

"One thing that our most sophisticated customers are struggling with is how to evaluate models," Barkley said.

To help customers choose, Google recently introduced some evaluation tools that allow users to put in a prompt and compare the way models respond.

The vendor is also working on AI reasoning techniques and sees that as moving the generative AI market forward.

AT&T's David C. Williams on how generative AI will force diversity in AI systems

Tue, 03 Sep 2024 09:00:00 -0300

The growth of generative AI has put diversity front and center.

In the last year, there have been concerns that GenAI systems such as ChatGPT and Google Gemini are not trained with enough diverse data sets.

For instance, the introduction of the Lensa app two years ago allowed people of color to generate avatars of themselves. Concerns were raised, however, after some users said Lensa's generated images changed their skin color.

Incidents with AI tools like Lensa show that AI creators might not have enough diversity in their data set.

Alternatively, there have also been incidents where it's clear that AI systems misrepresented diversity. For example, Google shut down Gemini's image generator earlier this year after users started generating inaccurate depictions of historical figures. For example, it generated images of well-known white people, such as the Pope, as Black people.

Google has since opened the model back up. Last week, the cloud provider revealed that its new AI model, Imagen 3, will be rolled out to its Gemini AI model. The model will produce images of people again but won't support generation of photorealistic identifiable individuals.

Despite the hiccup in the beginning stages of the technology, hope exists, said David C. Williams, assistant vice president of automation at AT&T.

While Williams leads a team that previously used RPA, or robotics process automation, to drive business needs at AT&T, the team is now pivoting to generative AI. The shift has given Williams a view of how GenAI could affect diversity.

"Generative AI is going to force diversity," Williams said on the latest Targeting AI episode.

Cloud providers such as Google must include diversity in their data sets because not having it could lead to alienation from people of color, he continued. If creators of these systems fail to have diverse systems that show representation, that could lead many people of color to simply stop using the systems, which won't help their business.

On the other hand, people of color and women will gain new opportunities because of generative AI.

"Those that embrace generative AI and figure out how to use it in the workplace will have an incredibly different value proposition than the rest," Williams said.

Generative AI fuels growth of online deepfakes threatening organizations and election integrity

Mon, 19 Aug 2024 08:00:00 -0300

The growth of deepfakes in the past few years is a threat to not only organizations but also the U.S. general election in November.

Information security vendor Pindrop saw a sharp rise in deepfakes in the first few months of the year compared to the previous year.

Deepfakes of Vice President Kamala Harris, former President Donald Trump, President Joe Biden and state-level candidates have circulated in the runup to the November U.S. general election.

"Last year, we were seeing about one deepfake every single month," Vijay Balasubramaniyan, co-founder and CEO at Pindrop, said on the Targeting AI podcast. "Starting this year ... we started seeing a deepfake every single day across every single customer."

A big reason for the stark increase is the growth of generative AI systems and voice cloning apps. Meanwhile, many people can't distinguish between a deepfake voice and an authentic one.

While about 120 voice cloning apps were on the market last year, this year users (both legitimate and illegitimate) can choose among more than 350 voice cloning apps.

Moreover, Balasubramaniyan said, fraudsters are using generative AI technology to scale their attacks.

For example, generative AI systems can create deepfakes in many different languages -- a series of large language models from Meta can translate some 4,000 languages. Fraudsters can use these systems to create deepfakes that can respond to questions depending on which words are spoken.

"They have managed to scale their attacks in massive ways, and in ways that we have not seen before generative AI. We're seeing that now," Balasubramaniyan said.

The massive progression of deepfake technology means organizations must remain aware and vigilant, said Harman Kaur, vice president of AI at Tanium, on the podcast. Tanium is a cybersecurity and management vendor based in Kirkland, Wash.

"You have to have a plan to respond," Kaur said. "Do you have the tools to understand what type of threat has been invited into your network, and do you have the tools to fix it?"

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Together, they host the Targeting AI podcast series.

Examining the tech stances of Kamala Harris and Donald Trump, plus J.D. Vance

Mon, 05 Aug 2024 08:00:00 -0300

Democratic presidential candidate Kamala Harris is a product of two decades of California politics who has longstanding ties to the tech and AI communities in her home state.

But in her role as President Joe Biden's vice president during the past four years, Harris was tasked with overseeing Biden's executive order on AI, with its emphasis on government regulation. And it was she who hosted leaders of tech giants at the White House last year and secured pledges from them to focus on AI safety.

In sharp contrast is the GOP presidential nominee, Donald Trump.

While Trump's running mate, Senator J.D. Vance (R-Ohio), has a background in tech venture capital, Trump himself has no tech experience but backs a largely hands-off approach to tech and AI companies.

In simple terms, Trump is anti-regulation, while Harris favors a moderate regulatory stance on big tech and the suddenly emergent generative AI sector, a view that roughly parallels that of Biden.

In this episode of the Targeting AI podcast from TechTarget Editorial, three commentators on the confluence of tech and AI and politics registered their analyses of the complex dynamics of the likely Harris-Trump faceoff.

Makenzie Holland, big tech and federal regulation senior news writer at TechTarget, emphasized that "there is a huge focus from the Biden-Harris administration on AI safety and trustworthiness."

Meanwhile, "we've obviously seen Trump attack the executive order," she noted.

For R "Ray" Wang, founder and CEO of Constellation Research, the choice for the tech industry is fairly clear.

"I stress the libertarian view because I think that's important to understand that tech doesn't necessarily want to be governed," Wang said.

The other guest on the podcast, Darrell West, a senior fellow in the Governance Studies program at the Brookings Institute, has authored a book about policy making in the AI era. He also pointed out the marked divergence of Harris and Trump on tech and AI issues.

"Even though she historically has been close to the tech sector, I actually think she will maintain Biden's tough line on a lot of issues because that's where the party is these days," West said. "And also that's where public opinion is on many tech issues."

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

A year in review with the Targeting AI podcast

Mon, 29 Jul 2024 08:00:00 -0300

For the past year, the Targeting AI podcast has explored a broad range of AI topics, none more than the fast-evolving and sometimes startling world of generative AI technology.

From the first guest, Michael Bennett, AI policy adviser at Northeastern University, the podcast has focused intently on the popularization of generative AI, while also touching on traditional AI.

While that first episode centered on the prospects of AI regulation, Bennett also spoke about some of the controversies then emerging in the nascent stages of generative AI.

"Organizations who have licenses to use and to sell photographers' works are pushing back,” Bennett said during the inaugural episode of the Targeting AI podcast.

While Bennett's point of view illuminated the regulatory and ethical dimensions of the explosively growing technology, Michael Stewart, a partner at Microsoft's venture firm M12, discussed the startup landscape.

