Narrative: Google is Ahead in AI
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New York Times Adopts Alphabet’s AI-Powered Content Moderation (Jun 13, 2017)
Just a quick one here: I wrote about Alphabet company Jigsaw’s machine learning-based approach to online content moderation a while back. At the time, I said it was nice to see AI and machine learning being applied to humdrum every problems that actually needed solving, but back then this was merely a concept that Jigsaw was making available. So it’s great validation for the technology that the New York Times is actually adopting it in a modified, customized form it’s developed with Jigsaw. That should both improve comment moderation on the Times website while also giving the underlying technology a boost, presumably making other news organizations more likely to try it.
via Poynter
Google Launches an AI Investment Program Separate from GV and CapitalG (May 26, 2017)
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Google To Bring Assistant to iPhone, Let Users Create Photo Books (May 16, 2017)
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Google’s Tensor Processing Unit Team Loses Key Members to Startup (Apr 21, 2017)
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Google Turns Image Search into an E-Commerce Funnel (Apr 13, 2017)
Google’s search advertising business is increasingly under threat from other sites pre-empting Google searches with their own search functions in specific areas, among them Amazon in e-commerce and Pinterest in fashion and other categories. As such, Google recently beefed up its image search function to serve up related results from its Shopping feature, and now also shows related images which show fashion products in use alongside other clothing or accessories. All of this is algorithmically generated without human curation, and leans on Google’s AI and machine learning technology. Google is going to have to get better and better at serving up results in these various categories if it’s to fend off the threat from the specialists, but if starting elsewhere has already become a habit for some users, they’ll never even see these Google advances.
via TechCrunch
Google Develops Federated Machine Learning Method Which Keeps Personal Data on Devices (Apr 6, 2017)
This is an interesting new development from Google, which says it has created a new method for machine learning which combines cloud and local elements in a way which keeps personal data on devices but feeds back the things it learns from training to the cloud, such that many devices operating independently can collectively improve the techniques they’re all working on. This would be better for user privacy as well as efficiency and speed, which would be great for users, and importantly Google is already testing this approach on a commercial product, its Gboard Android keyboard. It’s unusual to see Google focusing on a device-level approach to machine learning, as it’s typically majored on cloud-based approaches, whereas it’s been Apple which has been more focused on device-based techniques. Interestingly, some have suggested that Apple’s approach limits its effectiveness in AI and machine learning, whereas this new technique from Google suggests a sort of best of both worlds is possible. That’s not to say Apple will adopt the same approach, and indeed it has favored differential privacy as a solution to using data from individual devices without attributing it to specific users. But this is both a counterpoint to the usual narrative about Google sacrificing privacy to data gathering and AI capabilities and to the narrative about device-based AI approaches being inherently inferior.
via Google
Google Shares Performance Characteristics for its Machine Learning Chip (Apr 5, 2017)
It’s time to roll out that old Alan Kay maxim again: “those who are serious about software should make their own hardware”. Google started working on its own machine learning chip, which it calls a Tensor Processing Unit or TPU, a few years back, and has now shared some performance characteristics, suggesting that it’s more efficient and faster than CPUs and GPUs on the market today for machine learning tasks. While Nvidia and others have done very well out of selling GPU lines originally designed for computer graphics to companies doing machine learning work, Google is doing impressive work here too, and open sourcing the software framework it uses for machine learning. As I’ve said before, it’s extremely hard to definitively answer the question of who’s ahead in AI and machine learning, but Google consistently churns out evidence that it’s moving fast and doing very interesting things in the space.
via Google Cloud Platform Blog
Microsoft launches Sprinkles, a silly camera app powered by machine learning – TechCrunch (Apr 4, 2017)
As I mentioned recently in the context of Microsoft’s Indian AI chatbot, the company appears to be in an experimental mood as regards AI, trying lots of things in lots of separate spaces, without pushing all that hard in any particular direction. There’s nothing wrong with experimentation, but there is a worry that Microsoft both spreads itself a little thin and risks diluting its brand, which has become more focused of late around productivity. There’s an argument to be made that this Sprinkles app fits its other, newer focus on creativity, but it’s probably a bit of a stretch given the minimal ties into any of its other offerings. On the consumer side, Microsoft’s biggest challenge continues to be not just producing compelling offerings but finding ways to monetize them.
via TechCrunch
Facebook will launch group chatbots at F8 – TechCrunch (Mar 29, 2017)
This is yet another sign that Facebook feels its initial bot strategy from last year isn’t panning out (something I predicted at the time) and that it needs to try alternative approaches. It’s iterated fairly rapidly since then and added some functions to make interacting with bots easier, and it now sounds like it’s trying another different tack, allowing developers to integrate bots into group conversations. But those bots won’t be interactive AI-type creatures, but instead will provide updates on events or processes, such as sporting matches or food orders. Like earlier pivots, this seems more modest in its ambitions but also more likely to be successful. But Facebook’s direction here stands in marked contrast to Microsoft’s, which continues to work on AI-based chatbots.
via TechCrunch
Microsoft launches Ruuh, yet another AI chatbot – ZDNet (Mar 29, 2017)
It’s fascinating to watch Microsoft continue to experiment with AI chatbots after its first effort, Tay, went so badly wrong. But the company’s response to that embarrassment is a sign of the culture changes that have happened at Microsoft over the last few years, as this piece from USA Today a while back points out. Microsoft isn’t afraid of failing, picking itself up, and trying again, and that’s admirable in an area as competitively intense as AI. It’s also interesting to watch these chatbots be launched into markets outside the US with other languages and/or accents (its other recent effort in this space is based in China). There’s a long way to go until these chatbots become really useful, but Microsoft seems determined to keep trying until it gets it right, while another early proponent, Facebook, seems to be changing its strategy lately.
via ZDNet