yonkshi

Unfortunately any AI systems will encounter the verification dilemma, the more powerful an AI system becomes, the less verifiable it would be.

I agree. I think a big part of this problem is that smaller companies usually cannot afford AI research. I would even go as far as to say there are more AI companies than capable AI researchers, and this causes a large number of faux-AI companies poisoning the AI branding.

AGI is a gradient, not an arbitrary threshold.

We are not capable of recreating human level intelligence yet, but our modern algorithms had become magnitudes better at generalization and sample efficiency. And this trend is not showing any signs of slowing down.

Take PPO for example (powers the OpenAI 5 dota agent), the same algorithms can be used for robotic arms as it does with video games. Two completely different domains of tasks now generalizable under one algorithm. That to me is a solid step towards more general AI.

My guess is to prevent ad-fraud by website owners. It's a lot harder to detect fraudulent clicks/impressions if all data are routed through website.

The cost of potentially blocked by ad blockers is finite (A percentage of total revenue), but the cost of ad-fraud is not bounded.

Not only dupe, Forbes also has the most click baity title amongst them all. Forbes is like buzzfeed now

It appears they are using an algorithmic process to perform the heartbeat trading, and they are patentable[0] as long as they are not illegal. Tax loopholes are not illegal but very unethical, I think the policy makers share equal if not more blame for this.

[0] https://www.quora.com/Is-it-possible-to-patent-an-algorithm

This is such a cool concept, I really wish these guys would succeed. Though last time I heard they gave up developing their own rocket (as a second stage to space). Anyone who's in the loop, does Stratolaunch currently have any potential rockets lined up?

Elaborate? We are discussing event probability, not logical inference, so how can there be fallacy in the first place.

Occam's razor applies here. Your theory is that she had 1. intended to assassinate Assange, 2. attempted to cover it as a joke and then 3. further covering it up by implying she doesn't remember this statement.

Let me offer you another theory: she's almost certain that she didn't say it, but she couldn't remember every word she's said.

Which theory do you think is more likely?

Now I am not saying you are wrong, but you'd need external evidence to support your theory, until then, Occam's rule apply. If you agree that my theory is more probable but do not like it, then it's possible your current belief is skewed by your prior belief.

My friend Gibbs invented this really efficient way to learn.

More specifically Markov chain with monte carlo method (MCMC)

Sure theoretical advances may be evolutionary, but they resulted in exponential reduction of parameter space and sample complexity. These advances outpace Moore's law by a large margin.

Hardware served as a catalyst, but it was not a necessity.

Less than four years ago our state-of-the-art RL system (DQN) could only beat some Atari games. Now we can almost beat best human player in SC. That to me is very impressive.

Currently visiting China for Chinese New Year, and can't help but to notice the anti-US anti-Apple sentiment that arose recently. Although anecdotal, I think the nationalistic sentiment has a lot to do with the dip in sales.

To put in perspective, most Chinese netizens are generally pretty critical of the Chinese gov. Chinese with higher educations usually look up to western culture and products. However after the Huawei arrest incident, the Chinese netizens were pissed at the American government, I've never seen such anger towards the US gov, and huge waves of boycotts began. They think that American gov is bullying a Chinese company into obeying US law even outside US, and their response is to boycott American products.

During my two weeks here I've talked to a dozen random people at bars, friends gatherings and on the plane. Normally US-China politics never come up, but it has come up in almost every single conversation this trip. Most of these people mention that they recent switched to a Chinese phone or their next phone will be a domestic phone.

..could you elaborate? Are you saying ML is dangerous because it’s based on mathematics, and math is too clean to handle real world data?

Sure. I don't have any holistic survey to prove my point, but an example of recent progress in terms of sample efficiency is this paper[0]. Derivatives of this paper have been used to solve Sudoku[1], Starcraft II[2] and more [3]. This paper enabled more efficient use of data by creating a probabilistic graphical model between logical sets.

[0]https://arxiv.org/abs/1706.01427 [1]https://arxiv.org/abs/1711.08028 [2]https://arxiv.org/abs/1806.01830 [3]https://arxiv.org/abs/1806.01822

The sample efficiency of ML systems are increasing rapidly in the theoretical realm, though the available data are growing magnitudes faster than the progress in ML sample efficiency. The article has a valid point: It's far cheaper to hoard data than to invent a more efficient ML system, thus we will see people race towards data rather than technical complexity.

