threatripper

FPGA is not efficient in any way except producing very few pieces of a particular logic very quickly.

But I think the higher level logic could check out. If we can abstract the majority of the compute into dedicated ASIC then we might use something similar to FPGA glue to connect and reconfigure them to adapt to new model updates. A bit similar to LoRA layers that you find tune to adapt the model to your particular needs.

I assume that it starts making sense once you have big multi year contacts to run a particular model with only minor updates.

Tens of millions or billions? Either way that's not a blocker if it promises to pay off.

Earlier there was no AI to catch it.

The longer you wait the faster you will go.

Not sure if it's fine but when it pays it doesn't have to be.

I don't see a hard reason. If it works it works. No hard need for a good, universal, and long lasting solution. At some point you just stack slop on top of slop and it works for your use case - and if it doesn't you'll slop it out yourself.

Based on my own experience AI (Fable, Astra) still has major blind spots and is prone to draw obviously wrong conclusions from thin air. Conclusions based on weak evidence that are so obviously blatantly wrong that you question if it can think at all. At the same time it already completely outperforms humans in many cases in many areas. The utility of current AI is in what you need. Can you take the good parts and cheaply compensate for the weakness OR do the mistakes completely kill it for you?

I would therefore not call it AGI (Artificial General Intelligence) because clearly it isn't but ASI (Artificial Special Intelligence) which clearly it is in many areas.

The problem is that ASI may be enough to win wars but not enough to save the economy.

We need to start numbering these things. I opened the board today and thought "oh, another one again?!" but it's just the thread of yesterday.

It seems to start understanding how bicycles work. Look at the front fork. There are reasons why it's shaped the way it is in real bicycles. If you do physical simulations in 3D with reinforcement learning you will start understanding that most of the arrangements that the other agents use just won't work.

There is plenty of evidence that they have improved in all benchmarks and also in my private experience. But have they improved in the things they still fail at? No, they still fail at them. You need only one example of failure to prove that it still fails. They still fail a lot on many real world tasks.

So, depending on what you ask, they may have not improved even a tiny bit.

Apparently that's necessary and sufficient for getting views. In these interesting times when the future is very uncertain we like to hear people speaking with certainty about the future. Especially people who appear to not be paid for it.

Can he predict the future? No. Can he pretend to be able to predict the future? Yes.

Being early is the same as being wrong. Being lucky is the same as being right.

True, if you have a codebase that works in practice but has dozens of loose ends and poorly defined edge cases than it can chase off into rabbit holes because "oh wait, what if x is undefined instead of null? How is y defined? This outdated package has long known severe security holes and should not be used anymore, do we actually need it?".

Nobody except corporations who built workflows on top of it and don't care about the price because the developer already moved on and nobody wants to touch it.

Figure 1 made me laugh out loud.