Tag: ChatGPT

  • LLMs Get Trounced at Chess

    While Large Language Models (LLMs) have demonstrated they can do many things well enough, it’s important to remember that these are not “thinking machines” so much as impressively competent “writing machines” (able to figure out what words are likely to follow).

    Case in point: both OpenAI’s ChatGPT and Microsoft Copilot lost to the chess playing engine of an old Atari game (Video Chess) which takes up a mere 4 KB of memory to work (compared with the billions of parameters and GB’s of specialized accelerator memory needed to make LLMs work).

    It’s a small (yet potent) reminder that (1) different kinds of AI are necessary for different tasks (i.e. Google’s revolutionary AlphaZero probably would’ve made short work of the Atari engine) and (2) don’t underestimate how small but highly specialized algorithms can perform.

    Last month we reported on the somewhat-surprising news that an emulated Atari 2600 running the 1979 software Video Chess had “absolutely wrecked” an overconfident ChatGPT at the game of kings. Fans of schadenfreude rejoice, because Microsoft Copilot thought this was a chance to show its superiority to ChatGPT: And the Atari gave it a beating.

    After conquering ChatGPT, Atari 2600 Video Chess destroys Microsoft Copilot: ‘The vintage silicon mastermind bested me fair and square’
    Rich Stanton | PC Gamer

  • The “Large Vision Model” (LVM) Era is Upon Us

    Unless you’ve been under a rock, you’ll know the tech industry has been rocked by the rapid advance in performance by large language models (LLMs) such as ChatGPT. By adapting self-supervised learning methods, LLMs “learn” to sound like a human being by learning how to fill in gaps in language and, by doing so, become remarkably adept at solving not just language problems but understanding & creativity.

    Interestingly, the same is happening in imaging, as models largely trained to fill in “gaps” in images are becoming amazingly adept. A friend of mine, Pearse Keane’s group at University College of London, for instance, just published a model trained using self-supervised learning methods on ophthalmological images which is capable of not only diagnosing diabetic retinopathy and glaucoma relatively accurately, but relatively good at predicting cardiovascular events and Parkinson’s.

    At a talk, Andrew Ng captured it well, by pointing out the parallels between the advances in language modeling that happened after the seminal Transformer paper and what is happening in the “large vision model” world with this great illustration.

    From Andrew Ng (Image credit: EETimes)

    “The revolution we’ve seen for text will be coming to images,” renowned computer scientist Andrew Ng asserted in a keynote talk he gave at the recent AI Hardware Summit here.

    Andrew Ng: The AI Text Revolution
    Is Coming to Images
    Sally Ward-Foxton | EETimes