Are you suggesting distributed NN training through user's browsers? This is a very sketchy area along the lines of browser based bitcoin mining. You don't want to run any computation heavy code on client side without user's knowledge & approval.

Though if you only meant performing inference in user's browsers, then the challenge would be to find a way for TF/pytorch pre-trained models to port and perform accurately in js. If successfully done, I can see some use cases here.

There is currently no NN learning algorithm that can handle massively paralleled training. We can use some simple fixes such as mean gradient but they have severe limitations, and their limitation grows as you scale up. Currently even with a handful paralleled GPU training, the gradient computation needs to wait for all GPU batch to complete and then return to CPU before the next batch, so your idea of infinite scaling is just a pipe dream at the moment. Sure Python can't be run on a million pages, but heck, no NN architecture can even handle more than a dozen paralleled computation in a stable manner yet.

JS/V8 may beat Python in general operations but definitely not in math specialized libraries. A lot of python packages support OpenBLAS and AVX/FMA by default which gives huge boosts to math/tensor operations. Correct me if I'm wrong, I don't think the js ecosystem is quite there yet except for experimental projects.

I think we will also see proportionally more and more chips coming out of US companies as well.

When Google started their own chips (TPUs), they just kickstarted a vertical integration race amongst the tech giants. Apple, Intel, Nvidia were the old players, Google is dipping their toes in with TPU, now Amazon and probably soon Microsoft and FB.

I think overall we will see more chips from both Asia and US

I'd be interested as well, I've written a few private scrapers for *A/OW award search, currently have small scale MS going on, would love to connect with more fellow MS-ers. my email is nopctoday at gmail.

Correct me if I'm wrong, but I think the significance of this finding is not so much about temperature, rather it's a big piece of the puzzle in understanding superconductivity. This is the first time that we are able to achieve superconductivity via mechanical property rather than molecular property. Our current superconductors are based some crazy alloys, and we have little insight of why they are superconductive.

In this finding they were able to make graphene superconductive by simply shifting two sheets of graphene by 1.1 degree angle. Graphene consists of only carbon atoms, its atomic properties are much easier to model than an alloy, so by examining why this magical angle work, it can greatly help us understand why superconductivity occur, eventually helping us design higher temperature superconductors.

And Apple's own statement said that the journalist is probably confusion a separate, software based, incident with this incident. https://www.apple.com/newsroom/2018/10/what-businessweek-got...

I don't think most ML experts would agree with that, a big reason DL became popular are the huge improvements they brought to CV and NLP fields.

In many ways, traditional approaches were harder because you need huge amount of domain expertise in CV & NLP, whereas a ML expert can solve simple CV problems with almost no domain knowledge.

Now, a lot of the business data, especially time series data, I agree that an algorithm/heuristic approach is easier and more robust. E.g. recommendation systems.

I spent 10 minutes on your site, still have no idea what it's about. If you say it's the "decentralized facebook", I don't see anything about a feed/updates, no way to post/broadcast anything. The only thing I see are contact grouping and shared calendar?

Not necessarily. E.g. making Ramen noodles, much cheaper to go to a ramen shop than to attempt to make it yourself. Economy of scale.

AI can also be used to identify these fake videos, and they are probably better than human identification. There will be a rise of AI forensics.

The type of neural network they use (GAN) works by having two networks battling each other, one tries to generate fake, and another (discriminator) tries to identify fake, it's a constant arms race. As the generator gets better, the discriminator also gets better. Which means, if the fake video is this good, there must be a discriminator network that identifies fakes just as good.

We did a similar project using GAN, generating images from a text description. You can see the progression of generator and discriminator battling each other, and both get better with time.

https://github.com/yonkshi/text2imageNet

I had a discussion with a buddy about the California high speed rail and similar land prices issue. Surprisingly, eminent domain allows the gov to acquire land fairly cheaply.

https://en.wikipedia.org/wiki/Eminent_domain#United_States

I advise you to reflect on the bias of your own view points.

China has been pulling the same bullying tactics towards EU and S.Korea/Japan as well. IP transfer, forced partnership, etc. Notice how EU, South Korea and Japan haven't really been opposing this ordeal between China and US? They would also like to see China start playing fair, unfortunately there is very few economies can standup to China right now.

China has been quite the bully towards EU nations (though not EU as whole yet). Here in Sweden, China went on full on propaganda mode over some Chinese tourists being kicked out of a hostel, and this made it to the Chinese national news yesterday.