pakl

13 ms·

31.
by pakl 9y ago
What was the architecture of your predictive model? Was it designed to learn the underlying physical dynamics from the tons of sensor data?
32.
by pakl 9y ago
May I ask how you reached this insight? What field do you work in?
33.
by pakl 9y ago
> objc_msgSend is written in assembly. There are two reasons for this: one is that it's not possible to write a function which preserves unknown arguments and jumps to an arbitrary function pointer in C. Wow... this is a bit off top
34.
by pakl 9y ago
In the interview, Tim Cook says nothing about working on cars. He recognizes that a new core technology is needed for autonomous systems, but doesn't say anything to imply they're working on a car. Along with Cook recently saying
35.
by pakl 9y ago
If this interests you, you should also check out carbon co2, a lightweight object-oriented addition to C ( https://github.com/peterpaul/co2/ ). Code example: https://github.com/peterpaul/co2&#x
36.
by pakl 9y ago
If you actually try current vision systems out on real raw video data as opposed to clean datasets of "good" photos pre-selected by humans, you'll see that they are terribly far from human performance. Same goes for translati
37.
by pakl 9y ago
It's also stunning how poorly it performs in a real world (i.e., non-human-prefiltered photo) scenario. Like autonomous cars or security cameras or robotics.
38.
by pakl 9y ago
Change to non-canonical lighting and it fails on classification tasks. Add non-standard background noise, and it also fails on hearing tasks.
39.
by pakl 9y ago
It's also stunningly poor in lots of cases. Largely because the system has no idea of the reality it's talking about.
40.
by pakl 10y ago
I'm certainly not saying that deep learning is fundamentally flawed. It's a great method, very powerful. (Excellent algorithm.) I'm saying it's not reasonable to expect good generalization in deep convnets that learn ma
41.
by pakl 10y ago
Training on adversarial examples doesn't solve the fundamental problem, it merely tries to plug the holes. But in such high dimensional spaces there are many many holes to be plugged. :) Agreed the failure mode may seem esoteric, but n
42.
by pakl 10y ago
> I have this diffuse idea in my head that most possible images do not occur in the real world and that there are way more degrees of freedom into direction that just don't occur in the real world but this idea is just too diffuse s
43.
by pakl 10y ago
Adversarial examples are just one way to prove that deep learning (deep convolutional nets) fail at generalizable vision. It's not a security problem, it's a fundamental problem. Instead, ask yourselves why these deep nets fail a
44.
by pakl 10y ago
Alternative TL;DR: The experimental methods you use can color the results you get. E.g., lesioning different parts of a microprocessor may reveal "clusters of function" much as such studies do in the brain.
45.
What people really fear about AI(blog.piekniewski.info)
4 points by pakl 10y ago|0 comments
46.
by pakl 10y ago
Does anyone know the source for his Einstein quote on page 9: "You should be careful to distinguish what is true from what is real."?
47.
by pakl 10y ago
I believe Objective C's @property declarations brings some level of what you describe to C/C++. They generate the boilerplate for you. E.g., @property (readonly) NSString* myName;
48.
Can a deep net see a cat?(blog.piekniewski.info)
4 points by pakl 10y ago|1 comments
49.
by pakl 10y ago
People do write those, and even propose viable alternatives, but those articles don't get upvoted as much ;)
50.
by pakl 10y ago
If the shadow is predictable then the unsupervised (self supervised) part of our Predictive model will not have too much trouble learning to deal with it. But you're right, if it did have trouble, there would be good reason to for the
51.
by pakl 10y ago
Yes, that's right! The way you are using supervised learning here will force the neural networks to map from textures directly to human labels. A purely feedforward network, no matter how deep, can only rote memorize the effects of th
52.
by pakl 10y ago
Oh-- my comment wasn't about the simplicity of the first part. (In fact, this is a great tutorial, thanks for posting.) My comment is about the approach of using supervised learning to map directly from images to category labels.
53.
by pakl 10y ago
If he ever tries to train deeper models in this manner and test them on real video frames from a car, he will be in for some unpleasant surprises. Learning to map discrete snapshots of objects to labels won't yield a system that can de
54.
How to build AI that scales naturally(blog.piekniewski.info)
4 points by pakl 10y ago|0 comments
55.
Next Steps in Unsupervised Learning: Adversarial Networks vs. Predictive Dynamics(iot-for-all.com)
15 points by pakl 10y ago|0 comments
56.
by pakl 10y ago
Working on self-driving cars, much like working on advanced robots, requires tight integration between the software, sensors, and motors. These aspects cannot really be developed independently, especially if you want to end up with an optim
57.
by pakl 10y ago
This is a good point, I didn't know Alan Kay had said this. Logic can sometimes be used to describe (to model) some minute aspect of thinking. But it is nearly always a massive simplification, discarding much of what happened during t
58.
by pakl 10y ago
Lecun has identified a real problem for AI -- the need to understand the real world, the link between intelligence and prediction over time. But the tools he is using are not the right ones. Deep conv nets were not designed with prediction
59.
by pakl 10y ago
I work at LeEco US out of San Diego, and my colleagues work at other ML/AI companies also in San Diego. We originally met and collaborated at Brain Corporation.
60.
Recurrent neural networks, dreams, and filling in(blog.piekniewski.info)
14 points by pakl 10y ago|4 comments

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