- It wants to wield influence in the wider AI ecosystem, and to do so has put together an impressive stack of machine learning tools — from software to servers — that mean you can build an AI product from the ground up without ever leaving the Google playpen.
- The heart of this offering is Google’s machine learning software TensorFlow.
- They attract talent to Google and help make the company’s in-house software the standard for machine learning.
- “There are technical differences between [different AI frameworks], but machine learning communities live off community support and forums, and in that regard Google is winning,” he tells The Verge.
- Yesterday, for example, Google announced that Android now has a staggering two billion monthly active users, and to keep the software’s edge, the company is honing it with machine learning.
Google has always used its annual I/O conference to connect to developers in its sprawling empire. It announces new tools and initiatives, sprinkles in a little hype, and then tells those watching:…
Continue reading “Google’s latest platform play is artificial intelligence, and it’s already winning”
- Our goal is to ensure that the most promising researchers in the world have access to enough compute power to imagine, implement, and publish the next wave of ML breakthroughs.
- We’re setting up a program to accept applications for access to the TensorFlow Research Cloud and will evaluate applications on a rolling basis.
- The program will be highly selective since demand for ML compute is overwhelming, but we specifically encourage individuals with a wide range of backgrounds, affiliations, and interests to apply.
- The program will start small and scale up.
Researchers need enormous computational resources to train the machine learning models that have delivered
recent advances in medical imaging, speech recognition, game playing, and many other domains. The TensorFlow
Research Cloud is a cluster of 1,000 Cloud TPUs that provides the machine learning research community with
a total of 180 petaflops of raw compute power — at no charge — to support the next wave of breakthroughs.
Continue reading “Accelerating open machine learning research with Cloud TPUs”
- At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too.
- At Google I/O today, the search giant announced a new initiative to help its AI make this leap down to earth: a mobile-optimized version of its machine learning framework named TensorFlowLite.
- The newly announced version, TensorFlowLite, will build on this, helping users slim down their machine learning algorithms to work on-device.
- The company also announced that an API for making machine learning work better with phone chips would be coming sometime in the future — a clear sign that Google thinks your next phone will have an AI-optimized chip in it.
- TensorFlowLite should help Google (and the wider AI research community) bring even more interesting functions like this to our most-used and most-important devices.
At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too. At Google I/O today, the search giant announced a…
Continue reading “Google’s new machine learning framework is going to put more AI on your phone”
- More recently, the technology has been applied to machine learning models used to improve Google Translate, Google Photos, and other software that can make novel use of new AI training techniques.
- In that sense, the original TPU was designed specifically to work best with Google’s TensorFlow, one of many open-source software libraries for machine learning.
- Now, Google says the second version of its TPU system is fully operational and being deployed across its Google Compute Engine, a platform other companies and researchers can tap for computing resources similar to Amazon’s AWS and Microsoft’s Azure.
- Google will of course use the system itself, but it is also billing the new TPU as an unrivaled resource for other companies to make use of.
- Even on their own, the second-gen TPUs are capable of “delivering a staggering 180 teraflops of computing power and are built for just the kind of number crunching that drives machine learning today,” says Fei-Fei Li, Google’s chief scientist of AI and machine learning.
Google today is unveiling its second-generation Tensor Processor Unit, a cloud computing hardware and software system that underpins some of the company’s most ambitious and far-reaching…
Continue reading “Google’s next-generation AI training system is monstrously fast”
- But with AutoDraw, Google is launching a new experiment today that uses machine learning algorithms to match your doodles with professional drawings to make you look like you know what you’re doing.
- Artists who want to donate their drawings to the project can do that here, by the way.
- This project actually uses the same technology as Google’s QuickDraw experiment.
- QuickDraw is more of a game, though, where you’re trying to draw a given object and hope that the AI algorithms recognize it within 20 seconds.
- With AutoDraw, you get more freedom to experiment, and, while you could read all about it here, it’s probably best you head over to AutoDraw.com and give it a
Drawing isn’t for everyone. I, for one, am definitely not very good at it. But with AutoDraw, Google is launching a new experiment today that uses machine..
Continue reading “Google’s AutoDraw uses machine learning to help you draw like a pro”
- If you’re interested in learning more about the magic behind AutoDraw, check out “Quick, Draw!”
- (one of our A.I. Experiments).
- AutoDraw’s suggestion tool uses the same technology to guess what you’re trying to draw.Big thanks to the artists, designers, illustrators and friends of Google who created original drawings for AutoDraw.HAWRAF, Design StudioErin Butner, DesignerJulia Melograna, IllustratorPei Liew, DesignerSimone Noronha, DesignerTori Hinn, DesignerSelman Design, Creative StudioIf you are interested in submitting your own drawings, you can do that here.
- We hope that AutoDraw, our latest A.I. Experiment, will make drawing more accessible and fun for everyone.
AutoDraw is a new A.I. Experiment, built by Google Creative Lab, which uses machine learning and artists’ drawings, to help everyone create anything visual, fast.
Continue reading “Fast Drawing for Everyone”
- In short, in the universe of pages existing in a (at the time almost) shapeless web, Page and Brin wanted to organize that information to make it become knowledge.
- Out of the more than 200 factors that Google accounts for when deciding whether the content on the web is relevant, RankBrain became the third most relevant.
- First, the semantic web is a set of rules and standards that make human language readable to machines.
- In semantic web jargon an entity is a subject which has unambiguous meaning because it has a strong contextual foundation.
- In other words, instead of going from writing to web writing as unconsciously as the human race transitioned from hunter-gathering to farming, it is time to take this step forward deliberately and intentionally.
When Larry Page and Sergey Brin invented PageRank back in 1996, they had one simple idea in mind: Organize the web based on “link popularity.” In…
Continue reading “How Artificial Intelligence is Changing Web Writing”