With the rise of foundation model providers such as Anthropic, Cohere and OpenAI, generative AI startups for the last 12 months chose to partner with and be subsidized by cloud giants -- namely Microsoft, Google and AWS –-- instead of seeking to be acquired.

"This is a very ripe environment for startups that have a partnership mindset to work with the main tech companies,” Stewart said during the popular episode, which was downloaded more 1,000 times.

The early stages of generative AI were marked by accusations of data misuse, particularly from artists, writers and authors.

Our Targeting AI podcast hosts have also spoken to guests about data ownership and how large language models are affecting industries such as the music business.

The podcast also explored new regulatory frameworks like President Joe Biden's executive order on AI.

With some 27 guests from a diverse group of vendors and other organizations, the podcast took shape and laid the groundwork for a second year with plenty of new developments to explore.

Coming up soon are episodes on Democratic presidential candidate Kamala Harris’ stances on AI and big tech antitrust actions, election deepfakes and tech giant Oracle's foray into generative AI.

Listen to Targeting AI on Apple Podcasts, Spotify and all major podcast platforms, plus on TechTarget Editorial’s enterprise AI site.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Together, they host the Targeting AI podcast series.

AWS GenAI strategy based on multimodel ecosystem, plus Titan, Q and Bedrock

Mon, 15 Jul 2024 08:00:00 -0300

AWS is quietly building a generative AI ecosystem in which its customers can use many large language models from different vendors, or choose to employ the tech giant's own models, Q personal assistants, GenAI platforms and Trainium and Inferentia AI chips.

AWS says it has more than130,000 partners, and hundreds of thousands of AWS customers use AWS AI and machine learning services.

The tech giant provides not only the GenAI tools, but also the cloud infrastructure that undergirds GenAI deployment in enterprises.

"We believe that there's no one model that's going to meet all the customer use cases," said Rohan Karmarkar, managing director of partner solutions architecture at AWS, on the Targeting AI podcast from TechTarget Editorial. "And if the customers want to really unlock the value, they might use different models or a combination of different models for the same use case."

Customers find and deploy the LLMs on Amazon Bedrock, the tech giant's GenAI platform. The models are from leading GenAI vendors such as Anthropic, AI21 Labs, Cohere, Meta, Mistral and Stability AI, and also include models from AWS' Titan line.

Karmarkar said AWS differentiates itself from its hyperscaler competitors, which all have their own GenAI systems, with an array of tooling needed to implement GenAI applications as well as AI GPUs from AI hardware giant Nvidia and AWS' own custom silicon infrastructure.

AWS also prides itself on its security technology and GenAI competency system that pre-vets and validates partners' competencies in putting GenAI to work for enterprise applications.

The tech giant is also agnostic on the question of proprietary versus open source and open models, a big debate in the GenAI world at the moment.

"There's no one decision criteria. I don't think we are pushing one [model] over another," Karmarkar said. "We're seeing a lot of customers using Anthropic, the Claude 3 model, which has got some of the best performance out there in the industry."

"It's not an open source model, but we've also seen customers use Mistral and [Meta] Llama, which have much more openness," he added.

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving

coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 35 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. They co-host the Targeting AI podcast.

Walmart uses generative AI for payroll, employee experience

Mon, 01 Jul 2024 08:00:00 -0300

The biggest global retailer sees itself as a tech giant.

And with 25,000 engineers and its own software ecosystem, Walmart isn't waiting to see how GenAI technology will play out.

The company is already providing its employees -- referred to by the retailer as associates -- with in-house GenAI tools such as the My Assistant conversational chatbot.

Associates can use the consumer-grade ChatGPT-like tool to frame a press release, write out guiding principles for a project, or for whatever they want to accomplish.

"What we're finding is as we teach our business partners what is possible, they come up with an endless set of use cases," said David Glick, senior vice president of enterprise business services at Walmart, on the Targeting AI podcast from TechTarget Editorial.

Another point of emphasis for Walmart and GenAI is associate healthcare insurance claims.

Walmart built a summarization agent that has reduced the time it takes to process complicated claims from a day or two to an hour or two, Glick said.

An important area in which Glick is implementing GenAI technology is in payroll.

"What I consider our most sacrosanct duty is to pay our associates accurately and timely," he said.

Over the years, humans have monitored payroll. Now GenAI is helping them.

"We want to scale up AI for anomaly detection so that we're looking at where we see things that might be wrong," Glick said. "And how do we have someone investigate and follow up on that."

Meanwhile, as for the "build or buy" dilemma, Walmart tends to come down on the build side.

The company uses a variety of large language models and has built its own machine learning platform, Element, for them to sit atop.

"The nice thing about that is that we can have a team that's completely focused on what is the best set of LLMs to use," Glick said. "We're looking at every piece of the organization and figuring out how can we support it with generative AI."

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. They co-host the Targeting AI podcast.

Lenovo stakes claim to generative AI at the edge

Mon, 17 Jun 2024 08:00:00 -0300

While Apple garnered wide attention for its recent embrace of generative AI for iPhones and Macs, rival end point device maker Lenovo already had a similar strategy in place.

The multinational consumer products vendor, based in China, is known for its ThinkPad line of laptops and for mobile phones made by its Motorola subsidiary.

But Lenovo also has for a few years been advancing a “pocket to cloud” approach to computing. That strategy now includes GenAI capabilities residing on smartphones, AI PCs and laptops and more powerful cloud processing power in Lenovo data centers and customers’ private clouds.

Since OpenAI’s ChatGPT large language model (LLM) disrupted the tech world in November 2022, GenAI systems have largely been cloud-based. Queries from edge devices run a GenAI prompt in the cloud, which returns the output to the user’s device.

Lenovo’s strategy -- somewhat like Apple’s new one -- is to flip that paradigm and locate GenAI processing at the edge, routing outbound prompts to the data center or private cloud when necessary.

The benefits include security, privacy, personalization and lower latency -- resulting in faster LLM responses and reducing the need for expensive compute, according to Lenovo.

“Running these workloads at edge, on device, I'm not taking potentially proprietary IP and pushing that up into the cloud and certainly not the public cloud,” said Tom Butler, executive director, worldwide communication commercial portfolio at Lenovo, on the Targeting AI podcast from TechTarget Editorial.

The edge devices that Lenovo talks about aren’t limited to the ones in your pocket and on your desk. They also include remote cameras and sensors in IoT AI applications such as monitoring manufacturing processes and facility security.

“You have to process this data where it's created,” said Charles Ferland, vice president, general manager of edge computing at Lenovo, on the podcast. “And that is running on edge devices that are deployed in a gas station, convenience store, hospital, clinics -- wherever you want.”

Meanwhile, Lenovo in recent months rolled out partnerships with some big players in GenAI including Nvidia and Qualcomm.

