Targeting AI

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

With agentic AI and generative AI, enterprises are continually looking for ways to transform their workflows. In this episode of the Targeting AI podcast from AI Business, Esther Shittu and Shaun Sutner welcome Jason Olkowski of CRM and workforce automation vendor Creatio, to examine how enterprises can use agentic AI to improve decision-making within their workflows and unlock true value.

Featuring: Jason Olkowski, chief strategy officer at Creatio

In this episode, we cover:

Enterprise AI adoption gaps and organizational maturity

The concept of the unlimited enterprise and removing platform limits

Creatio's new AI agents for banking and their governance frameworks

Prioritizing AI initiatives for business impact

The role of AI in decision-making and worker augmentation

To learn more about using agentic AI in enterprise workflows, 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 Creatio is Redefining CRM for Financial Institutions

Turning Agentic AI From Idea to Essential Enterprise Process

Nvidia Launches Workflows for Organizations to Build Their Own AI

In this episode of the Targeting AI podcast from AI Business, Shaun Sutner and Esther Shittu host Ben Schreiner from AWS to discuss the evolving landscape of AI adoption among small and medium-sized businesses (SMBs). They delve into AI safety, AI adoption by SMBs, practical use cases, choosing the right partners, managing costs, and the future of AI tools tailored for SMBs -- emphasizing responsible deployment, the importance of trust in data, and how AI can augment human effort rather than replace it.

Featuring: Ben Schreiner, head of AI and modern data strategy business development at AWS

In this episode, we cover:

AI adoption challenges for SMBs

AI safety and guardrails

Partner selection and vendor lock-in

AI tools and real-world SMB applications

To learn more about AWS and AI for enterprises and SMBS, 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:

AWS Launches Frontier Agents

AWS Simplifies Agent Building With Model Customization

How AI Can Lead to Operational Transformation in Smaller US Companies