7 ms·

I am surprised by how dismissive the whole post sounds. For example: > OpenAI could in fact have a breakthrough that fundamentally changes the world Well, it

• naasking 3y ago
  The article is from Gary Marcus, a well known AI skeptic. Not surprising if he's being dismissive.
  • psbp 3y ago
    Skeptic and completely reactionary. I had to unfollow him on Twitter because he always has to have a "take" on every AI headline, and he's often contradictory between "AI is useless" and "AI is a huge threat".
    • throw310822 3y ago
      Yes, skepticism is healthy and useful, but this sounds more like intellectual dishonesty.
      • naasking 3y ago
        I think his position is that "AI is not intelligent but is nevertheless dangerous, and even moreso because people think it's intelligent".
    • thatguysaguy 3y ago
      It was super weird to see him align with the "regulate AI people" after years of being one of the main "this is going nowhere" guys
      • pvdoom 3y ago
        I think his stance is pro-regulation, but not in the same way that AI doomers and OpenAi want.
      • skywhopper 3y ago
        The people behind LLMs believe in it so thoroughly that they are pushing it to do things that aren’t safe. So it can easily be true that it’s both overhyped and needs better regulation. The fact that LLMs can’t actually solve the problems they are being used for is, in fact, a problem that may require government intervention.
        • _heimdall 3y ago
          If LLMs aren't fit for the types of problems they're being used for, what would the government be needed for? Users would pretty quickly give up and move on if LLMs continue to suck at solving the problems people want them to solve.
          • danaris 3y ago
            Not if those people are "true believers". Once you get emotionally attached to the idea that something must work, you don't respond to failures rationally.
            • _heimdall 3y ago
              If we're delving into the realm of belief and therefore religion, pulling in the government to shut it down goes against everything that America was (supposedly) founded on.
    • aidaman 3y ago
      I tend not to listen to people who have no fucking clue what they're talking about.
• golol 3y ago
  >Gary Marcus
• lucubratory 3y ago
  >I am surprised by how dismissive the whole post sounds. I wouldn't be surprised, it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful, who's only widely publicly known now because AI has had a backlash and media needed an expert to quote for "the other side". Same as the computational linguistics researchers who always get quoted for the same reason. In general, academics in competing fields whose funding threatens to get tanked or eclipsed by research approaches that work on principles they have fundamental academic disagreements with are going to talk negatively about the tech, no matter what it is achieving. Where I think it can be valuable to listen to them is when they're giving the technology credit - generally they'll only do that when it's something really undeniable or potentially concerning.
  • Ukv 3y ago
    > it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful I wouldn't bet on loss of perceived expert on the other side status or prestige no matter how poorly his gloomy LLM forecasts fare. It hasn't happened from any of his previous consistently pessimistic predictions around deep learning since the beginning of its resurgence (which has clearly far surpassed its state in 2012, even if currently overhyped). To quote Gary Marcus quoting Gary Marcus: > Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)
  • tomhoward 3y ago
    This is such a case of a bad-comment that seems clever and insightful. It boils down to saying we don't need to debate or even consider the content of his arguments because we can assume he's only motivated by prestige and money (but without considering the second-order effects on his credibility and funding if he actually turns out to be proven substantially wrong in the future). I don't know how right or wrong he is - none of us do. That's why it's all still being debated. The one thing I know is that we can only truly understand a topic by fully understanding arguments for and against all the claims. I also know the pro-LLM set have way more money (double-digit billions as we saw just this week) and credibility to lose over this topic than Gary Marcus does.
• jksk61 3y ago
  I don't know, it is an opinion and it seems kind of well-founded (i.e. there's no evidence for groundbreaking research on OpenAI part except for scaling things up).
