EsssM7QVMehFPAs's comments

That's the wet dream of western credit card companies.

Sure in some perspective getting people not used to debt at all to easily overspend might be called empowering from a twisted neoliberal point of view.

I'd guess they accelerate simulations on the GPU and/or over multiple cores.

Rarely revolutionary since this is certainly best practice in any serious application domain. Might make it much more accessible to implement though.

We'll see, I also remain rather sceptical.

Our compilers should long have included static analysis on par with optimization efforts, because - as you say - people tend to produce errors in code. Strong type systems and memory safety are a very nice first step though.

I am certain this will change over the years, but then the trend (that I am also prone to) is to go dynamic for the sake of productivity anyways.

Implementing censored search for an oppressive regime or providing research to killer drone programs is quite a different ball than "pissing off a few users".

Megacorps are only driven by one goal: growth. And Google has certainly proven that a lot is flying under the radar way on the wrong side of the good vs. evil dichtonomy.

Maybe simplified versions work with this paradigm with significant performance gains, but I doubt that you could simply plug Varnish or nginx in and see much of an improvement.

There is still quite a complexity gap between e.g. interpreting firewall filter rules and a full blown web reverse proxy..

I'd rather see this as a way to ingest performance critical code pieces into the kernel space more easily, with virtualization and verification options providing safety within an otherwise dangerous/complicated domain.

I would not agree with the article that this kind of paradigm is new - neither inside not outside of kernel land.

  tre-agrep -3 "Joohn Doe" contacts.txt|cut -d':' -f1

The basis of reinforcement learning algorithms is the exploratory nature of learning due to the initial application of largely random moves.

Only after some time the agent is given confidence into his learned ability and grafually moved into a more deterministic behaviour mode.

This is the exact opposite of your statement. Star Craft players have noted that the fleet of different AlphaStar instances training in ensemble observed very different behaviour due to this property of RL.

What makes recent breakthroughs in AI agents playing adversarial games possible is the fact that deep neural networks are able to develop patterns that yield short- and long-term strategic planning. And the ability to self train without human intervention to reach unprecedented training levels.

Point taken

As a take on your interpretation of creativity: I would argue that the act of forming new and valuable propositions is not related to emotion or aesthetics per se.

Aesthetic theory is observing a very narrow subset of creative processes. And even there, our transition from modernism into the uncertainty of the post-modernist world defies any sound definition of the "aesthetic criteria". Yet we perceive aesthetic human-creativity all the time.

In similar vain is the application of generative machine learning that spurs debate about computational aesthetics today. Nothing proofs better the incapability of modern ML forming real creativity than the imitating nature of adversarial networks spitting out (quite beautiful) permutations of simplified data structures underlying the body of Bach's compositions.

Now we could start on the assumed role of complex neurotransmitters in the creative process of the brain and the trivial way reinforcement learning rewards artificial agents, but that would push the scope of this comment.

How can we assume that a simulated evolutionary process of a simple mathematical model or some arbitrarily sized multi-dimensional matrices yields similar evolutionary results?

Just think of the ongoing debate about quantum entanglement effects inside the neural signaling process. On a rather onthological level, we are still unable to formulate a mere definition of our consciousness or things like creativity that lasts longer than a few academic decades..

I personally do not believe in AGI since I also do not believe in psychology, sociology or neurobiology being anywhere near understanding the holistic nature of our own intelligence. We are getting better at emulating human traits for specific tasks with ML. We lack the specific knowledge of what the algorithm should mimic to become equal to us in terms of our intellect though.

Sounds like a huge step forward to me. And Bill Gates is usually on track with his investments in transformative tech..

I have no idea about the domain but 30k rules seems more than sufficient to map from a 150k set of uncompressed wildcard patterns.