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Comment by mtkd

2 days ago

An enterprise using RAG, fine tuning etc. to leverage their data and rethinking how RL and vector DBs etc. can improve existing ops ... is likely going to make some existing moats much better moats

If your visibility on current state of AI is limited to hallucinogenic LLM prompts -- it's worth digging a bit deeper, there is a lot going on right now

9 comments

mtkd

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ptx  2 days ago

What specifically is going on right now in AI that's not based on "hallucinogenic LLM prompts"?

  • rv3392  1 day ago

    ML is still a thing. I believe that most AI research is still non-LLM ML-related - things like CNN+Computer Vision, RL, etc. In my opinion, the hype around LLMs has a lot to do with its accessibility to the general public compared to existing ML techniques which are highly specialised.

    • flessner  1 day ago

      To be fair, I remember that some 5 years ago a lot of ML was quite accessible to programmers as it was often just a couple lines of python using tensorflow, or later pytorch.

      I am almost in disbelief that LLMs are the thing that reached the "tipping point" for most companies to magically care for ML. The amount of products, that could have been built properly 5 years ago, that exist now in a slower form because of "reasoning" LLMs, is likely astonishing.

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  • milesrout  1 day ago

    Convolutional neural networks for image recognition and more generally image processing. They are much better than they were a few years ago, when they were all the rage, but the hype has disappeared. These systems improve the performance of radiologists at detecting clinically significant cancers. They can be used to detect invasive predators or endangered native wildlife using cameras in the bush, in order to monitor populations, allocate resources for trapping of pests, etc.

    ML generally is for pattern recognition in data. That includes anomaly detection in financial data, for example. It is used in fraud detection.

    Image ML/AI is used in your phone's facial recognition, in various image filtering and analysis algorithms in your phone's camera to improve picture quality or allow you to edit images to make them look better (to your taste, anyway).

    AI image recognition is used to find missing children by analysing child pornography without requiring human reviewers to trawl through it - they can just check the much fewer flagged images.

    AI can be used to generate captions on videos for the deaf or in text to speech for the blind.

    There are tons of uses of AI/ML. Another example: video game AI. Video game upscaling. Chess and Go AI: NNUE makes Chess AI far stronger and in really cool creative ways which have changed high level chess and made it less drawish.