  • teleforce 3y ago
    It seems to me that the breakthrough claim was a desperate attempt of OpenAI staffs to get their beloved boss back to OpenAI. If anything it most probably will be incremental things rather than a breakthrough but definitely not a bad things but just call a spade, a spade [1]. Ironically the breakthrough happened somewhere else in Google and for some unknown reasons as of now, Google has missed the boat of commercializing its very own invention. Personally I have been recently have been using the ChatGPT-4 on a daily basis, and I considered myself a long time and ardent user of Google products (mainly search) for the past two decades. However it becomes increasingly frustrating for the past several years since it is getting more difficult to perform "search that matters" (going to trademark this motto). What frequently irked me the most is not when doing random or targetted search with Google, is when looking for something that you discovered previously but it's forgotten and apparently it does really matter now. It was quoted that about 10% of our time looking for items that we lost or misplaced, and the same can be said to our acquired knowledge and information. Some times we crave so much for the knowledge or the info we had but very difficult or impossible to recall. ChatGPT-4, in particular the online search with Bing feature, is extremely useful in this regard but at the same time is very limited since it is not a well supported features with many failed attempts, perhaps due to limited online data scrapping capability, and thus sub-par results. This specific feature, I call it search that matters, is like Google search with steroid and the fact that Google, with its Deepmind subsidiary, failed to utilize and monetize on this opportunity until now is just beyond me. If ChatGPT, call it ChatGPT-5 if you like, can perform this operation intuitively and seamlessly it will be a game changer but not a breakthrough. Apparently according to ChatGPT-4 you can have a game changer not a breakthrough. The breakthrough, however, is to fundamentally improve AI or LLM itself as mentioned by Stephen Wolfram in his tutorial article on ChatGPT, not merely enhancing its existing operations [2]: When it comes to training (AKA learning) the different “hardware” of the brain and of current computers (as well as, perhaps, some undeveloped algorithmic ideas) forces ChatGPT to use a strategy that’s probably rather different (and in some ways much less efficient) than the brain. And there’s something else as well: unlike even in typical algorithmic computation, ChatGPT doesn’t internally “have loops” or “recompute on data”. And that inevitably limits its computational capability - even with respect to current computers, but definitely with respect to the brain. [1] Eight Things to Know about Large Language Models: https://wp.nyu.edu/arg/eight-things-to-know-about-large-language-models/ https://wp.nyu.edu/arg/eight-things-to-know-about-large-lang... [2] What Is ChatGPT Doing and Why Does It Work: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...
• simne 3y ago
  Must agree with you. Even when GPT-2 not impressed me much, but with GPT-3 and GPT-4 we constantly see significant progress, not seen on near every other technologies. Even if GPT-5 will be fatally expensive, but still show such leap as GPT-4, it will be enough to consider OpenAI most successful AI research in human history. And for about expenses, we just need to wait some 10 years, and GPT-3 class tech will be in every smartphone, and in 30 years, GPT-4 will become also affordable for everyone, GPT-5 will be just ordinary business workhorse.
  • okdood64 3y ago
    How expensive is a "typical" GPT-4 query in Azure pricing? Edit: Oh I see: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/ https://azure.microsoft.com/en-us/pricing/details/cognitive-...
• gexla 3y ago
  One thing OpenAI has now that it didn't have 4 years ago is a lot more compute power at its disposal. Sam Altman has already said "I think we're at the end of the era where it's going to be these, like, giant, giant models." If that's actually true, then the GPTX tech has largely hit a wall in which throwing more compute at it won't get the same increase in capability. Bill Gates predicted that GTP5 won't be much better than GPT4. So, any "breakthrough" really could be incremental rather than game changing.
  • visarga 3y ago
    I think there are two main source of learning for AI - the web scrape datasets, which contain our historical experiences and communications, and AI feedback generated from deployed agents. The web text is almost exhausted, or we can't scale it 100x more, but the feedback is just starting to ramp up. Every day millions of chat sessions are recorded, and they are exceptional training examples. They would contain the kind of errors LLMs do, and the kind of demands people have, and include a human reaction to each LLM message. The OpenAI move to create "GPTs" is showing they are actively working on improving the feedback signals by empowering the LLM with RAG, code execution and API access. In such a setup it is possible to use a model at level N to generate data at level N+1. The keyword here is learning from feedback, which aligns with recent talk of using RL methods like AlphaZero with LLMs. A RL agent would create its own data as it goes. I think progress will be gradual, as we need to wait for the world to produce the learning feedback signal. Of course in domains where we can speed that up, AI will progress faster. Interesting thought: by making LLMs available to the public, they are going to assist people in many ways and create effects that will percolate in the next training set: LLM inference -> text -> effects in the world -> text -> LLM training. So there is already an implicit feedback loop when we retrain the base models. GPT-5 will train on data from a world influenced and shaped with GPT-4.
  • oceanplexian 3y ago
    The corollary of this is optimistic though. If increasingly large models don't make a big difference, this is hinting at the fact that data quality is a lot more important than the raw quantity of data. This is good news for open source models, because it would be possible to run or train viable models on less expensive hardware, and having billions of dollars in GPUs isn't as much of a moat as they are suggesting.