Monthly Archives: March 2017

When it comes to the intersection of smartphones and AI, Motorola had the most surprising news at the show. In case you missed it, Motorola is working with Amazon (and Harman Kardon, most likely) to build a Moto Mod that will make use of Alexa. Even to me, someone who cooled on the Mods concept after an initial wave of interesting accessories slowed to a trickle, this seems like a slam dunk. Even better, Motorola product chief Dan Dery described what the company ultimately wanted to achieve: a way to get assistants like Alexa to integrate more closely with the personal data we keep on our smartphones.

In his mind, for instance, it would be ideal to ask an AI make a reservation at a restaurant mentioned in an email a day earlier. With Alexa set to be a core component of many Moto phones going forward, here's hoping Dery and the team find a way to break down the walls between AI assistants and the information that could make them truly useful. Huawei made headlines earlier this year when it committed to putting Alexa on the Mate 9, but we'll soon see if the company's integration will attempt to be as deep.

Speaking of Alexa, it's about to get some new competition in Asia. Line Inc., makers of the insanely popular messaging app of the same name, are building an assistant named Clova for smartphones and connected speakers. It will apparently be able to deal with complex questions in many forms: Development will initially focus on a first-party app, but should find its way into many different ones, giving users opportunities to talk to services that share some underlying tech.

LG got in on the AI assistant craze too, thanks to a close working relationship with Google. The LG V20 was the very first Nougat smartphone to be announced ... until Google stole the spotlight with its own Nougat-powered Pixel line. And the G6 was the first non-Pixel phone to come with Google's Assistant, a distinction that lasted for maybe a half-hour before Google said the assistant would roll out to smartphones running Android 6.0 and up. The utility is undeniable, and so far, Google Assistant on the G6 has been almost as seamless as the experience on a Pixel.

As a result, flagships like Sony's newly announced XZ Premium will likely ship with Assistant up and running as well, giving us Android fans an easier way to get things done via speech. It's worth pointing out that other flagship smartphones that weren't announced at Mobile World Congress either do or will rely on some kind of AI assistant to keep users pleased and productive. HTC's U Ultra has a second screen where suggestions and notifications generated by the HTC Companion will pop up, though the Companion isn't available on versions of the Ultra already floating around. And then there's Samsung's Galaxy S8, which is expected to come with an assistant named Bixby when it's officially unveiled in New York later this month.

While it's easy to think of "artificial intelligence" merely as software entities that can interact with us intelligently, machine-learning algorithms also fall under that umbrella. Their work might be less immediately noticeable at times, but companies are banking on the algorithmic ability to understand data that we can't on a human level and improve functionality as a result.

Take Huawei's P10, for instance. Like the flagship Mate 9 before it, the P10 benefits from a set of algorithms meant to improve performance over time by figuring out the order in which you like to do things and allocating resources accordingly. With its updated EMUI 5.1 software, the P10 is supposed to be better at managing resources like memory when the phone boots and during use -- all based on user habits. The end goal is to make phones that actually get faster over time, though it will take a while to see any real changes. (You also might never see performance improvements, since "performance" is a subjective thing anyway.)

Even Netflix showed up at Mobile World Congress to talk about machine-learning. The company is well aware that sustained growth and relevance will come as it improves the mobile-video experience. In the coming months, expect to see better-quality video using less network bandwidth, all thanks to algorithms that try quantify what it means for a video to "look good." Combine those algorithms with a new encoding scheme that compresses individual scenes in a movie or TV episode differently based on what's happening in them, and you have a highly complex fix your eyes and wallet will thank you for.

And, since MWC is just the right kind of absurd, we got an up-close look at a stunning autonomous race car called (what else?) RoboCar. Nestled within the sci-fi-inspired body are components that would've seemed like science fiction a few decades ago: There's a complex cluster of radar, LIDAR, ultrasonic and speed sensors all feeding information to an NVIDIA brain using algorithms to interpret all that information on the fly.