The vendor is also heavily invested in working with neural processing units, or NPUs, in edge devices and innovative cooling systems for AI servers in its data centers.

Shaun Sutner is a journalist with 35 years of experience, including 25 years as a reporter for daily newspapers. He is a senior news director for TechTarget Editorial's information management team, covering AI, analytics and data management technology. Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.

The importance of open source in GenAI

Fri, 31 May 2024 15:41:00 -0300

The rise of generative AI has also brought renewed interest and growth in open source technology. But the question of open source is still "open" in generative AI.

Sometimes, the code is open -- other times, the training data and weights are open.

A leader in the open source large language model arena is Meta. However, despite the popularity of the social media's giant's Llama family of large language models (LLMs), some say Meta's LLMs are not fully open source.

One vendor that built on top of Llama is Lightning AI.

LightningAI is known for PyTorch Lightning, an open source Python library that provides a high level of support for PyTorch, a deep learning framework.

Lightning in March rolled out Thunder, a source-to-source compiler for PyTorch. Thunder speeds up training and serves generative AI (GenAI) models across multiple GPUs.

In April 2023, Lightning introduced Lit-Llama.

The vendor created the Lit-Llama model starting with code from NanoGPT (a small-scale GPT for text generation created by Andrej Karpathy, a co-founder of OpenAI and former director of AI at Tesla). Lit-Llama is a fully open implementation of Llama source code, according to Lightning.

Being able to create on top of Llama highlights the importance of "hackable" technology, Lightning AI CTO Luca Antiga said on the Targeting AI podcast from TechTarget Editorial.

"The moment it's hackable is the moment people can build on top of it," Antiga said.

However, mechanisms of open source are yet to be fully developed in GenAI technology, Antiga continued.

It's also unlikely that open source models will outperform proprietary models.

"Open source will tend to keep model size low and more and more capable, which is really enabling and really groundbreaking, and closed source will try to win out by scaling out, probably," Antiga said. "It's a very nice race."

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

A vision of an AI future that may not include humans

Mon, 20 May 2024 08:00:00 -0300

In intellectual tech circles, a debate over artificial general intelligence and the AI future is raging.

Dan Faggella is in the middle of this highly charged discussion, arguing on various platforms that artificial general intelligence (AGI) will be here sooner than many people think, and it will likely take the place of human civilization.

"It is most likely, in my opinion, that should we have AGI, it won't follow too long from there that humanity would be attenuated. So, we would fade out," Faggella said on the Targeting AI podcast from TechTarget Editorial.

"The bigger question is how do we fade out? Is it friendly? Is it bad?" he said. "I don't think we'll have much control, by the way, but I think maybe we could try to make sure that we've got a nice way of bowing out."

In addition to his role as an AI thinker, Faggella is a podcaster and founder and CEO of AI research and publishing firm Emerj Artificial Intelligence Research.

In the podcast episode, Faggella touches on a wide range of subjects beyond the long-term AI future. He takes on election deepfakes (probably not as dangerous as feared, and the tech could also be used for good) and AI regulation (there should be the right amount of it), as well as robots and how generative AI models will soon become an integral part of daily life.

"The constant interactions with these machines will be a wildly divergent change in the human experience," Faggella said. "I do suspect absolutely, fully and completely that most of us will have some kind of agent that we're able to interact with all the time.

Meanwhile, Faggella has put forth a vision of what an AGI-spawned "worthy successor" to humans could look like in the AI future. He has written about the worthy successor as "an entity with more capability, intelligence, ability to survive and (subsequently) moral value than all of humanity."

On the podcast, he talked about a future inhabited by a post-human incarnation of AI.

"Keeping the torch of life alive would mean a post-human intelligence that could go populate galaxies, that could maybe escape into other dimensions, that could visit vastly different portions of space that we don't currently understand," he said.

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.

Salesforce open model AI strategy aims for trust, automation

Mon, 06 May 2024 08:00:00 -0300

Salesforce was an early adopter of generative AI, seizing on large language model technology from OpenAI to integrate into its own applications.

But the CRM and CX giant quickly evolved an open model strategy. It now gives customers access to multiple third-party LLMs while providing its own AI trust layer to try to ensure that Salesforce users can safely rely on AI-generated outputs.

Jayesh Govindarajan, senior vice president at Salesforce AI, calls this approach "BYOLLLM," or bring your own LLLM.

"The Salesforce LLM strategy is to provide an open-model ecosystem for our customers," Govindarajan said on the Targeting AI podcast from TechTarget Editorial.

"Salesforce-developed models are, of course, available out of the box on the AI stack, but customers can also bring their own LLMs. And to support this level of choice and diversity, the trust layer is model-agnostic," he continued.

As befits its core customer base, Salesforce sees sales, marketing and customer service applications as most ripe for generative AI, and that is where the vendor is focusing on the technology as a productivity engine, Govindarajan said.

Similar conversations, whether taking place in email or other messaging formats, can be automated with generative AI so the technology is embedded in daily workflows.

An example Govindarajan cited is using generative AI to let a marketing person easily make a marketing campaign multilingual.

"How do we make a customer service person more efficient? How do we make a rock star salesperson 10 times more successful? How do we make a marketing manager create campaigns that convert really well?" Govindarajan said.

"It's not easy to do that. You want to do it with safety, security, and trust," he said. "As you know, the systems can go off. So, you want to have the right guardrails in place to be able to shape it into the right form."

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

Examining developers' perception of AI tools at GitHub

Mon, 22 Apr 2024 08:00:00 -0300

The explosive popularity of generative AI has been accompanied by the question of whether developers are finding great uses for the new technology.

While the hype around GenAI has grown, the perception of its usefulness for developers has changed.

"Developers are eager to kind of embrace AI more into their complex tasks, but not for every part, and they're not open to the same degree," GitHub researcher Eirini Kalliamvakou said on the Targeting AI podcast from TechTarget Editorial.

On Jan. 17, Kalliamvakou released new findings that showed the evolution of developers' expectations of and perspectives on AI tools.

For many developers, GenAI tools are like a second brain and serve mainly to reduce some of the cognitive burden they feel performing certain tasks. Cognitive burden in coding is produced by tasks that require more energy than developers would like to invest.

"They feel that it is not worth their time," Kalliamvakou said. "This is a sort of task that is ripe for automation."

Many developers are also using AI tools to quickly make sense of a lot of information and understand the context of what they need to do.

While many developers find AI tools helpful, others experience AI skepticism, she added.

Developers who are skeptical about AI had tried AI tools and were not satisfied.

"They felt the tools are not good enough," Kalliamvakou continued.

This is because the tools sometimes gave inaccurate responses and were not helpful.

"What they were saying was AI [tools] at the moment, they cannot be trusted, they cannot give ground truths," she said.

