Narrative: Google is Ahead in AI
Each narrative page (like this) has a page describing and evaluating the narrative, followed by all the posts on the site tagged with that narrative. Scroll down beyond the introduction to see the posts.
Google’s AI Explosion in One Chart – MIT Technology Review (Mar 27, 2017)
One of the big problems with evaluating which company is ahead or behind in a field like AI is that there are few external signals – companies work on a variety of AI projects behind closed doors in their R&D departments, and many of them only surface when they’re built into products and services they bring to market. Some have suggested using patents as a way to measure leadership, and this article cites publication in scientific journals as another. Certainly, Google’s publishing is a sign that there’s lots of work going on, but it also reflects the (deliberately) quasi-academic culture at DeepMind, its big AI acquisition, while Apple is also slowly moving in this direction with regard to AI specifically. Neither patent filings nor academic papers, however, have a direct connection to using AI to provide better products and services, and that remains very difficult to measure.
Intel Consolidates its AI Teams (Mar 23, 2017)
Intel is announcing that it’s taking its various AI teams and consolidating them under Naveen Rao, who ran the AI company Nervana which Intel acquired last year. This feels like a sensible move from a company which has dabbled in AI in various parts of the organization but hasn’t become known as an AI powerhouse. Where things get slightly less credible is where Intel talks in the announcement about rallying the industry around a set of standards for AI as it has with past computational trends. Whereas Intel was a major player in personal computing, one of the examples it cites, it’s not nearly in the same position of influence with regard to AI, and so this feels like hubris rather than realism on what Intel’s role will be. Intel also talks, though, about bringing AI to more people, which sounds a lot like the “democratization of AI” message we’ve been hearing a lot from Microsoft lately, and which others including Google have also started parroting lately. This feels like it’s going to become an increasingly important theme in AI: less about individual companies owning capability and more about packaging up and making that capability available to anyone who wants to use it.
via Intel
A.I. Expert at Baidu, Andrew Ng, Resigns From Chinese Search Giant – The New York Times (Mar 22, 2017)
This story is notable for two reasons. Firstly, Baidu especially and Chinese companies in general are often overlooked completely in discussions of who’s making big investments in AI and machine learning, and yet Baidu has made massive investments in this area, and recently hired former Microsoft exec Qi Lu to be its COO and to oversee its AI efforts. Secondly, despite Qi Lu’s recent arrival, the trend of former Silicon Valley execs joining big Chinese tech companies still has fewer long-term success stories than short-term fizzles, as this article points out. Both Hugo Barra and Andrew Ng’s move to Chinese companies were seen as highly symbolic, and as such it’s inevitable that their departures should be too. The big Chinese companies are doing good work, and in some cases pioneering new product and service categories, across a number of different areas, but attracting and keeping high-profile talent from the US (even those with ties to Greater China – Ng was born in the UK to parents from Hong Kong) remains tough.
via New York Times
Google Announces Progress in Using Deep Learning to Detect Cancer (Mar 3, 2017)
Yet another story about using either AI or deep learning (or both) to solve a real-world problem, from Google. This time, it’s an application miles away from any of Google’s current businesses (though perhaps a little relevant to some of the Other Bets), but the point is that Google is finding a very broad set of applications for its capabilities here, which can of course be applied back to lots of things which are relevant to the core Google business (as well as providing tangible human benefits if adopted by other organizations).
via Google
Google Cousin Develops Technology to Flag Toxic Online Comments – The New York Times (Feb 23, 2017)
I love the term “Google cousin” to describe the non-Google companies under the Alphabet umbrella (though confusingly Jigsaw’s website makes it seem as if it’s actually part of Google despite no longer being called Google Ideas). The bigger point here is that this is a clever use of machine learning to solve a real problem, which I’m always a big fan of. Online comments can be horrible and very time consuming to moderate, and this API can be used by publishers to filter out the most “toxic” of those moments. Having said that, the sample comments Jigsaw shows to demonstrate the tool highlight just how inane most online comments are regardless of whether they’re actually toxic, calling into question for me at least whether they’re worth having at all. But this Perspective tool seems to be part of a broader push around technologies for increasing “safety” in various scenarios – that’s definitely the message you get at the Jigsaw website.
