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Is General Artificial Intelligence a Breakthrough or just a Financial Hype?

HKPhysicist

According to my understanding, AI hype big corps spend a whole country's energy resource to produce learning software and hardware which can do a very little things for us.  Laughing

On the other hands, a parrot can learn how to talk with men with just a few grams of nutrition.  Also, my little puppy learnt how to do simple house hood things for me in just a few minutes and a few grams fo nutrition. Blush

Which one of these examples are more intelligent and energy efficient?

Had we better use those astronomical sum of money and resources on investigating deeper natural laws such as room temperature superconductivity, novel material and crystal, higher energy experiments beyond the Large Hadron Collider, nuclear fusion experiments, etc.?

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  • People (including some tech experts) thought the cloud was hype and a fad, and it took less than a decade for it to become crazy to not use the cloud for most businesses. Others assumed IoT was useless, and yet there are now hundreds of devices all around you wherever you go, being monitored or controlled over combined wireless and Internet, from energy meters to vending machines. Probably around 60% of my search engine queries have now been replaced by LLM interaction, it just works better. I use AI to help me with all manner of tasks (non-technical and technical).

  • in reply to shabaz

    To be fair for every example you have of a tech that people doubted, but ended up becoming crucial, there's an example of one that was touted as the next best thing but failed. Ruby on Rails was supposed to be the future of web apps, 3D displays were the future of TV but were never widely adopted. AR/VR never really took off even when Facebook(now Meta) believed in it so much that it shaped its new identity around it; the blockchain was hyped to hell and back, and now is quietly in a corner, only used for the few crypto outlets that survived, etc.

    Now, I'm not fully against LLMs as a technology. I'm very well involved in the development of AI-based systems and I deal with different LLMs on a daily basis. I've also tested them in different tasks that are part of my job, and there's only been a very small subset of tasks that they perform adequately, or where they are actually a help, and not a hindrance.

    Right now they are struggling to make sense as a business model, and I don't see them being as widely adopted as investors/tech bros want them to be unless they solve several problems with them (from ethics/morals to financial, technical and practical). So, repeating a bit of what I said earlier, unless they find a way of solving the shortcomings and cutting their running costs down, they will slowly stop being used everywhere, and it will become tools only used in a handful of contexts, for a limited number of tasks (like summarizing reports and meetings in the corporate world, auto translation of videos on social media, etc).

    Part of their problem is they keep overpromising and overselling the tech, hoping to increase adoption and then "solve the issues" once the user base increases, but that's been a very bad strategy so far.


    Luckily, "regular" AI (i.e: Not gen-ai) continues helping science and will do so even if the LLM hype dies. What could be a problem, however, is that maybe at one point people will get so "sick" of hearing the word AI that actual AI-based research will receive less funding because of this poisoned association.

  • in reply to battlecoder
    battlecoder said:
    What could be a problem, however, is that maybe at one point people will get so "sick" of hearing the word AI that actual AI-based research will receive less funding because of this poisoned association.

    Back to the 90s and the first "AI Winter"...

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