LLMs brought this new revolution where it's not immediately obvious you're chatting with a machine, but, just like most humans, they still severely lack the ability to decompose unstructured data into logic statements and prove anything out. It would be amazing if they could write some datalog or prolog to approximate more complex neural-network-based understanding of some problem, as logic based systems are more explainable

It would be interesting if all these models were finetuned on basic datalog which is a very simple language. That way they could demonstrate their logic/reasoning capabilities as well as ability to learn from mistakes and iterate.

https://cdn.leonardo.ai/users/36088783-2be9-4497-9dd0-0d23fd...

Lazily evaluated until there's a probability it has to interact with something. Since you can never really see the value of the actual function, but only see what it looks like when it's forced to evaluate a computation in some context, an interaction, you can never get a precise definition of the function

https://docs.racket-lang.org/datalog/

https://docs.racket-lang.org/racklog/

https://www.npr.org/2019/03/25/706715377/man-pleads-guilty-t...

I remember del.icio.us from a long time ago was chocked full of top 100 lists, for example giant directories of free OCW courses. Stuff no one ever goes through, but everyone feels like they have to bookmark to go through it later.

Instead, right after the debt ceiling, there was a massive short squeeze, parabolic AI tech pump, and we're at 4567 on the S&P.

As it turns out the people just trading off momentum, technical analysis, and liquidity expectations, did way better than those betting on certain industries to go down. Sure, it can still go down, but there are plenty of money managers, macro experts, that looked at the big picture based and made data driven decisions based on historical data, and still got completely burned because they were trading against the technicals (massive upward momentum since after October).

I wonder if they'll start using LLMs while ingesting new data. eg asking the LLM if the content is helpful, cites sources, respectful, positive, not-thin content, common or often duplicated content, etc etc, before each content import.

One day some new startup will train on all of libgen and torrent networks, but it will be very hard to prove. You'll keep getting these gaps up in questionable morality and legality, and even openai will complain about playing fair

Crawling sites to index the FAQ's and knowledge bases, into the vector search, isn't as intimidating as it sounds, at least on linux systems. Sometimes a thin wrapper function over plain old wget will get you 99% of the way

    wget -rnH -t 1 --waitretry=0 'https://{{domain}}' -P '{{domain}}'