AI alignment researcher, ML engineer. Masters in Neuroscience.
I believe that cheap and broadly competent AGI is attainable and will be built soon. This leads me to have timelines of around 2024-2027. Here's an interview I gave recently about my current research agenda. I think the best path forward to alignment is through safe, contained testing on models designed from the ground up for alignability trained on censored data (simulations with no mention of humans or computer technology). I think that current ML mainstream technology is close to a threshold of competence beyond which it will be capable of recursive self-improvement, and I think that this automated process will mine neuroscience for insights, and quickly become far more effective and efficient. I think it would be quite bad for humanity if this happened in an uncontrolled, uncensored, un-sandboxed situation. So I am trying to warn the world about this possibility.
See my prediction markets here:
I also think that current AI models pose misuse risks, which may continue to get worse as models get more capable, and that this could potentially result in catastrophic suffering if we fail to regulate this.
I now work for SecureBio on AI-Evals.
relevant quote:
"There is a powerful effect to making a goal into someone’s full-time job: it becomes their identity. Safety engineering became its own subdiscipline, and these engineers saw it as their professional duty to reduce injury rates. They bristled at the suggestion that accidents were largely unavoidable, coming to suspect the opposite: that almost all accidents were avoidable, given the right tools, environment, and training." https://www.lesswrong.com/posts/DQKgYhEYP86PLW7tZ/how-factories-were-made-safe
I've been using a remineralization toothpaste imported from Japan for several years now, ever since I mentioned reading about remineralization to a dentist from Japan. She recommended yhe brand to me. FDA is apparently bogging down release in the US, but it's available on Amazon anyway. It seems to have slowed, but not stopped, the formation of cavities. It does seem to result in faster plaque build-up around my gumline, like the bacterial colonies are accumulating some of the minerals not absorbed by the teeth. The brand I use is apagard.
Thanks faul-sname. I came to the comments to give a much lower effort answer along the same lines, but yours is better. My answer: lazy local evaluations of nodes surrounding either your current position or the position of the goal. So long as you can estimate a direction from yourself to the goal, there's no need to embed the whole graph. This is basically gradient descent...
Personally, I most enjoyed the first one in the the series, and enjoyed listening to Joe's reading of it even more than when I just read it. My top three are 1, 6, 7.
I generally agree, i just have some specific evidence which I believe should adjust estimates in the report towards expecting more accessible algorithmic improvements than some people seem to think.
"What Dragons?", says the lion, "I see no Dragons, only a big empty universe. I am the most mighty thing here."
Whether or not the Imagined Dragons are real isn't relevant to the gazelles if there is no solid evidence with which to convince the lions. The lions will do what they will do. Maybe some of the lions do decide to believe in the Dragons, but there is no way to force all of them to do so. The remainder will laugh at the dragon-fearing lions and feast on extra gazelles. Their children will reproduce faster.
Something else to play around with that I've tried. You can force the models to handle each digit separately by putting a space between each digit of a number like "3 2 3 * 4 3 7 = "
As part of a team of experts building private biorisk evals for AI, and doing private red-teaming experiments, I appreciate this post.
The interesting thing to me about the question, "Will we need a new paradigm for AGI?" is that a lot of people seem to be focused on this but I think it misses a nearby important question.
As we get closer to a complete AGI, and start to get more capable programming and research assistant AIs, will those make algorithmic exploration cheaper and easier, such that we see a sort of 'Cambrian explosion' of model architectures which work well for specific purposes, and perhaps one of these works better at general learning than anything we've found so far and ends up being the architecture that first reaches full transformative AGI?
The point I'm generally trying to make is that estimates of software/algorithmic progress are based on the progress being made (currently) mostly by human minds. The closer we get to generally competent artificial minds, the less we should expect past patterns based on human inputs to hold.
As a population of AGI copies, the obvious first step towards 'taking over the world' is to try to improve oneself.
I expect that the described workforce could find improvements within a week of clock time including one or more of:
Improvements to peak intelligence without needing to fully retrain.
Improvements to inference efficiency.
Improvements to ability to cooperate and share knowledge.