MAXPOOL

16 ms·

121.
by MAXPOOL 7y ago
"just"? Neuroscience is full of problems of the hardest kind.
122.
by MAXPOOL 7y ago
I think you have a point. AGI is a scientific problem of the hardest kind, not an engineering problem where you just use existing knowledge to build better and better things. Marving Minsky once said that in mathematics just five axioms is
123.
Dynamic Time Warping(en.wikipedia.org)
1 points by MAXPOOL 7y ago|0 comments
124.
by MAXPOOL 7y ago
For numerical sequences, see dynamic time warping (DTW) algorithm. https://en.wikipedia.org/wiki/Dynamic_time_warping
125.
by MAXPOOL 7y ago
You don't need to memorize rules when studying math. Just like you don't need to spend any time to memorize syntax for programming languages. You automatically remember things you use a lot. Once you have spent countless hours
126.
by MAXPOOL 7y ago
Capsule networks are attempt to get past the limits of current approach to deep learning. It took 20 years until the the current paradigm in deep learning started to generate results. You start with toy problems and explore. Hinton's &
127.
Layer rotation: a surprisingly powerful indicator of generalization?(arxiv.org)
2 points by MAXPOOL 7y ago|0 comments
128.
by MAXPOOL 7y ago
Learning multilayer convolutional representations of statistical features is roughly equal to taking few first few layers in visual cortex and stacking them. Creating higher and higher stacks is not going to solve vision. We are essentially
129.
by MAXPOOL 8y ago
Did he really make this claim? I didn't listen the podcast. If true, this sounds completely unrealistic especially with the way Tesla is approaching self-driving cars. It's possible that Musk will showcase something within year, b
130.
by MAXPOOL 8y ago
I found this incredible book draft because Scott Aaronsons glowing praise of Avi Wigderson made me curious https://www.scottaaronson.com/blog/?p=4156 >Congrats to Avi Wigderson for winning the Knuth Prize. When I w
131.
Mathematics and Computation [pdf](math.ias.edu)
241 points by MAXPOOL 8y ago|14 comments
132.
by MAXPOOL 8y ago
In the 19th century William Whewell developed concept of "Consilience of Inductions". Evidence from independent, unrelated sources can "converge" on strong conclusions. We should feel more confident about our conclusio
133.
by MAXPOOL 8y ago
Open Review https://openreview.net/forum?id=SkfMWhAqYQ
134.
When will computer hardware match the human brain? Hans Moravec, (1998)(jetpress.org)
3 points by MAXPOOL 8y ago|0 comments
135.
by MAXPOOL 8y ago
The human retina communicates to brain with 10 Mbps. The 4-year old's brain has experienced 9 months in a womb and 4 years outside the womb. That's less than 150 million seconds. More than third of that while sleeping. Assuming t
136.
by MAXPOOL 8y ago
"for all intents and purposes"
137.
by MAXPOOL 8y ago
When you get brain damage that changes your behavior, it's for all intents and purposes 100% environment.
138.
by MAXPOOL 8y ago
Almost always both. Environment triggers changes in gene expression and reverse is also true. There are multiple ways of how nature and nurture interact. Take for example stress during childhood. It seems that stressful environment leads e
139.
by MAXPOOL 8y ago
Not my exact field, but I keep track of the research for potential applications. The general vibe is that there are potentially large welfare gains to be achieved if algorithms, ML or statistical methods are integrated into human decision
140.
by MAXPOOL 8y ago
ReLU is very simple in this regard. In the plain form it's just affine transformation followed by 'a viewport'. The mapping trough multiple layers is alternating affine transformations and windows into data. Learning is com
141.
by MAXPOOL 8y ago
They are not hyped. Capsule nets are in early phase of research. The bleeding edge deep learning research that tries to push the science forward is trying to develop new ideas. Hinton et. al developed the current deep learning revolution
142.
by MAXPOOL 8y ago
> The cerebellum had been far too neglected in AI research. Cerebellar Model Articulation Controller (CMAC) https://en.wikipedia.org/wiki/Cerebellar_model_articulation_... A Historical Review of Forty Years of Resea
143.
by MAXPOOL 8y ago
It's possible to infer causation from correlation without experiments if you add some general assumptions. One trick in causal discovery is additive noise. If X and Y are noisy correlating variables and X is causing Y, assumption th
144.
by MAXPOOL 8y ago
It's deep and large model. Maybe unnecessarily so. In image processing a single pixel or region almost always belongs to one object. Car can be behind a tree, or tree can be behind a car but they are not transparent and mixed together
145.
by MAXPOOL 8y ago
You still may want to replace softmax layer with support vector machine for classification sometimes.
146.
by MAXPOOL 8y ago
The problem is not the data. The problem is the need for high quality data. Current ML is data driven statistical learning. ML tries to learn a model that describes the distribution. It's impossible to get similar performance as th
147.
by MAXPOOL 8y ago
I'm just eyeballing it, but it seems like linear trend line of the nowcast (blue line), would be very close to the official forecast (red dot).
148.
by MAXPOOL 8y ago
Yes.
149.
by MAXPOOL 8y ago
Deep learning is not mimicking how our brains work. It's just brain inspired. It may mimic of how some primitive areas of brain work in very low level in very crude way. Take for example machine vision. ConvNET is inspired by the lowe
150.
by MAXPOOL 8y ago
> important results are simply bugs Probably none. If the paper is important and collects citations, the algorithm is in use. Computer science != working code. Code is required when you produce something where the scientific importance i

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