Slashdot Mirror

Google has announced that it's absorbing DeepMind Health, a part of its London-based AI lab DeepMind. "In a blog post, DeepMind's founders said it was a 'major milestone' for the company that would help turn its Streams app -- which it developed to help the UK's National Health Service (NHS) -- into 'an AI-powered assistant for nurses and doctors' that combines 'the best algorithms with intuitive design,'" reports The Verge. "Currently, the Streams app is being piloted in the UK as a way to help health care practitioners manage patients." From the report: DeepMind says its Streams team will remain in London and that it's committed to carrying out ongoing work with the NHS. These include a number of ambitious research projects, such as using AI to spot eye disease in routine scans. The news is potentially controversial given the upset in the UK caused by one of DeepMind's early deals with the NHS. The country's data watchdogs ruled in 2017 that a partnership DeepMind struck with the NHS was illegal, as individuals hadn't been properly informed about how their medical data would be used.

Another consistent worry for privacy advocates in the UK has been the prospect of Google getting its hands on this sort of information. It's not clear what the absorption of the Streams team into Google means in that context, but we've reached out to DeepMind for clarification. According to a report from CNBC, the independent review board DeepMind set up to oversee its health work will likely be shut down as a result of the move. More broadly speaking, the news clearly signals Google's ambitions in health care and its desire to get the most of its acquisition of the London AI lab. There have reportedly been long-standing tensions between DeepMind and Google, with the latter wanting to commercialize the former's work. Compared to Google, DeepMind has positioned itself as a cerebral home for long-sighted research, attracting some of the world's best AI talent in the process.

Researchers from Google's DeepMind subsidiary have developed deep neural networks that "have a remarkable capacity to understand a scene, represent it in a compact format, and then 'imagine' what the same scene would look like from a perspective the network hasn't seen before," writes Timothy B. Lee via Ars Technica. From the report: A DeepMind team led by Ali Eslami and Danilo Rezende has developed software based on deep neural networks with these same capabilities -- at least for simplified geometric scenes. Given a handful of "snapshots" of a virtual scene, the software -- known as a generative query network (GQN) -- uses a neural network to build a compact mathematical representation of that scene. It then uses that representation to render images of the room from new perspectives -- perspectives the network hasn't seen before.

Under the hood, the GQN is really two different deep neural networks connected together. On the left, the representation network takes in a collection of images representing a scene (together with data about the camera location for each image) and condenses these images down to a compact mathematical representation (essentially a vector of numbers) of the scene as a whole. Then it's the job of the generation network to reverse this process: starting with the vector representing the scene, accepting a camera location as input, and generating an image representing how the scene would look like from that angle. The team used the standard machine learning technique of stochastic gradient descent to iteratively improve the two networks. The software feeds some training images into the network, generates an output image, and then observes how much this image diverged from the expected result. [...] If the output doesn't match the desired image, then the software back-propagates the errors, updating the numerical weights on the thousands of neurons to improve the network's performance.

Pete Warden, engineer and CTO of Jetpac, shares his view on how deep learning is already starting to change some of the programming is done. From a blog post, shared by a reader last week: The pattern is that there's an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution. I can only point to examples within Alphabet that we've made public, like upgrading search ranking, data center energy usage, language translation, and solving Go, but these aren't rare exceptions internally. What I see is that almost any data processing system with non-trivial logic can be improved significantly by applying modern machine learning. This might sound less than dramatic when put in those terms, but it's a radical change in how we build software. Instead of writing and maintaining intricate, layered tangles of logic, the developer has to become a teacher, a curator of training data and an analyst of results. This is very, very different than the programming I was taught in school, but what gets me most excited is that it should be far more accessible than traditional coding, once the tooling catches up. The essence of the process is providing a lot of examples of inputs, and what you expect for the outputs. This doesn't require the same technical skills as traditional programming, but it does need a deep knowledge of the problem domain. That means motivated users of the software will be able to play much more of a direct role in building it than has ever been possible. In essence, the users are writing their own user stories and feeding them into the machinery to build what they want.