That these developments spanned the realms of smartphones, media and cars in a single, formerly focused trade show speak to how big a deal machine learning and artificial intelligence have become. There's no going back now -- all we can do is watch as companies make better use of the data offered to them, and hold those companies accountable when they inevitably screw up.

Click here to catch up on the latest news from MWC 2017.

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AI continued its world domination at Mobile World Congress - Engadget

Quora Questions are part of a partnership between NewsweekandQuora, through which we'll be posting relevant and interesting answers from Quora contributors throughout the week. Read more about the partnershiphere.

Answer from Eric Jang, Research engineer at Google Brain:

Who is leading in AI research among big players like IBM, Google, Facebook, Appleand Microsoft?First, my response contains some bias, because I work at Google Brain, and I really like it there. My opinions are my own, and I do not speak for the rest of my colleagues or Alphabet as a whole.

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I rank leaders in AI research among IBM, Google, Facebook, Apple, Baidu, Microsoft as follows:

I would say Deepmind is probably #1 right now, in terms of AI research.

Their publications are highly respected within the research community, and span a myriad of topics such as deep reinforcement learning, Bayesian neural nets, robotics, transfer learningand others. Being London-based, they recruit heavily from Oxford and Cambridge, which are great ML feeder programs in Europe. They hire an intellectually diverse team to focus on general AI research, including traditional software engineers to build infrastructure and tooling, UX designers to help make research tools, and even ecologists (Drew Purves) to research far-field ideas like the relationship between ecology and intelligence.

They are second to none when it comes to PR and capturing the imagination of the public at large, such as with DQN-Atari and the history-making AlphaGo. Whenever a Deepmind paper drops, it shoots up to the top of Reddits Machine Learning page and often Hacker News, which is a testament to how well-respected they are within the tech community.

Before you roll your eyes at me putting two Alphabet companies at the top of this list, I discount this statement by also ranking Facebook and OpenAI on equal terms at #2. Scroll down if you dont want to hear me gush about Google Brain.

With all due respect to Yann LeCun (he has a pretty good answer), I think he is mistaken about Google Brains prominence in the research community.

"But much of it is focused on applications and product development rather than long-term AI research."

This is categorically false, to the max.

TensorFlow (the Brain teams primary product) is just one of many Brain subteams, and is to my knowledge the only one that builds an externally-facing product. When Brain first started, the first research projects were indeed engineering-heavy, but today, Brain has many employees that focus on long-term AI research in every AI subfield imaginable, similar to FAIR and Deepmind.

FAIR has 16 accepted publications to the ICLR 2017 conference track (announcement by Yann: Yann LeCun - FAIR has co-authors on 16 papers accepted at...), with 3 selected for orals (i.e. very distinguished publications).

Google Brain actually slightly edged out FB this year at ICLR2017, with 20accepted papers and fourselected for orals. I'm excited that the Google Brain teamwill have a decent presence at ICLR 2017.

This doesnt count publications from Deepmind or other teams doing research within Google (Search, VR, Photos). Comparing the number of accepted papers is hardly a good metric, but I want to dispel any insinuations by Yann that Brain is not a legitimate place to do deep learning research.

Google Brain is also the industry research org with the most collaborative flexibility. I dont think any other research institution in the world, industrial or otherwise, has ongoing collaborations with Berkeley, Stanford, CMU, OpenAI, Deepmind, Google X and a myriad of product teams within Google.

I believe that Brain will soon be regarded as a top tier institution in the near future. I had offers from both Brain and Deepmind, and chose the former because I felt that Brain gave me more flexibility to design my own research projects, collaborate more closely with internal Google teams, and join some really interesting robotics initiatives that I cant disclose yet.

Microsoft claims its new speech recognition software has reached parity with humans but still isn't perfect. Microsoft/ YouTube

FAIRs papers are good and my impression is that a big focus for them is language-domain problems like question answering, dynamic memory, Turing-test-type stuff. Occasionally there are some statistical-physics-meets-deep-learning papers. Obviously they do computer vision type work, as well. I wish I could say more, but I dont know enough about FAIR besides their reputation is very good.

