41–50 of 123 posts
Earlier quoted context omitted.
Do you ever have novel ideas while walking or in the shower? Well, you’re learning from data you generate.
Well put. Also, ever read your old journals? You are training on generated data.
Of course we still need real world data, but it seems like generated data should also play a role. Humans don’t weight dreams equally with reality, however, and that’s a distinction I feel is missing here.
Quite a few people saw this coming. It is still early to tell if we reached AI winter again or not, but at least we can see that news are slowing down.
Yes sheer quantity has a quality of its own but that won't produce optimal results.
My biggest worry is the idea of generating data as training data. We're obviously already unwittingly doing this, but once someone decides to augment low-volume segments of the dataset with generative input, we're going to start getting some really crappy feedback loops.
Shouldn't there be enough known-good training content that we can use it to determine if a document is worth including in a training set?
If you want ML to do it... well that's a bit of a catch-22. How would an ML algorithm know if data is good enough to be trained on unless it has already been trained on that data?
My long-standing observation has been that while nature may abhor a vacuum, she also really, really loves sigmoids. That performance vs. training data is not linear, but logarithmic, doesn't exactly come as a surprise.
Every exponential is really an s curve?
Quite a few people saw this coming. It is still early to tell if we reached AI winter again or not, but at least we can see that news are slowing down.
RAG seems to be all the rage. Not to mention the quest for the cooking up the correct cocktail of smaller MoE/ensemble models, and ... there's decades' worth of optimization work ahead (a few years of it seems to be already VC and edu grants funded), no?
There are some major limitations to LLMs that aren't going to be "optimized" away. At the end of the day LLMs are Monte Carlo samplers over a latent, compressed representation existing human text data. Many of the tasks people hope LLMs will achieve require major leaps in our current understanding of language modeling.
One great example limitation (which shocks me sometimes when I think about it): generating output is still ultimately stuck in looking at the probability of the next token rather than the much more useful, probability of the generated statement. There are techniques to improve this, but we're missing a major piece of generating highly probable statements with no hint about how to really get there. Consider how you might write SQL. You conceive of the high level query first and start sketching out the pieces. An LLM can only look at each token and can't, statistically speaking, think in terms of the entire query.
Personally I think LLMs are very underutilized/exploited for what they are good at, and there is way too much focus on what they can't do. Hopefully we'll dodge an AI winter by using LLMs to solve the wide range of classical NLP problems that make many tasks that just a few years ago nearly impossible, rather simple today. Unfortunately the irrational hype around these models makes me skeptical of that scenario.
Quite a few people saw this coming. It is still early to tell if we reached AI winter again or not, but at least we can see that news are slowing down.
I think that depends what your expectations are, and what you mean by another AI winter. We're just scratching the surface of what's possible with the current state of the art. Even if there are no major advances or breakthroughs in the near future, LLMs and associated technologies are already useful in many use cases. Or close enough to useful where engineering rather than science will be sufficient to overcome many…
But you would think after 70 years AI practitioners would learn some humility! It is very obvious that GPT-4 is dumber than a honeybee, let alone a cat, let alone a crow. But for over a year I've heard dozens of tech folks insist it's smarter than most people.
Quite a few people saw this coming. It is still early to tell if we reached AI winter again or not, but at least we can see that news are slowing down.
I hope this means that people actually start curating their training sets. The quality control is horrible on all of the datasets I've looked through. Especially image captions. Yes sheer quantity has a quality of its own but that won't produce optimal results.