Pete Warden, engineer and CTO of Jetpac, shares his view on how deep learning is already starting to change some of the programming is done. From a blog post, shared by a reader last week: The pattern is that there's an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution. I can only point to examples within Alphabet that we've made public, like upgrading search ranking, data center energy usage, language translation, and solving Go, but these aren't rare exceptions internally. What I see is that almost any data processing system with non-trivial logic can be improved significantly by applying modern machine learning. This might sound less than dramatic when put in those terms, but it's a radical change in how we build software. Instead of writing and maintaining intricate, layered tangles of logic, the developer has to become a teacher, a curator of training data and an analyst of results. This is very, very different than the programming I was taught in school, but what gets me most excited is that it should be far more accessible than traditional coding, once the tooling catches up. The essence of the process is providing a lot of examples of inputs, and what you expect for the outputs. This doesn't require the same technical skills as traditional programming, but it does need a deep knowledge of the problem domain. That means motivated users of the software will be able to play much more of a direct role in building it than has ever been possible. In essence, the users are writing their own user stories and feeding them into the machinery to build what they want.

Last year, Google's AI subsidiary DeepMind said it was going to work with Starcraft creator Blizzard to turn the strategy game into a proper research environment for AI engineers. Today, they're opening the doors to that environment, with new tools including a machine learning API, a large game replay dataset, an open source DeepMind toolset and more. TechCrunch reports: The new release of the StarCraft II API on the Blizzard side includes a Linux package made to be able to run in the cloud, as well as support for Windows and Mac. It also has support for offline AI vs. AI matches, and those anonymized game replays from actual human players for training up agents, which is starting out at 65,000 complete matches, and will grow to over 500,000 over the course of the next few weeks. StarCraft II is such a useful environment for AI research basically because of how complex and varied the games can be, with multiple open routes to victory for each individual match. Players also have to do many different things simultaneously, including managing and generating resources, as well as commanding military units and deploying defensive structures. Plus, not all information about the game board is available at once, meaning players have to make assumptions and predictions about what the opposition is up to.

It's such a big task, in fact, that DeepMind and Blizzard are including "mini-games" in the release, which break down different subtasks into "manageable chunks," including teaching agents to master tasks like building specific units, gathering resources, or moving around the map. The hope is that compartmentalizing these areas of play will allow testing and comparison of techniques from different researchers on each, along with refinement, before their eventual combination in complex agents that attempt to master the whole game.

After "narrowly" beating the world's top Go player, what's left for Google's AlphaGo AI? Engadget reports: Now that it has nothing left to prove, the AI is hanging up its boots and leaving the world of competitive Go behind. AlphaGo's developers from Google-owned DeepMind will now focus on creating advanced general algorithms to help scientists find elusive cures for diseases, conjure up a way to dramatically reduce energy consumption and invent new revolutionary materials. Before they leave Go behind completely, though, they plan to publish one more paper later this year to reveal how they tweaked the AI to prepare it for the matches against Ke Jie. They're also developing a tool that would show how AlphaGo would respond to a particular situation on the Go board with help from the world's number one player. While you'll have to wait a while for those two, you'll soon be able to watch 50 games AlphaGo played against itself when it was training
The first ten games that AlphaGo played against itself are already online. Shi Yue, 9 Dan Professional and World Champion, described them as "Like nothing I've ever seen before -- they're how I imagine games from far in the future." Google announced that this week's competition "has been the highest possible pinnacle for AlphaGo as a competitive program. For that reason, the Future of Go Summit is our final match event with AlphaGo... We hope that the story of AlphaGo is just the beginning."

"We are very excited about contributions from the community," announced Alphabet's DeepMind, open sourcing a new library to make it easier to build complex TensorFlow neural networks. An anonymous reader writes: "DeepMind foresees Sonnet to be used by the community as a research propellant," reports FossBytes. "Also, it would allow easy sharing of other models created by DeepMind with the community." Sonnet uses an object-oriented approach, a recent blog post explained, pointing to more details on GitHub. "The main principle of 'Sonnet' is to first construct Python objects which represent some part of a neural network, and then separately connect these objects into the TensorFlow computation graph."

DeepMind sees this as part of their broader commitment to open source AI research. "In recent months we've also open-sourced our flagship platform DeepMind Lab, and are currently working with Blizzard to develop an open source API that supports AI research in StarCraft II."

