An Invitation to Washington

Three and a half weeks ago I got an email asking me if I’d testify at a hearing of the US Senate Commerce Committee’s Subcommittee on Communications, Technology, Innovation and the Internet. Given that the title of the hearing was “Optimizing for Engagement: Understanding the Use of Persuasive Technology on Internet Platforms” I wasn’t sure why I’d be relevant.

But then the email went on: “The hearing is intended to examine, among other things, whether algorithmic transparency or algorithmic explanation are policy options Congress should be considering.” That piqued my interest, because, yes, I have thought about “algorithmic transparency” and “algorithmic explanation”, and their implications for the deployment of artificial intelligence.

Generally I stay far away from anything to do with politics. But figuring out how the world should interact with AI is really important. So I decided that—even though it was logistically a bit difficult—I should do my civic duty and go to Washington and testify. Continue reading

The first workshop to define what is now the Santa Fe Institute took place on October 5–6, 1984. I was recently asked to give some reminiscences of the event, for a republication of a collection of papers derived from this and subsequent workshops.

It was a slightly dark room, decorated with Native American artifacts. Around it were tables arranged in a large rectangle, at which sat a couple dozen men (yes, all men), mostly in their sixties. The afternoon was wearing on, with many different people giving their various views about how to organize what amounted to a putative great new interdisciplinary university.

Here’s the original seating chart, together with a current view of the meeting room. (I’m only “Steve” to Americans currently over the age of 60…):

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Someone from the House Permanent Select Committee on Intelligence recently contacted me about a hearing they’re having on the subject of deep fakes. I can’t attend the hearing, but the conversation got me thinking about the subject of deep fakes, and I made a few quick notes….

What You See May Not Be What Happened

The idea of modifying images is as old as photography. At first, it had to be done by hand (sometimes with airbrushing). By the 1990s, it was routinely being done with image manipulation software such as Photoshop. But it’s something of an art to get a convincing result, say for a person inserted into a scene. And if, for example, the lighting or shadows don’t agree, it’s easy to tell that what one has isn’t real.

What about videos? If one does motion capture, and spends enough effort, it’s perfectly possible to get quite convincing results—say for animating aliens, or for putting dead actors into movies. The way this works, at least in a first approximation, is for example to painstakingly pick out the keypoints on one face, and map them onto another.

What’s new in the past couple of years is that this process can basically be automated using machine learning. And, for example, there are now neural nets that are simply trained to do “face swapping”:

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What the Wolfram Language Makes Possible

We’re on an exciting path these days with the Wolfram Language. Just three weeks ago we launched the Free Wolfram Engine for Developers to help people integrate the Wolfram Language into large-scale software projects. Now, today, we’re launching the Wolfram Function Repository to provide an organized platform for functions that are built to extend the Wolfram Language—and we’re opening up the Function Repository for anyone to contribute.

The Wolfram Function Repository is something that’s made possible by the unique nature of the Wolfram Language as not just a programming language, but a full-scale computational language. In a traditional programming language, adding significant new functionality typically involves building whole libraries, which may or may not work together. But in the Wolfram Language, there’s so much already built into the language that it’s possible to add significant functionality just by introducing individual new functions—which can immediately integrate into the coherent design of the whole language.

To get it started, we’ve already got 532 functions in the Wolfram Function Repository, in 26 categories:

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