Monthly Roundup: New LLMs, GTC 2024, Constraint-Driven Innovation, Model Safety, and GraphRAG
Apr 18, 2024
auto_awesome
Paco Nathan, Founder of Derwen, discusses updates on large language models and advancements in efficiency and scalability. Topics include Constraint-Driven Innovation, GTC 2024 highlights, and lessons from AI workload security exploits. Exciting discussions on model improvements, generative AI tools, and the importance of data engineering for AI safety.
Planning for future upgrades in large language models is essential for flexibility and efficiency.
Proactive measures and ongoing monitoring are crucial to address security vulnerabilities in AI workloads.
Deep dives
Trends in Large Language Models
Continuous improvements in large language models are driving the need for flexibility in model usage, emphasizing the importance of planning for future upgrades and trends such as a mixture of experts approach for increased efficiency and cost-effectiveness.
Insights from AWS Chief Scientist
James Hamilton's retrospective on constraint-driven innovation highlights the significant resources and costs involved in training large language models, shedding light on the complexities and challenges faced in developing AI technologies.
NVIDIA's Shift Towards AI Platform
NVIDIA's transition from a chip provider to a comprehensive AI platform signifies a strategic move to shape the AI ecosystem and introduce new hardware offerings, showcasing a commitment to advancing AI capabilities across a range of applications.
Security Vulnerabilities in AI workloads
The discovery of critical security vulnerabilities in AI workloads, particularly in the Ray framework, highlights the importance of proactive measures and ongoing monitoring to address potential exploits, underscoring the need for robust defenses beyond model-specific safeguards.
Paco Nathan is the founder of Derwen, a boutique consultancy focused on Data and AI. This episode is part of our series of monthly roundups and covers: recently released large language models, Constraint-Driven Innovation, highlights from GTC 2024, and Lessons from the First AI Workload Security Exploit.