dnc
Joined 15 years ago
Joined 15 years ago
I'd like this to be true, but Socrates immediately comes to mind as a perfect counter example of someone who was opposed to writing and was arguably one of the greatest thinkers ever.
- the Cul-de-Sac: AI progress flattens as scaling data and compute, RL and algorithmic improvements hit diminishing returns.
- democratization: LLMs decentralize, mirroring the shift from mainframes to personal computers.
- AI creates new jobs and thus new dependencies for the capitalist class
- Any combination of the above.
"The researchers also explored the possibility that index changes are more predictable today than in the past, leading sophisticated market participants to trade ahead of the events. They concluded that this played only a minor role in their findings, however."
The two main factors being:
"...an increase in migrations over time from the S&P MidCap 400 index, and an overall increase in the market's ability to provide liquidity to those investors seeking to buy and sell around the time of index changes.
The additional liquidity has 'made it easier for everyone to trade, and as a result, the prices move less.' says Dr. Greenwood.
And with more stocks migrating to the S&P 500 from the S&P MidCap 400, midcap funds are selling as S&P 500 funds are buying, leading to 'a wash' in demand, he says.
"
That makes at least two of us. :) But, I suspect there are quite a few more HN readers who like Kafka's works and literature in general (the first is arguably very correlated to the other), judging from how often literature related articles appear on the HN front page. Often, when that is the case I also can't help but wonder the same about the correlation between being a fiction and HN reader.
[1] https://bryanafern.files.wordpress.com/2016/12/on-writing-ra...
This is incorrect in general case and it entirely depends on the model that is used to produce word vectors and the text corpus the model is trained with.
For instance, fastText model is trained on words, but also their parts (n-grams), so it should produce word vectors that would be close (in cosine-distance) to vectors of their corresponding typos and partials, even if the text corpus that was used to train the model doesn't contain same typos and partially typed words verbatim.
This is an awesome project, congratulations!
Could you share details about the machine translation engine that is used (or where to find out more about it)? Are there any plans to open source the extension code (with the WebAssembly optmizations that are mentioned in the article)?
Thanks.
[1]: https://news.ycombinator.com/item?id=21509144 [2]: https://news.ycombinator.com/item?id=9015092