neptvn

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".

Sure, but that's a non-sequitur. Sight and hearing are the most important human senses that practically inform consciousness in the sense of where we live, and what we can do. The other 3 (touch, smell and taste) are at best secondary and cannot provide the groundedness in our world experience - you are the easiest prey in the animal kingdom. Language is primarily a tool for communication, not thought [0], and "thought" is a slippery concept when you're lacking a guidance that informs you of the world if you can't see or hear it.

Super interesting quote and link though!

[0] Fedorenko et al, https://www.nature.com/articles/s41586-024-07522-w

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

  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

</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?

Are your clients based globally or solely in Germany?

I would look into Stripe Atlas in any case.

https://stripe.com/atlas

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?