The two groups of developers are important to keep in mind for GitHub and other AI vendors creating tools that developers will use.

Musicians and the fight for fairness in the age of GenAI

Mon, 08 Apr 2024 08:00:00 -0300

The growth of generative AI technology has led to concerns about the data AI technology companies use to train their systems.

Authors, journalists and now musicians have accused generative AI vendors of using copyrighted material to train large language models.

More than 200 musicians signed an open letter released Tuesday by the Artists Rights Alliance calling on AI developers to stop their "assault on human creativity."

While the artists argue that responsible use of generative AI technology could help the music industry, they also maintain that irresponsible use could threaten the livelihoods of many.

The problem is permissions, said Jenn Anderson-Miller, co-founder and CEO of music licensing firm Audiosocket, on the Targeting AI podcast from TechTarget Editorial.

"It's widely understood that a lot of these training models have trained on copyrighted material without the permission of the rights holders," Anderson-Miller said.

While it's true that the musicians did not produce evidence of how their works have been infringed on, generative AI vendors such as OpenAI have failed to prove that they didn't infringe on copyrighted works, she said.

For Anderson-Miller, one solution to the problem is creating a collaborative effort with musicians that would include licensing.

As a company that represents more than 3,000 artists, Audiosocket recently inserted an AI clause in its artist agreement.

In the clause, Audiosocket defined traditional and generative AI and said it plans to support the ecosystem of traditional AI.

"We don't see this as directly threatening our artists," Anderson-Miller said. "We see this as, if anything, it's helping our artists."

Security, bias risks are inherent in GenAI black box models

Mon, 25 Mar 2024 08:00:00 -0300

From bias to hallucinations, it is apparent that generative AI models are far from perfect and present risks.

Most recently, tech giants -- notably Google -- have run into trouble after their models made egregious mistakes that reflect the inherent problem with the data sets upon which large language models (LLMs) are based.

Microsoft faced criticism when its models from partner OpenAI generated disturbing images of monsters and women.

The problem is due to the architecture of the LLMs, according to Gary McGraw, co-founder of the Berryville Institute of Machine Learning.

Because most foundation models are a black box that contain security flaws within their architecture, users have little ability to manage the risks, McGraw said on the Targeting AI podcast from TechTarget Editorial.

In January, the Berryville Institute published a report highlighting some risks associated with LLMs, including data debt, prompt manipulation and recursive pollution.

"These are some risks that need to be thought about while you're building your LLM application so that you don't put your business, your enterprise, your business, at more risk than you want to take on when you adopt this technology," McGraw said.

The risks are embedded in both closed and open source models and small and large language models, he added.

"When people build their own language model, what they're often doing ... is taking a foundation model that's already developed and they're training it a little bit further with their own proprietary prompting," he continued. "These steps do not eradicate the risks that are built into the black box. In fact, all they do is hide them even further."

These risks can be dangerous for real-world situations such as the 2024 election, McGraw said. Since the language models are built from data from all over the web -- both good and unreliable -- LLMs trained on that data can be used to produce false and malicious information about the election.

"Using this technology, we need some way of controlling the output so that it doesn't get back out there into the world and just cause more confusion among people who don't know which way is up," he said.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

A look at independent AI hardware and software vendor SambaNova's open source strategy

Mon, 11 Mar 2024 09:03:57 -0300

AI hardware and software provider SambaNova Systems seeks to put enterprise customers in charge of their data while using open source models.

A smaller competitor of AI hardware vendor Nvidia, the AI vendor is trying to distinguish itself by helping enterprises train and deploy large models that they can't train on Nvidia's systems.

"What we try to focus on is how do we actually create a hardware platform that allows these companies to take these hard problems where the models are really big and deploy them in a reasonable way," co-founder and CEO Rodrigo Liang said on the Targeting AI podcast from TechTarget Editorial.

One way the vendor does this is by focusing on open source models.

"What we decided to do some years ago was [go] fully into open source," Liang said. "We want to open the model so that everybody at any given point in time can look at the entire model and how it was trained."

SambaNova introduced Sambaverse on March 6.

In SambaNova's terms, Sambaverse is a playground and API where developers can test available open source large language models from a single endpoint and compare their responses for any given application.

The new playground comes one week after the vendor unveiled Samba-1, a trillion-parameter generative AI model for the enterprise. The model comprises more than 50 open source generative AI models.

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a journalist with 35 years of experience, including 25 years as a reporter for daily newspapers. He is a senior news director for TechTarget Editorial's information management team, covering AI, unified communications software, analytics and data management technology. Together, they host the Targeting AI podcast.

It's looking like 2024 is the year of ROI for generative AI

Mon, 26 Feb 2024 08:00:00 -0400

Generative AI vendors and investors have turned their attention from last year's innovative frenzy to ROI, monetizing the language models that have revolutionized the tech world in a short time.

That's the outlook on 2024 from Kashyap Kompella, founder and analyst at RPA2AI Research, who was a guest on the Targeting AI podcast from TechTarget Editorial.

"If we think about it, 2023 really was the year of shock and awe for AI technology," Kompella said on the podcast. "But I think in 2024, there is going to be some amount of focus -- if not sole focus -- on return on investment."

At the same time, the tech landscape is seeing in 2024 an astonishing profusion of AI language models, from the ever-expanding power of large language models (LLMs) to the rise of small and open source models, and even models adapted for mobile devices, Kompella noted.

"The burst of technological innovation will continue," he said.

Investors looking at generative AI tech vehicles to pump venture funds into are hoping to hit "pay dirt" this year, as Kompella put it.

"But the businesses and the organizations that are looking to implement AI systems, they're going to be also focused on business value and return on investment," he said.

Meanwhile, 2024 is seeing a continuation and even ramping up of the litigation surrounding generative AI systems. There is also a growing emphasis on making generative AI systems safe by attempting to reduce or eliminate bias and inaccurate outputs.

Everyone from comedian and author Sarah Silverman and best-selling novelist John Grisham to The New York Times are suing generative AI vendors for misappropriating their work.

"Businesses are … becoming aware of some of the risks of using the AI systems." Kompella said. "So we'll see more indemnity clauses being offered by AI vendors."

Looking at the swelling generative AI market, Kompella also noted that venture capital activity in the arena is accelerating after a strong year in 2023.

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas.

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems.

Together, they host the "Targeting AI" podcast series.

AI, hiring and the need for humans in the loop

Mon, 12 Feb 2024 08:00:00 -0400

The fear of AI technology eliminating thousands of jobs or affecting the hiring process continues to prevail in the age of generative AI.

While many believe that AI technology will augment workers, some are already seeing the effect of AI in the job market. Indeed, tech companies and other large enterprises have laid off thousands of workers in recent months, though staffing levels are mostly still higher than before the COVID-19 pandemic.

