- Two years later the tool, which is used in building machine-learning software, underpins many future ambitions of Google and its parent company, Alphabet.
- But just months after TensorFlow was released to Google’s army of coders, the company also began offering it to the world for free, as an open source tactic.
- S. Somasegar, a managing director at venture fund Madrona who was previously head of Microsoft’s developer division, says TensorFlow’s prominence poses a genuine challenge to Google’s cloud rivals.
- The company has created specialized processors to make TensorFlow faster and reduce the power it consumes inside Google’s data centers.
- Since Google released TensorFlow, its competitors in cloud computing, Microsoft and Amazon, have released, or started supporting, their own free software tools to help coders build machine-learning systems.
Google has pinned its cloud computing hopes on a bit of software that helps programmers build artificial intelligence apps called TensorFlow.
Continue reading “Google’s plan to best Amazon rests on one particular piece of software”
- Capping off the slew of updates it unveiled this year at VidCon, YouTube announced at a session on Saturday evening that it is rebuilding its Creator Studio on desktops and revamping comments with new machine learning technology.
- The all-new YouTube Creator Studio — which the company is renaming YouTube Studio — will still feature a suite of channel management tools aimed to make creators’ lives easier.
- YouTube says it’s rebuilding the Studio with creator input, and is inviting interested parties to sign up for a beta test right here.
- YouTube said that updates to comments, including the ability for creators to bestow hearts and pin comments, are seeing great success.
- The technology will also parse through comments to look for common themes and topics, giving creators a sense of what viewers are talking about at a glance.
YouTube announced it is rebuilding its Creator Studio on desktops and revamping comments with new machine learning technology.
Continue reading “YouTube Adds Machine Learning To Comments, Rebuilds Its Desktop Creator Studio”
- Foggy bottom: When Narayen became CEO, “you could see there were some dark clouds on the horizon,” he says.
- Adobe switched to a subscription model, opening the door to a new way to deliver software in which customers could more easily receive updates and new features.
- Adobe CEO Shantanu Narayen.Courtesy of Adobe Finding Wall Street: Investors were concerned Adobe was spending too much on data centers, but Narayen convinced them it would pay off.
- Double Duty: Adobe’s board elected Narayen as its chairman this year on top of his CEO duties.
- Narayen is quick to mention Adobe couldn’t be successful without his staff’s hard work.
As Adobe arrives on the Fortune 500 for the first time, CEO Shantanu Narayen shares how the cloud and AI lead it to a sunnier future.
Continue reading “Fortune 500: Adobe CEO Hints at Artificial Intelligence on Photoshop”
- That was the inspiration behind the company’s Cognitive Toolkit (previously CNTK) for deep learning, and on Thursday it got a major upgrade.
- The Microsoft Cognitive Toolkit 2.0 is now generally available, open-source.
- Though version 2 of the toolkit has been in beta since October, the full release builds on previous functionality.
- It improves the performance for neural nets outside of speech recognition and also makes it easier for Microsoft to extend it later.
- With this release, utilization should only grow, as will the toolkit’s functionality.
The general available of the Microsoft Cognitive Toolkit 2.0 adds a number of new features, including Java language bindings for model evaulation, Keras support, performance improvements, and more.
Continue reading “The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support”
- After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions about your data, it’s now expanding this feature to also automatically build charts for you.
- All of this is backed by the same natural language understanding tech that already powered the “Explore” feature.
- It’s worth noting that the previous version of “Explore” could already build graphs for you, but those focused on your complete data set.
- With this new version, Google also is making it easier to keep in sync data from Sheets that you use in Docs or Slides.
- You could already update charts you copy into Docs and Slides with just a click, but now you also can do the same with tables.
Google Sheets is getting smarter today. After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions..
Continue reading “Google Sheets now uses machine learning to help you visualize your data”
- Which is why it makes sense that Apple has announced it’s expanding its offices in Seattle, where much of its AI and machine learning work is done.
- Last August, Apple even bought a Seattle-based machine learning and artificial intelligence startup named Turi for an estimated $200 million, and the team is said to be moving into Apple’s offices at Two Union Square as part of the expansion.
- Carlos Guestrin, a University of Washington professor, former Turi CEO, and now director of machine learning at Apple, told GeekWire: “There’s a great opportunity for AI in Seattle.”
- He added: “We’re trying to find the best people who are excited about AI and machine learning — excited about research and thinking long term but also bringing those ideas into products that impact and delight our customers.”
- As part of the news, the University of Washington also announced a $1 million endowed professorship in AI and machine learning named after Guesterin.
In many ways, the tech world’s AI arms race is really a fight for talent. Skilled engineers are in short supply, and Silicon Valley’s biggest companies are competing to nab the best minds from…
Continue reading “Apple is expanding its Seattle offices to focus on AI and machine learning”
- SINGAPORE — In the brave new world of smart homes, Dyson thinks it’s got a way to get a leg up over the competition: artificial intelligence.
- “Almost every product can benefit from AI, lighting, purification, cleaning — everything that you see in a room needs artificial intelligence,” declared Sir James Dyson, its founder said at its launch event.
- Sir James Dyson, speaking at the Singapore launch.Dyson’s already started dipping its toes in the connected home scene.
- My dream is much rather that everything is automatic, and sets itself for you to your preference,” Dyson said.
- Testing has already begun for Dyson’s new home lighting and fan systems, using face and voice recognition to detect the user, then adjusting its temperature and speed to their precise preferences.
Imagine never having to input a setting again.
Continue reading “Dyson is quietly working artificial intelligence into all of its home gadgets”
- There are two competing nightmare scenarios for any company unveiling a new brand or logo.
- So a search for a logo involving Apple might return thousands of similar logos, or many of them owned by Apple Inc. itself.
- Designers can even search for color combinations to find out if their palette is similar to other trademarks.
- The startup bypasses old-fashioned keyword searching and uses deep learning to match your logo or trademark with millions of trademarks it may infringe upon.
- Rather than just searching for 2D images, designers will be able to upload multiple views of a 3D design and find out if it’s closely matched to an existing design patent.
It’s an omnipresent fear for companies and designers alike. Deep learning might hold the solution.
Continue reading “Design Plagiarism Is A Serious Problem–This Startup’s AI Could Help”
- If you are deploying a model to a cloud environment, you want to know that your model can execute on the hardware available to it, without unpredictable interactions with other code that may access the same hardware.
- For example, the Udacity tutorials and the RNN tutorial using Penn TreeBank data to build a language model are very illustrative, thanks to their simplicity.
- For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
- But good programmers know it is much harder to write code that humans will use, versus code that a machine can compile and execute.
- We appreciate their strategy of integrating new features and tests first so early adopters can try things before they are documented.
A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff) from Dan Kuster, one of indico’s deep learning researchers.
Continue reading “The Good, Bad, & Ugly of TensorFlow”
- Google supercharges Play Music with machine learning for smarter recommendations
- Google Play Music is getting a much-needed overhaul starting this week, both inside and out.
- The company noted the update will roll out starting this week, so you should be able to try the revamped interface soon.
- The company’s music service will now use those smarts to bring you suitable playlists for every activity it can reliably detect.
- Its Android , iOS and Web apps are getting a new interface that’s powered by machine learning to recommend music based on what you’re doing and where you are.
Google Play Music’s Android, iOS and Web apps are getting a new interface that recommends music based on what you’re doing and where you are.
Continue reading “Google supercharges Play Music with machine learning for smarter recommendations”