ARCHITECHT Daily: AI research is becoming a victim of its own success

Apologies in advance if this post is long and kind of unorganized, but I have a lot of thoughts about
ARCHITECHT Daily: AI research is becoming a victim of its own success
By ARCHITECHT • Issue #94
Apologies in advance if this post is long and kind of unorganized, but I have a lot of thoughts about this issue, which I first started covering in 2013 …
Artificial intelligence is so hot right now that even some researchers in the space think the hype is getting out of control. Case in point: A researcher Yoav Goldberg’s recent takedown of a natural-language processing paper published on the popular Arxiv repository. Criticism about the specifics of the research aside, Goldberg raised some big questions about the prolific nature of AI papers on Arxiv (often before, or in lieu of, peer review) and the notion of “flag-planting"—a race by researchers to stake their claim on a particular method or field, legitimacy of their approach or their results be be damned.
Goldberg’s post generated a fair amount of commentary and criticism, including on the Reddit machine learning forum and a post by Facebook AI head and deep learning guru Yann LeCun. Goldberg himself added some clarifications within a day of his original post. There’s some good discussion (including LeCun’s post) about the pros and cons of Arxiv’s publishing model—especially with regard to who gets credit for what ideas, and how anybody is supposed to keep up with the glut of work being published.
I also want to address a related point brought up by some other commenters, which is a concern that well-known institutions like Google/DeepMind or OpenAI also complement their papers with blog posts, thus leading to amplified attention and credit for their work. Of course they do—and, generally speaking, this is fantastic for the field of AI. 
AI needs blog posts because people need AI
There’s a whole world of people who are not steeped in AI research but are nonetheless interested in the field. And how could they not be, with the CEOs of every company under the sun going on about how AI and machine learning will be the foundation of their companies going forward. Or, better yet, with well-known scientists, technologists, and businesspeople going on about how AI might present an existential risk to mankind, and at best will turn our economy on its head.
Blog posts are the vehicle for putting sometimes ridiculously complex math and computer science into a format that’s easier (even if only slightly so) to consume for lay readers. Blog posts are also a great vehicle for getting journalists to pay attention. Blog posts by large companies or well-known institutions are even better. 
Sometimes, as often happens with Google, this is because blog posts are tied to improvements in applications that millions of people use. When we’re talking about conceptually or technically difficult concepts, the only thing better than a good analogy is a good example. "Deepmind just built a machine that can kick your ass at Pong” or “Android phones can now recognize your voice even on the floor of a Slayer concert” (not real headlines, that I know of) are always going to be more interesting, more relatable and, for most people, more explanatory stories than “Human-level control through deep reinforcement learning” (an actual DeepMind paper title).
Are some of these AI results overhyped in blog posts and media stories. Yes. Are some of these stories reported breathlessly? Absolutely. From a public relations standpoint, that can lead to inflated expectations about what’s possible (for example, some stuff is still very much reserved to the lab; AlphaGo is still just a great Go-playing system; and Watson perhaps should have stuck to Jeopardy!) and perhaps an undue assignment of credit for making a breakthrough that hasn’t actually solved any problem.
However, it’s largely because of the work of companies like Google, DeepMind, Facebook and Baidu, and their willingness to talk about it publicly, that there’s so much activity in the AI field right now. Regardless where they’re publishing, a lot of people* doing AI research right now, especially if it has anything to do with neural networks, can probably thank these companies for that opportunity. 
(*By the way, that is a lot of people. This blog post does a good job visualizing some of the trends, including the fact that nearly 2,000 papers were submitted to Arxiv in March 2017 alone. Also, probably not coincidentally, the number of papers really starts to pick up after November 2015, when Google open sourced its TensorFlow framework.)
Keeping a clear head in a sea of hype
All that being said, I do think the AI community—researchers, companies, reporters and investors, alike—should take these concerns about peer-review and proper credit seriously. Mostly because we live in a world where open source software is a dominant force in enterprise IT, and where AI is fast becoming one. Being until relatively recently primarily an academic pursuit, AI is also rooted in concepts of openness, at least in terms of publishing research results.
But a big difference between research and software development is that research results are not necessarily an analog to beta products or features. When a software company announces a new product that won’t ship until six months from now or is available as a pre-GA release, there’s a good chance it doesn’t work as advertised at the moment. But you can also bet its engineers are actually working on delivering what they promised, because vaporware is bad for business.
When researchers publish results, they might sound amazing but not be tied to anything beyond that specific research, which itself might have very little real-world application or be just a minor improvement on a minor improvement. That’s fine when you’re steeped in the field and can parse the through the good, the bad and the ugly, but probably less fine when the field is one of the hottest things on the planet. All of a sudden, the pace and scope of research becomes nigh impossible for any single human to track (Hello, AI model!), and competition among researchers is augmented by reporters and investors ready to jump on any sign of the next big thing. 
