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    Unsupervised Learning

    Ep 12: EleutherAI's Aran Komatsuzaki on Open-Source Models' Future and Thought Cloning

    Wed Jul 19 2023
    AI researchopen-source modelsgenerous modelsencoding processinglanguage modelsAI community

    Description

    Iran Komatsuzaki discusses the gap between top AI companies and others, open-source models, generous models, encoding processing, limitations of language models, and engaging with the AI community.

    Insights

    The Gap in AI Research

    There is a significant gap between top AI companies and smaller players, with resources and expertise being major factors.

    Open-Source Models and the Gap

    Open-source models may struggle to catch up with proprietary models due to the gap in resources and expertise. Investing in open AI may not be a wise choice due to the significant lead they have.

    Generous Models and Thought Cloning

    Machine learning models are becoming more generous and outperforming specialized models. Thought cloning aims to replicate human thought processes in large-scale models.

    Encoding Processing and Video Models

    Encoding processing ideas include barberizing code steps and letting models learn from that data. Video models using computer activity data sets have the potential to complete long video games.

    PhD Programs and Limitations of Language Models

    PhD programs are a major source of young researchers in machine learning. Open source models tend to be overhyped, and language models have limitations in context and memory.

    Engaging with the AI Community

    Engaging with the AI community through platforms like Twitter helps stay updated on news and events.

    Chapters

    1. The Gap in AI Research
    2. Open-Source Models and the Gap
    3. Generous Models and Thought Cloning
    4. Encoding Processing and Video Models
    5. PhD Programs and Limitations of Language Models
    6. Engaging with the AI Community
    Summary
    Transcript

    The Gap in AI Research

    00:00 - 08:21

    • Iran Komatsuzaki, lead scientist for eluthir AI, discusses the incredible gap between top AI companies and everyone else.
    • Researchers and engineers in AI prioritize working with other top researchers and engineers over money.
    • The idea of Cloud-Crowny is an exciting development in AI research.
    • Iran predicts that it won't take long until we achieve extra ACI (Artificial Creative Intelligence).

    Open-Source Models and the Gap

    08:02 - 16:49

    • Luther AI is an open-source research community focused on reproducing and open-sourcing GPT-3.
    • They have developed models like GPT-J, GPT NeoX, and P-theor.
    • Open-source models may struggle to catch up with proprietary models due to the gap in resources and expertise.
    • Compute, dataset quality, and top researchers/engineers are major factors in model development.
    • There is a clear gap between top players like OpenAI and smaller players.
    • Investing in open AI may not be a wise choice due to the significant lead they have.
    • Companies may opt to fine-tune existing LLMs rather than creating their own for specific use cases.

    Generous Models and Thought Cloning

    16:25 - 25:04

    • Machine learning models are becoming more generous and multicasting, outperforming specialized models.
    • GPT-4 performs better than specialized models in the medical domain.
    • Generous models allow for more investment and higher returns on multiple tasks.
    • Open source models put pricing pressure on closed source models, especially for easier tasks.
    • Thought cloning is an exciting area of research that aims to replicate human thought processes.
    • Humans spend time brainstorming, refining ideas, and making decisions before writing, unlike GPT models.
    • Encoding human thought processes into large-scale models is possible with proper annotation.

    Encoding Processing and Video Models

    24:35 - 33:00

    • Ideas for encoding processing to large-scale models include barberizing code steps in articles and letting models learn from that data.
    • A video model is being developed using computer activity data sets, including video, audio, mouse and keyboard inputs, and eye movement tracking.
    • The approximation of human eye input in video processing can reduce computational requirements.
    • Generalized perception models in the video space could have a significant impact.
    • In the future, there may be AI models capable of completing long video games like Legend of Zelda or Dark Souls.
    • Combining language models with video models has shown improvements in capacity.
    • The influence of academia on large language models is diminishing but remains important as a source of young researchers.

    PhD Programs and Limitations of Language Models

    32:42 - 41:32

    • PhD programs are a major source of young researchers and engineers in the field of machine learning.
    • Many engineers at companies like Google choose to pursue a PhD before becoming machine learning researchers.
    • The quality of faculty members and their research is not consistently high outside of top labs.
    • Open source models in AI tend to be overhyped, especially for knowledge-intensive tasks like math research.
    • There was an overhyped Asian type model based on GPT4 that has faded from attention.
    • Influences from other areas like crypto are negatively impacting the efficiency of machine learning.
    • Contributions from individual researchers often go overlooked, with credit being given to CEOs or famous figures instead.
    • Building a machine learning model that can do research better than humans is a goal before achieving AGI.
    • Current language models have limited context and struggle with remembering information from previous time steps.
    • Incorporating human-like third-person perspective into language models would be beneficial.
    • Vision input may not be necessary for all processes, but it could be useful in some cases.
    • The primary source of news for many people in the space is archive, followed by conversations with different people.

    Engaging with the AI Community

    41:11 - 43:16

    • The speaker talks with many different people and has built a network of machine learning researchers.
    • Twitter and Discord are used to communicate with people across the world.
    • The speaker sometimes hears leaks from companies like Google and OpenAI through these platforms, although they may not be completely reliable.
    • Engaging with others on Twitter helps the speaker stay updated on news and events.
    • Listeners can find more about the speaker's work by visiting their Twitter page, where they also discuss their own projects.
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