Machine learning engineer, AMAA

Yeah Einstein was a bad example. What about Feynman or any mathematician?

All that Tesla stuff sounds like bullshit, do you really believe that?

More like the latter. I think emotions are fundamentally linked to biology, so they could be hardcoded into a system, simulating the biological system, but they wouldn't arise spontaneously from generic artificial intelligence. Not that I personally believe in a soul, but there is something special about biological life that goes beyond abstract "intelligence".

Much easier with a degree (that applies to most fields), but if not a formal degree at least take online courses.

Not any one thing in particular, but I will say that ML is inherently math-heavy. Sure you can bullshit your way through some things, but the more math background you have the better. Some things to aim for (even if you don't get there are)
- having enough background in stats/probability/math able to prove major theorems in probability such as the central limit theorem or law of large numbers
- being able to do all the math/programming of standard deep learning (e.g. fully connected layers) by hand.

He was murdered for his research, but yeah, seems far out. But honestly, so would ML to someone from the 1900's. Still more believable by far than the flat earth CIA red-herring op though.

Attached: 11202cea0eccacef92a2984d7ff0e2c4f3073a773e682865a7dc3d615ad6789f.jpg (770x643, 124.94K)

It's not that far away. I think within 3-5 years we will have that + being able to generate it in real time.

Language is a more difficult problem because language contains within it all the complexity of the world as humans understand it. Note I made no mention of language in because the question was what I expected to see in 10 years, and I don't expect large advances in language understanding/processing in the next 10 years.

Self driving cars can for the most part function without a high level understanding of the world, they only need to understand physics, and the kind of objects they would usually encounter. Sure there are edge cases, e.g. a complicated sign, but physics and object detection alone would probably out perform humans by a large margin if done well enough.

No, when I refer to machine learning I mean for the most part deep neural networks, because these are where by far the most advances have happened in the last 20 years. This cannot be done on an old computer because it requires fast floating point math. I personally used a 1080 Ti GPU for my own work though I don't have time to do my own research anymore.

I don't have much more to say on this, but I would suggest studying physics, it's not that hard and you should then be able to judge claims made about Tesla better. I also love physics, if I hadn't ended up in ML I would be glad to study physics instead. Studying Lie groups and differential geometry also have helped me in ML a lot.

Agreed, 3-5 years is a better estimate.

I still have apprehensions. Language doesn't put anyone's lives at risk.

You're right to have apprehensions. It's not going to be as simple as plugging in a deep learning system. It will require a combination of pure ML, other algroithmic techniques (e.g. see youtube.com/watch?v=B8R148hFxPw), and new hardware systems (e.g. radio beacons).

But in the end, we have to accept this technology if it can significantly reduce accidents (eg. 10x), even if it sometimes causes accidents even in cases where a human wouldn't have.

that reads as propaganda. if a human wouldn't have caused the accident, then the technology is not solving a problem, it's creating one. i am not worried about car accidents. it's not the driving that's the problem, it's the cars themselves. they need to be made safer, not the drivers.

I'm speaking about reducing accidence by 10x (e.g. 1.25 deaths per 100 million miles => 0.25 deaths per 100 million miles). I believe this is possible, but at the same time, accidents will still happen and the 0.25 deaths per 100 million miles might be in circumstances that a human might have avoided.

I don't see why this is "propaganda" it's more like utilitarianism, but not even that because utilitarianism would accept 0.999x change in fatalities.

actually that should be 1.25 deaths per 100 million miles => 0.125 deaths per 100 million miles

OP here, thanks for the questions and comments, got to go now but will continue to monitor the thread. I haven't signed my posts up til now but I will start now so it's clearer when the thread gets longer.

OP have you worked on zeromq, TANGO, the atacama submilimeter array, or any of the software suites associated with said botnets?

I'm confused, those all seem very different and none of them are botnets (or even networks) as as far as I can tell. zeromq is a low level IPC library. I don't know what TANGO is and it's a common name for products so Google doesn't help. The atacama submilimeter array is a radio telescope.

utility is cold. some circumstances require it, but if a technology brings an outcome that a human wouldn't, that technology needs to be refined. as i am saying, it is much easier to make cars safer as they are now without needing to invest in a new and untested technology.