They almost lost the deep learning framework wars with the widespread adoption of TensorFlow, but well see if Pytorch is able to successfully capture back market share.

One weakness of FAIR, in my opinion, is that its very difficult to have a research role at FAIR without a PhD. A FAIR recruiter told me this last year. Indeed, PhDs tend to be smarter, but I dont think having a PhD is necessary to bring fresh perspectives and make great contributions to science.

OpenAI has an all-star list of employees: Ilya Sutskever (all-around deep learning master), John Schulman (inventor of TRPO, master of policy gradients), Pieter Abbeel (robot sent from the future to crank out a river of robotics research papers), Andrej Karpathy (Char-RNN, CNNs), Durk Kingma (co-inventor of VAEs), Ian Goodfellow (inventor of GANs), to name a few.

Despite being a small group of around 50 people (so I guess not a Big Player by headcount or financial resources), they also have a top-notch engineering team and publish top-notch, really thoughtful research tools like Gym and Universe. Theyre adding a lot of value to the broader research community by providing software that was once locked up inside big tech companies. This has added a lot of pressure on other groups to start open-sourcing their codes and tools as well.

I almost ranked them as #1, on par with Deepmind in terms of top-research talent, but they havent really been around long enough for me to confidently assert this. They also havent pulled off an achievement comparable to AlphaGo yet, though I cant overstate how important Gym/Universe are to the research community.

As a small nonprofit research group building all their infrastructure from scratch, they dont have nearly as much GPU resources, robots, or software infrastructure as big tech companies. Having lots of compute makes a big difference in research ability and even the ideas one is able to come up with.

Startups are hard and well see whether they are able to continue attracting top talent in the coming years.

Baidu SVAIL and Baidu Institute of Deep Learning are excellent places to do research, and they are working on a lot of promising technologies like home assistants, aids for the blindand self-driving cars.

Baidu does have some reputation issues, such as recent scandals with violating ImageNet competition rules, low-quality search results leading to a Chinese student dying of cancer, and being stereotyped by Americans as a somewhat-sketchy Chinese copycat tech company complicit in authoritarian censorship.

They are definitely the strongest player in AI in China though.

Before the Deep Learning revolution, Microsoft Research used to be the most prestigious place to go. They hire very experienced faculty with many years of experience, which might explain why they sort of missed out on deep learning (the revolution in deep learning has largely been driven by PhD students).

Unfortunately, almost all deep learning research is done on Linux platforms these days, and their CNTK deep learning framework havent gotten as attention as TensorFlow, torch, Chainer, etc.

Apple is really struggling to hire deep learning talent, as researchers tend to want to publish and do research, which goes against Apples culture as a product company. This typically doesnt attract those who want to solve general AI or have their work published and acknowledged by the research community. I think Apples design roots have a lot of parallels to research, especially when it comes to audacious creativity, but the constraints of shipping an insanely great product can be a hindrance to long-term basic science.

I know a former IBM employee who worked on Watson and describes IBMs cognitive computing efforts as a total disaster, driven from management that has no idea what ML can or cannot do but sell the buzzword anyway. Watson uses deep learning for image understanding, but as I understand it the rest of the information retrieval system doesnt really leverage modern advances in deep learning. Basically there is a huge secondary market for startups to capture applied ML opportunities whenever IBM fumbles and drops the ball.

No offense to IBM researchers; youre far better scientists than I ever will be. My gripe is that the corporate culture at IBM is not conducive to leading AI research.

To be honest, all the above companies (maybe with the exception of IBM) are great places to do deep learning research, and given open source software and how prolific the entire field is nowadays, I dont think any one tech firm leads AI research by a substantial margin.

There are some places like Salesforce/Metamind, Amazonthat I heard are quite good but I dont know enough about to rank them.

My advice for a prospective deep learning researcher is to find a team/project that youre interested in, ignore what others say regarding reputation, and focus on doing your best work so that your organization becomes regarded as a leader in AI research.

