> Unfortunately , too few people understand the distinction between memorization and understanding. It's not some lofty question like "does the system have an internal world model?", it's a very pragmatic behavior distinction: "is the system capable of broad generalization, or is it limited to local generalization?"
-- a thread from François Chollet

> by popular demand: a starter set of papers you can read on the topic.

"Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks": https://arxiv.org/abs/2311.09247

"Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve": https://arxiv.org/abs/2309.13638

"Faith and Fate: Limits of Transformers on Compositionality": https://arxiv.org/abs/2305.18654

"The Reversal Curse: LLMs trained on "A is B" fail to learn 'B is A'": https://arxiv.org/abs/2309.12288

"On the measure of intelligence": https://arxiv.org/abs/1911.01547 not about LLMs, but provides context and grounding on what it means to be intelligent and the nature of generalization. It also introduces an intelligence benchmark (ARC) that remains completely out of reach for LLMs. Ironically the best-performing LLM-based systems on ARC are those that have been trained on tons of generated tasks, hoping to hit some overlap between test set tasks and your generated tasks -- LLMs have zero ability to tackle an actually new task.

In general there's a new paper documenting the lack of broad generalization capabilities of LLMs every few days.

"Noisy TV problem" is solvable by introducing yet another level of abstraction :)
Curiosity-Driven Exploration via Latent Bayesian Surprise
https://arxiv.org/abs/2104.07495

More on the topic: https://lilianweng.github.io/posts/2020-06-07-exploration-drl/

a nice take on AI/ML from outside the Valley
https://wz.ax/bridgewater-on-ai

oh interesting. makes sense as all addictions are driven by the same neurochemicals
https://twitter.com/tenobrus/status/1738364449122357365

> In this study, we show that when aiming for limited precision, existing approximation methods can be outperformed by programs automatically discovered from scratch by a simple evolutionary algorithm.
https://arxiv.org/abs/2312.08472

that's a diploma. ~$30K budget
https://mobile-aloha.github.io/

> I made this game to teach my daughter how buffer overflows work
DANGER: NERD LEVEL 80
https://punkx.org/overflow/

⚠️ GitLab arbitrary account takeover, CVSS 10
TLDR: Upgrade your gitlab instance ASAP, likely it's an open door right now.
https://about.gitlab.com/releases/2024/01/11/critical-security-release-gitlab-16-7-2-released/

if you thought Z̵̋̄ ̱̬͗̐̃͗͋͋͐͂͛̍̀͛̒͘ą̵͔̗͍̝̲͈̘͉͓̰͍̯͑͐ͅĺ̵̢̨̦̫͈͓̖̼̟͎̤̦̖̔͗̓̏̌̾̑̈́͆̎͘͝g̸ ̨̠̠͓͚͙̣̟̪̺̗̺̻̖͆̾͋̽͐̑́͌̚͠ơ̶̋͝ ̞͖ is bad...
https://stackoverflow.com/a/6163129

slow but steady
https://delvingbitcoin.org/t/untraceable-taprootized-atomic-swaps/458

insecure boot, huh

https://arstechnica.com/security/2024/02/critical-vulnerability-affecting-most-linux-distros-allows-for-bootkits/

TIL this is possible in the general case. Neat!

> SQL-99 allows for nested subqueries at nearly all places within a query.
From a user’s point of view, nested queries can greatly simplify the formulation of complex queries.

However, nested queries that are correlated with the outer queries frequently lead to dependent joins with nested loops evaluations and thus poor performance.

We present a generic approach for unnesting arbitrary SQL queries. As a result, the de-correlated queries allow for much simpler and much more efficient query evaluation.

https://btw-2015.informatik.uni-hamburg.de/res/proceedings/Hauptband/Wiss/Neumann-Unnesting_Arbitrary_Querie.pdf

TRUFFLE–1 $ 1,299
Truffle-1 is an AI inference engine designed to run opensource models at home, on 60 Watts.

https://preorder.itsalltruffles.com/features

super detailed explanation of the CVE-2024-1086 Linux v5.14-v6.7 privilege escalation exploit

I hope beginners will learn from my VR workflow and the seasoned researchers will learn from my techniques.

https://pwning.tech/nftables/