Are you saying there is some alternative technology that would reduce accident rates more lives than self-driving cars? If so, what is it?

Let me make this clearer do you work on any of the below projects?
archive.fo/I4iSe
archive.fo/onjU5
archive.fo/ATf3p
archive.fo/NF9MZ
archive.fo/kB7hc
archive.fo/VIIOb
archive.fo/91fTx
archive.fo/toCeJ
archive.fo/p6nVV
archive.fo/NlFfx
archive.fo/WJClM
archive.fo/6yMj0
archive.fo/1TAPe
I ask because I was wondering how developement on TANGO was going after it was shoahed from the internet. TANGO being a form of virus that uses the IPC cababilities of things like zeromq to spread.

In biological systems you'd have to factor in the evolution of systems themselves, and imagine the brain more like improvisations of many simple systems (useful and/or redundant) evolving and consolidating over time. The special characteristics would likely be the result of how these systems jury rigged themselves to adapt to changing environments.

No never worked on any of that. From what I can see, TANGO is a distributed control system (not really sure exactly what that means) that is built on top of zeromq. TANGO is used by the atacama submilimeter array (I assume to control their physical equipment?). Why do you say TANGO was shoahed from the internet, isn't this it: en.wikipedia.org/wiki/TANGO

My only knowledge of any of these things is that I've used zeromq for local IPC on a single machine.

The original github page for the COBRA project was shoahed from here archive.fo/6yMj0 is why I said that.
Yea, if you had gone through all the links or if your were familiar with the projects you would know that it's not just radio telescope equipment being controlled, it is android, mac fags, and other libraries used by all things compatibile with zeromq they use and use for "remote access" which is to say botnet. From archive.fo/WJClM

Why would any of these features be in a ssh shortcut to access a server controlling radio telescope equipment? It wouldn't be.

Yes that's exactly how I think about the brain. Human thinking, especially emotion, is to some extent an accident of evolution.

I think that ML is a crappy language. You should really use Scheme or Haskell.

In this context ML stands for Machine Learning, not Meta Language, you absolute fag.

All this sounds like that meme where in the future we'll be having flying cars.
Its all hype
Most technology of today isn't being applied to amazing innovative things to fight diseases or robotics. Shit, Cancer, HIV, STD's, and the common flu still exist in the 'Year 2018'. The most complex robotics we use on a daily basis is probably the self check out machine at Walmart.

Self driving cars get in accidents all the time. They had one at my city downtown that was transporting people for an event and it got in an accident due to "human error". Sure, machines can learn effectively and can be smart, but the most we'll probably get out of an algorithm is having bots comment on social media or the chan boards.

There seems to be a huge disconnect between meatspace and cyberspace that will never be filled

Huge advances have been made in HIV. Similarly cancer survival rates have increased significantly for individual cancers, though there is selection bias since this changes as screening/diagnosis rates change.
That's consumer technology. Look at Amazon's use of robots in its factories, or Tesla's automation in its factories.

Fully self driving? The fact that we have self-driving cars at all is amazing, and if they can perform at roughly human level now, there it still lots of room for improvement since the underlying technology, especially deep learning, is so new and still improving. I also believe that self-driving cars is such a hard problem, that it cannot be solved without lidar. Doing it with vision alone or vision + radar requires the computer to be too smart, even for someone who is overall optimistic about ML.

I used to get frustrated that all of ML was narrowly target at this sort of thing. It's natural for startups to focus on text-based ML because there is no barrier to entry: they don't need to invest in physical hardware. But that is changing and I'm seeing a lot of startups involved in both software and hardware, e.g. agricultural robotics.

There is indeed a huge gap between meatspace and cyberspace, but deep learning is bridging that gap. Currently we have the ability to reliable classify objects in a scene. For robotics, we need neural networks with the ability to represent 3d geometry, not just analyze single 2d images.

It's possible an AI could also exhibit such emergent traits through hierarchical learning, accidentally adopting seemingly redundant or irrelevant behaviors and/or rewards as it transfers knowledge in multitask learning. Those "junk" behaviors/rewards could also serve as a disposition to better training of new tasks as a side-effect.

It would be like how a child learns and becomes obsessed with a specific hobby, and is then exposed to a new unfamiliar concept. It's likely they'll learn it faster and with more motivation, if there's a strong familiarity with their hobby at any abstract level, even if the tasks seems radically different.