ResumeBuilder.com found in a November 2023 survey that of 750 business leaders, 44% reported AI technology would cause layoffs in 2024. The presence of AI in the hiring process has also led to laws like New York's Local Law 144. It prevents employers from using an automated employment decision tool unless they prove they performed a bias audit beforehand.

This law and others are among the ways of proving accountability in the hiring process, said Cliff Jurkiewicz, vice president of global strategy at Phenom, an AI recruiting vendor.

"We must be accountable for the use of artificial intelligence, and the recommendations that it may be making in our decision-making," Jurkiewicz said on TechTarget Editorial's Targeting AI podcast.

While accountability is needed, removing all bias in hiring and recruiting is almost certainly unattainable, Jurkiewicz said.

"It is impossible to do that," he said. "It requires humans in the loop ... to be examining how these tools are functioning and being used in organizations."

B Corp Sama responds to AI data labeling criticism

Mon, 29 Jan 2024 10:21:24 -0400

Data labeling and annotation vendor Sama seeks to make an impact not only in the tech market but also in parts of the world where it's hard for people to partake in the digital economy.

As a women-led B Corporation chartered to do social and environmental good, Sama employs numerous people in countries such as Kenya and has created, said CEO Wendy Gonzalez on the latest episode of the Targeting AI podcast from TechTarget Editorial. She said the company has created more than 10,000 jobs in those regions.

Yet Sama has faced intense criticism for paying substandard wages to workers in Africa and also subjecting them to inhumane work environments by requiring them to view and then label offensive and violent images.

On the podcast, Gonzalez blamed some of the practices on its former client, generative AI giant OpenAI. She also argued that her company created decently paying jobs for people who otherwise would have trouble gaining employment.

"It went beyond the boundaries of work that we were comfortable doing," Gonzalez said. "It was only in existence for a handful of months."

Meanwhile, Sama's business mission is to help enterprises minimize the risk of AI model failure using its data annotating services.

New multi-cloud integration

Most recently, on Jan. 24, the vendor introduced a multi-cloud integration strategy in its platform to increase the speed of new project onboarding.

The integration allows enterprises to keep their data on one of the three top cloud providers – AWS, Microsoft and Google -- while still giving Sama access to the data.

It also enables faster onboarding to the Same platform and an integration suite compatible with Python SDKs and the Databricks platform.

The integration reduces the cost of data egress because it eliminates the need for organizations to move data around in a multi-cloud model deployment, said Gartner analyst Sid Nag.

"It speeds up application development via integration with other SDKs and programming language models while conforming to compliance and security models," Nag added.

However, it's unclear how the Sama product gets access to the data contained in an organization's primary cloud provider, Nag continued.

Ethics of data annotation and labeling

While Sama has found success in the data annotation niche, it has navigated a turbulent history in Africa.

Sama came under fire while performing contracted work for OpenAI in November 2021.

On behalf of OpenAI, Sama hired data labelers in Kenya for a take-home pay of about $2 per hour.

The labelers were charged with trying to remove toxic data from the training data sets of tools such as ChatGPT.

However, some of the workers accused Sama of making them read sexually disturbing texts while paying them unfairly low wages.

Although the work was beyond the norms of what Sama says it usually does in regions like Kenya, the incident still raised questions about the ethical implications of data labeling and what human workers are asked to do when removing toxic data from generative AI systems like ChatGPT.

For Gonzalez, it has to do with the types of jobs available for workers like those in Kenya and how those workers can be a part of the digital economy.

"If there were plentiful jobs, meaning you sort of take it or leave it, then that would be amazing," she said on the podcast "But that's not the situation. Being able to have people from around the world, globally in particular, the ones that have the greatest barriers to employment have access to the digital economy is important."

Complete and effective data is also important, she continued.

"You need a human in the loop to then validate that the AI or the model is interpreting that data as expected," Gonzalez said. "If it isn't, then you need to be able to flag that and then reflect and retrain that model."

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a journalist with 34 years of experience, including 25 years as a reporter for daily newspapers. He is a senior news director for TechTarget Editorial's information management team, covering artificial intelligence, customer experience and unified communications software, and analytics and data management technology. Together, they host the Targeting AI podcast.

Examining Microsoft venture fund M12’s AI investment approach

Tue, 16 Jan 2024 08:00:00 -0400

In the age of generative AI, Microsoft has become one of the lead investors after its massive investment in ChatGPT creator OpenAI.

Since Microsoft's $13 billion investment in OpenAI, the AI market has seen changes including a tilt toward smaller and open source AI language models. Meanwhile, the tech giant's venture fund, M12, (which did not take part in the tech giant's deal with OpenAI) is still keeping its eye out for other AI startups that could be just as big as OpenAI.

M12 seeks technologies that are new and transformative in the market, said partner Michael Stewart.

"These are usually technologies where Microsoft does not have an existing large product," Stewart said on TechTarget Editorial's Targeting AI podcast. "[There's] less of a worry that Microsoft would be left behind in this unfolding story, as much as making sure they are aware of the most attractive, most competitive newest technologies that they could partner with."

In the hot AI market, there are more opportunities for AI startups to partner with big tech companies via investments than in the past, Stewart added.

"This is a very ripe environment for startups that have a partnership mindset to work with the majors," he said.

It's also critical that AI startups looking for investment understand where the generative AI technology is going, even if they are not all incorporating the technology.

Furthermore, startups must be willing to partner with investors and accept their input in the structure of their business model, Stewart said.

"It's very difficult for me to accept that investors who are buying a portion of the company have no say or even protection of their own investment as the company grows," he said. "We do look critically at structures that are really intended to foil the influence of boards."

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the Targeting AI podcast series.

Guiding generative AI toward responsible use

Tue, 02 Jan 2024 08:00:00 -0400

When Juliette Powell and Art Kleiner started working on their book, The AI Dilemma: 7 Principles for Responsible Technology, generative AI had not yet exploded into the public consciousness.

But after OpenAI released its blockbuster AI chatbot, ChatGPT, in October 2022, the co-authors went back to revise their narrative to accommodate the sudden emergence of a transformative force in business and society, one that needs guidelines and regulations for responsible use perhaps more than any other new software technology.

"Now that we have generative AI in our hands … we also have to have the responsibility of how they will impact not just the people around us, but also the billions of people that are coming online every year who have no idea to what extent algorithms shape their lives," Powell said on the Targeting AI podcast from TechTarget Editorial. "So I feel like we have a larger responsibility."

Powell, like Kleiner, with whom she is a partner in a tech consultancy, is an adjunct professor at New York University's Interactive Telecommunications Program.

The authors' second principle, "Open the closed box," is about transparency and explainability -- the ability to look into AI systems and understand how they work and are trained, Kleiner said.

