In today's edition: What does research say about how far we are from AGI? News from the usual subjects: Anthropic, Cohere, OpenAI, etc. The freshest AI&ML research papers from the week of Mar 4 — Mar 10 Though the question, “ ” predates the term “artificial intelligence” itself, it saw a significant resurgence on Twitter last week, prompted by the Musk vs. OpenAI lawsuit (Musk accuses OpenAI of abandoning open-source principles and prioritizing profit over safety, hindering the safe development of AGI.) . Today, we will discuss “How Far Are We from Intelligent Visual Deductive Reasoning?” “How Far Are We From Automating Front-End Engineering?” and Stephen Wolfram’s article “Can AI Solve Science?” These papers offer fascinating explorations of the differences between human and artificial intelligence. How far are we from achieving human-level intelligence in machines (or AGI, or ASI)? But far more interesting were the papers and an article that came out last week tackling this question Intelligent Visual Deductive Reasoning In “ ?”, researchers from Apple explore Vision-Language Models (VLMs), like GPT-4V, in visual-based deductive reasoning, a complex yet less studied area, using Raven’s Progressive Matrices (RPMs)*. How Far Are We from Intelligent Visual Deductive Reasoning *Raven’s Progressive Matrices is a nonverbal intelligence test measuring abstract reasoning, using patterns to assess cognitive functioning without language. What caught my attention was the finding that . The paper notes, “ ” This inability to deal with abstract concepts marks a fundamental difference between computational processing and human cognitive abilities. AI systems like VLMs struggle with tasks requiring abstract pattern recognition and deduction VLMs struggle to solve these tasks mainly because they are unable to perceive and comprehend multiple, confounding abstract patterns in RPM examples. Being a sophisticated pattern recognizer doesn’t equate to sentience. The observation that “ ” highlights , suggesting a nuanced aspect of intelligence that current AI lacks. Another intriguing point was the models’ overconfidence. all the tested models never express any level of uncertainty the importance of doubt and uncertainty in human cognition Automating Front-End Engineers In “ ”, researchers from Stanford University, Georgia Tech, Microsoft, and Google DeepMind have developed a benchmark for Design2Code, aiming to evaluate how well multimodal LLMs convert visual designs into code. Here, the replacement of humans came closer. Despite some limitations, there were considerable advancements in using generative AI to convert designs into front-end code. It’s remarkable that “ .” This finding challenges traditional notions of artistic and creative value, Design2Code: How Far Are We From Automating Front-End Engineering? annotators think GPT-4V generated webpages can replace the original reference webpages in 49% of cases in terms of visual appearance and content; and in 64% of cases, GPT-4V generated webpages are considered better questioning whether creativity is uniquely human or can be algorithmically reproduced — or even surpassed. However, . VLMs struggle with “ ” posing questions about understanding and interpretation. significant limitations persist recalling visual elements from the input webpages and generating correct layout designs. So, the important question is actually not how far we are from AGI (whatever it is), but how we embrace human-AI collaboration most effectively. AI Solving Science In that sense, Stephen Wolfram’s blog post “ ” serves as an excellent example. In the very beginning, he plainly states that AI cannot solve all scientific questions. However, there is significant value in AI assisting scientific progress. He discusses how LLMs can serve as a new kind of linguistic interface to computational capabilities, providing high-level “autocomplete” for scientific work. As he usually does, he emphasizes the transformative potential of representing the world computationally and suggests that pockets of computational reducibility* can be found by AI as well. Can AI Solve Science? *A pocket of computational reducibility — a fascinating concept introduced by Wolfram — is a situation or problem within a complex system where, despite the system’s overall unpredictability, predictable patterns or simplified behaviors emerge, allowing for easier understanding or calculation. Wolfram argues that AI can significantly aid scientific discovery by providing new tools for analysis and exploration, but its ability to completely “solve” science is limited by fundamental principles such as computational irreducibility. The future of AI in science lies in its integration with human creativity and understanding, leveraging its strengths to uncover new knowledge within the constraints of what is computationally possible. We might be able to survive without front-end developers (no offense intended), but scientists remain indispensable! To summarize: https://x.com/pmddomingos/status/1766945083314827455?s=20&embedable=true News from The Usual Suspects © Cohere and its commitment to the research community https://x.com/cohere/status/1767275128813928611?s=20&embedable=true Hugging Face Starting an ambitious open robotics project and hiring , a former Tesla scientist, to expand beyond software into robotics, including work on humanoid robots like Tesla’s Optimus. Remi Cadene Offers an amazing tool: https://x.com/maximelabonne/status/1767124527551549860?s=20&embedable=true Russia’s talent is invisible According to “The Global AI Talent Tracker 2.0”, Russia has no chance in the AI race: this analysis