Slashdot Mirror

A new computing tool developed by Google will let developers build AI-powered apps. The upside is it's doing so without sucking up all of your information. From a report: Google on Wednesday released TensorFlow Federated, open-source software that incorporates federated learning, an AI training system. It works by using data that's spread out across a lot of devices, such as smartphones and tablets, to teach itself new tricks. But rather than send the data back to a central server for study, it learns on your phone or tablet itself and sends only the lesson back to the app maker.

Federated learning runs "part of the machine learning algorithm right next to where the data is on the device," Alex Ingerman, a product manager at Google Research, said in an interview. The algorithm applies what it already knows to the data on your phone, such as suggesting replies to emails, and creates a summary of what it learned in the process to send back.

theodp writes: Back in ye olde days of the information superhighway," begins Clive Thompson in It's Time to Make Code More Tinker-Friendly, "curious newbies had an easy way to see how websites worked: View Source." But no more. "Websites have evolved into complex, full-featured apps," laments Thompson. "Click View Source on Google.com and behold the slurry of incomprehensible Javascript. This increasingly worries old-guard coders. If the web no longer has a simple on-ramp, it could easily discourage curious amateurs." What the world needs now, Thompson argues, are "new tools that let everyone see, understand, and remix today's web. We need, in other words, to reboot the culture of View Source." Thompson cites Fog Creek Software's Glitch, Chris Coyier's CodePen, and Google's TensorFlow Playground as examples of efforts that embrace the spirit of View Source and help people recombine code in useful ways. Any other suggestions?

Long-time Slashdot reader Giant Robot writes: Google Brain's latest experiment is a neural network that allows you to collaboratively draw with it inside of your web browser in real-time. The neural network is trained using the drawings collected from an earlier web game called Quick, Draw! released a few months earlier.
"Once you stop doodling, the neural network takes over and attempts to guess the rest of your doodle," explains Google's page about the project, adding "You can take over drawing again and continue where you left off."

Long-time Slashdot reader Giant Robot writes: Google Brain's latest experiment is a neural network that allows you to collaboratively draw with it inside of your web browser in real-time. The neural network is trained using the drawings collected from an earlier web game called Quick, Draw! released a few months earlier.
"Once you stop doodling, the neural network takes over and attempts to guess the rest of your doodle," explains Google's page about the project, adding "You can take over drawing again and continue where you left off."

Qbertino writes: I'm about to move on in my career after having a "short rethink and regroup break" and was for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something. But I have the seriously growing suspicion that artificial intelligence is coming for us programmers and IT experts faster than we might want to admit. Just last weekend I heard myself saying to a friend who was a pioneer on the web, "AI is today what the web was in 1993" -- I think that to be very true. So just 20 minutes ago I started thinking and wondering about what types of jobs there are in AI. Is anything popping up in the industry from the AI hype and what are these positions called, what do they precisely do and what are the skills needed to do them? I suspect something like an "AI Architect" for planning AI setups and clearly defining the boundaries of what the AI is supposed to do and explore. Then I presume the requirements for something like an "AI Maintainer" and/or "AI Trainer," which would probably resemble something like an admin of a big data storage, looking at statistics and making educated decisions on which "AI Training Paths" the AI should continue to explore to gain the skill required and deciding when the "AI" is ready to be let go on to the task. You're seeing we -- AFAIK -- don't even have names for these positions yet, but I suspect, just as in the internet/web boom 20 years ago, that is about to change *very* fast.

And what about Tensor Flow? Should I toy around with it or are we past that stage already and will others do AI setup and installation better than me before I know how this thing really works? Because I also suspect most of the AI work for humans will closely be tied to services and providers such as Google. You know, renting "AI" as you rent webspace or subscribe to bandwidth today. Any services and industry vendors I should look into -- besides the obvious Google that is? In a nutshell, what work is there in the field of AI that can be done and how do I move into that? Like now. And what should I maybe get a degree in if I want to be on top of this AI thing? And how would you go about gaining skill and knowledge on AI today, and I mean literally, today. I know, tons of questions but insightful advice is requested from an educated slashdot crowd. And I bet I'm not the only one interested in this topic. Thanks.

Reader BrianFagioli writes: One of the best way to digest and present data is with visualizations and dashboards. Not everyone is a data scientist, so how you tell a story matters. Today, Google is making a rather nifty data visualization tool an open source project. Called "Embedding Projector", it can show what the search giant calls high-dimensional data. "To enable a more intuitive exploration process, we are open-sourcing the Embedding Projector, a web application for interactive visualization and analysis of high-dimensional data recently shown as an A.I. Experiment, as part of TensorFlow. We are also releasing a standalone version at projector.tensorflow.org, where users can visualize their high-dimensional data without the need to install and run TensorFlow," says Google.

An anonymous reader writes: Google's Project Magenta, which aims to use machine learning to create music and art, just created its first song. The song, which can be more appropriately described as a 90-second melody, is quite simplistic and reminiscent of an old Nokia ringtone. It's impressive for a machine! Magenta is built on top of its TensorFlow system, and all the open-sourced materials one could ever need are available through its Github. The team wants to be able to tell stories from the art it creates similar to that of artists. "The design of models that learn to construct long narrative arcs is important not only for music and art generation, but also areas like language modeling, where it remains a challenge to carry meaning even across a long paragraph, much less whole stories," the team wrote. "Attention models like the Show, Attend and Tell point to one promising direction, but this remains a very challenging task."

An anonymous reader writes from a report via PCWorld: Google says its Tensor Processing Unit (TPU) advances machine learning capability by a factor of three generations. "TPUs deliver an order of magnitude higher performance per watt than all commercially available GPUs and FPGA," said Google CEO Sundar Pichai during the company's I/O developer conference on Wednesday. The chips powered the AlphaGo computer that beat Lee Sedol, world champion of the game called Go. "We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law)," said Google's blog post. "TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation. Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models, and apply these models more quickly, so users get more intelligent results more rapidly." The chip is called the Tensor Processing Unit because it underpins TensorFlow, the software engine that powers its deep learning services under an open-source license.

smaxp writes: Google has announced the open source release of TensorFlow, its machine learning software library. "TensorFlow has extensive built-in support for deep learning, but is far more general than that -- any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface." This comes alongside some dramatic speed increases (PDF). The code is available at GitHub under an Apache 2.0 license. "Deep learning, machine learning, and artificial intelligence are all some of Google's core competencies, where the company leads Apple and Microsoft. If successful, Google's strategy is to maintain this lead by putting its technology out in the open to improve it based on large-scale adoption and code contributions from the community at large.

smaxp writes: Google has announced the open source release of TensorFlow, its machine learning software library. "TensorFlow has extensive built-in support for deep learning, but is far more general than that -- any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some examples). Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface." This comes alongside some dramatic speed increases (PDF). The code is available at GitHub under an Apache 2.0 license. "Deep learning, machine learning, and artificial intelligence are all some of Google's core competencies, where the company leads Apple and Microsoft. If successful, Google's strategy is to maintain this lead by putting its technology out in the open to improve it based on large-scale adoption and code contributions from the community at large.