"That doesn't just mean the algorithm, it means also the company that created it and the people who engineered it and the whole system of sociotechnical activity, people and processes and code that fits together and creates it," he said.

Another of the principles at the core of the book is "people own their own data."

"One of the things that human beings do is hold biases and assumptions, especially about other people. And that when it's frozen into an AI system has dramatic effect, particularly on vulnerable populations," Kleiner said. "We are our own data."

The book is largely based on Powell's undergraduate thesis at Columbia University about the limits and possibilities in self-regulation of AI and drew on her consulting work at Intel.

As for regulation of AI technology, Powell and Kleiner are proponents to the extent that it fosters responsible use of AI.

"It's important that companies be held accountable," Powell said. "And I also think that it's incredibly important … for computer and systems engineers to actually be held accountable for their work, to actually be trained in responsible work ethics so that if people get harmed, there's actually some form of accountability."

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the "Targeting AI" podcast series.

Looking ahead: 2024 will see generative AI mature

Mon, 18 Dec 2023 08:00:00 -0400

With 2023 being the year for generative AI, 2024 will be the year the technology grows and develops.

Many industry experts think that instead of the hype slowing, it will blossom.

"In 2024, there will not be a trough of disillusionment with this tech, ever," said Mike Leone, an analyst at TechTarget's Enterprise Strategy Group, on the Targeting AI podcast from TechTarget Editorial. "We're jumping from hype to seeing productivity enhancements and improvements."

However, the year will likely bring about many more AI models with both mature and immature enterprise capabilities. Enterprises may also see cost and regulation policies that could affect enterprise adoption of generative AI, Leone added.

One development in the new year is a move away from large language models towards smaller models, said Usama Fayyad, executive director of The Institute for Experiential AI at Northeastern University.

"[There will be] a realization that bigger is not necessarily better all the time," Fayyad said. "Having more parameters makes a model less portable, less maintainable, often unstable, requires a lot more data and a lot more guidance."

Alternatively, smaller models are cheaper to train, maintain and revise, Fayyad added.

Regulation will also continue to develop in 2024, said Ricardo Baeza-Yates, director of research at The Institute for Experiential AI.

While the EU is already introducing AI policies, countries like China are expected to join in next year, Baeza-Yates said.

There will also be a push toward "grey models" instead of black box models, he added. Black box models are models that are unexplainable, while with grey models, there's a level of understanding of how the models work.

AI-assisted driving here long before autonomous vehicles

Mon, 04 Dec 2023 08:00:00 -0400

Wide use of autonomous vehicles is far off in the hazy future.

But truck and "last-mile" delivery van fleets serving online shoppers are already using advanced AI technology to guide drivers to their destinations safely.

As Stefan Heck, CEO of Nauto, vendor of an AI-powered driver and fleet safety system, explained it on the Targeting AI podcast from TechTarget News, Nauto uses the same driving tools as autonomous vehicles, but leaves human drivers in charge.

"We're not trying to replace the driver at all. We're a co-pilot or a guide, an advisor or safety warning system for the driver," Heck said on the podcast. "We use similar AI to what an autonomous vehicle does in terms of understanding what's happening."

Nauto's predictive AI package uses sensors, a dual-facing camera, computer vision and neural network technology to see, understand and anticipate driving conditions in real time and issue verbal assist alerts to drivers if they take their eyes off the road or hands off the steering wheel or act sleepy.

But unlike the tech in expensive autonomous vehicles, which are still largely in the testing phase and have run into serious safety and other operating problems in San Francisco and elsewhere, Nauto's system is more approachable at a cost of about $500 per vehicle.

As for privacy considerations, while drivers are fully aware the AI system is there and can't turn it off while they're driving, Heck said the vendor tries to make it as non-intrusive as possible so drivers don't get annoyed.

And the Nauto onboard box, mounted on the windshield, is polite, Heck argued.

"It is an algorithm looking in real time for certain risks and behaviors only," he said. "We don't have an algorithm that says … 'Stefan's picking his nose today.' But we do look for, did you fall asleep? Did you not see the stop sign where you're not paying attention?

Diving into Wayfair’s machine learning and AI odyssey

Mon, 20 Nov 2023 08:00:00 -0400

Wayfair's machine learning strategy has been critical to its growth.

The online furniture retailer's machine learning and AI journey started in 2013.

"It was about 'We think we can do better business, make our dollars go longer if we actually optimize this toolkit,'" said Tulia Plumettaz, Wayfair's director of machine learning, during the Targeting AI podcast from TechTarget News.

Wayfair started with putting machine learning technology to work to enhance its marketing. This meant using machine learning and AI technology to find the best medium to place its ads.

Soon, the online retail giant was expanding its use of the technology to price algorithmically and understand how price changes will change demand.

When Wayfair first engaged with AI, the company was mostly a "build shop," meaning it developed its AI and machine learning systems in-house, Plumettaz said.

However, the company has since pivoted to a hybrid approach and started partnering with third-party vendors, notably Google Cloud. Wayfair has also tested generative AI technology from OpenAI, even though the company has historically been a Google shop, Plumettaz said.

"We see the longevity of these partnerships as a mechanism of saying, 'Hey, we can use that to inform product,'" she said. "We see ourselves pretty much with a lot of vendors, as we want to be a partner as you're building your product rather than a transactional relation of, 'I buy a service from you.'"

Regarding generative AI, the retailer has integrated the technology into products such as Decorify, a generative AI design tool. It is also incorporating the technology internally and in some sales operations.

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the "Targeting AI" podcast series.

Tech industry reaction to Biden’s AI executive order mixed

Mon, 06 Nov 2023 17:46:49 -0400

The tech industry is dealing with the implications of an executive order on AI signed by President Joe Biden Oct. 30.

The order aims to establish new standards for AI safety and security, while protecting the privacy of American citizens, promoting innovation and spurring development of responsible AI.

"It's really looking at developing guidelines and best practices really across the whole field," said Katherine Hendrickson, a senior research lead at EpiSci, an AI military and aerospace software and hardware vendor, on the Targeting AI podcast from TechTarget News. While the order holds much promise for AI system developers, Hendrickson said its main value is its focus on research and the government partnering with research centers, while also appearing to fund a number of AI sectors.

The order also shows how the federal government is promoting AI technology internally, said Forrester analyst Alla Valente.

"From the language of this EO, what's clear is that the federal government is now being mandated to leverage AI, and then use that AI to improve how it does everything it does," she said.

However, AI vendors in both the private and federal sectors should pay attention to the order, especially in the areas in which there is a call for standards in AI safety and security, Valente added.

The executive order discusses the need for new standards to test AI, built on the National Institute of Standards and Technology's framework.

"What the executive order is hoping to do is identify some of the risks as early as possible," Valente said. If that's accomplished, risk and security management practices can be embedded earlier in the development cycle of the AI lifecycle, she added.

