Targeting AI

While physical AI has been accelerating in China, the technology is just starting to gain momentum in the U.S. In this Targeting AI episode, Sce Pike, vice president of AI growth and solutions at Telus Digital, discusses the future of physical AI and robotics, focusing on data collection challenges, standardization efforts, and the cultural acceptance of robots in different regions.

In this episode we discuss:

Data collection problems for physical AI

Standardization efforts in robotics data

Training data for generalist robotic models

Safety and quality issues in robotics data

Cultural acceptance of robots in different regions

To dive more into topics about robotics, check out TechTarget AI 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:

Humanoid robots: The next frontier in physical AI

Physical AI challenge: Making humanoid robots work in the real world

Smarter robots: agentic and physical AI converge in business

With Google one of the leading model makers in the AI race, the tech giant is continually addressing challenges in interoperability, governance and the future of enterprise AI. On this episode of Targeting AI, Michael Clark, director of product management at Google Cloud, dives into how AI agents are evolving, their applications in enterprise environments, and the importance of trust and security.

In this episode, we discuss:

AI agent ecosystem and interoperability challenges

Google Cloud's Gemini Enterprise Agent Platform

Governance, security, and trust in AI systems

Building and scaling enterprise AI agents

Future trends in AI agent development

To read more into topics about Google 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:

Google Cloud Bets Big on the Agentic Enterprise

What Google’s Release of Gemini 3.8 Says About the AI Market

In Catch-up Mode, Google Intros AI Agents for Financial, Legal Services

Most enterprises are familiar with probabilistic AI systems such as generative AI and agentic AI. According to Maha Achour, CEO and founder of enterprise AI platform vendor Kodamai, another form of AI could help enterprises better trust these systems: math-based AI. In this episode of Targeting AI, Achour discusses how math-based AI can be trusted more than other forms of AI because it is harder to hack or be manipulated. She dives deep into the mathematical principles that Kodamai uses, including category theory and type theory.

In this episode, we discuss:

Mathematical principles such as category theory and type theory could help remove the black box behind current forms of AI.

The importance of using neuro-symbolic AI.

What grounding AI systems in mathematical certainty rather than probabilistic approximations means for challenges such as hallucinations, governance and security.

Why artificial general intelligence and artificial superintelligence require human collaboration.

The superiority of human intuition.

To learn more about generative 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:

Startup Pioneering Neuro-Symbolic AI Secures Bridge Funding

Mathematical Superintelligence Startup Valued at $1.45B
An Explanation of the Different Types of AI

The explosion of AI in 2022 coincided with the introduction of image-generating models such as Dall-E, which were met with controversy. However, in recent years, AI companies have partnered with legacy image vendors such as Getty Images and Shutterstock. On this episode of Targeting AI, Daniel Mandell of Shutterstock explains how data licensing is changing in the age of generative AI. Mandell explains how Shutterstock has evolved from a stock content company into a data licensing partner for model training, inference and increasingly agentic workflows.

We discuss how Shutterstock approaches creator compensation, how it filters synthetic data, why inference is becoming as important as training, and why high-quality rights-cleared content still matters even as image generators improve.

Featuring: Daniel Mandell, senior vice president of data licensing and AI at Shutterstock

In this episode, we cover how:

Shutterstock’s AI business grew from image licensing into a broader multimodal data licensing model covering video, audio, 3D, fonts and templates.

The company now serves both model training and inference use cases, with inference becoming a major part of the business.

Demand has shifted from broad volume requests to highly specific, niche, and metadata-rich content for real-world applications.

Mandell says Shutterstock is not trying to be a model builder, but rather a content and data partner that helps customers solve practical AI problems.

The company sees its role as combining stock assets with AI-generated content to offer more optionality to customers.

Compensation for creators remains an open challenge, but Shutterstock says it is trying to ensure contributors stay part of the AI ecosystem and continue to monetize their work.

Synthetic data is useful for edge cases, but Mandell argues models still need real rights-cleared human-made data to perform well.

To learn more about AI and finance, 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:

And Now it Begins: Shutterstock Unveils Text-to-Image AI Platform

The need for tools such as Getty Generative AI by iStock

The Perplexity-Getty Images Licensing Deal is Different

In this episode, Don Muir of AI-native private market investment platform vendor F2, discusses how AI is transforming private markets investing by automating data processing, standardizing unstructured data, and building a system of agentic workflows tailored for financial institutions. He explains the importance of AI native solutions for private credit and equity, and how F2's platform helps decision-making and operational efficiency.

Featuring: Dan Muir, co-founder and CEO of F2

In this episode, we cover:

AI's role in automating private market workflows

Standardization of unstructured financial data

Agentic AI systems tailored for finance

Impact of AI on private credit and equity markets

F2's platform and its customization for firms

Balancing probabilistic AI with deterministic financial data

Pricing models for AI-driven financial services

Future growth and market convergence in private markets

AI's role in risk management during market dislocation

To learn more about AI and finance, 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:

Native AI on Horizon for Finance, Accounting Teams

Robinhood Will Let Agents Trade -- It Could Be a Trend

Global study reveals biggest risks of AI in finance sector