Curious about the real history of artificial intelligence and how it has woven itself into the fabric of modern life? Join us as Erick Galinkin returns to share his insights on the evolution of AI, from its early conceptual stages to its present-day applications like self-driving cars. We promise you'll walk away with a deep understanding of the various levels of autonomous driving and the enormous strides AI has made, surpassing even the most ambitious expectations of the past. This is not just a technical conversation; it's a philosophical journey questioning AI's origins and contemplating its future.
Discover the transformative role of massively parallel processing in AI, especially within computer vision. Learn how CUDA, initially designed for computer graphics, has become indispensable for deep learning by efficiently handling complex computations. We break down neural networks and activation functions, explaining how frameworks like TensorFlow and PyTorch leverage specialized hardware to achieve remarkable performance improvements. If you've ever wondered how deep learning mimics human neural behavior or how AI-specific hardware is optimized, this segment will be invaluable.
In the latter part of our episode, we tackle the intricate relationship between AI and cybersecurity. Hear about the challenges of training machine learning models to detect malware and the dual-use nature of AI models that can serve both defensive and offensive purposes. We shed light on the complexities of securing AI systems, emphasizing the need for specialized risk management strategies distinct from traditional cloud security. From tools like Garak to frameworks like Nemo Guardrails, we explore various solutions to secure large language models and ensure they operate safely within an organization. This episode will arm you with the knowledge to understand and mitigate the risks associated with deploying AI technologies in your own projects.
https://github.com/leondz/garak
https://github.com/nvidia/nemo-guardrails
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