I haven't read the act and am not going to, but, for this size community I'm pretty sure having a flag/report button would do the trick, and to go the extra mile, with very cheap LLM's generating a "dodgy content" score on every message would be pretty trivial. This seems a bit knee-jerk of a reaction to delete the whole site.
I’m on Zwift a lot with the Zwift Ride and Wahoo Kickr core. But I’m looking for a recumbent setup so that I can also work while riding or game on the ps5 in a more comfy position. For keyboard I have the Ultimate Hacking Keyboard which is perfect since it has a mouse layer built in. Haven’t figured out the recumbent setup yet though. Any tips appreciated. I’m in the EU.
We could run something like TensorFlow.js in a Chrome extension to identify the person in the image and replace it in the dom. A little resource intensive for inference on every image in but probably worth it in this case.
I know some people may find the introduction engaging, but any article that begins: "There comes a moment in life..." you just know that the author is going to lay out some pretentious self-scripture.
I'm sad that you've never had singular moments in life that have caused fundamental reflection and re-evaluation of things you took for granted. I've had many. They're delightful, terrifying, hilarious, sombre, banal and life-changing, sometimes several of these at the same time.
I didn't find the article particularly pretentious, or "self-scripture", whatever that may be.
Perhaps dive in, reflect, and then ask after you've read the whole article whether it was pretentious "self-scripture" before assuming it is. As a heuristic, your current method seems a bit over-fitted to a false premise.
This is an overly rigid view. "There comes a moment in life" can be used as a relatable, humanizing introduction, drawing readers into a shared experience or insight. It doesn't necessarily lead to pretentiousness—what matters is how the idea is developed after that.
Except that the development of deep neural networks took direct inspiration biological neuroscience with neurons and synapses. Neural is even in the name. https://en.wikipedia.org/wiki/Deep_learning
DL did not take 'direct inspiration' from neurosciences. Maybe some ideas were borrowed such as the integrate-and-fire nature of neurons and Hebb's very vague rule, but those are very old ideas. Most of neuroscience research in past decades is in molecular biology , and particularly in the study of neural diseases (that's where all the funding goes). Learning and biological plasticity is notoriously complex and difficult to study, it's still very much undeciphered, and none of that plasticity research has made its way into ANN training.
In fact it is the reverse: the recent success of deep learning has sparked a race in neuroscience to try to find processes in the nervous that might mimic deep learning and in particular to build biologically plausible models about how the brain might implement gradient descent or more generally credit assignment.
People always repeat these stupid things like they're lore. Ok let's suppose this is true. What else is true is that neurology itself was inspired by phrenology and the practice of exorcisms. Should we now start recognizing and exalting those connections given how divorced modern (useful!) neurology is?
Hinton's most recent paper on forward-forward acknowledges Peter Dayan explicitly for his feedback on the paper, and cites a paper they cowrote together back in the 90s. Dayan being the author of the canonical textbook on theoretical neuroscience.
Major distinction given those practices have been abandoned as pseudo science or even worse, so they aren't fields of science continued to be developed which further useful connections might be found.
In psychiatry, there is a certain amount that we continue to study social standards of normalcy in other (including historic) societies to determine what should count as a mental disorder, but more to make sure we aren't doing a 21st century equivalent of labeling something as a demon possession because it contrasts with our current deeply held social norms.
So what is the meaning of to do with and nothing to do with? Inspiration seems to be a relationship.
Consider a different relationship between cellular biology and the Cells at Work anime. Clearly any relationship is unidirectional. Any cellular biology learns nothing from the anime, but the anime wouldn't exist without cellular biology.
Do we say the show has nothing to do with cellular biology? That doesn't seem right to me, given it depends upon it despite taking an amazing degree of artistic freedom.
The downvotes are very unusual to say the least. All the historical material on the subject unambiguously points to neural networks emerging from work done to formalize actual brain neurons. That formalism turns out not to be a great way to explain biological brains but the abstraction it provided proved highly effective for tasks like pattern recognition, classification, and decision making.