Who is leading in AI research among big players like IBM, Google, Facebook, Apple, and Microsoft? originally appeared on Quorathe place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google+. More questions:

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Quora Question: Which Company is Leading the Field in AI Research? - Newsweek

Photo courtesy Concept Rise

Rise, the impressive robot uprising short film starring the late Anton Yelchin, is being adapted into a movie... with the original director on board to helm the production.

The five-minute film takes an updated version of the special effects from A.I. with the storyline of The Second Renaissance from The Animatrix. Its all about a dystopian future where artificially intelligent robots are hunted and killed, after the government determined they were becoming too emotional and, therefore, human. Unfortunately, its not working, as Yelchins A.I. helps trigger a war for the future of their species.

David Karlak, who directed the original short, has signed on to direct the feature-length adaptation. Its being produced by Johnny Lin (American Made) and Brian Oliver (Hacksaw Ridge, Black Swan), with original writers Patrick Melton and Marcus Dunstan returning to pen the script. No word who would replace Yelchin, who sadly passed away last year, but I am hoping Rufus Sewell (The Man in the High Castle) reprises his role as the government interrogator. Ill watch him in anything.

You can watch the original short film below.

[The Hollywood Reporter]

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AI SciFi Short Rise Is Being Turned Into a Movie - Gizmodo

A fresh Texas Holdem-playing AI terrorhas emerged barely a month after a supercomputer-powered bot claimedvictory over four professional poker players. But insteadof relying ona supercomputers hardware, the DeepStack AI has shown how it too can decisively defeat human poker pros while running on a GPU chip equivalent to those found in gaming laptops.

The success of anypoker-playing computer algorithm inheads-up, no-limit Texas Holdem is no small feat. Thisversion of two-player poker with unrestricted bet sizes has 10160possible plays at different stages of the gamemore than the number of atoms in the entire universe. But the Canadian and Czech reseachers who developed the new DeepStack algorithm leveraged deep learning technology to create the computer equivalent of intuition and reduce the possible future plays that needed to be calculated at any point in the gameto just 107. That enabled DeepStacks fairly humble computer chip to figure out its best move for each playwithin five seconds and handily beat poker professionals from all over the world.

To make this practical, we only look ahead a few moves deep,saysMichael Bowling, a computer scientist and head of the Computer Poker Research Groupat the University of Alberta in Edmonton, Canada.Instead of playing from there, we useintuition to decide how to play.

This is a huge deal beyond just bragging rights for an AIs ability to beat the best human poker pros. AI that can handle complex poker games such as heads-up, no-limit Texas Holdem could alsotackle similarly complex real-world situations by making the best decisionsin the midst of uncertainty. DeepStacks poker-playing success while running on fairly standard computer hardware could make it much more practical for AI to tackle many other imperfect-information situations involving business negotiations,medical diagnoses andtreatments, or even guiding military robotson patrol. Full details of the research are published in the 2 March 2017 online issue of thejournalScience.

Imperfect-information games have represented daunting challenges for AI until recently because of the seemingly impossible computing resources requiredto crunch all the possible decisions. To avoidthe computing bottleneck, most poker-playing AI have used abstraction techniques that combine similar plays and outcomes in an attempt to reduce the number of overall calculations needed. They solved for a simplified version of heads-up, no-limit Texas Holdem instead of actually running through all the possible plays.

Such an approach has enabledAI to play complex games from a practical computing standpoint, but at the cost of having huge weaknesses in their abstracted strategies that human players can exploit. An analysis showed that four of the top AI competitors in the Annual Computer Poker Competition were beatable by more than 3,000 milli-big-blinds per game in poker parlance. That performance is four times worse than if the AI simply folded and gave up the pot at the start of every game.

DeepStack takes a very different approach that combines both old and new techniques. The older technique isanalgorithm developed by University of Alberta researchers that previously helped come up with a solution for heads-up, limit Texas Holdem (a simpler version of poker with restricted bet sizes). This counterfactual regret minimization algorithm, called CFR+ by its creators, comes up with the best possible play in a given situation by comparing different possible outcomesusing game theory.

