xabush

"To me it would be more impressive if we could define hard problems that need to be solved up front and see how the models deal with that."

I was recently listening to BBC Radio 4's episode on the Poincare Conjecture[1] and the guests on the program were discussing how the problem that looked deceptively simple eluded the great mathematicians of the time (including Poincare himself) for nearly a century and how Grigori Perelman cleverly came up with the proof. It took other mathematicians working in groups years after Perelman's publication to understand and validate his proof. The mathematicians on the program were speaking of highly of his proofs and admiring the originality of his work. This made me think of one neat experiment where if we cut-off a frontier model's training data 2002 or anytime before Perelman posted his proofs on arXiv and check if it can come up with the solution by itself. That would surely be a great signal to see if these LLMs aren't just solving interesting puzzles and that they can came up with something truly novel.

P.S I highly recommend Misha Green's "Perfect Rigor" for anyone interested in the history of the problem and the genius behind the proofs of the conjecture - Perleman. I found it an entertaining read and could digest its description of the problem as a layperson (with undergrad level math).

[1] https://www.bbc.co.uk/programmes/p0038x8l

I don't understand (the point of) your example. In all branches of the search `X > 5` will never be `true` so yeah `slow_computation` will not be reached. How does that relate to your point of it being "brute force"

>> but if it did, it would come up with the same result

Meaning either changing the condition or the order of the clauses. How do you expect Prolog to proceed to `slow_computation` when you have declared a statement (X > 5) that is always false before it.

Amazing design! Just joined the mailing list

Is the software open-source? If so, can you share a link to it?

Thanks for the comment. Have you run cplint on a kb of the similar size before and gotten it to finish in reasonable time?

How does Scallop scale on large knowledge bases (KBs) for doing probabilistic reasoning? I'm currently working on large KB with ~ 12M facts and trying to do probabilistic inference on it. So far I've been using [cplint](https://friguzzi.github.io/cplint/_build/html/index.html) which is based on SWI-Prolog. It works fine for toy examples, however, it doesn't finish running for the large KB - even after waiting for it for more than a week. Does know any Probabilistic Logic Programming (PLP) libraries that are fast and scale to large KBs? Preferably in Prolog ecosystem, but not a hard requirement.

IM are legendary! I still listen to them now and then. My favorite tracks from them are - 'Deeply Disturbed', 'Becoming Insane' and 'Where do I belong'. Not exactly the same, but checkout Astrix and Neelix if you haven't already.

This is an interesting anecdote. I have gotten high several times during my college years and less so since I started my career. In all of the cases I got high, it was primarily for entertainment purposes. However, whenever I am high, I get some sense of clarity about problems that were irking me the week before (I usually do weed over the weekends). This led me to wonder, esp. lately, what if there is actually some connection between productivity and marijuana use (not abuse) and your experience seems to be inline with that. Ofcourse, there are trove of other anecdotes about how weed is used for creative/artistic purposes but I haven't come across ones related to programming.

Youtube, Gmail and Drive pages open in incognito mode but can't sign in. The issue seems with the account manager.

Or use Rust as it fits this particular use case where you need a language that’s low-level and safe.

This excellent video delves into the economics of nuclear power plants and compares them with the alternatives. https://www.youtube.com/watch?v=UC_BCz0pzMw

Although nuclear energy is profitable of the LONG term, it is financially infeasible over the short-to-medium term.

Very interesting and helpful !

This is great tutorial. I used to be confused by the macros in scheme

Thank you for this! I was looking for a similar tool and glad I discovered Drools.