Deep Work

The fact that [David Heinemeier] Hannson might be working from Marbella, Spain, while your office is in Des Moines, Iowa, doesn’t matter to your company, as advances in communication and collaboration technology make the process near seamless. (This reality does matter, however, to the less-skilled local programmers living in Des Moines and in need of a steady paycheck.) This same trend holds for the growing number of fields where technology makes productive remote possible — consulting, marketing, writing, design and so on. Once the talent market is made universally accessible, those at the peak of the market thrive while the rest suffer.

From “Chapter One: Deep Work is Valuable.”

Newport, Cal. Deep Work: Rules for Focused Success in a Distracted World. First edition. New York: Grand Central Publishing, 2016.

Oh, we’re really making an AI

Around 2002 I attended a private party for Google — before its IPO, when it was a small company focused only on search. I struck up a conversation with Larry Page, Google’s brilliant cofounder. “Larry, I still don’t get it. There are so many search companies. Web search, for free? Where does that get you?” My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defense this was before Google had ramped up its ad auction scheme to generate real income, long before YouTube or any other major acquisitions. I was not the only avid user of its search site you thought it would not last long. But Page’s reply has always stuck with me: “Oh, we’re really making an A.I.”

I’ve thought a lot about that conversation over the past few years as Google has bought 13 other AI and robotics companies in addition to DeepMind. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search constitutes 80 percent of its revenue. But I think that’s backward. Bather than use AI to make its search better, Google is using search to make its AI better. Every tie you type a query, click on a search-generated link, or create a link on the web, you are training the Google AI. When you type “Easter Bunny” into the image search bar and then click on the most Easter Bunny-looking image, you are teaching the AI what an Easter Bunny looks like. Each of the 3 billion queries that Google conducts each day tutors the deep-learning AI over and over again. With another 10 years of steady improvements to its AI algorithms, plus a thousandfold more data and a hundreds more computing resources, Google will have an unrivaled AI. In a quarterly earning conference call in the fall of 2015, Google CEO Sundar Pichai stated that AI was going to be “a core transformative way by which we are rethinking everything we are doing… We are applying it to all of our products, be it search, be it YouTube and Play etc.” My prediction: By 2026, Google’s main product will not be search but AI.

Kelly, Kevin. The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, 2016.

Track Changes #28 – Rational Geographic

[32:31]

Aaron: Nobody understands why a gazetteer is important until they suddenly need one and then they’re, like, wait, oh what, how do we…

Paul: That’s been the miracle of the web, to me, right, it’s that you’d be like I want to build this thing  and then you very rapidly stumble into the need for a large set of data with a lot of tasks. Like, I need historical texts or I need a list of places or whatever and it’s just amazing how often you get back to that.

And that whole part of our world is surprisingly untended. Right? And you go, oh get this a list of businesses but it’s from 2010 and no one has adopted it since. I’ve been actually thinking, like there isn’t really, as far as I can tell – maybe you know better than I would, but there’s an idea that I’m going to adopt this open source project, or give this into to the commons, or I’m going to open this thing but there’s no culture of adopting big data sets and taking care of them in the same way as there is as putting things on github and doing releases as open source software… that I know about.

….

[34:10]

Aaron: I guess the example of people who are doing that are the New York Public Library.

Paul: They are. That’s true.

Aaron: That’s a good example of trying to deal with both just processing the data – whether its the Menus project or the Theatre Bills or Building Inspector…

Paul: Their Labs is very strong…

Aaron: … and then providing tools for letting people work in little atomic units but even then some of it is a question of scale, I mean for all that the NYPL does amazing work they’re pretty reluctant to offer those services outside of New York City.

Paul: No, of course. What’s bugging me is I think that everyone sees code as the infrastructure for creativity and doing new work online, and I think it’s also data, and we don’t really, that’s not a conversation that people really have that much.

Track Changes – Podcast #28: Rational Geographic — Map Chat with Aaron Straup Cope
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The AI Revolution: The Road to Superintelligence

Secondly, you’ve probably heard the term “singularity” or “technological singularity.” This term has been used in math to describe an asymptote-like situation where normal rules no longer apply. It’s been used in physics to describe a phenomenon like an infinitely small, dense black hole or the point we were all squished into right before the Big Bang. Again, situations where the usual rules don’t apply. In 1993, Vernor Vinge wrote a famous essay in which he applied the term to the moment in the future when our technology’s intelligence exceeds our own—a moment for him when life as we know it will be forever changed and normal rules will no longer apply. Ray Kurzweil then muddled things a bit by defining the singularity as the time when the Law of Accelerating Returns has reached such an extreme pace that technological progress is happening at a seemingly-infinite pace, and after which we’ll be living in a whole new world. I found that many of today’s AI thinkers have stopped using the term, and it’s confusing anyway, so I won’t use it much here (even though we’ll be focusing on that idea throughout).

The AI Revolution: The Road to Superintelligence, Tim Urban, Wait but why