By itself, CFR+ would stillruninto the same problem of the computing bottleneck in trying to calculate all possible plays. But DeepStack gets around this by only having the CFR+ algorithm solve for a few moves ahead instead of all possible moves until the end of the game. For all the other possible moves, DeepStack turns to its own version of intuition that is equivalent to a gut feeling about the value of the hidden cards held by both poker players. To train DeepStacks intuition, researchers turned todeep learning.

Deep learning enables AI to learn from example by filtering huge amounts of data through multiple layers of artificial neural networks. In this case, the DeepStack team trained their AI on the best solutions of the CFR+ algorithm for random poker situations. That allowed DeepStacks intuition to become a fast approximate estimate of its best solution for the rest of the game without having to actually calculate all the possible moves.

Deepstack presents the right marriage between imperfect information solvers and deep learning, Bowling says.

But the success of the deep learning componentsurprised Bowling. He thought the challenge would prove too tough even for deep learning. His colleaguesMartin Schmid and Matej Moravcikboth first authors on the DeepStack paperwere convinced that the deep learning approach would work. They ended upmakinga private bet on whether or not the approach would succeed. (I owe them a beer, Bowling says.)

DeepStack proved its poker-playing prowess in 44,852 games played against 33 poker pros recruited by the International Federation of Poker from 17 countries. Typically researchers would need to have their computer algorithms play a huge number of poker hands to ensure that the results are statistically significant and not simply due to chance. But the DeepStack team used a low-variance technique called AIVAT that filters out much of the chance factor and enabled them to come up with statistically significant results with as few as 3,000 games.

We have a history in group of doing variance reduction techniques, Bowling explains.This new technique was pioneered in our work to help separate skill and luck.

Of all the players, 11 poker pros completed the requested 3,000 games over a period of four weeks from November 7 to December 12, 2016. DeepStack handily beat 10 of the 11 with a statistically significant victory margin, and still technically beat the 11th player. DeepStacks victory as analyzed by AIVATwas 486 milli-big-blinds per game (mbb/g). Thatsquite a showing given that 50 mbb/g is considered a sizable margin of victoryamong poker pros. This victory margin also amounted to over 20 standard deviations from zero in statistical terms.

News of DeepStacks success is just the latest blow to human poker-playing egos. ACarnegie Mellon University AI called Libratus achieved its statistically significant victory against four poker pros during a marathon tournament of 120,000 games totalplayedin January 2017. That heavily publicized eventled some online poker fans to fret about the possible death of the gameat the hands of unbeatable poker bots. But to achieve victory, Libratus still calculatedits main poker-playing strategy ahead of time based on abstracted game solvinga computer- and time-intensive process that required15 million processor-core hours on a new supercomputer called Bridges.

Worried poker fans may have even greater cause for concern with the success of DeepStack.Unlike Libratus, DeepStacks remarkably effective forward-looking intuition means itdoes not have to do any extra computing beforehand. Instead, it always looks forward by solvingforactualpossible plays several moves ahead and then relies on its intuition to approximate the rest of the game.

This continual re-solving approach that can take place at any given point in a game is a step beyond the endgame solver that Libratus used only during the last betting rounds of each game. And the fact that DeepStacks approach works on the hardware equivalent of a gaming laptop could mean the world will see the rise of many more capable AI bots tackling a wide variety of challenges beyond pokerin the near future.

It does feel like a breakthrough of the sort that changes the typesof problems we can apply this to, Bowling says. Most of the work of applying this to other problems becomes whether can we get a neural network to apply this to other situations, andI think we have experience with using deep learning in a whole variety of tasks.

IEEE Spectrum's award-winning robotics blog, featuring news, articles, and videos on robots, humanoids, drones, automation, artificial intelligence, and more. Contact us:e.guizzo@ieee.org

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Texas Hold'em AI Bot Taps Deep Learning to Demolish Humans - IEEE Spectrum