Prof Gary Marcus revisited his keynote from AGI-21, noting that many of the issues he highlighted then are still relevant today despite significant advances in AI. This is part 1, we will be releasing an in-depth interview with Gary in the coming weeks. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Gary Marcus criticized current large language models (LLMs) and generative AI for their unreliability, tendency to hallucinate, and inability to truly understand concepts. Marcus argued that the AI field is experiencing diminishing returns with current approaches, particularly the "scaling hypothesis" that simply adding more data and compute will lead to AGI. He advocated for a hybrid approach to AI that combines deep learning with symbolic AI, emphasizing the need for systems with deeper conceptual understanding. Marcus highlighted the importance of developing AI with innate understanding of concepts like space, time, and causality. He expressed concern about the moral decline in Silicon Valley and the rush to deploy potentially harmful AI technologies without adequate safeguards. Marcus predicted a possible upcoming "AI winter" due to inflated valuations, lack of profitability, and overhyped promises in the industry. He stressed the need for better regulation of AI, including transparency in training data, full disclosure of testing, and independent auditing of AI systems. Marcus proposed the creation of national and global AI agencies to oversee the development and deployment of AI technologies. He concluded by emphasizing the importance of interdisciplinary collaboration, focusing on robust AI with deep understanding, and implementing smart, agile governance for AI and AGI. Pre-order Gary's new book here: Taming Silicon Valley: How We Can Ensure That AI Works for Us https://amzn.to/4fO46pY Filmed at the AGI-24 conference: https://agi-conf.org/2024/ Refs: Closed source vs open-source models slide ~24 mins Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth (Maxime Labonne/Liquid AI) https://huggingface.co/blog/mlabonne/sft-llama3 TOC: