sapphireblue

9 ms·

31.
by sapphireblue 10y ago
>A CPU for games would have very fast cores, larger cache, faster (less latency) branch prediction, The CPU industry stayed on this path for as long as it were physically possible, even long after the time when it hit diminishing returns
32.
by sapphireblue 10y ago
Because CPUs are designed around benchmarks representative of real workloads people are running on their computers. Naturally, multimedia and games are a large part of these workloads, this is what drives SIMD adoption.
33.
by sapphireblue 10y ago
This may be an unpopular opinion, but I find it completely fine and reasonable that CPUs are optimized for games and weakly optimized for crypto, because games are what people want. Sometimes I can't help but wonder how the world where
34.
by sapphireblue 10y ago
C# is just a glue language, physics is run inside the engine as optimized C++, it is vectorized where possible.
35.
by sapphireblue 10y ago
You have some good points, but this naive approach of handcoding "cognitive modules" was tried many times in 20th century, and it didn't work at all. But look at what Deepmind does: it takes these ideas (and also ideas from s
36.
by sapphireblue 10y ago
This is not a question of "consciousness", this is a question of Reinforcement Learning.
37.
by sapphireblue 10y ago
I agree to your points. Your comment is a quality one, I mostly talked about other ones. >Turing-completeness is quite broad and nonspecific, like I said. It is, but feedforward models (and almost every Bayesian/statistical model) d
38.
by sapphireblue 10y ago
IMHO It is unproductive to rigidly split problems into "merely requiring algorithmic solution" and "AI-complete". Even with language there is a whole spectrum of language skill. Some animals like parrots, crows, great ap
39.
by sapphireblue 10y ago
>Are the image-recognition AI's described in the article capable of selective attention? Yes they are: https://indico.io/blog/sequence-modeling-neural-networks-par... https://github.com/harvardn
40.
by sapphireblue 10y ago
It is already succeeding on language tasks, see https://research.facebook.com/research/babi/ It is funny how every AI post on HN turns into a speculative discussion forum full of words "I think", "l
41.
by sapphireblue 10y ago
Thank you! I found a great list of deep learning paper summaries that is mostly about learning algorithms from data and other cutting edge models: http://colinraffel.com/wiki/tag:neural_networks?do=showtag&t... all
42.
by sapphireblue 10y ago
IMHO talking about AGI here sounds like "all or nothing" type of thinking. Almost all problems can be formulated as [perhaps very hard] machine learning problems of 3 basic types https://en.wikipedia.org/wiki/
43.
by sapphireblue 10y ago
3 years ago many systems that exist today would be regarded as "scifi" or "maybe 10 years into the future". One of the most striking examples is Visual Question Answering with dynamic memory networks [1]. The model is gi
44.
by sapphireblue 10y ago
The corpus can be acquired from companies that host coding competitions. In this paper http://arxiv.org/pdf/1510.07211v1.pdf (I already mentioned it many times, but it is very relevant) researchers took the data from a
45.
by sapphireblue 10y ago
I have an old one written some time ago by myself too, in node.js/javascript: https://github.com/crystalline/dnnjs It is a simple multilayer perceptron with ReLU nonlinearity, it can achieve 1.7% error on MNIST wh
46.
by sapphireblue 10y ago
The author proposes a lot of vague ideas in this article (for example "I believe one of the biggest problems is the use of Error Propagation and Gradient Descent") without references or any solid proofs why they are necessary to s
47.
by sapphireblue 10y ago
I find it disappointing that there is this aversive sentiment in tech-scene towards groundbreaking, physical technologies like segways, self-driving cars, and now magic leap. I like Peter Thiel's point of view on this issue: world need
48.
by sapphireblue 10y ago
With their budget they can afford to make a custom SLAM ASIC, push more cores into their SoC and create some special GPU. Such capital-heavy ASIC approach can give 10-100x performance boost.
49.
by sapphireblue 10y ago
Low-hanging fruit is becoming increasingly more rare. The only real path forward is hard technology. Hard technology requires tolerance for risk, given the necessity of hardtech taking such risks should be celebrated.
50.
by sapphireblue 10y ago
There are signs, if you know were to look for them: http://arxiv.org/abs/1510.07211 https://arxiv.org/abs/1410.4615 http://arxiv.org/abs/1605.06640 https://arxiv.o
51.
by sapphireblue 10y ago
Isn't doing programming projects much less cumbersome when you hire a professional programmer and express a high-level project spec to him/her? Why cannot a sufficiently advanced machine-learning model behave just like such a pr
52.
by sapphireblue 10y ago
Seems like a gross overestimation of time needed for said advances and complexity of such systems. There is already a research-grade approach to learn to generate short programming-challenge-like programs from natural language description:
53.
by sapphireblue 10y ago
That's a good idea, TPU and Nervana show that there is a large potential in such accelerators.
54.
by sapphireblue 10y ago
This is incorrect. With custom ASIC that implements ultra-wide low precision SIMD arithmetic (f16, i16, i8) it is possible to squeeze an order of magnitude or more speedup over conventional GPGPUs using same area and power. GPGPUs have to h
55.
by sapphireblue 10y ago
Autoencoders are a long known approach to use neural networks for lossy compression. Some papers about using these models as aids for compression are googleable.
56.
by sapphireblue 10y ago
There is an award-winning compressor that uses many statistical models (and a 3-layered dense neural network) to compress the data losslessly: http://www.byronknoll.com/cmix.html Also there is a free book that contains desc
57.
by sapphireblue 10y ago
Actually the success of deep learning has made us very optimistic again. Just read this blog post: https://deepmind.com/blog there is a screenshot of environment that deepmind uses to train its agents. It's like scienc
58.
by sapphireblue 10y ago
An interesting but not widely known fact is that the Dartmouth conference was also attended by Ray Solomonoff. Contrary to his colleagues he focused on questions of machine learning before learning became perceived as a worthwhile research
59.
by sapphireblue 10y ago
Explaining how neural network models generate their output is possible, see http://cs231n.github.io/understanding-cnn/ https://auduno.github.io/2016/06/18/peeking-inside-convnets/
60.
by sapphireblue 10y ago
I think the grandparent has a valid point. IMHO learning is a necessary feature of any intelligence, artificial or natural. That's why modern AI is 90% Machine Learning. All non-learning approaches (frames, symbolic AI) have failed 20

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