Interconnects

Full post: https://www.interconnects.ai/p/olmo-2-and-building-language-model-trainingOLMo 2 demo: https://playground.allenai.org/OLMo 2 artifacts: https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edcChapters00:00 Building AI Teams06:35 OLMo 2FiguresFig 1, pretrain plot: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/pretrain.webpFig 2, pretrain table: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/pretrain-table.webpFig 3, post-train table: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/postrain-table.webp This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.interconnects.ai/subscribe

Dive into the fascinating evolution of open post-training for language models! Discover how techniques like direct preference optimization are reshaping the landscape post-chatGPT. The conversation unveils innovative methodologies such as scaling prompts and the role of reinforcement learning with verifiable rewards. Get a sneak peek into future developments aimed at enhancing open weight models, and see how this competitive drive is pushing the boundaries of what AI can achieve!

Dive into the debate surrounding AI scalability versus AGI expectations. Discover the successes and limitations of large AI models, and why specialized models might hold the key to future advancements. Engage with insights on how the landscape of artificial intelligence is evolving amidst varying expectations. This thought-provoking discussion sheds light on the complexities of the AI field and its potential.

Explore the vital role of the National AI Research Resource in shaping the future of AI in the U.S. The discussion emphasizes the importance of accountability and transparency in AI policy. Additionally, the potential impact of political changes on AI research and development is examined, providing insights into what the future may hold for the industry.

Join Tim Dettmers, a leading figure in open-source AI development and a future Carnegie Mellon professor, as he shares insights on the transformative potential of open-source AI models. He discusses the challenges of quantization and GPU resource efficiency, emphasizing their role in driving innovation. Tim also explores the evolving landscape of AI technology, comparing its impact to the internet revolution, while addressing the delicate balance between academic research and real-world applications. His passionate perspective offers a fresh take on the future of AI!

Andrew Carr, co-founder and chief scientist at Cartwheel AI, is on a mission to create innovative text-to-motion models for creative fields. He dives into how generative AI can enhance creativity through niche applications, like AI-generated poetry. Andrew shares insights from his time at OpenAI and discusses the fascinating interplay between AI and art, emphasizing the need for human oversight. He also explores the evolving AI landscape and the importance of fostering a positive research culture in tech companies to drive impactful innovations.

Explore the compelling motivations behind the creation of open language models, where inclusivity and transparency are key. Discover how open-source systems can challenge corporate dominance while promoting diversity in tech. The urgency of engaging the public in developing these models is highlighted, stressing collaboration as essential for addressing regulatory challenges and ensuring responsible AI research. Tune in for insights on fostering impactful advancements in the realm of artificial intelligence!

Explore the exciting frontier of AI as the podcast delves into the latest on Claude 3.5, Anthropic's cutting-edge model. Discover how it stacks up against Google's Gemini and OpenAI's systems. The discussion highlights the strengths, weaknesses, and future potential of these models. Who will dominate the AI landscape? Tune in for insights on the evolution of these powerful technologies and their implications for automation and reasoning.

Arvind Narayanan, a computer science professor at Princeton and director of the Center for Information Technology Policy, delves into the realities of AI amidst the hype. He discusses the pitfalls of AI policy, emphasizing the need for harm-focused research. The conversation covers the risks of open-source foundation models, critiques of traditional AI in risk prediction, and the implications of scaling laws. Narayanan also sheds light on the balance between innovation and societal impact, highlighting the necessary collaboration between researchers and policymakers.

Explore the complexities of evaluating language models in the fast-evolving AI landscape. Discover the hidden issues behind closed evaluation silos and the hurdles faced by open evaluation tools. Learn about the cutting-edge frontiers in evaluation methods and the emerging risks of synthetic data contamination. The conversation highlights the necessity for standardized practices to ensure transparency and reliability in model assessments. Tune in for insights that could reshape the evaluation process in artificial intelligence!