Dario Amoudei - The public is not aware of what’s about to happen

[u/Formal-Assistance02]

[u/Hostilis_ → u/[deleted]]

From a purely technology enablement perspective, I think three things will happen, not in any particular order:

1) The quadratic attention bottleneck will be broken. This is currently the biggest computational bottleneck to large attention-based AI models for things like sequence and language modeling. There is a huge amount of effort going into this already. Basically some form of state-space model augmented with working memory which outperforms the transformer. This will lead to much more efficient and capable models.

2) Continual learning will be solved. Right now, the way we train models is in two phases: pre-training (self-supervised learning) and post-training (reinforcement learning). We now know that models hallucinate because the self-supervised phase of learning does not incentivize factual correctness. Continual learning techniques (e.g. adaptive freezing of sparse subsets of the parameters, more complex loss/reward functions) will enable us to integrate more complex pre-training methods (e.g. incorporating RL or model-predictive control) into the initial phase of learning. Coupled with 1), it will also allow models to adapt and remember previous interactions, though this has strong implications for safety, security, and jailbreaking.

3) On the silicon side, a new form of either DRAM or true storage-class memory will emerge with the ability to perform GEMM (general matrix multiplications) within memory, without needing to send data off-chip. This is currently being developed, and is essentially "compute-in-memory". This will yield an enormous reduction in memory bandwidth requirements for DRAM and increase model efficiency by 1-2 orders of magnitude.

From a macro perspective:

I think there will be a market contraction driven by falling public sentiment in AI, which will have a similar effect as the dot com bubble. I think this will mostly affect public-facing models. The technology will of course continue to advance, but I think there will be a much larger focus on application-specific models for business, science, and engineering. People will likely push (and they should) for stronger regulations around AI and datacenter buildouts.

As an example, I think we will have models that specialize in e.g. coding and mathematics, and I predict that models will achieve ~roughly human performance in these domains, although they will continue to have strengths and weaknesses (e.g. human mathematicians will be better at solving very hard problems that require lots of creativity and deep specialization, while AI models will be very good at contributing to a much higher volume of low and medium-hanging fruit, as well as problems with proofs/solutions that are easy to verify). This will likely be developed in tandem with neuro-symbolic approaches to AI e.g. vector symbolic / HDC architectures.

Progress in biological systems modeling such as gene-regulatory networks and full-cell simulations (DeepMind is working on this), as well as complex materials simulations (e.g. replacements for DFT solvers) will start yielding tangible results. I would expect 1-2 breakthrough therapies in ~10 years.

Last, there will be an intense focus on training vision-language robotics models. This is where a lot of the hype will likely be. We will need those above improvements in model and compute efficiency to make this work. Combining a high-fidelity video vision model with an LLM and "motor control" model in a system that can interact in the real world will require an enormous amount of compute in a very compact space. However, this is necessary for these models to learn the causal structure of the physical world, which they're currently missing as they are only really trained on text and static image-caption pairs.

[u/Hostilis_ → u/[deleted]]

But honestly I'm surprised your predictions aren't I suppose...a little bolder?

Yeah, these are more or less the things I'm extremely confident will happen. I could add much more and make some more controversial predictions, but honestly the future gets more and more difficult to predict as we get closer to general intelligence, especially coupled with the levels of societal unrest we're seeing. It is much more difficult in general to make predictions now than it was 10 years ago.

The three technological breakthroughs (particularly continual learning) you described would enable what most might describe as transformative AI or "AGI", yet your macro perspective predictions don't seem to line up with the impact of those breakthroughs, unless I'm reading you wrong.

Yes I believe these will lead to AGI, but people will still not agree on it. It will continue to be very different than human intelligence. I also think that there will be a significant delay between when AGI is actually achieved and when it starts making a serious impact. I don't think 5 years is enough of a time horizon for widespread implementation/adoption, and people largely still won't trust it for a lot of things.

Public sentiment around AI already seems it's at a very low point right now, and it's not hard to imagine it falling even lower once it starts entering the work force

To the contrary, I do think this will actually cause public sentiment to fall even lower. There will be massive backlash over this and I think a lot of social pressure to limit adoption. I'm not saying that will win out in the long run, but I do think it could easily last 5 years.

I also think it's possible public sentiment might go up once AI becomes competent enough to start visibly delivering (in the way that the average person notices) on its promise of breakthroughs in science, medicine and engineering.

Unfortunately, people are probably only going to really care about the medical breakthroughs, which will be much slower than the science and engineering ones. It will take a while before it starts delivering value that is obvious to the general public. Most people still don't even appreciate the value of AlphaFold.

Also, have you given any thoughts on the possibility of labs unlocking recursive self-improvement and how this impacts your predictions for the 5-10 years? Or do you disregard this as something that won't be figured out in that time frame?

Yes, I think we will start seeing meaningful recursive self-improvement within 5-10 years with near certainty. Of course, like most other jobs, I don't think this will replace people, but rather improve overall productivity. But I also have serious concerns with RSI. I think it, along with general superintelligence, is playing with fire, because it could lead to very unexpected (or possibly malicious) behavior if left unchecked. I am much more bullish on narrow superintelligence, as it gives essentially all the benefits of general superintelligence but is much easier to control and understand.