While the intent of the executive order is to create standards and safety guardrails around AI systems, the lack of actionable steps stood out to Gopi Polavarapu, chief solutions officer at Kore.ai.

"From a vendor perspective, it's a welcome governance that's coming from the government, but at the end of the day, we need to know what those standards are, how that's going to be enforced," Polavarapu said. Kore.ai is a startup vendor of conversational AI tools for enterprises.

Venture capital firm helps launch early AI startups

Mon, 23 Oct 2023 16:35:41 -0300

The success of an AI startup depends on not only the technology and the problem the startup seeks to solve within the market, but also the support it has from investors and venture capital firms.

One venture capital (VC) firm that prides itself on working closely with the founders of startups is Glasswing Ventures.

As an early VC firm, Glasswing is focused on investing in AI-enabled companies and what it calls "frontier tech" B2B companies, according to Kleida Martiro, a partner at the company.

"We have built strong convictions around certain areas where AI could really revolutionize certain industries," Martiro said during a Targeting AI podcast discussion. Those convictions have led Glasswing to create a mission oriented toward "connecting and protecting" building AI startups.

When in the connect part of the mission, the VC firm looks for startups developing smart data infrastructure and automation, and vertical applications. The protect part is centered around security, which includes data governance and cybersecurity.

Glasswing focuses on seed and pre-seed financing of startups in earliest stages. It guides startups by connecting them to customers, talent and more fundraising.

"We serve as a true partner, we get involved as much as the startup wants us to get involved and we step aside when they don't need our help," Martiro said. "We're very much founder-first. They're part of our extended family."

Startups working with Glasswing need to demonstrate that their technology addresses critical need in the market. The startups also need to start with real talent.

"When investing at such an early stage, it really comes down to the team," Martiro said. "Backing a team that can execute, has the vision, has the technical chops and the technical skills very much married with the business ... and backing good people who are hustlers is truly what makes it at this stage."

Generative AI and CX co-existing, carefully

Tue, 10 Oct 2023 08:00:00 -0300

Customer experience chatbots that not only fail to deliver but also fall short of their human counterparts are the bane of CX designers' vision of an automated future.

Now, the arrival of generative AI technology is promising to correct dysfunctional chatbots' missteps, ease the burden on overworked and underappreciated human customer service agents and satisfy frustrated consumers.

But CX expert Don Fluckinger, a veteran tech journalist who has also worked as a CX industry analyst, casts a skeptical eye on claims made on behalf of generative AI and takes a cautionary view of automation and chatbots themselves.

"Losing jobs is never all right," Fluckinger said on TechTarget News' Targeting AI podcast. "But would it be OK for generative AI to more effectively answer customer questions so that humans could monitor what it's doing and not spewing out deceptive or wrong information? That would be good."

Many call centers already have AI-powered interactive voice response (IVR) systems, Fluckinger noted.

And yet, these don't work all that well.

"I've seen demos of these at conferences, on exhibition floors. I've read about them, but I have never run into it in real life yet," Fluckinger said. "The IVRs I hit are always pretty dumb."

Meanwhile, better IVR systems could be on the horizon, and generative AI could help.

Fluckinger noted, though, that while better call center and other CX platforms infused with generative AI technology are coming, they have to be tested and integrated with current systems.

And, finally, companies have to buy the new technology. But the industry isn't there yet.

Note: At the time this podcast was recorded, Fluckinger was a CX analyst for TechTarget's Enterprise Strategy Group. He now covers digital experience systems, end-user computing and the CPU/GPU market for TechTarget Editorial's news unit.

Global hiring tech company Oyster treads carefully with generative AI

Mon, 25 Sep 2023 08:00:00 -0300

Oyster is keeping its distance from the generative AI craze, at least for now.

When the vendor, whose platform helps companies with hiring, paying and managing employees in 180 countries around the world, recently came out with a new chatbot, Pearl, it fueled it with basic conversational AI, not the generative variety.

That's largely because Oyster wanted to skirt generative AI's by now well-known risks of outputting inaccurate and biased information, said Michael McCormick, senior vice president of product and engineering at Oyster, on this week's episode of TechTarget Editorial's Targeting AI podcast.

The vendor is a certified B Corporation with a mandate to focus on social and environmental performance.

"One of the big problems with generative AI that everyone knows about is the tendency it can hallucinate," McCormick said. "We've seen examples of people resting control away from the intent of the generative AI programmers, and convincing the generative AI to do and say all sorts of awful things.

"And there is not enough data capturing the experience of underserved and underrepresented groups," he added. "And so there's a huge amount of risk if you try to have guidance from systems like that in the HR space."

Pearl is Oyster's first public foray into using AI to interact with users of its platform. Essentially, the chatbot answers, in conversational format, questions about hiring and remote employment regulations in a world of distributed work in dozens of far-flung countries.

The chatbot is trained on Oyster's wealth of static information about global HR policies, taxes and benefits. So essentially it functions as a private large language model, with Oyster employees serving as "humans in the loop" to ensure that Pearl gives simple, consistent and accurate advice, thus further minimizing generative AI risk.

"If you give an individual the ability to have a direct conversation with a generative AI, you give up control of what might happen," McCormick said. "And you're at the mercy of OpenAI or Bard or whomever in terms of how they try to control that."

Digital assistant platform vendor talks AI disruption

Mon, 11 Sep 2023 08:00:00 -0300

Much of the world became aware of generative AI and large language models with the release of Dall-E and ChatGPT last year, but Conversica CEO Jim Kaskade has known about the technology since 2019.

During a walk with a top AI executive at Google, Kaskade said he learned about a lot about where the tech giant was heading with generative AI technology.

Once he became CEO of the AI vendor specializing in digital assistants, he looked for ways to apply the technology in a way that was disruptive on the scale of earlier world-changing technologies.

Kaskade's company's brand of disruption is conversational AI and the generative AI-powered digital assistants that he sees as an automated workforce that will eventually ease the burden of much menial work now done by humans.

The application of LLMs in the form of OpenAI's ChatGPT and other similar systems has seen quick adoption worldwide compared to similarly disruptive technologies such as electricity, telephone communications and television, but not all organizations are comfortable with the technology.

That uneasiness is analogous with the discussion in recent years about public cloud versus private and hybrid cloud, Kaskade said.

"It's just a sequence of been there, done that," he said on Tech Target Editorial's Targeting AI podcast. "Once people get really comfortable with the amount of governance that's put around the public application [product], the public cloud solutions, then the big enterprises will start to move from private LLM to public LLM. It'll take the same period of time as it did with cloud."

The more comfortable companies and people are with AI technology, the more benefits they can gain from it.

