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A good historical overview of the Deep Learning revolution. If you think the quote above is an exageration, here are some fresh news from Microsoft: Internal email: Microsoft forms new 5,000-person AI division

"The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, is a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society." (...) "The report is designed to address four intended audiences. For the general public, it aims to provide an accessible, scientifically and technologically accurate portrayal of the current state of AI and its potential. For industry, the report describes relevant technologies and legal and ethical challenges, and may help guide resource allocation. The report is also directed to local, national, and international governments to help them better plan for AI in governance. Finally, the report can help AI researchers, as well as their institutions and funders, to set priorities and consider the ethical and legal issues raised by AI research and its applications."

A good idea for a card in one of our ACT magic decks!!! In his words AI is "our biggest existential threat" - lol

the "even" in your comment is the key word

Edge.org recently had an interesting piece that ties into all of this recent AI fear mongering: http://edge.org/conversation/the-myth-of-ai

Cool repository with python source code for GNC applications of AI technology.

Super LOL for AI !!!!

We are living in the future when live broadcasts are being censored by AI programs in real-time. I'm sure dictators everywhere are looking forward for these technologies to mature. Having a firewall over reality is so convenient.

Oh, and look - the copyright AI doesn't likes Democrats as well: http://www.wired.com/threatlevel/2012/09/youtube-flags-democrats-convention-video-on-copyright-grounds/

What this tells is that we should not take AI seriously until smart Luis's (or his son) managed to make something decent out of it ... "This was, of course, absurd. First of all, the clips had been provided by the studios to be shown during the award ceremony. The Hugo Awards had explicit permission to broadcast them. But even if they hadn't, it is absolutely fair use to broadcast clips of copyrighted material during an award ceremony. Unfortunately, the digital restriction management (DRM) robots on Ustream had not been programmed with these basic contours of copyright law. And then, it got worse. Amid more cries of dismay on Twitter, Reddit, and elsewhere, the official Worldcon Twitter announced: Chicon 7@chicon_7 We are sorry to report that #Ustream will not resume the video feed. #chicon7 #hugos #worldcon 3 Sep 12 ReplyRetweetFavorite And with that, the broadcast was officially cut off. Dumb robots, programmed to kill any broadcast containing copyrighted material, had destroyed the only live broadcast of the Hugo Awards. Sure, we could read what was happening on Twitter, or get the official winner announcement on the Hugo website, but that is hardly the same. We wanted to see our heroes and friends on that stage, and share the event with them. In the world of science fiction writing, the Hugo Awards are kind of like the Academy Awards. Careers are made; people get dressed up and give speeches; and celebrities rub shoulders with (admittedly geeky) paparazzi. You want to see and hear it if you can. But Ustream's incorrectly programmed copyright enforcement squad had destroyed our only access. It was like a Cory Doctorow story crossed with RoboCop 2, with DRM robots going crazy and shooting indiscriminately into a crowd of perfectly innocent broadcasts."

Let an AI build up your web page for just 96$ per year!

Amazing! Not even coding jobs are safe from AI anymore! AI programming will be the last bastion in the struggle against automation. Get ready :)

"Without the relevant data for reasoning about the state of AI technology, we are essentially "flying blind" in our conversations and decision-making related to AI [...] The AI Index is an initiative to track, collate, distill and visualize data relating to artificial intelligence. "

You erase it, AI reconstructs it.

Google is taking position here, but can they live up to their own standards?

" Avoid creating or reinforcing unfair bias." Thats the very definition of the AI used today. If you learn from a dataset, you are biased to that data set. No escape from it.

I'm afraid I take some issue with this rather glorious title. I've only skimmed this paper but looking at the experimental details, their 'imagined universes' comprise of a ball confined in a 2D space as well as a double pendulum. Work along this area is nothing new - it's known in the control community as Inverse Reinforcement Learning - learning dynamics of some system from demonstration trajectories. The paper title ('Toward an AI Physicist for Unsupervised Learning') is more moderate so yet again the issue is how AI is reported in the media...

MEGA CONSTELLATION and AI ... China again :)

and we did not even propose it ...

This is an interesting viewpoint on the applicability (usefulness) of AI models devised by competitions, backed up by easy statistics. Worth a read!

We made it to Slashdot's frontpage !!! :)

Congratulations, gentlemen!

Home page of teodorov.... He believes optimal control is the path to robot movement.... Not AI!!!

Isn't it obvious! :)

I need to modify the first comment "He belives the path to AI goes through optimal control"... Big difference...

new interesting neuroscience article ... how can we learn from this for AI?

> how can we learn from this for AI? After reading the abstract only... it appears a bit too low-level to be relevant...

The Expressive Intelligence Studio at UC Santa Cruz will be hosting a StarCraft competition: This competition enables academic researchers to evaluate their AI systems in a robust commercial RTS environment. The final matches will be held live with commentary. Exhibition matches will also be held between skilled human players and the top performing bots.

I mainly see neural networks in this... maybe some evolutionary stuff :))

love this one ... it seems to take physicist to explain to the AI crowd what they are actually doing ... Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.

Protein Folding solved by AI?