I'm curious if such a thing has been explored yet.

Are you licensed and bonded? If not, you’re not an engineer.

Yes all these ideas are pretty common, and go by a number of names. "Transfer learning" is when you take a model trained on one dataset (usually large) and reuse the models weights with a different problem. For example people issue use the ImageNet (competition for object classification) winners for this as ImageNet is huge dataset and also object classification requires learning a broad set of features and so should be good for generalizing.

"Multi-task learning" involves learning lots of tasks at the same time where the models share some weights in common.

"Intrinsic rewards" refers to training a neural network or other model where it gets rewards for essentially noticing patterns, instead of just solving a specific task. This somewhat mirrors the idea of human curiosity.

No and I'm not interested in arguments over words. If you want to talk about whether what I do while require professional certification that's a more interesting topic.

s/while/should

Why do you help the botnet? Muh diseases is such BS, this is mostly used for analyzing consumer behaviour to get them to spend money and follow specific politics, curing illnesses isn't profitable. Is what you work on proprietary?

Of course curing diseases is profitable, don't fall for anti-corporate rhetoric. Not saying the medical industry is perfect but overall we are healthier and have better lives thanks to medical technology.

Privacy is very important to me and I'm torn between the benefits of ML and the fact that the tech industry as a whole is not doing enough about privacy issues.

Most of my work is open source.

Are you a faggot? AI was already hyped in the 70s and look what it got us, 1 millimeter of progress. Self driving cars are the stupidest thing since ECUs and remote control cars (via uConnect vulnerabilitiy and friends). The default behavior is to crash and kill you, and they add a new edge case each time someone crashes, while claiming they're better because the population of drunk people crash more often.

While ML does solve certain problems, just as any new engineering technique or technology, it is indeed mostly hype. Opposing retarded popular opinions is not even close to "being contrarian".

The industry doesn't have to do anything about privacy. Privacy isn't something the world works together to provide for you. It's something you have by default until the government makes it illegal to have privacy (for example making it illegal to change IMEI of your phone 10 years ago, or making Freenet illegal because muh CP). Creating technology that is inevitable does not degrade privacy because that technology was already inevitable. Literally the only way to stop such technology from emerging is by either mind controlling all 7 billion people to convince each and every one of them not to do it, or in some cases such tech may be too expensive to create so you just need to control who has capital. But none of that matters because anyone who cares can always easily get his privacy back. The only real hurdle is when the government starts saying you're a terrorist because you don't upload daily photos to facebook and instagram.

Contrary to what normalfags everywhere think, there is no question of regulation. For example, any real content based addressing network such as Freenet physically can't have any way to enforce contraband material or accountability. Same for Tor. Cuckflare was asking tor devs to "regulate behavior of its users", which of course makes no sense to anyone with the slightest clue of how onion routing works.

Did the government force the 2 billion googlers to sign up to a data miner that makes their money off of personal profiling?

fuck off reddit. nobody cares about normalfags losing their privacy. even normalfags don't care, otherwise they'd literally spend 5 minutes fixing their privacy issues

How much manual work is there? I assume you always need some data set where categorization or whatever was done by humans that the AI then "learns" from. Do these need to be large? How sensitive are they to errors? What else do you have to do with some ML program on github or whatever to e.g. get a decent picture tagging program?Why do they need so much compute power? Is that the reason they were not doing this before?

What do you mean "the default behavior". One Uber self-driving car has crashed an killed someone (I'm not counting Tesla because they aren't true self-driving). We don't know yet whether this makes them more or less dangerous than average.

ML (especially deep learning) is different from the AI hype of the 70s because we have already made very impressive progress. In the fields of self-driving cars, human-like speech generation, speech recognition, object recognition and machine translation, we are approaching human-level capability thanks to to deep learning.

Currently, ML is highly dependent on large human-labelled datasets. These are expensive to create. There have always been techniques (generally called "semi-supervised learning") developed by academics that claim not to require huge datasets. Up til now these have not been successful in industry. But semi-supervised techniques using neural networks are starting to actually work, e.g. I think that some speech synthesis does this.