"Look at what happened with the computer, the PC, look what happened with the phone, look what happened with the world wide web," Kaskade said. "AI is going to be more disruptive than any of those or all of them added together."

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas.

AI facing ”turtles all the way down”

Mon, 28 Aug 2023 08:00:00 -0300

AI technology has become a “turtles all the way" down problem.

It's a dilemma in which AI technology is created to solve a particular problem. But in order to test the first AI tool, the tester has to use another AI technology, and then a third, and so on.

According to Johna Till Johnson, CEO of advisory and IT consulting firm Nemertes Research, most enterprises try to avoid this problem by first providing proprietary data to the input of the first AI technology and testing the output, eliminating the need to have an AI tester test constantly.

"The problem is, as you expand your AI outside of private data, the outputs can vary much more wildly," Johnson said during an interview on the Targeting AI podcast from TechTarget News. "You still need some form of AI to test the outputs and then you need some form of AI to test the AI that's testing the outputs, and you get your turtles all the way down again."

Enterprises looking to get away from this endless feedback loop might need to stick with performing manual testing of the output of the initial AI technology, Johnson continued.

Moreover, enterprises must ensure that the data they input into the technology from the beginning is trustworthy, she said.

So using an AI tool like OpenAI's ChatGPT is not advisable.

"ChatGPT has been abused horribly," Johnson said, adding that if the tool is used at her small business, it will need to be checked by a human, a time-costly activity. "If you think about the best use of ChatGPT at the moment, it's writing really bad term papers."

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas.

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.

AI: good and evil in the movies, and in reality

Mon, 14 Aug 2023 08:49:04 -0300

Whether AI is good and helpful or evil and dangerous is the stuff of endless debate in tech circles during this year's "generative AI moment."

In the movies, though, it's been pretty consistent: AI is the kind of malevolent force as embodied the HAL 9000 computer in the 1968 sci-fi classic 2001: A Space Odyssey.

But CX analyst Liz Miller of Constellation Research, who recently wrote a blog about AI and the movies and Salesforce, says AI should be seen as more like Meryl Streep's helpful assistant in the 2006 film The Devil Wears Prada.

Andy, the human assistant played by Anne Hathaway, whispers useful information about a prospective customer in the Streep character's ear -- and Miller thinks we should let AI technology do the same.

Indeed, it already is in some ways, in the form of digital assistants and generative AI-supported systems such as Microsoft's Copilot and Salesforce's various GPT tools.

"There's this fallacy that AI was going to take everything over, when in reality what AI needed to do was take over the stuff that we did not have the capacity to do in the time that we had to do it," Miller said on TechTarget Editorial's Targeting AI podcast.

"I think that's where we're starting to see AI take shape. And that's what I meant by that analogy," Miller added. "There's nothing wrong with HAL 9000. It's a great villain."

Meanwhile, beyond AI and the movies, Miller touches on other topics during the podcast, including the fast-moving saga of the X social media platform (formerly known as Twitter). For her, the AI story there is not about X itself but about what happens with mercurial X owner Elon Musk's nascent AI venture, xAI.

Tesla, federal highway officials deficient on autonomous vehicle safety: an interview with industry analyst Sam Abuelsamid

Mon, 31 Jul 2023 08:00:00 -0300

Sam Abuelsamid thinks Tesla's driver assist technology is unsafe.

The mobility ecosystem analyst at Guidehouse Insights is a vocal critic of the electric vehicle giant's AI-powered "Autopilot" technology.

A former mechanical engineer, automotive journalist and Ford and General Motors employee, Abuelsamid also charges that the National Highway Traffic Safety Administration (NHTSA) has grossly undervalued safety considerations for self-driving and partially self-driving vehicles.

While Abuelsamid acknowledges that Tesla has advanced society's views on driving technology by appealing to consumers and popularizing electric vehicles, he also refuses to call such vehicles "autonomous." Instead, he refers to them as "automated," because, as he points out, few fully driverless vehicles are on the road.

In addition, Abuelsamid contends that Tesla has tried to do safety "on the cheap" by relying on cameras only to power Autopilot features and not using considerably more expensive sensor arrays.

"I think they've been utterly reckless and irresponsible in their approach to automated driving by putting experimental software in the hands of average consumers who are not trained in how to properly test and evaluate this kind of safety critical software," Abuelsamid says

Meanwhile, autonomous vehicle technology vendors including Cruise, Waymo, Zoox and Motional are using multiple types of sensors, he says.

One Tesla fan and investor, Ross Gerber, CEO of Gerber Kawasaki Wealth and Investment Management, has disputed Tesla safety critics. He argues that autonomously driven Teslas will get increasingly safer with hundreds of thousands of consumers driving and testing out the beta version of the popular carmaker's full self-driving capability.

But Abuelsamid faults NHTSA for failing to effectively oversee safety aspects of autonomous vehicle technology vendors.

"I think the National Highway Traffic Safety Administration has been negligent in not doing more to require sharing of data from these test vehicles to build an understanding of how these things function," he says. "At a minimum what we need is the electronic equivalent of what we have to do as humans to get a driver's license."

Go to TechTarget News for reports on autonomous vehicle technology and other AI developments.

An Interview with AI Expert Michael Bennett of Northeastern University

Fri, 28 Jul 2023 14:08:09 -0300

Our guest is Michael Bennett, director of education curriculum and business lead for responsible AI at the Institute for Experiential AI at Northeastern University. Bennett, a practicing lawyer, holds a law degree from Harvard Law School and a PhD from Rensselaer Polytechnic University in Philosophy -- Science, Technology and Society. Bennett is also an occasional TechTarget contributing writer.

During the 45-minute episode, Bennett discusses the impact of New York City's new Law 144 governing the use of AI in automated employment decision tools, which he helped draft before it went into effect on July 5, 2023. The local law is likely to have a wide-reaching effect on employers across the U.S. if only because a large number of corporations are based in or have a significant presence in the country's largest city, Bennett says.

The law prohibits "employers and employment agencies from using an automated employment decision tool unless the tool has been subject to a bias audit within one year of the use of the tool, information about the bias audit is publicly available, and certain notices have been provided to employees or job candidates." Law 144 has already spun off a thriving new niche of law and audit firms providing services to employers to comply with the measure,

Bennett also zeroes in on the hottest topic in the tech world at the moment: generative AI. He talks about various efforts, including projects he's involved in, to rein in, regulate and harness for effective use large language models and the AI chatbots such as ChatGPT and Google Bard that have become ubiquitous in the business and consumer spheres over the last year.

On another front, AI and the arts, Bennett discusses the latest developments in copyright law as it relates to AI and also touches on the Hollywood TV writers strike and writers' concerns about generative AI systems taking over their jobs.

Podcast intro/outro music by Six Umbrellas: "Joker." This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.