Tags: machines

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Ways to think about machine learning — Benedict Evans

This strikes me as a sensible way of thinking about machine learning: it’s like when we got relational databases—suddenly we could do more, quicker, and easier …but it doesn’t require us to treat the technology like it’s magic.

An important parallel here is that though relational databases had economy of scale effects, there were limited network or ‘winner takes all’ effects. The database being used by company A doesn’t get better if company B buys the same database software from the same vendor: Safeway’s database doesn’t get better if Caterpillar buys the same one. Much the same actually applies to machine learning: machine learning is all about data, but data is highly specific to particular applications. More handwriting data will make a handwriting recognizer better, and more gas turbine data will make a system that predicts failures in gas turbines better, but the one doesn’t help with the other. Data isn’t fungible.

Untold AI: The Untold | Sci-fi interfaces

Prompted by his time at Clearleft’s AI gathering in Juvet, Chris has been delving deep into the stories we tell about artificial intelligence …and what stories are missing.

And here we are at the eponymous answer to the question that I first asked at Juvet around 7 months ago: What stories aren’t we telling ourselves about AI?

‘Black Mirror’ meets HGTV, and a new genre, home design horror, is born - Curbed

There was a time, circa 2009, when no home design story could do without a reference to Mad Men. There is a time, circa 2018, when no personal tech story should do without a Black Mirror reference.

Black Mirror Home. It’s all fun and games until the screaming starts.

When these products go haywire—as they inevitably do—the Black Mirror tweets won’t seem so funny, just as Mad Men curdled, eventually, from ha-ha how far we’ve come to, oh-no we haven’t come far enough.

Failing to distinguish between a tractor trailer and the bright white sky | booktwo.org

James talks about automation and understanding.

Just because a technology – whether it’s autonomous vehicles, satellite communications, or the internet – has been captured by capital and turned against the populace, doesn’t mean it does not retain a seed of utopian possibility.

Design in the Era of the Algorithm | Big Medium

The transcript of Josh’s fantastic talk on machine learning, voice, data, APIs, and all the other tools of algorithmic design:

The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do.

Josh put together ten design principles for conceiving, designing, and managing data-driven products. I’ve added them to my collection.

  1. Favor accuracy over speed
  2. Allow for ambiguity
  3. Add human judgment
  4. Advocate sunshine
  5. Embrace multiple systems
  6. Make it easy to contribute (accurate) data
  7. Root out bias and bad assumptions
  8. Give people control over their data
  9. Be loyal to the user
  10. Take responsibility

The Eccentric Genius Whose Time May Have Finally Come (Again) - Doug Hill - The Atlantic

A profile of Norbert Wiener, and how his star was eclipsed by Claude Shannon.

Percussive Maintenance on Vimeo

Have you tried turning it off and on again?

Percussive Maintenance

dConstruct 2013: “It’s the Future. Take it.” | matt.me63.com - Matt Edgar

This is a terrific write up of this year’s dConstruct, tying together all the emergent themes.

NSA: The Decision Problem by George Dyson

A really terrific piece by George Dyson taking a suitably long-zoom look at information warfare and the Entscheidungsproblem, tracing the lineage of PRISM from the Corona project of the Cold War.

What we have now is the crude equivalent of snatching snippets of film from the sky, in 1960, compared to the panopticon that was to come. The United States has established a coordinated system that links suspect individuals (only foreigners, of course, but that definition becomes fuzzy at times) to dangerous ideas, and, if the links and suspicions are strong enough, our drone fleet, deployed ever more widely, is authorized to execute a strike. This is only a primitive first step toward something else. Why kill possibly dangerous individuals (and the inevitable innocent bystanders) when it will soon become technically irresistible to exterminate the dangerous ideas themselves?

The proposed solution? That we abandon secrecy and conduct our information warfare in the open.

MachineDrawing DrawingMachines : Pablo Garcia

In which twelve drawings of historical drawing machines are drawn by a computer numerical controlled machine.

The Robot-Readable World – Blog – BERG

Wonderful musings from Matt on meeting the emerging machine intelligence halfway.