Inflection enhances its Pi Inflection , enhancing its personal AI, Pi, with IQ capabilities alongside its empathetic EQ. They claim to compete with GPT-4, achieving significant efficiency and performance improvements, especially in STEM areas, with less computational resource usage. launches Inflection-2.5 Chips TSMC (based in Taiwan), the world’s largest contract chipmaker, is for an Arizona chip plant. This funding, part of the CHIPS and Science Act of 2022, aims to boost domestic semiconductor production. TSMC’s $40 billion investment in the plant marks one of the largest foreign investments in U.S. history. expected to receive over $5 billion in U.S. federal grants OpenAI: new members on the board Sam Altman is reinstated as a member of the OpenAI board. , with a rich history as the CEO of the Bill and Melinda Gates Foundation, brings extensive experience in healthcare and philanthropy. , a seasoned executive with roles at Sony Entertainment and as a lawyer, offers legal and entertainment industry insights. , leading Instacart and with a background at Meta Platforms, including as head of Facebook, contributes expertise in technology and e-commerce. This diverse trio enhances OpenAI’s governance with their wide-ranging expertise and perspectives. He is joined by three women: Sue Desmond-Hellmann Nicole Seligman Fidji Simo Elon’s Grok https://x.com/elonmusk/status/1767108624038449405?s=20&embedable=true Anthropic Offers a great collection of prompts in its Prompt library Enjoyed This Story? I write a weekly analysis of the AI world in the newsletter. We aim to equip you with comprehensive knowledge and historical insights, so you can make informed decisions about AI and ML. Turing Post 🎁 Bonus: The freshest research papers, categorized for your convenience Enhancements in Language Models and Multimodal Understanding : Showcases an LLM’s intrinsic ability to understand and generate music, expanding LLMs’ applications beyond text. ChatMusician Read the paper : Demonstrates advanced multimodal understanding by processing extensive contexts of text, video, and audio. Gemini 1.5 Read the paper : Enhances TTS synthesis with a factorized diffusion model for zero-shot natural speech generation. NaturalSpeech 3 Read the paper : Utilizes fine-tuned MLMs for superior image-text data filtering, improving dataset quality for MLM training. Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters Read the paper : Introduces the first LLM designed for the legal domain, demonstrating proficiency in legal text understanding and generation. SaulLM-7B Read the paper Novel Training and Evaluation Techniques : Proposes a method for scaling instruction tuning in mathematical reasoning, significantly enhancing LLMs’ problem-solving abilities. MathScale Read the paper : Reveals redundancy in LLM layers and proposes a pruning strategy, maintaining performance while reducing model size. ShortGPT Read the paper : Enhances memory efficiency in LLM training without sacrificing performance by applying gradient low-rank projection. GaLore Read the paper : Develops a method for collaborative decoding among multiple LLMs, improving performance across various tasks. Learning to Decode Collaboratively with Multiple Language Models Read the paper : Advocates for training RL value functions through classification instead of regression, boosting performance and scalability. Stop Regressing Read the paper Advances in Generative Models and Data Synthesis : Introduces a framework for generating synthetic multimodal datasets, overcoming limitations in data privacy and diversity. MAGID Read the paper : Trains a generative model to create interactive virtual worlds from text, images, or sketches, advancing interactive environment simulation. Read the paper Genie Scalability and Efficiency in AI Systems : Details a system for training LLMs on over 10,000 GPUs, tackling challenges in training efficiency and stability at large scales. MegaScale Read the paper : Improves State Space Models with dense hidden connections for efficient large language models, enhancing performance with minimal parameter increase. DenseMamba Read the paper Exploring New Frontiers in AI and Machine Learning : Illustrates that planning and prediction in time series can be simplified through learned contrastive representations. Inference via Interpolation Read the paper : Employs simulated trial and error for tool learning, significantly enhancing LLMs’ practical application capabilities. LLMs in the Imaginarium Read the paper : Aims to improve context length generalization in LLMs by refining interpolation techniques for out-of-distribution token positions. Resonance RoPE Read the paper : Enhances the localization of behaviors in LLMs to specific components, improving diagnostic capabilities and model understanding. AtP Read the paper : Investigates using world models for enhancing visual representation learning, beyond reinforcement learning applications. Learning and Leveraging World Models in Visual Representation Learning Read the paper Platforms and Tools for Model Evaluation and Interaction : Provides a platform for evaluating LLMs based on human preferences, offering insights into model rankings through pairwise comparisons. Chatbot Arena Read the paper : Explores enhancing LLMs’ reasoning capabilities using RLHF, comparing different algorithms and reward strategies. Teaching Large Language Models to Reason with Reinforcement Learning Read the paper Also published . here

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How Far Are We From Human-level Intelligence in AI?

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