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You can compare the current state of LLMs to the days of chess machines when they first approached grandmaster level play. The machine approach was very brute f
by Isamu 1y ago
You can compare the current state of LLMs to the days of chess machines when they first approached grandmaster level play. The machine approach was very brute force, and there was a lot of work done to improve the sheer amount of look ahead that was required to complete at the grandmaster level.
As opposed to what grandmasters actually did, which was less look ahead and more pattern matching to strengthen the position.
Now LLMs successfully leverage pattern matching, but interestingly it is still a kind of brute force pattern matching, requiring the statistical absorption of all available texts, far more than a human absorbs in a lifetime.
This enables the LLM to interpolate an answer from the structure of the absorbed texts with reasonable statistical relevance. This is still not quite “what humans do” as it still requires brute force statistical analysis of vast amounts of text to achieve pretty good results. For example training on all available Python sources in github and elsewhere (curated to avoid bad examples) yields pretty good results, not how a human would do it, but statistically likely to be pertinent and correct.
- gowld 1y agoOne key difference is that an LLM really is better than any human at almost everything, simply because the LLM is kind of OKish at millions of things that each single human is just terrible at or completely ignorant of. The common LLMs are not trying to be a better human than a single human. They are trying to be more useful per $ cost than a bevy of humand doing some basket of human tasks. Making a replacement for an individual complete human, an effort that was popular before the Internet made that idea seem quaint, is very different challenge, and more niche, because it's not as economically efficient.