vedant

The title of this article feels like "has electricity killed oil lamps"?

There is absolutely no way to achieve the impact of Henry Ford without actively trying extremely hard to be the next Henry Ford.

From the paper:

"All models still underfit WebText and held-out perplexity has as of yet improved given more training time."

I'm thankful for all the extremely improbable things that had to happen for me to have the experience I'm having.

I am thankful

- To have been born to loving, educated parents

-To live in a time and place with economic and social liberty

- To have a loving and growing group of friends and family around the world who live interesting and inspiring lives

-To live in a time where so many of us can travel at the speed of sound and communicate at the speed of light

-To be able to ask any question, purchase any common object, or want to see any loved one face-to-face, and have my wish fulfilled by a vast global network of machines that do our bidding

-For the millions of seekers, of wise men and women, who created wonders from the energy of the sun and the materials of the Earth, and showed us the way through the darkness

-That after 1000 trillion creatures and eons of failed attempts, one particular hairless ape on one particular wet rock evolved a 3 pound mass of flesh into the most sophisticated computational apparatus in the universe

- That it feels like anything at all to be thinking meat.

This is a heap of nonsense. Not only does this summarily dismiss the enormous challenges in digital signal processing required for removing arbitrary background audio, it exposes some confusion associated with the ideas of correlated random variables, inner products, and affine transformations.

This has the smell of a comment written by someone with limited real-world experience. Simply writing down the list of problems you would have to solve to build Shazam would take an entire afternoon.

Yes, deep neural networks have proven remarkably useful for machine perception, but you would still need to collect a colossal amount of audio data, fingerprint all of it, build a low-latency processing infrastructure for making inferences, and convince a hundred million people to install your software to feed you copious real-world training data that you can use to improve model performance.

Alternatively, it helps illustrate the complexity of the problems solved by natural selection over long periods of iteration.

One reason is that the amount of training data is many many orders of magnitude smaller.

FWIW it seems the structure you're talking about exploiting is at a morphological and syntactic level, which modern language models tend to effectively handle. Semantics are a much harder problem.

Theravada Buddhism.

High modularity refers to when a system's components can be cleanly separated and recombined.

If I were to guess, I'd say that amelius is saying that it becomes increasingly common as companies grow for them to expand into functions that were originally outside their core function. Framed this way, "selling stuff online" may have once set an effective upper bound on what Amazon does, but now they appear to be expanding into "moving stuff from place to place."

This concept is related to vertical integration.