ML is also very sensitive to errors in manual labelling, it is a major problem since the more accuracy you want, the better (and higher paid) humans you need. Machine translation is very limited by this.

ML is expensive because it involves a lot of large matrix multiplications, which end up in a lot of floating point multiply-add operations on the hardware. A typical image tagging network will be mostly a "convolutional neural network" or convnet. Each "layer" in the network will be an image with width, height and "depth" (e.g. the input RGB image would have depth 3 but hidden layers would have any depth you choose).

Suppose two hidden layers both have shape 64 x 64 x 16. A typical set of connection between them might be a 3x3 kernel. This means that for each output "pixel" (with 16 components) you look at a 3x3x16 patch. Since each component is independent this means a 3x3x16x16 matrix represents the entire filter. And since the original image is 64x64, this means approximately 64x64x3x3x16x16 or approx 9 million mutilply-add operations. This is just an example, there are different kinds of variations on this kind of filter, but it just goes to show how even for a small image, the combination of the filter size, and the image depths combine to create a lot of arithmetic operations. And this is just for the forward-pass.

So deep learning really relies on very fast computation, and research is limited by compute power.

I see your point, basically you are saying that physical communication enables privacy unless the government imposes restrictions, and that if people choose to give information to big companies freely, that's not a violation of their privacy.

I think it's a bit more complex that that because people give away so much information without even realizing it. E.g. when you visit a website it's not obvious that that website is using third party services that track your browsing habits across websites. Similarly it's not obvious that owning and Android phone means Google is able to track your location.

One thing I'm hoping is that decentralized systems such as IPFS become more popular. Up til now, decentralized systems tended to be less efficient than centralized systems (in spite of a lot of wishful thinking). But I think this is slowly changing as network connectivity becomes cheaper.

Do you know of any good guides for doing deepfakes on Linux? This is the only one I've seen written for AMD GPUs then he updates with
"The standard deepfakes code requires CUDA-compatible NVIDIA graphics cards. That means ATI graphics card users were left unable to participate." I've tried going through this with an Nvidia card ignoring the AMD bits, but can't get it to work.
deepfakes.club/deepfakes-with-amd-graphics-card/

I don't know anything about deepfakes, but if it's based on TensorFlow, then the instructions at tensorflow.org/install/install_linux might be helpful. This shows you how to install the right drivers, as well as cudnn, to use TensorFlow with Nvidia hardware. I did this about a year ago and it mostly worked (instructions were not always accurate but I got everything working).

I'm fucking sick of all these debbie-downer contrarians and wolf-crying-never-making-solutions fuckwits so I'll ask you this:

How can we the people use machine learning as a means of security and search-engine streamlining? Clickbait is fucking everywhere, and who knows what their ads are hiding, maybe machine learning can help us get them the fuck away. How can we use machine learning to filter clickbait, domain squatters, scammers, spammers, trackers and deceptive scripting? How else can we use it to protect ourselves from increasingly idiotic security holes?

The only thing that is possible to be owned "by the people" is browser plugins or scripts. This is the only way you can change the web experience without being a search company or browser developer.

So what your would do is create a browser plugin where people report bad stuff and then an ml model is trained on these reports, and the ml model runs in the browser and flags bad stuff.

But then scammers could themselves mess with your system so you need some way to get around this like liquid reputation.

That said, scammers are already in a cat and mouse game with search engines. Whatever you think of search engines, they don't actually want to display scammers or domain squatters. So I don't actually think a community based system could do a better job that search engines are already doing.

Being able to find out where the left and right lane are is not "human-level capability", also they said the exact same thing in the 70s. Self driving cars are not just general brains that get scolded 1 billion times per second until they figure out how to drive. They are programs constructed from a bunch of ML algos and shit.

A website is not a real concept. Android and other "smart" phone products will disappear as soon as the company decides they aren't profitable anymore. We can't talk about regulating such systems because they come and go in different forms every year. The web itself is absurdly designed and if you create regulation for it, when more sane systems emerge they will be regulated in ways that only made sense for the web and make no sense at all in the new context.
Why am I surprised that you name IPFS instead of anything else?
What does this mean? Bittorrent has always been efficient. Bitcoin is decently efficient and has fixes. Tor has always had decent bandwidth (though this is not a necessary condition to view poorly engineered high latency websites). Freenet is slow because nobody uses it.