So much about computer science has been inspired from other fields such as biology. Polymorphism and object oriented programming, reification, neural networks and in particular convolutional neural networks, genetic algorithms...
If anything, it teaches the value in learning a topic and then applying it directly within computer science. The strength of computer science lies in its ability to adapt and incorporate ideas from other domains to push the boundaries of technology.
There are a lot of downvotes going around because a large contingent is thinking the Nobel Prize for "Physics" should not go to something involving Computer Science. That it was awarded as it was, was an error.
Seemingly because even if the math or algorithms came from a physicist solving physics problems . Since it didn't involve some theoretical particles, it isn't physics'y enough to get a Nobel in Physics.
Many of those are unfulfilled promises of the type that have been around for 30 or 40 years at least. And climate modelling: what's the point? You can't predict climate change from history. That's the whole point of the research.
So then wait until those promises have been fulfilled, as has so often been the case in Nobel prizes. Remember Higgs?
But the negative effects have been clear. Might just as well give the Nobel Peace Prize to Zuckerberg.
it already had, bottom-quark tagging has improved O(10)x in efficiency in the last decade without any new "physics" understanding, just from training with more low-level data and better ML arch (now using Transformers)
but we haven't found new physics with or without ML, making this prize a little weird.
Maybe it's a prize for hope and change that physics will be revolutionized by neural networks? Similar to how Obama got a Nobel Peace Prize in order to repudiate Bush's legacy in Iraq and Afghanistan. While Bush's legacy absolutely deserved to be repudiated, I don't think awarding a new president the Peace Prize was the best way to do it, especially because in the foreign policy realm, he ended up not so different from Bush.
"With their breakthroughs, that stand on the foundations of physical science, they have showed a completely new way for us to use computers to aid and to guide us to tackle many of the challenges our society face. Simply put, thanks to their work Humanity now has a new item in its toolbox, which we can choose to use for good purposes. Machine learning based on ANNs is currently revolutionizing science, engineering and daily life. The field is already on its way to enable breakthroughs toward building a sustainable society, e.g. by helping to identify new functional materials. How deep learning by ANNs will be used in the future depends on how we humans choose to use these incredibly potent tools, already present in many aspects of our lives"
Whether or not the original Higgs discovery decay channels used ML, confirming that it was in fact the Higgs required measuring the decay to b-quarks, which has used ML since the LHC started taking data.
Over the lifetime of the LHC, he backgrounds got around 10x smaller for the same "efficiency" (fraction of true b-quarks tagged) if you want to be pedantic about the definitions. We've used NNs in b-tagging for decades now, so it was always possible to dial in a threshold for tagging that was e.g. 70% efficient.
Transformers gave us a factor of a few smaller backgrounds in the last few years though [1].
I sort of agree in principle but in practise they've always taken a broad view.
Kissinger was one of the most prominent disrupters of world peace in the postwar era but that didn't stop him winning the peace prize. Churchill won the literature prize for defeating Hitler. The blue led guys a few years back didn't do much except make a thing that would go on every single consumer gadget and disrupt my sleep but they won the physics prize.
Even when they get it right they often get it wrong. For example I believe Einstein supposedly won for "especially his work on the photoelectric effect" rather than relativity.
Einstein’s work on the photoelectric effect was incredibly important, and incredibly influential on other research at the time. He proposed that light was quantised - essentially the foundation of quantum mechanics.
It’s no exaggeration that Einstein’s work on the photoelectric effect was as important as special or general relativity, and it had the advantage of strong experimental verification by 1921.
The main reason that prize is remarkable is that Einstein himself hated quantum mechanics - but that doesn’t dispute the work’s importance.
The discovery of the photoelectric effect was certainly as important as relativity in terms of how much it affects society. But it was only an incremental advanced on top of Planck work on blackbody radiation.
