Stephan Gillich

Bringing AI Everywhere

The biggest barrier to enterprise AI isn't the models—it's infrastructure complexity. An open, hybrid approach provides the solution.

Bringing AI Everywhere
#1about 3 minutes

Understanding the current barriers to AI adoption

Despite AI's potential, low production deployment rates and infrastructure challenges are significant hurdles for many organizations.

#2about 3 minutes

The three evolutionary phases of enterprise AI

Enterprise AI is evolving from individual copilots to integrated workflow automation and finally to complex AI-driven functions.

#3about 4 minutes

Intel's strategy for bringing AI everywhere

Intel's approach to democratizing AI is built on four pillars: innovation, value maximization, ubiquitous deployment, and responsible use.

#4about 3 minutes

Choosing the right hardware for different AI workloads

Intel offers specialized hardware like Gaudi for power users and integrated accelerators like AMX and NPUs for general-purpose AI tasks.

#5about 3 minutes

Enabling hybrid AI with an open software stack

Hybrid AI splits workloads between client and cloud, enabled by an open software stack featuring tools like OpenVINO and oneAPI.

#6about 4 minutes

A case study on AI-powered code optimization

Turing Tech uses Intel's Gaudi accelerators and oneAPI to power an AI platform that automates code refactoring and performance optimization.

#7about 3 minutes

Using RAG for secure enterprise data integration

Retrieval-Augmented Generation (RAG) allows enterprises to use private data with LLMs securely by augmenting prompts without retraining models.

#8about 2 minutes

Building trusted environments for responsible AI

Hardware-level security features like Intel TDX create trusted compute environments essential for data privacy and regulatory compliance.

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Matching moments

Moving beyond consumer AI to its real-world impact
01:08 MIN

Moving beyond consumer AI to its real-world impact

AI in Africa: How can we bounce back?

Navigating AI regulation and open source
04:04 MIN

Navigating AI regulation and open source

The AI Hype Filter: What’s Real, What’s Investable, What’s Noise?

Building trust and skills for the future of AI
06:43 MIN

Building trust and skills for the future of AI

Tackling the Risks of AI - With AI

Overcoming legal and security roadblocks for AI adoption
02:46 MIN

Overcoming legal and security roadblocks for AI adoption

The AI Skills Gap: What Tech Leaders Must Get Right

Why AI giga factories are not enough for sovereignty
03:27 MIN

Why AI giga factories are not enough for sovereignty

AI Sovereignty: What Does It Take?

Defining what intelligence everywhere means for consumers
02:19 MIN

Defining what intelligence everywhere means for consumers

Intelligence Everywhere: The Future of Consumer Tech

Addressing legal challenges and building trust in AI systems
07:11 MIN

Addressing legal challenges and building trust in AI systems

From Monolith Tinkering to Modern Software Development

Overcoming key challenges in cloud AI adoption
05:17 MIN

Overcoming key challenges in cloud AI adoption

Reference Architecture of AI in the Cloud

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From learning to earning

Jobs that call for the skills explored in this talk.

AI Engineer / AiOps

AI Engineer / AiOps

Diehl Stiftung & Co. KG
Röthenbach a.d. Pegnitz, Germany

Keras
DevOps
Python
Docker
PyTorch
+5