Sam Altman & Khosla Ventures - AI: Evolution, Disruption, and the Future of Work
In this insightful episode, Sam Altman and Vinod Khosla delve into the world beyond 2035, discussing the astonishing rate of technological change driven by AI and its profound implications for industries and jobs. They explore the rapid acceleration of AI capabilities, the vision for AI as a "default personal AGI," and its potential to revolutionize enterprise operations and scientific discovery. The conversation also touches on the global spread of AI's benefits, the promise of a hugely deflationary economy, and the critical role of government in navigating this transformative era.
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Distilling Step-by-Step: Outperforming LLMs with Less Data
Join us as we explore LLM knowledge distillation, a groundbreaking technique that compresses powerful language models into efficient, task-specific versions for practical deployment. This episode delves into methods like TinyLLM and Distilling Step-by-Step, revealing how they transfer complex reasoning capabilities to smaller models, often outperforming their larger counterparts. We'll discuss the benefits, challenges, and compare distillation with other LLM adaptation strategies like fine-tuning and prompt engineering.
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😵💫 Why Language Models Hallucinate
In this episode, we delve into why language models "hallucinate," generating plausible yet incorrect information instead of admitting uncertainty. We'll explore how these overconfident falsehoods arise from the statistical objectives minimized during pretraining and are further reinforced by current evaluation methods that reward guessing over expressing doubt. Join us as we uncover the socio-technical factors behind this persistent problem and discuss proposed solutions to foster more trustworthy AI systems.
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Attention Is All You Need
Join us as we unpack "Attention Is All You Need," a pivotal paper introducing the Transformer, a novel neural network architecture. This groundbreaking model redefines sequence transduction by relying solely on attention mechanisms, completelydispensing with recurrence and convolutions. Discover how it achieves superior quality, greater parallelizability, and significantly faster training times, setting new state-of-the-art results in machine translation and generalizing well to other tasks like English constituency parsing.
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LoRA: Low-Rank Adaptation of Large Language Models
In this episode, we dive into LoRA, a groundbreaking technique that makes fine-tuning massive language models like GPT-3 more accessible and efficient. Discover how this method drastically reduces the number of trainable parameters and GPU memory needed, all without adding any extra delay during inference. We'll explore how LoRA freezes the original model and injects small, trainable matrices, achieving results on-par with or even better than full fine-tuning.
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