This has been true for years, Intel CPU's can't efficiently perform math operations when hyperthreading is involved. Generally there is only a single shared FPU/AVX/SSE unit doing the math over two hyperthreads. Since the Eigen implementation often can keep that unit 100% busy, it makes no sense to try and run two threads at full tilt through the units.
I tested all this very heavily before Eigen had AVX-512 support. In that environment there might be some differences and I would suggest you benchmark both configurations.
Performance of DOM based rendering is very problematic and not unified across browser implementations. Canvas rendering will likely increase the performance of Google Docs, and make the UX more unified across platforms. Google Docs is really an application built on the web platform. HTML DOM rendering was never intended to give developers the control they need to build fully featured high performant applications, we just shoe horned things until they sort of work. I think this is a positive thing, UX will be better and the integration API's will become much cleaner and not depend on structure of how they design the UI. Concerns will be separated and the end result is something much cleaner, more performant, and more supportable.
Could be interested in the right opportunity, very experienced dev and manager with Ankylosing Spondylitis who has been greatly helped by medical mj. Would be remote for now, not in LA area. Would potentially be open to relocate after the dust settles on the current situation. Email is in profile, can you send me some more info?
Depends on the cameras, but I would imagine Facebook and Google's tech + data to be able to positively identify 99% or more of the people. Especially with multiple shots per person. Accuracy might be as high as 99.9% if you have drones go over the crowds, and have high res cameras at chokepoints.
Correlate this with cell phone data and probably they have 100% accuracy. There is a reason Google Fi exists.
I say all this as a happy Google User and a developer of Face Recognition Technology. Those in the know realized privacy has been dead for awhile now. Generally computers can do most anything we can do in say 200 ms or less with the proper training data, and they generally do it better than humans. The giants have all the data they need, they just need to spend time annotating training sets and running analysis over larger and larger sets until they reach super human performance.
I know of several hackers that have gone through YC have autoimmune conditions, and I would say there is a larger population of people that have autoimmune conditions then our parents generation. I myself have ankylosing spondylitis, and live with chronic pain, back and neck problems. Probably just really interesting to people like us, who want to hack our way out of problems that medicine doesn't have good answers for yet.
Regarding 1, there are systems @ Facebook and inside Google that can do this reasonably well and I know the technology intimately. I would consider this a nearly solved problem, I give it 1 to 2 years at most to be more generally available.
There is absolutely no reason to store anything for 100,000 years. If we were sane and reprocessed our fuel (like France), something like 400-500 years would put us below standard ambient radiation levels. Still a long time, but no we don't need to store anything for 100k years. Remember, almost all the long lived Transuranics are generally usable as further fission fuel. The other Transuranics and fission by products will typically decay rapidly to something stable or barely radioactive within a reasonable timeframe.
Justin,
I love the concept of Whale, but many of us don't have iOs phones. Could you run a more traditional AMA at some point?When will there be a web player or an Android app for Whale? I feel that many people in the early user base for Whale are being alienated here.
I use a tiny bit of python, a little more LUA, and a TON of C++ in the machine learning work I do. Things like opencv, fbthrift, folly, boost, fblualib, and thpp make writing this sort of code in C++ very time efficient and if you know what you are doing it will end up performing much better than the alternatives. I only use python for some light scripting, data collating and reformatting type tasks, and LUA due to using Torch as my Neural network framework of choice.
I'm not saying its easy, I'm saying a developer using the tools and processes available at a large internet corporation building something small need not be insecure. Basically until we look at the code and the security processes in place, or do pentesting, we have no clue one way or the other. I'm mostly just saying we shouldn't discount something like this automatically as insecure. It might be terrible, it might be very well thought out and have security be one of the most important considerations that was top of mind when he coded it.
He did reuse things that exist inhouse, like the face recognition piece. To rebuild a service with Google/Facebook/Microsoft/Amazon level accuracy in that task, it is definitely more effort than 100 hours by itself.
To use an existing private service, not nearly as much effort.
That being said: Of course he could have learned how to train a model himself, but even in that case he is using training infrastructure and data that is available to him that is not public. I highly doubt he had the time to build those sorts of things himself. But his goal was more talking to his home and using tools to build an interactive home assistant. Sounds like he was very successful at doing that. He wasn't out to build the individual AI pieces, doesn't make it not cool or interesting.
Actually, there are some ways to make this very secure that don't take much effort that in are common use around facebook. It is quite easy to build secure thrift services, and to design a thrift api it is just creating a text file with the necessary idl. You can then access the service from most any language by generating the right client. I'm sure same thing goes for gRPC and Stubby on the google side (more familiar with the fbthrift, folly, wangle, proxygen tech stack myself, but I'm sure its just as easy.)
I think you should also mention work queues and producer / consumer thread pools. These are very powerful concepts you don't mention here, and they are often the first tool I use. You very often don't need anything else.
Dunno about unicorn, but Brad Feld was an very early Angel investor in Harmonix music systems and they were working for many years before the had breakout success.
I wouldn't say no distributed object storage system is particularly tuned for that. Facebook's haystack and systems patterned after it can work very well for tiny files.
Doing the dev is the only way, I would recommend though reading the Hinton dropout paper and trying to implement a non full featured Neural Network framework to implement those ideas. Then pick papers to implement in your toy framework. Once you have done this a few times, you will get a really good idea how all this stuff works and ideas to extend things. It also REALLY helps if you have a very strong linear algebra background, so if your weak there do lots of reading and practice getting those skills up to snuff. I may not be the best example, because I worked in computer graphics heavily over my career. My suggestion though is to try and apply DNN's and ML techniques in somewhere you have domain experience. It really will help a lot with the intuition part since you have valuable domain experience to give you ideas to try.
This is a path I took around 4 years ago, and I have built some seriously valuable stuff in that time.
Also I would personally say to not discount the value of backend stuff, doing scalable ML processing is not off the shelf and lots of value can be created by making scalable reusable systems that can run machine learning models. Most of those ML PHd's can't (or don't want to) build scalable distributed systems. If you can train experiments faster, or actually do the work to make an end to end system work outside the lab, you can make real measurable contributions.
3 generations ahead of moore law??? I really wonder how they are accomplishing this beyond implementing the kernels in hardware. I suspect they are using specialized memory and an extremely wide architecture.
Sounds they also used this for AlphaGo. I wonder how badly we were off on AlphaGo's power estimates. Seems everyone assumed they were using GPU's, sounds like they were not. At least partially. I would really LOVE for them to market these for general use.
I tested all this very heavily before Eigen had AVX-512 support. In that environment there might be some differences and I would suggest you benchmark both configurations.