You’re assuming that it would make sense to have a globally learning model, one constantly still training, when that drastically increases the cost of running the model over present approaches. Cost is already prohibitive, and to reach that many parameters any time soon exorbitant (but that will probably happen eventually). Plus, the sheer amount of data necessary for such a large one is crazy, and you aren’t getting much data per interaction. Note that Chinchilla recently showed that lack of data is a much bigger issue right now for models than lack of parameters so they probably won’t focus on parameter counts for a while.
Additionally, there are many fundamental issues we haven’t yet solved for DL-based AI. Even if it was a huge advancement over present model, which I don’t believe it would be at that size, it would still have massive weaknesses around remembering, or planning, and would largely lack any agency. That’s not scary. It could be used for ill-purposes, but not at human (or above) levels.
I’m skeptical of AI in the near term because we are not close. (And the results of scaling are sublinear in many ways. I believe that mathematically, it’s a log, though how that transfers to actual results can be hard to guess in advance.)
I agree that current models seem to be missing some critical pieces (thank goodness!). I think perhaps you might be overestimating how hard it will be to add in those missing pieces if the capabilities research community focuses their primary intention on them. My guess is it’d be more like 5-10 years than 20-30 years.
I was replying to someone asking why it isn’t 2-5 years. I wasn’t making an actual timeline. In another post elsewhere on the sight, I mention that they could give memory to a system now and it would be able to write a novel.
Without doing so, we obviously can’t tell how much planning they would be capable of if we did, but current models don’t make choices, and thus can only be scary for whatever people use them for, and their capabilities are quite limited.
I do believe that there is nothing inherently stopping the capabilities researchers from switching over to more agentic approaches with memory and the ability to plan, but it would be much harder than the current plan of just throwing money at the problem (increasing compute and data.).
It will require paradigm shifts (I do have some ideas as to ones that might work) to get to particularly capable and/or worrisome levels, and those are hard to predict in advance, but they tend to take a while. Thus, I am a short term skeptic of AI capabilities and danger.
You’re assuming that it would make sense to have a globally learning model, one constantly still training, when that drastically increases the cost of running the model over present approaches. Cost is already prohibitive, and to reach that many parameters any time soon exorbitant (but that will probably happen eventually). Plus, the sheer amount of data necessary for such a large one is crazy, and you aren’t getting much data per interaction. Note that Chinchilla recently showed that lack of data is a much bigger issue right now for models than lack of parameters so they probably won’t focus on parameter counts for a while.
Additionally, there are many fundamental issues we haven’t yet solved for DL-based AI. Even if it was a huge advancement over present model, which I don’t believe it would be at that size, it would still have massive weaknesses around remembering, or planning, and would largely lack any agency. That’s not scary. It could be used for ill-purposes, but not at human (or above) levels.
I’m skeptical of AI in the near term because we are not close. (And the results of scaling are sublinear in many ways. I believe that mathematically, it’s a log, though how that transfers to actual results can be hard to guess in advance.)
I agree that current models seem to be missing some critical pieces (thank goodness!). I think perhaps you might be overestimating how hard it will be to add in those missing pieces if the capabilities research community focuses their primary intention on them. My guess is it’d be more like 5-10 years than 20-30 years.
I was replying to someone asking why it isn’t 2-5 years. I wasn’t making an actual timeline. In another post elsewhere on the sight, I mention that they could give memory to a system now and it would be able to write a novel.
Without doing so, we obviously can’t tell how much planning they would be capable of if we did, but current models don’t make choices, and thus can only be scary for whatever people use them for, and their capabilities are quite limited.
I do believe that there is nothing inherently stopping the capabilities researchers from switching over to more agentic approaches with memory and the ability to plan, but it would be much harder than the current plan of just throwing money at the problem (increasing compute and data.).
It will require paradigm shifts (I do have some ideas as to ones that might work) to get to particularly capable and/or worrisome levels, and those are hard to predict in advance, but they tend to take a while. Thus, I am a short term skeptic of AI capabilities and danger.