I don't deny the advances in AI, I just think it will ruin society. Cameras being able to automatically detect your face and know where you are at any point in time. Instantly comparing your behavior to a database of suspicious ones.

Self driving cars controlling where you can or can't go. No manual option since it will be considered "dangerous".

Detecting and fixing "bad genetics" which of course will be someone's opinion in the end.

Robotics killing jobs and sending people to the streets.

AI writers, painters, musicians which will outclass people and make them lose their life's meaning.

And many more.

Current self-driving cars already do a lot more than lane following.
I work in ML so I'm well aware that ML is not the same as human intelligence. As I said in , self driving cars will require lidar, which will given them an inherent technological edge over humans in spite of the limitations of ML.
As far as I can see, the web has not fundamentally changed in 20 years. So why shouldn't we try to think about the privacy issues inherent in the web as it is.
Why don't you just say what you are trying to say?
I mean that the cost (in terms of compute and network IO) of distributed systems like bittorrent is higher than centralized systems such as YouTube.

Agreed these are real dangers. I don't have an answer here. I believe the positives outweight the negatives, but I don't have specific solutions to these problems.

One thing I will say is this: when I raise these concerns with my co-workers, one thing people said is that making the technology open would at least allow people to create defenses. But I don't think there are any defenses to mass-surveilance.

I am doing everything I can to limit the spread of mass surveillance.

Why do you think this is bad?

The nature of all technological advancements is that it "kills jobs". It's not a side effect, it's an inherent part of economic and technological progress. The nature of technology is that it replaces human effort with (cheaper) machinery, and this is a net positive for society because human labor is freed up for other tasks.

AI cannot replace true artistic expression because it's not that advanced. It can replace rote work, but see my point above.

Well I might disagree with someone's opinion (or the AI's opinion). What if it considers being short (for example) "bad", but I don't, and if my child came out short I want it to be that way.

Not if the people die first.

To expand, I am really scared of something like: I have a child and go to the hospital, and there it is automatically "enhanced" genetically, and I am not even informed of what the supposed "enhancements" are, and of course I have no choice to deny them (like with vaccines - not saying vaccines are bad, just an example). But I don't intend to have children so fuck it, but other people will have a problem.

To expand even more - it would be easy for the elites to create a population of people having the exact features they want (and lacking the ones they don't want) using the AI and the promise of "enhancement". Creativity - bad trait, kill it. Replace with submissiveness. Etc...a conspiracy scenario, but can't deny the possibility.

People don't die because their job was replaced by a machine. At worst they go on welfare. Look at the percentage of the US workforce employed as farm labor over time. Did the farm laborers die?

I misunderstood your original point, I didn't realize you were saying the people would be forced to have their children enhanced by AI.

I've heard stories of hospitals "enhancing" children without parental consent by removing the foreskin of said children. But even that doesn't seem common, usually it's done with the consent of the parents.

I don't see why you think this will happen. Current laws focus more on banning selective abortions (e.g. if the baby has Downs syndrome, which it doesn't take an AI to see is a bad thing) not forcing people to have them.

What you do think about neuromorphic chips?

I don't think they are a promising technology. The promise of neuromorphic cheap is cheaper compute-per-watt for neural network style computation. But low precisions floating point (e.g. 8 or 16 bit) seems to deliver better in this regard.

I think OP works in academia. Really interesting thread, thanks!

Glad you enjoyed the thread. I actually work for a tech company, but there are close connections between industry and academia, e.g. many of my colleagues present at conferences.

Personally I think its promising applications are the possible spiking neural network designs that could be built-in at a hardware level, as well as power efficiency. If I'm right, your future (if not current) work will probably see you get very involved with integrating/converting deep learning concepts and algorithms into spiking neural network designs, and have direct collaboration with neuroscientists and chip manufacturers.

go back to /r/ama
>>>/reddit/

As a software engineer that works on Machine Learning, do you operate using C and ASM?

However the brain works, it uses a lot less power to do equivalent computations than "deep neural networks". So maybe spiking neural networks would be an improvement, but I haven't seen any evidence yet.

Most ML is done in, C++, Python and JS/TypeScript (for frontend stuff). E.g. Tensorflow is C++/CUDA "kernels" wrapped in Python. I don't know PyTorch that well but I assume it works in a similar way.

