ackbar03

No, the comments are not confused. I wouldn't consider myself as a expert, but I've worked a lot with evolutionary algorithms. This is not a good work and should be rejected from ICLR, although again it is my personal opinion. Terminologies are there to help distinguish between different concepts, not to be obscured in some handwavy way to seem deep and knowledgable to sucker some ICLR paper reviewer with no scientific rigor. It is indeed possible to optimize NNs using GAs but claiming diffusion is an evolutionary algorithm is a huge stretch, and they even didn't draw the connection properly in the paper. There might be something worthwhile in their work but I would say portraying it this way is just flat-out not correct.

I don't really know much about ic manufacturing.

Are you sure university labs are really able to to this? If so how come only a few companies like tsmc and that one Dutch company are able to manufacture microchips? Or are those two completely different things and I'm just confusing myself?

Hey man, remember to type out that play before the universe dies

are there out-of-the-box software tools to do this?

I thought about something like this before, but its non-trivial to train an image recognition system to do this, let alone the commands to mimic it through hardware, its quite an engineering feat in itself.

I think finding cofounders is one of those things where the best time to start was always last year or two years ago

I know the version where you try not to kill yourself chasing the cheese. Are you saying there's one where the cheese chases and tries to kill you?

Now I am going to be forever haunted by all those trucks I destroyed

physics?

I was just wondering when they were going to award the alphafold2 guys the nobel after after seeing Hinton win the physics one. 100% agree, all three of them totally deserve this one. Baker's lab is pretty much keeping Deepmind in check at this point and ensuring open source research is keeping up. Hats off

> Fuzzing is often a special case of genetic algorithms

Yes, that was sort of why I thought RL guided fuzzing could work, and possibly better. Also, for things like XSS fuzzing (which I have a little experience in), it is possible for an experienced attacker to intelligibly guide the fuzzing to a payload, which theoretically could be mimicked through RL.

There wasn't really anything novel in the proposal, it was just for a graduate cyber-security course, and one of the deliverables was a project proposal for something related. There were already some existing works that time (around 2-3 years ago) where people tried combining RL with fuzzing, and I just mish-mashed some ideas together so I could hand in something.

My main concern at the time however was that with fuzzing the positive signal would be so rare compared to the negative signal, since most randomly fuzzed inputs would just return the same negative feedback. I wasn't sure that would be enough signal to train an RL system. I'm not quite sure what new progress has been made in the field since then.

I proposed using reinforcement learning to guide coverage as a potential phd topic, but didn't really go down that path, no idea if it could work

What your basically describing is HackerOne but for kids. And I actually don't think it's a bad idea, they could consider doing a teenager version or do some program aimed at high schoolers. I'm sure it would be very well received, I would have thought it was the coolest thing ever.

I went out and borrowed this book after Carlo Rovelli was recommended, so far it has been indeed quite mind-bending

Does anyone have a good recommendation for an introductory book on these concepts?

I read a bit of The Fabric of Reality but had trouble progressing too far with that one. The author talks about the quantum slit experiment, somehow arrives at the explanation of parallel universes, and then claims anyone who disagrees with this conclusion must have faulty logic. A huge chunk of reasoning behind parallel universes seems to be skipped and I have trouble taking it seriously every time it is brought up.

I'm a bit surprised, since it seems the fed is about to start a easing cycle

what happens if you don't invalidate them?

real question is did Michael Lewis happen to be hanging around the OpenAI water-coolers again when all this happened

The Forbes family tree or the publication?

In hk too

So deepmind is doing more of the fundamental stuff and isomorphic the commercialization? I thought isomorphic would have been carrying the protein projects now since Google brain/deepmind, now Google deepmind, is focusing on catching up with openai and stuff and less on fundamental research

The universe blue screens cause those keys were never supposed to be dropped

I think what's interesting about it is the inter-relation between different disciplines and how the ideas are connected. The connection between the heat-equation and the generative diffusion models we see to day, and its relation to the Fourier Transform would not have been immediately obvious to me.

I guess the idea will be somewhat similar, going from coarse to fine details, such as for 3D structures.

Maybe the original author benanne could give his insight.

How do you guys actually hire the superstars to join your startup as the early employees/partners? And not just the typical friend you know who's "a pretty smart guy" and his friend and so on

Worms still use them as homing missiles for their Armageddon battles

When you need to train that pytorch model in the middle of syria

>but they are complex valued and change when you sample from them

I see, that does sound pretty different

Thanks for that, that's super interesting.

With quantum mechanics it's just infinity in the opposite direction, going infinitely small. My very pedestrian understanding is that the field of quantum mechanics came about because people were having trouble explaining the behavior of atomic particles, particularly electrons, using newtonian mechanics, and quantum mechanics were able to explain everything in a more comprehensive framework. At first I always found the idea that electrons are 'nowhere' until they are observed very mysterious, but it made a lot more sense when I understood that probability densities are involved in qm equations. There's usually a similar source of confusion when we move from "probabilities" of discrete distrubutions, which is quite easy to understand, to probability densities, which can be done by taking limit of number of possible states to infinity, and where you can get "probabilities" larger than one.

>To understand the universe, scientists look to its outliers. “You always want to know about the extreme cases — the special cases that lie at the edge,”

Some of the books I've read recently touch upon things like quantum mechanics and black-holes and that kind of stuff.

As a decently technical person but with no formal training in physics, can I generally interpret the study of things like black-holes and quantum physics as the idea of understanding how the physical world behaves as we take limit towards zero or infinity? Is that a correct way to think about it?

For example, I've studied probability and statistics somewhat formally in undergrad. The idea that electrons taken on a distribution and are technically "nowhere" until they are observed (schrodingers cat) sounds just like the description of a continuous variable, or alternatively where you take limit on a discrete variable such that it approaches a continuous distribution. The probability of the variable being any value is technically 0 but its state can be observed. It's hard to "truly" comprehend in a realistic since but its what allows us to build statistical models of things

Which article do you recommend from this issue?