An anonymous reader quotes a report from VentureBeat: Google and Blizzard are opening up StarCraft II to anyone who wants to teach artificial intelligence systems how to conduct warfare. Researchers can now use Google's DeepMind A.I. to test various theories for ways that machines can learn to make sense of complicated systems, in this case Blizzard's beloved real-time strategy game. In StarCraft II, players fight against one another by gathering resources to pay for defensive and offensive units. It has a healthy competitive community that is known for having a ludicrously high skill level. But considering that DeepMind A.I. has previously conquered complicated turn-based games like chess and go, a real-time strategy game makes sense as the next frontier. The companies announced the collaboration today at the BlizzCon fan event in Anaheim, California, and Google's DeepMind A.I. division posted a blog about the partnership and why StarCraft II is so ideal for machine-learning research. If you're wondering how much humans will have to teach A.I. about how to play and win at StarCraft, the answer is very little. DeepMind learned to beat the best go players in the world by teaching itself through trial and error. All the researchers had to do was explain how to determine success, and the A.I. can then begin playing games against itself on a loop while always reinforcing any strategies that lead to more success. For StarCraft, that will likely mean asking the A.I. to prioritize how long it survives and/or how much damage it does to the enemy's primary base. Or, maybe, researchers will find that defining success in a more abstract way will lead to better results, discovering the answers to all of this is the entire point of Google and Blizzard teaming up.

Artem Tashkinov writes: Researchers behind Google's DeepMind company have been creating AI algorithms which could hardly be applied in real life aside from pure entertainment purposes -- the Go game being the most recent example. However, their most recent development, a speech synthesis AI algorithm called WaveNet, beats the two existing methods of generating human speech by a long shot -- at least 50% by Google's own estimates. The only problem with this new approach is that it's very computationally expensive. The results are even more impressive considering the fact that WaveNet can easily learn different voices and generate artificial breaths, mouth movements, intonation and other features of human speech. It can also be easily trained to generate any voice using a very small sample database. Quartz has a voice demo of Google's current method in its report, which uses recurrent neural networks, and WaveNet's method, which "uses convolutional neural networks, where previously generated data is considered when producing the next bit of information." The report adds, "Researchers also found that if they fed the algorithm classical music instead of speech, the algorithm would compose its own songs."

braindrainbahrain writes: Having conquered checkers, chess, and more recently Go, artificial intelligence research now looks at the next frontier: the popular real-time strategy game of StarCraft.
Blizzard Entertainment's president reached out to Google's DeepMind researchers last month, who are now describing StarCraft as "our likely next target". But many top StarCraft experts believe AIs will fail because "Unlike machines, humans are good at lying," reports the Wall Street Journal. An executive at the Korea e-Sports Association tells them "It's going to be hard for AI to bluff or to trick a human player."

One University of Alberta computer scientist David Churchill counters that âoeWhen the AI finds that the only way to win is to show strength, it will do that. If you want to call that bluffing, then the AI is capable of bluffing, but there's no machismo behind it." Unfortuantely, for five years Churchill has been running AI-vs-human StarCraft tournaments, and "So far, it hasn't even been close... Using a mouse and keyboard, the world's top players can issue 500 or more commands a minute," the Journal reports. But they add that now both Facebook and Microsoft are also working on small StarCraft AI projects.

An anonymous reader writes: Google's London-based AI group DeepMind has launched DeepMind Health, teaming up with the NHS to work on its first project. The "neuroscience-inspired" company, bought by Google in 2014, said of the collaboration: "We want to see the NHS thrive, and to ensure that its talented clinicians get the tools and support they need to continue providing world-class care." In its first initiative alongside kidney experts at London's Royal Free Hospital, DeepMind Health has introduced a mobile app called Streams. The software is designed to support the provision of critical information to doctors and nurses in order to help detect the presence of acute kidney injuries (AKI). To support the development of the Streams app, the AI group has also acquired clinical task management app company Hark.

TechCrunch reports that Google has acquired London-based artificial intelligence firm Deep Mind. TechCrunch notes that the purchase price, as reported by The Information, was somewhere north of $500 million, while a report at PC World puts the purchase price lower, at mere $400 million. Whatever the price, the acquisition means that Google has beaten out Facebook, which reportedly was also interested in Deep Mind. Exactly what the startup will bring to Google isn't clear, though it seems to fit well with the emphasis on AI that the company underscored with its hiring of futurist Ray Kurzweil: "DeepMind's site currently only has a landing page, which says that it is 'a cutting edge artificial intelligence company' to build general-purpose learning algorithms for simulations, e-commerce, and games. As of December, the startup had about 75 employees, reports The Information. In 2012, Carnegie Mellon professor Larry Wasserman wrote that the 'startup is trying to build a system that thinks. This was the original dream of AI. As Shane [Legg] explained to me, there has been huge progress in both neuroscience and ML and their goal is to bring these things together. I thought it sounded crazy until he told me the list of famous billionaires who have invested in the company.'"