I mostly write Python because I don't write the TensorFlow kernels. I did write some handwritten assembly for ARM NEON for computer vision before I got into deep learning. I would guess that handwritten assembly is only used for mobile. There is no reason to write x86 assembly by hand because when training a model all the heavy lifting is done by GPUs anyway.

how do you debug a neural network implementation?
I tried to do one from the ground up for a coursework (in java because it was a java course), but it does not seem to work: it can train itself on simple stuff like the XOR function, but when I give it MNIST, the error just starts to oscillate around 10^-3. I am supposed to using correct hyper-parameters, I took from the net. Also it seems the more training examples I give it the worse it gets.

Fasttext +CommonCrawl. Fight me

There is a thread already.

As much I agree that this is a duplicate, this specific thread is actually a good quality tech thread with lots of good knowledge being shared. This thread is far better than the shitting Firefox threads that happen on every Firefox event.

Fucking posseur you know high tech stuff for white non Pajeets have to be done in ASM right?

Some debugging ideas:

Log curves of loss function.

Log the activations: sometimes neural networks get stuck in a region where activations of each neuron are always max, and so the network cannot learn as the gradient is always zero. This is usually caused by bad initialization.

Even though you're building this yourself, you can check it tensorboard for inspiration.

Finally check your own code, both for the forward pass or gradients. John Carmack has some useful advice on rolling your own deep learning code facebook.com/permalink.php?story_fbid=2110408722526967&id=100006735798590

That's a good way to get word embeddings but word embeddings are a tiny part of understanding natural language.

You've noted that you feel you are acting against mass surveillance (Props I guess) but isn't machine learning the cutting edge of data collection right now?

Don't get me wrong I think the tech is cool and it is one of a vast many things my pleb ass wants to know more about, but there are plenty of applications for it beyond just camera surveillance, including traffic analysis and profiling. How do you think the average user should counteract this?

I think it's necessary to distinguish between ML and data collection. E.g. the police can operate fake cellphone towers to track people's location, but this doesn't require machine learning. So a lot of issues around data collection are not related to ML.

That said, ML can be used to analyze video and track people, number plates etc. And it can also go through data that was collected (e.g. location data) and extract even more info from it.

The only protection for a person is to not reveal that info in the first place. For video collected in public there is no way to prevent this. I'm not an expert but I think there are ways to prevent or minimize how much location, browsing etc. data is collected about you.

The work I do is general purpose so I can't control how it used. But when I do work on specific applications I avoid things that could be used for the purposes.

Question on feature selection on text classification: TF-RF for supervised, TF-IDF for unsupervised in Neural Network...
In what situations are Information Gain, Odds Ratio or Chi Squared useful (SVM, kNN, Neural Networks)
Base assumption is that there is no feature over-load aaai.org/Papers/AAAI/2006/AAAI06-121.pdf

Adagrad vs Adadelta vs RMSProp vs Adam vs adamax vs amsgrad
Which one is the cheapest to run? Which one is the most optimal?

PyTorch also uses a C++ tensor library (ATen). I would say PyTorch is better compared to TensorFlow. How do you like your static compute graphs? :)

The problem with gradient descent is that it wants to work, even if you computed your partial derivative incorrectly. Debugging this crap is pretty hard due to the stochasticity that is typically involved with this stuff. However, in handwritten implementations this is the main source of error (so you probably need to check your math). That's why these auto-differentiation are so popular, you program the compute graph, and it computes the partial derivatives with respect to the variables that you assign to the problem.

What loss function are you using, regular log cross entropy? 10^-3 training loss is pretty good. You probably reached the capacity of your network.

How to get started in machine learning? I can probably get a meme job that pays $200k/year from this. Any good resources?

Usually it is a meme that these Coursera lectures are good. However, I would recommend the lectures from EPFL in Switzerland, and the resources from Kaparthy.

edu.epfl.ch/coursebook/en/machine-learning-CS-433
github.com/epfml/ML_course
cs231n.stanford.edu/

It has been shown that regular (fine-tuned) SGD with momentum) outperforms these. However, typically Adam is just fine. Nevertheless, for large jobs that span several days, and for which I do not have a-priori knowledge I typically use momentum SGD since the memory requirements are a bit smaller.