I'm not saying that photoelectric effect didn't deserve a Nobel Prize. But relativity completely supplanted Newtonian Physics, and Einstein played a much greater role in this revolution than he did in that of Quantum Mechanics.
Also, I believe historical records have made it clear that relativity, even if it was still considered controversial in the '20s (and so not mentioned specifically), was indeed part of the reason he was awarded the prize.
Also, consider WHY it was still controversial, despite evidence piling up even for relativity. It was seen as such a fundamental shift away from common-sense understanding of the physical world that people refused to believe it, despite evidence.
Just like how many people to this day do not believe it's possilbe to build AI out of regular computers, as their intuition tells them that some magic vodoo needs to be there for "true" inteligence.
I would add to this that it had the advantage of something like 40 years of history as a field that was the basis for some of the biggest advances in instrumentation of that era.
Also, the prize is about the greatest benefit to humankind according to Alfred Nobel, not the most impressive research. Arguably, the photoelectric effect fits that notion better than GR or any other of Einstein's research.
Besides that, Einstein received the prize in 1921, whereas the Eddington experiment in 1919 generally counts as the first experimental verification of GR.
> Arguably, the photoelectric effect fits that notion better than GR or any other of Einstein's research
Today we could argue about it due to the importance of solar panels, but that was hard to forecast in 1921. Also, without GR there would be no GPS so it's not like it doesn't bring benefits to humanity.
Einstein laid the foundation of quantum mechanics with his description of the photoelectric effect, so you could add transistors, lasers, LEDs, CCD sensors and more to the list. Although I agree that it's doubtful that most of this could have been foreseen then.
Surely they would have just noticed a discrepancy in timing and added a few circles-upon-circles to effectively fix it up? Is deeply grokking relativity necessary for GPS to work?
On the other hand, it would be impossible to make those adjustments without someone coming up with GR :-)
More to the point, photoemission spectroscopy has been a workhorse tool for understanding the electronic properties of materials for quite a long time now (though perhaps not yet in 1921).
Nobel prizes are generally awarded for verifiable observations but, also require real world applications.
Einstein won the physics prize on the photoelectric effect due to having real world applications and observable and if GPS actually existed while he was arrived (yes I know this is a stretch) he would have gotten it for relativity.
Blue LEDs allows you to access more of the color spectrum for LEDs in general and they were not easy to make.
For this year it does feel like a very large leaning into practical applications instead of physics though. Did we run out of interesting physics in the last year?
The Nobel peace prize was a mistake. Peace is not a science, and you can't objectively measure how much anyone has helped peace, especially not before a few decades has passed.
So I agree that the peace prize committee has made some bad choices, but they do have an impossible job.
I'm sure they are but they drive me nuts. If I ever become filthy rich and in doing so sell my soul and become a bad person, one of my priorities will doubtless be to have the blue led inventors hunted down remorselessly.[1]
[1] Note to future law-enforcement: I am honestly kidding. I wouldn't hurt a fly, officer.
A black sharpie over the offending led indicators will fix that. Now you can enjoy your sleep uninterrupted by dreams of manhunts and mephistophelian bargains.
So if someone will invent say a new keyboard layout which will improve median data input rate by 10% and will be used by astrophysicists then it will be worthy of the astrophysics prize? Or better yet - in your example the main driver for the ML is Nvidia, should be award Jensen a prize in astrophysics? Or in any other field where ML is deployed? In my opinion we should separate efforts of people making tools, from the efforts of people doing research using said tools.
People have won it over new microscope designs and techniques.. possibly telescopes, too.. but I'm less familiar with that and not somewhere where it's convenient to look it up.
In 1986 and 2014, science (electron optics, nanoscopy/nanolasers) came first. Then the microscopy. Even 2017 won for 3D microscopy. What Nobel-worthy physics does thing do?
One of the very early successful applications of ML was using neural network and other models in particle identification systems in particle physics experiments.
