The standard way to do this is Reinforcement Learning: we do not teach the model how to do the task, we let it discover the _how_ for itself and only grade it based on how well it did, then reinforce the attempts where it did well. This way the model can learn wildly superhuman performance, e.g. it's what we used to train AlphaGo and AlphaZero.
Well, growth has been on this exponential already for 5+ years (for the METR eval), and we are at the point where models are very close to matching human expert capabilities in many domains - only one or two more years of growth would put us well beyond that point.
Personally I think we'll see way more growth than that, but to see profound impacts on our economy you only need to believe the much more conservative assumption of a little extra growth along the same trend.
The argument is not that it will keep growing exponentially forever (obviously that is physically impossible), rather that:
- given a sustained history of growth along a very predictable trajectory, the highest likelihood short term scenario is continued growth along the same trajectory. Sample a random point on an s-curve and look slightly to the right, what’s the most common direction the curve continues?
- exponential progress is very hard to visualize and see, it may appear to hardly make any progress while far away from human capabilities, then move from just below to far above human very quickly
> You don’t have to randomize the first part of your object keys to ensure they get spread around and avoid hotspots.
Sorry, this is absolutely still the case if you want to scale throughput beyond the few thousand IOPS a single shard can serve. S3 will automatically reshard your key space, but if your keys are sequential (eg leading timestamp) all your writes will still hit the same shard.
It means you can for example no longer do things like get multiple disjoint references into the same buffer for parallel reads/writes of independent chunks.
Or well you can, using unsafe, Arc and Mutex - but at that point the safety guarantees aren’t much better than what I get in well designed C++.
Don’t get me wrong, I still much prefer Rust, but I wish async and references worked together better.
Source: I recently wrote a high-throughput RPC library in Rust (saturating > 100 Gbit NICs)
Requiring ownership transfer gives up on one of the main selling points of Rust, being able to verify reference lifetime and safety at compile time. If we have to give up on references then a lot of Rusts complexity no longer buys us anything.
On many natural language tasks there can be significant overlap, making it difficult to judge performance.
That's why I like more complex code generation tasks such the dataset we used for AlphaCode.
Yes, this is how you increase prosperity (see: agricultural revolution, industrial revolution, etc). You can now create more with the same number of people.
If you are interested in this topic, I recommend "Crazy Like Us" by Ethan Watters - it describes how mental illnesses get exported by media coverage and cultural attitudes from Western countries, and how cultural expectations shape the manifestation of mental illness. It was also reviewed in depth by Scott Alexander: https://astralcodexten.substack.com/p/book-review-crazy-like...
Self diagnosis waves on tiktok seem like the next evolution of this, but even faster and more globalised.
I've been wondering if this phenomenon could be harnessed to reduce the impact of mental illness: purposely spread content that encourages self-diagnosis with the mildest possible symptoms.
Works for an amazingly large set of products!