Illustration by Lisk Feng for Google

By David Weinberger

AI Outside In is a column by PAIR’s former writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

Let’s say you’re an Orc in the Lord of the Rings and bought a copy of Morc the Orc’s Sauron: The Delights of Our Very Evil Leader from Mordor’s leading online bookseller, Harnen Books. When you return to that store, it’s likely to recommend more books praising Sauron. …


This is the year in which the epic battle between theater and TV was settled. Television has won. Not everything, should, or will turn into TV, but we have decisively turned a corner.

Boy and his TV, 1950s John Atherton, CC BY-SA 2.0 via Wikimedia Commons

Good. It’s about time. We have been living in a world that has valued theater over TV as more real, more profound, and more human. When it’s Hamilton versus Young Sheldon, few would argue. But TV isn’t just the shows on the networks or streamers. TV is a sophisticated set of rhetorical forms made possible by video. It’s often packaged into commercial entertainment, but we’re now living…


Illustration by Kati Szilagyi for Google

By David Weinberger

AI Outside In is a column by PAIR’s former writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

Making online objects more understandable?

I love how many machine learning systems require explicit decisions about what level of confidence we want them to assume as they make their classifications and correlations. That makes these machines more consistently humble than we humans are.

As I said in my previous post, I even hold the hope that we’re going to learn from these machines to attach confidence…


Illustration by Janik Sollner for Google

AI Outside In is a column by PAIR’s writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

Machines can learn, but what do they know?

Machine learning systems may be portrayed in much of the media as our new overlords, dictating policy while blithely driving our cars for us, but these systems are actually quite humble. It may seem counterintuitive, but we could learn from their humility.

Machine learning works by inspecting the data we provide it, looking for relationships among the many small points of data, and connecting them…


Illustration by Playmetric for Google

By David Weinberger

AI Outside In is a column by PAIR’s writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

Suppose you want a machine learning system to suggest paint names based on any color you specify. This has been done hilariously by Janelle Shane — “burf pink,” “navel tan” — but let’s say we want to do it more seriously (and without any reference to how Shane actually did it).

Machine learning, at least of the common sort called “supervised learning”…


Cake in the shape of a standup comedian
Cake in the shape of a standup comedian
Photo by Eldriva @ Flickr CC-B Y-ND

I just listened to an excellent Marc Maron interview with Jerry Seinfeld.

When I first saw Seinfeld’s post-series 2002 documentary, Comedian, I really enjoyed it, although like many, I was made queasy by the film’s portrayal of the far lesser-known comedian Orny Adams — talk about punching down! …


Illustration by Joao Fazenda of a woman archer missing her target, the arrow stuck in a cloud
Illustration by Joao Fazenda of a woman archer missing her target, the arrow stuck in a cloud
Illustration by Joao Fazenda for Google

By David Weinberger

AI Outside In is a column by PAIR’s writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

Machine learning’s superpower

When we humans argue over what’s fair, sometimes it’s about principles, sometimes about consequences, and sometimes about trade-offs. But machine learning systems can bring us to think about fairness — and many other things — in terms of three interrelated factors: two ways the machine learning (ML) can go wrong, and the most basic way of adjusting the balance between these potential…


Illustration by Giacomo Bagnara for Google

By David Weinberger, Writer-in-Residence, Google PAIR

AI Outside In is a series of columns from PAIR’s writer-in-residence, David Weinberger, who offers his outsider perspective on key ideas in machine learning. His opinions are his own and do not necessarily reflect those of Google.

How would you say these two images are alike?


And gives us the tools for our next evolutionary step

Credit: peepo/Getty Images

The program “Deep Patient” doesn’t know that being knocked on the head can make us humans dizzy or that diabetics shouldn’t eat 5-pound Toblerone bars in one sitting. It doesn’t even know that the arm bone is connected to the wrist bone. All it knows is what researchers fed it in 2015: the medical records of 700,000 patients as discombobulated data, with no skeleton of understanding to hang it all on.

Yet, after analyzing the relationships among these blind bits, Deep Patient was not only able to diagnose the likelihood of individual patients developing particular diseases, it was in some…


Maximizing the benefits of machine learning without sacrificing its intelligence

Note: Wired.com has simultaneously run an op-ed version of this paper.

Imagine your Aunt Ida is in an autonomous vehicle (AV) — a self-driving car — on a city street closed to human-driven vehicles. Imagine a swarm of puppies drops from an overpass, a sinkhole opens up beneath a bus full of mathematical geniuses, or Beethoven (or Tupac) jumps into the street from the left as Mozart (or Biggie) jumps in from the right. Whatever the dilemma, imagine that the least worst option for the network of AVs is to drive the car containing your Aunt Ida into a concrete…

David Weinberger

I mainly write about the effect of tech on our ideas

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