Inside Facebook’s AI Machine – Backchannel (Feb 23, 2017)
Backchannel (and Steven Levy in particular) seems to be becoming the default outlet (I was going to say channel) for these access-y pieces on AI and machine learning. Levy previously did something very similar for Apple last August, Amazon in November, and Google in October. And there continues to be a perceived need for this kind of thing because AI continues to be something that’s mostly talked about rather than seen by consumers. That’s not to say that it’s not in products – it clearly is, and the money quote from this article is that “Facebook today cannot exist without AI” – but that it’s not intuitively obvious to consumers that AI is behind a lot of what they use. Companies still need to tell their AI stories, particularly because narratives have emerged about Google being ahead or Apple being behind, and those narratives need to be countered. There are several interesting things in this particular article, but as that quote indicates the biggest thing that comes out of it for me is how central AI and machine learning are becoming to almost everything at Facebook. Secondarily, it’s interesting to see Facebook in some cases do complex processing on the phone itself, something Apple has pioneered but which others have largely eschewed in favor of cloud processing.
via Backchannel
Google debuts Cloud Search, a smart search engine for G Suite customers – TechCrunch (Feb 7, 2017)
The article doesn’t mention Microsoft once, but talks about Google’s consumer products several times, which makes it feel like this is rather missing the point. This is an enterprise offering and therefore goes head on against various Microsoft products and services intended to achieve similar aims (as well as those from Box and other smaller, more specialized enterprise software and service providers). Both Google and Microsoft are focusing on their AI skills as a source of differentiation in enterprise file management, by promising to help employees find the files they need. Search is, of course, a core Google skill, but it operates very differently in an enterprise file system from on the open web, and Microsoft may actually be better placed here given its long history and the massive investment many companies have made in Microsoft tools in this setting.
via TechCrunch
Inside Libratus, the Poker AI That Out-Bluffed the Best Humans – WIRED (Feb 2, 2017)
When most of your news about AI comes from the tech world, it’s easy to imagine that big tech companies are the only ones doing interesting things in the field, but here as in autonomous driving there’s also lots of amazing work being done in academia, as in this case. Carnegie Mellon researchers have developed a poker-playing AI which combines three different methods for learning the game and ultimately beating human players. The piece is worth reading for the details of how this was done, but it’s also a good reminder that neither any single tech company nor the tech industry as a whole has a monopoly on big breakthroughs in AI.
via Wired
Apple Officially Joins Partnership on AI (Jan 27, 2017)
I commented on the reports that Apple was about to join the Partnership on AI yesterday, so I won’t revisit all of this today. Two notable things from today’s announcement, though: Apple’s representative will be Tom Gruber, who runs Siri at Apple, and that may be indicative of where Apple sees ownership of AI residing within the company (it has no formal head of AI); secondly, Apple has been involved with the Partnership from the outset, but hadn’t formalized its membership until today. That might signify that there were some details of Apple’s membership which needed to be worked out before it felt comfortable joining -I’d love to know what those were. Separately from Apple’s involvement, it’s worth noting that the board now has representatives from a number of other organizations beyond tech companies including several universities. So the Partnership won’t just be about driving the agenda of the tech industry here.
Alphabet Announces Fourth Quarter and Fiscal Year 2016 Results – Alphabet (Jan 26, 2017)
One of the things I was most interested in as part of Alphabet’s results was what happened to the Google Other category of revenues, because that’s where sales of the new hardware devices will be reported. That category grew 62% year on year, but also includes Play store revenues as well as Google’s enterprise cloud service revenues, and has been growing at a decent clip already. I’d estimate around $600-700m in revenue from the new hardware products, which probably translates into 600-700k Pixel sales and sales of Home, WiFi, and Daydream hardware. That’s not a bad start, but of course supply was constrained and distribution limited, so there’s clearly potential for more here. Back in the core business, it’s striking how the number of paid “clicks” on Google’s own properties remains the one big driver of ad revenue growth, while total paid clicks on third party sites and the cost per click on all sites continues to fall. YouTube is the major driver here (those clicks include views of video ads where no-one actually clicks), offsetting the erosion of revenues from the shift from desktop to mobile, and was an obsession among analysts on the call. Sundar Pichai focused his remarks on machine learning rather than AI, although the two topics are closely related – it was interesting to hear Satya Nadella kick off the Microsoft earnings call an hour later with talk of AI.
You might also be interested in the Alphabet Q4 2016 deck which is part of the Jackdaw Research Quarterly Decks Service.
via Alphabet (more on Techmeme)