How difficult is it to get ML algorithms to not recognize niggers as the apes they are?

Attached: 1490335600875.png (7x7, 338)

It's always weird seeing his real life name instead of his screen name as I knew him before he got into all this machine learning stuff.

how close are we to the first neural-network generated otoMAD?
youtube.com/watch?v=nIZAQDBPD-U

He always did ML related work according to the best of my knowledge? I'm talking about Andrej Karpathy (made a typo), maybe there was some confusion.

I only heard about his machine learning related work a few years ago, but at that time I didn't recognize him because I didn't know him by his real life name. I had actually spoken with him before ~7-8 years ago about a completely different topic than machine learning. He may have been doing ML related stuff back then, but at least I didn't know about it.

Quit your job you fucking jew. You are responsible for ALL the evil in the world and it's destruction.

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In the past he also did physics on the side (undergraduate / graduate). He's till interested in that stuff though, especially if he can make a connection with machine learning. I had the pleasure as well to "meet" him, i.e., attend some seminars on the union of those fields at a physics laboratory. They were quite high-level, but it was intended for physicists after all :)

AI doesn't spy on people, people do :^)

you mean people who fund this shit

I see other people, some with more knowledge than me, answering questions so I won't reply to everything

I'm not familiar with any of those terms apart from TF-IDF (are they all from that paper?). But generally I'm not that interested in classical feature selection. In my work I select features by frequency alone. Like in that paper, advanced feature selection is often paired with less advanced models (e.g. standard SVMs). Instead of spending time on feature selection I think it's more productive to think about feature selection and modeling jointly. So for example, if you have a small dataset, maybe it makes more sense to use pretrained embeddings from a larger dataset. Or maybe you should construct a vocab, but for out of vocab words, use pre-trained embeddings.

I don't think static compute graphs are a problem per se. TensorFlow is somewhat strange in how it's implemented: some things you would expect to be part of the graph aren't (like primitives for distributed models) while some things that you would expect not to be part of the graph are (like ops for saving/loading models). Unfortunately I only think things will get worse as TF grows. But it's still the best framework out there due to its support for distributed training.

Good point, John Carmack's article says something similar.

I don't know if that's true, but if you can, go for it. I took a very specific path to learning ML so I can't be much help here, see .

I don't know but I guess 10-30 years from neural network generated video in general.

You're going to have to explain what you think is responsible for the world's destruction and why. Simply calling someone a Jew doesn't mean you have a coherent or valid point. And generally speaking being strongly against the status quo doesn't make you smarter or excuse you from having to explain your reasoning.

Assuming you do want high accuracy with large machines while wasting little time, which one is the best? What if the accuracy-memeroy-speed trade-off is changed?

Well memory-time trade-off IS an issue for hobbyist like me, TF-RF would save time for supervised learning, no?

Also, speaking as a Zig Forumsack for there are already lots of info how Machine Learning is used to Mass censor information and target people by topic sentiments or "writeprints".These information is a given in >>>Zig Forums
Examples include pre-cambridge-analytica days of Facebook/Obama, and SPLC/ADL's keyword tracking system.
Lurk Moar, Zig Forums is Zig Forums: The Imageboard, get used to it

github.com/pinkeshbadjatiya/twitter-hatespeech

More info adl.org/news/press-releases/adl-uc-berkeley-announce-groundbreaking-project-using-ai-machine-learning-to
engadget.com/2017/08/18/google-uses-machine-learning-to-help-journalists-track-hate/

Muh China using Machine Learning to censor
scmp.com/news/china/policies-politics/article/2113377/its-seen-cool-place-work-how-chinas-censorship-machine
theglobeandmail.com/news/world/china-using-ai-to-censor-sensitive-topics-in-online-group-chats/article33116794/
reuters.com/article/us-china-congress-censorship-insight/tea-and-tiananmen-inside-chinas-new-censorship-machine-idUSKCN1C40LL

SInce ELU > Leaky ReLU > RELU, I was thinking, why not reduce the complexity of ELU by replacing the exp where x < 0 with soft-sign? that would save time in computation while keeping the shape of f(0,a) = 0, f'(0,a) = 1, f(-inf,a)=-a and f'(-inf,a)=0 ?