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...

At LanguageTool, Bartmoss St Clair (Head of AI) is pioneering the use of Large Language Models (LLMs) for grammatical error correction (GEC), moving away from the tool's initial non-AI approach to create a system capable of catching and correcting errors across multiple languages.

LanguageTool supports over 30 languages, has several million users, and over 4 million installations of its browser add-on, benefiting from a diverse team of employees from around the world.

Episode Summary -

1. LanguageTool decided against using existing LLMs like GPT-3 or GPT-4 due to cost, speed, and accuracy benefits of developing their own models, focusing on creating a balance between performance, speed, and cost.
2. The tool is designed to work with low latency for real-time applications, catering to a wide range of users including academics and businesses, with the aim to balance accurate grammar correction without being intrusive.
3. Bartmoss discussed the nuanced approach to grammar correction, acknowledging that language evolves and user preferences may vary, necessitating a balance between strict grammatical rules and user acceptability.
4. The company employs a mix of decoder and encoder-decoder models depending on the task, with a focus on contextual understanding and the challenges of maintaining the original meaning of text while correcting grammar.
5. A hybrid system that combines rule-based algorithms with machine learning is used to provide nuanced grammar corrections and explanations for the corrections, enhancing user understanding and trust.
6. LanguageTool is developing a generalized GEC system, incorporating legacy rules and machine learning for comprehensive error correction across various types of text.
7. Training models involve a mix of user data, expert-annotated data, and synthetic data, aiming to reflect real user error patterns for effective correction.
8. The company has built tools to benchmark GEC tasks, focusing on precision, recall, and user feedback to guide quality improvements.
9. Introduction of LLMs has expanded LanguageTool's capabilities, including rewriting and rephrasing, and improved error detection beyond simple grammatical rules.
10. Despite the higher costs associated with LLMs and hosting infrastructure, the investment is seen as worthwhile for improving user experience and conversion rates for premium products.
11. Bartmoss speculates on the future impact of LLMs on language evolution, noting their current influence and the importance of adapting to changes in language use over time.
12. LanguageTool prioritizes privacy and data security, avoiding external APIs for grammatical error correction and developing their systems in-house with open-source models.