neptvn

It's a nice article that basically (rage)baits the readers before they realize the author actually disagrees with the premise of coding being solved. So here goes, my higher-level rant on "solving" something with AI.

I strongly dislike all the "X is solved" narratives. What does it mean for something to be solved? A math problem (or any kind of problem), a riddle, a mystery, a dispute. Those are all instances of a particular situation that requires a "solution", but new situations will always come up. I understand that by "cancer/coding/X is solved" in this new age of (gen)AI that means the ability to streamline or speed up the "solution finding" procedure, but even that presupposes a fixed, static, fully deterministic space of the things we are trying to "solve".

Even cancer cannot be fully solved - Demis Hassabis slowly started drifting away from using the word "diseases can be solved" because they can't be eliminated - we can only speed up the process for finding a cure for any particular disease, be it existing or new and/or evolving.

Is bridge-building solved? Architecture? Why are architects still employed? Is solving "civil engineering" or designing an optimally running machine a thing to be solved? What are we trying to do when we talk about "progress with AI"? Even when the "recursively-self-improving-AIs" and "perfect" robots do arrive, we're still bound to work with them, and they'll have to evolve to find new solutions to new problems.

To be clear, I work with and rely on LLMs every day, from coding custom RAG architectures with CC and Pi to research and agentic data science. These bombastic conversations, however need to quiet down a bit so we can get back to work :)

So the article seems to pose more questions than it answers. I was expecting some observations from the plastic-free lab they built, and at least some evidence of the comparative impact vs. other labs that use plastic to study blood and other fluids (besides the 100-times-plastic/phthalates concentration in the air).

What exactly are they studying in the lab? The biggest takeaway from all this is that "we [still] don’t have enough information yet".

Wolfram might be a genius, but I personally find his writing and lectures extremely dull. The only thing he seems to write is how everything is "computational", and how we need to embrace his idea that computing is what the whole universe is about. For example in this blog post alone, we can find this following word count:

  computer - 19x
  computation* - 96x
  computational - 63x

Make everything computational!

  computational intelligence - 2x
  computational contracts - 7x
  computational universe - 7x
  computational language - 18x
  computational thinking - 2x
  computational fact - 1x
  computational acts - 1x
  computational equivalence - 8x
  computational irreducibility - 6x
  computational system - 1x
  computational process - 1x
  computational work - 1x
  computational essays - 2x
  computational law - 1x

Throwing all these words around may sound smart but they lose any meaning or relevance that they were supposed to deliver if being overused in such a larger-than-life manner.

</rant>

Is `hashbrown::HashMap` now completely ported/identical with `std::collections::HashMap`? I just compared their performance (on yesterday's beta channel) on a hashmap with over 200k keys and didn't notice any relevant speedup.

Can you describe the mess Atlas creates?

Congrats to the Rust team and all contributors! Awesome work!

Is the non-lexical lifetime improvements work locked to a particular release yet?

Also, now that the async/await RFC has been merged, is its implementation in nightly or stable going to be squeezed in for the 2018 roadmap plans?

> Apart from the fact that nobody wants to risk having false positives, there’s a simple argument as old as machine learning itself: whatever a human can do, a machine can be taught to do. Humans have no problem correctly interpreting adversarial examples, so there must be a way to do it automatically.

So by all means, the authors (and a vast majority of researchers) seem to be confident that ML/DL is the road to AGI, hence can "solve" human intelligence (given it is computational)? For how long are we gonna drag the adage that mimicking a human (Turing test) is equal to reaching human levels of intelligence?