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#computerscience

10 posts10 participants0 posts today

People say it's a misconception that quantum computers can evaluate a function on all its possible inputs in parallel. But actually a lot of quantum algorithms do begin by applying a function to a superposition of all possible inputs. It's just that after that point you need to do some difficult linear algebra and you can't always extract the information you want.

In fact, you can define a lot of important complexity classes in this way. The set of problems solvable in polynomial time with an oracle that evaluates a given circuit on all its possible inputs and tells you …

P: … nothing.
BPP: … a randomly chosen output.
PP: … a majority output.
NP: … if any of the outputs is nonzero.
co-NP: … if all of the outputs are nonzero.
PSPACE: … a fixed point.

Unfortunately, my horse power computer is now offline. And I am in another city so no access to it. It's likely that there has been a #poweroutage which is now very common in my country #iran

I am doing experiments with #wakegp to see if my simple method for parsimony pressure is effective. Till now, it seems that its effect is very little. I'm thinking of other methods for parsimony pressure such as bucketing and tournament selection(for size instead of fitness).

I am expecting to deliver results in summer, God willing.

"Learn how to do it yourself. Use the mechanical aids, but learn what you’re doing. A general problem with these large language models is that they will give you confident answers that are total nonsense, and if you don’t understand what you’re doing well enough, you will not recognize what the nonsense is."

The undefeatable Brian Kernighan on the joys of teaching and the sadness of genAI.

Thanks to @aka_pugs
for the link.

dailyprincetonian.com/article/

The PrincetonianTeaching keeps you young, or so says Professor Brian KernighanComputer science professor Brian Kernighan GS ’69 left a career in emerging computer science research to pursue his passion for teaching. He shared his perspectives on the evolution of the field of computer science, his interdisciplinary approach to this work, and his advice to aspiring programmers.

Dear people. Please understand that LLMs are not AI. Don't use phrases like "code generation with AI". Use the term LLM instead. LLMs are just a tiny subset of machine learning which itself is a subset of AI science.

#ai#ml#llm

So I'm curious. I'm thinking about using #nix as a way to reproducibly manage scientific results and the computational dependencies between those, specifically simulations and their outcomes.

Some of those simulations involve measuring the execution time of a given algorithm, and plotting those times on a graph. That graph would then be integrated into a report or a paper of some sort, which would be the build artefact.

Issue is... those execution time measurements aren't 100% deterministic. Not down-to-the-microsecond reproducible. Hence, the results are technically not reproducible either.

Obviously, Nix is a software build tool, not meant for what I'm using it for here, but... I still find the notion of these reproducible, cacheable units with pure-functional relationships of one deriving from another to be very appealing.

Thoughts?

#academia #computerscience #nix #nixos

semester is winding down. My goal for summer is to read a bunch of #computerscience and #programming books over summer and build stuff. I need to find a job in software, IT, or ideally games. I need to build my resume and skill set through making stuff and learning stuff and learning stuff through making stuff.

The modern tech industry is very weird (Not just the big name MEGACORPS) and finding a job seems to be a multi-stage boss fight. I gotta get some decent “gear” by building my skills.