Wiki Contributions


Relevant tweet/quote from Mustafa Suleyman, the co-founder and CEO:

Powerful AI systems are inevitable. Strict licensing and regulation is also inevitable. The key thing from here is getting the safest and most widely beneficial versions of both.

Thanks for writing and sharing this. I've added it to the doc.

What happened to black swan and tail risk robustness (section 2.1 in "Unsolved Problems in ML Safety")?

It's hard to say. This CLR article lists some advantages that artificial systems have over humans. Also see this section of 80k's interview with Richard Ngo:

Rob Wiblin: One other thing I’ve heard, that I’m not sure what the implication is: signals in the human brain — just because of limitations and the engineering of neurons and synapses and so on — tend to move pretty slowly through space, much less than the speed of electrons moving down a wire. So in a sense, our signal propagation is quite gradual and our reaction times are really slow compared to what computers can manage. Is that right?

Richard Ngo: That’s right. But I think this effect is probably a little overrated as a factor for overall intelligence differences between AIs and humans, just because it does take quite a long time to run a very large neural network. So if our neural networks just keep getting bigger at a significant pace, then it may be the case that for quite a while, most cutting-edge neural networks are actually going to take a pretty long time to go from the inputs to the outputs, just because you’re going to have to pass it through so many different neurons.

Rob Wiblin: Stages, so to speak.

Richard Ngo: Yeah, exactly. So I do expect that in the longer term there’s going to be a significant advantage for neural networks in terms of thinking time compared with the human brain. But it’s not actually clear how big that advantage is now or in the foreseeable future, just because it’s really hard to run a neural network with hundreds of billions of parameters on the types of chips that we have now or are going to have in the coming years.

The cyborgism post might be relevant:

Executive summary: This post proposes a strategy for safely accelerating alignment research. The plan is to set up human-in-the-loop systems which empower human agency rather than outsource it, and to use those systems to differentially accelerate progress on alignment. 

  1. Introduction: An explanation of the context and motivation for this agenda.
  2. Automated Research Assistants: A discussion of why the paradigm of training AI systems to behave as autonomous agents is both counterproductive and dangerous.
  3. Becoming a Cyborg: A proposal for an alternative approach/frame, which focuses on a particular type of human-in-the-loop system I am calling a “cyborg”.
  4. Failure Modes: An analysis of how this agenda could either fail to help or actively cause harm by accelerating AI research more broadly.
  5. Testimony of a Cyborg: A personal account of how Janus uses GPT as a part of their workflow, and how it relates to the cyborgism approach to intelligence augmentation.

Does current AI hype cause many people to work on AGI capabilities? Different areas of AI research differ significantly in their contributions to AGI.

I've grown increasingly alarmed and disappointed by the number of highly-upvoted and well-received posts on AI, alignment, and the nature of intelligent systems, which seem fundamentally confused about certain things.

Can you elaborate on how all these linked pieces are "fundamentally confused"? I'd like to see a detailed list of your objections. It's probably best to make a separate post for each one.

That was arguably the hardest task, because it involved multi-step reasoning. Notably, I didn't even notice that GPT-4's response was wrong.

I believe that Marcus' point is that there are classes of problems that tend to be hard for LLMs (biological reasoning, physical reasoning, social reasoning, practical reasoning, object and individual tracking, nonsequiturs). The argument is that problems in these class will continue to hard.

Yeah this is the part that seems increasingly implausible to me. If there is a "class of problems that tend to be hard ... [and] will continue to be hard," then someone should be able to build a benchmark that models consistently struggle with over time.

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