I don’t think there’s any easy answer to any of this, but it’s a big part of the reason this newsletter exists. I’m trying to cut through the noise and share stuff I think is particularly interesting, well-reasoned and, in the case of research, reasonably likely to have some commercial or societal impact. AI being a feeding frenzy makes this goal both necessary and difficult, but to anybody trying to make a go of a career in AI (even if on the periphery), activity and excitement are probably better than the alternatives.

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Chef is fairly ubiquitous, but maintaining its base means helping customers navigate the transition from cloud to containers to whatever‘s next, without disrupting their businesses in the process.
Artificial intelligence
WIRED has published a couple of good articles lately talking about the interaction between man and machine in autonomous vehicles. One of them, a discussion with Audi’s CEO about self-driving cars, raises some good, if not rose-colored, visions about our AI-powered driving futures. The other, about Boeing’s planned pilot-free airplane, tries to be reassuring but it really just terrifying. I predict fear, regulation and consumer demand will force both cars and airplanes to have humans behind the wheel in some capacity for the foreseeable future.
Like many people, I tend to give IBM’s Watson efforts a hard time. This, however, is a good idea that—assuming it works—will look even better once it’s shrunk to the size of one of those Secret Service earpieces.
This is not the first research of this kind, and the results tend to be similar every time. But the fact that these systems can parse and answer questions well enough to pass—in a fraction of the time that human test-takers have—is the headline, not that it scored worse than most humans do.  •  Share
If I were a CISO, I think my head would explode. FWIW, the Las Vegas CIO did vouch for one product on the podcast recently. So maybe there’s a starting point …
There are lots of reasons I wouldn’t worry about this too much if I were a spy, ranging from political to budgetary to the fact that intelligence still means human. But having machines comb through images, messages, etc, is a good idea.
This guy tested Keras with a backend of Google’s TensorFlow and Microsoft’s CNTK. The results were very similar. It doesn’t make TensorFlow less popular, but more data like this could make Microsoft shops more comfortable in their AI futures.
Small sample size, but this research suggests surprise questions might help identify attempts at online ID theft, especially in situations where users answer personal info. When they don’t know info offhand, people will scroll around more to find it.  •  Share
It’s using fuzzy logic rather than deep learning, but it’s remarkably effective across domains. The actual numbers are a bit, well, fuzzy, but the model is very good at predicting how well bipolar patients will respond to lithum treatment.
As far as research around specialized AI chips goes, this is a pretty novel approach. But you have to assume somebody is going to hit upon the design that can rival GPUs, at least in power/space-constrained applications.
The problem they solved, as well as the novel computation method, is reminiscent of what some hope quantum computing can achieve. I can tell you which one has more resources and momentum right now, though.
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Cloud and infrastructure
This is a good take on why Amazon would sue a former AWS VP for heading to startup Smartsheet, and what AWS might have to disclose in order to win its lawsuit.
The company’s idea makes sense—moving from the public cloud and procuring data center space, bandwidth, etc., is hard—but I’m not certain how big that market really is.
Speaking of moving off the public cloud … I think Facebook has pretty much figured out how this whole infrastructure thing works, so this is not surprising at all. (But I didn’t know WhatsApp ran on IBM!)
Startup Cronitor explains how it keeps AWS spend at about 12.5 percent of monthly revenue. It’s a lot of investment on infrastructure planning, but might be worth it for startups with fluctuating revenue streams.
If you want another open source alternative to Cloud Spanner (beyond CockroachDB, that is), you can check out Comdb2. Of course, I don’t suspect Bloomberg is offering commercial support.  •  Share
We’ve been hearing about culture for a decade re: cloud computing, and it still comes up all the time as an obstacle to real adoption. Here’s some advice on actually solving it.
All things data
I’ve said before that Hortonworks is doing some smart stuff around winning streaming/IoT workloads and embracing new revenue models, and here is more proof. 
CrowdFlower presents itself as an AI company (for obvious reasons—that’s where all the hype is), but it’s solving a problem applicable to any situation involving data analysis. “Garbage in, garbage out” is universal.
This is adds some context to the CrowdFlower news, IMHO. Getting hold of the right data is still a huge hurdle for many companies, and then they have to make it usable.
I’m pretty sure this is something we can all get behind. It’s also a great use case for using data not to uncover some new insight, but to optimize the production process and save lots of money.
This is possibly only interesting to those of us with experience using LexisNexis or WestLaw for legal research. Having no info about the terms/circumstances of this sale, I’ll say I am kind of sad to see Ravel integrate into the complex and costly world of big-time legal tech, but Lexis does have a lot of data to turn this tech on if they so desire. Also, I think breaking into the world of legal tech, especially on the research side, is probably really difficult.
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