Early Interest in Generative AI

• Martin's initial exposure to Generative AI in 2016 through a conference talk in Milano, Italy, and his early work with Generative Adversarial Networks (GANs).

Development of GANs and Early Language Models since 2016

• The evolution of Generative AI from visual content generation to text generation with models like Google's Bard and the increasing popularity of GANs in 2018.

Launch of GenerativeAI.net and Online Course

• Martin's creation of GenerativeAI.net and an online course, which gained traction after being promoted on platforms like Reddit and Hacker News.

Defining Generative AI

• Martin’s explanation of Generative AI as a technology focused on generating content, contrasting it with Discriminative AI, which focuses on classification and selection.

Evolution of GenAI Technologies

• The shift from LSTM models to Transformer models, highlighting key developments like the "Attention Is All You Need" paper and the impact of Transformer architecture on language models.

Impact of Computing Power on GenAI

• The role of increasing computing power and larger datasets in improving the capabilities of Generative AI

Generative AI in Business Applications

• Martin’s insights into the real-world applications of GenAI, including customer service automation, marketing, and software development.

Retrieval Augmented Generation (RAG) Architecture

• The use of RAG architecture in enterprise AI applications, where documents are chunked and queried to provide accurate and relevant responses using large language models.

Technological Drivers of GenAI

• The advancements in chip design, including Nvidia’s focus on GPU improvements and the emergence of new processing unit architectures like the LPU.

Small vs. Large Language Models

• A comparison between small and large language models, discussing their relative efficiency, cost, and performance, especially in specific use cases.

Challenges in Implementing GenAI Systems

• Common challenges faced in deploying GenAI systems, including the costs associated with training and fine-tuning large language models and the importance of clean data.

Measuring GenAI Performance

• Martin’s explanation of the complexities in measuring the performance of GenAI systems, including the use of the Hallucination Leaderboard for evaluating language models.

Emerging Trends in GenAI

• Discussion of future trends such as the rise of multi-agent frameworks, the potential for AI-driven humanoid robots, and the path towards Artificial General Intelligence (AGI).

In this episode, we sit down with Steve Orrin, Federal Chief Technology Officer at Intel Corporation. Steve shares his extensive experience and insights on the transformative power of AI and its parallels with past technological revolutions. He discusses Intel’s pioneering role in enabling these shifts through innovations in microprocessors, wireless connectivity, and more.

Steve highlights the pervasive role of AI in various industries and everyday technology, emphasizing the importance of a heterogeneous computing architecture to support diverse AI environments. He talks about the challenges of operationalizing AI, ensuring real-world reliability, and the critical need for robust AI security. Confidential computing emerges as a key solution for protecting AI workloads across different platforms.

The episode also explores Intel’s strategic tools like oneAPI and OpenVINO, which streamline AI development and deployment. This episode is a must-listen for anyone interested in the evolving landscape of AI and its real-world applications.

Intel's Legacy and Technological Revolutions

• Historical parallels between past tech revolutions (PC era, internet era) and current AI era.
• Intel's contributions to major technological shifts, including the development of wireless technology, USB, and cloud computing.

AI's Current and Future Landscape

• AI's pervasive role in everyday technology and various industries.
• Importance of computing hardware in facilitating AI advancements.
• AI's integration across different environments: cloud, network, edge, and personal devices.

Intel's Approach to AI

• Focus on heterogeneous computing architectures for diverse AI needs.
• Development of software tools like oneAPI and OpenVINO to enable cross-platform AI development.

Challenges and Solutions in AI Deployment

• Scaling AI from lab experiments to real-world applications.
• Ensuring AI security and trustworthiness through transparency and lifecycle management.
• Addressing biases in AI datasets and continuous monitoring for maintaining AI integrity.

AI Security Concerns

• Protection of AI models and data through hardware security measures like confidential computing.
• Importance of data privacy and regulatory compliance in AI deployments.
• Emerging threats such as AI model poisoning, prompt injection attacks, and adversarial attacks.

Innovations in AI Hardware and Software

• Confidential computing as a critical technology for securing AI.
• Research into using AI for chip layout optimization and process improvements in various industries.
• Future trends in AI applications, including generative AI for fault detection and process optimization.

Collaboration and Standards in AI Security

• Intel's involvement in developing industry standards and collaborating with competitors and other stakeholders.
• The role of industry forums and standards bodies like NIST in advancing AI security.

Advice for Aspiring AI Security Professionals

• Importance of hands-on experience with AI technologies.
• Networking and collaboration with peers and industry experts.
• Staying informed through industry news, conferences, and educational resources.

Exciting Developments in AI

• Fusion of multiple AI applications for complex problem-solving.
• Advancements in AI hardware, such as AI PCs and edge devices.
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• Potential transformative impacts of AI on everyday life and business operations.

In this episode we talk to Kirk Marple about the power of Knowledge Graphs when combined with GenAI models. Kirk explained the growing relevance of knowledge graphs in the AI era, the practical applications, their integration with LLMs, and the future potential of Graph RAG.

Kirk Marple a veteran of Microsoft and General Motors, Kirk has spent the last 30 years in software development and data leadership roles. He also successfully exited the first startup he founded, RadiantGrid, acquired by Wohler Technologies.

Now, as the technical founder and CEO of Graphlit, Kirk and his team are streamlining the development of vertical AI apps with their end-to-end, cloud based offering that ingests unstructured data and leverages retrieval augmented generation to improve accuracy, domain specificity, adaptability, and context understanding – all while expediting development.

Episode Summary -

• Introduction to Knowledge Graphs:
• Knowledge graphs extract relationships between entities like people, places, and things, facilitating efficient information retrieval.
• They represent intricate interactions and interrelationships, enabling users to "walk the graph" and uncover deeper insights.
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• Importance in the AI Era:
• Knowledge graphs enhance data retrieval and filtering, crucial for feeding accurate data into large language models (LLMs) and multimodal models.
• They provide an additional axis for information retrieval, complementing vector search.
• Industry Use Cases:
• Commonly used in customer data platforms and CRM models to map relationships within and between companies.
• Knowledge graphs can convert complex datasets into structured, easily queryable formats.
• Challenges and Limitations:
• Familiarity with graph databases and the ETL process for graph data integration is still developing.
• Graph structures are less common and more complex than traditional relational models.
• Integrating Knowledge Graphs with LLMs:
• Knowledge graphs enrich data integration and semantic understanding, adding context to text retrieved by LLMs.
• They can help reduce hallucinations in LLMs by grounding responses with more accurate and comprehensive context.
• Graph RAG (Retrieval Augmented Generation):
• Combines knowledge graphs with RAG to provide additional context for LLM-generated responses.
• Allows retrieval of data not directly cited in the text, enhancing the breadth of information available for queries.
• Scalability and Efficiency:
• Effective graph database architectures can handle large-scale graph data efficiently.
• Graph RAG requires a robust ingestion pipeline and careful management of data freshness and retrieval processes.
• Future Developments:
• Growing interest and implementation of knowledge graphs and Graph RAG in various industries.
• Potential for new tools and standardization efforts to make these technologies more accessible and effective.
• Graphlit: Simplifying Knowledge Graphs:
• The platform focuses on simplifying the creation and use of knowledge graphs for developers.
• Provides APIs for easy integration, supporting domain-specific vertical AI applications.
• Offers a unified pipeline for data ingestion, extraction, and knowledge graph construction.
• Open Source and Community Contributions:
• Recommendations for...