How neural networks work - A good explainer video

How does a neural network connect perceptions to concepts? In other words, how can you make something that accepts an array of pixels as an input and correctly outputs "dog" or "cat?" This video from Art of the Problem does a good job of explaining how neural networks are able to do this, and why it's important to have neural networks with many layers. Read the rest

Machine learning app turns face sketches into realististic photos

University researchers from Hong Kong and China created an application called DeepFaceDrawing that "allows users with little training in drawing to produce high-quality images from rough or even incomplete freehand sketches."

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Web-based sketching app uses machine learning to detect penises

If a website called Do Not Draw a Penis senses that you are trying to sketch a penis it will erase it. Otherwise, it attempts to describe what you are drawing. It's pretty easy to sneak a penis in by having it after you draw something it identifies. Read the rest

This algorithm coins new words every time you click

Thomas Dimson used the GPT-2 language model to make a website that creates a new word every time you refresh the page. Some of them are really good -- I could imagine them entering the lexicon.

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A new instructional video series from Google: machine learning foundations

All you need to know before taking Google's Machine Learning Foundations course is "a little bit of Python." In this first episode, you'll learn what machine learning is and how it works.

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Teenagers interview A.I experts about the future of thinking machines

The young journalists at YR Media (formerly Youth Radio) were curious about "what artificial intelligence means for race, art, and the apocalypse." So they asked the opinion of a a few experts, including tech journalist Alexis Madrigal, engineer Deb Raji of New York University's AI Now Institute, artist/programmer Sam Lavigne, and AI ethicisit Rachel Thomas. You can read (and listen to) bit from the lively conversation at the Youth Media feature "In the Black Mirror." Here's an excerpt:

RACE + BIAS

Deb Raji: There was a study released where we evaluated the commercial facial recognition systems that were deployed. And we said, "How well does this system work for different intersectional demographics?" So, how well does it work for darker skinned woman versus lighter skinned woman versus darker skinned men and lighter skinned men? And it figures that there was a 30 percent performance gap between lighter skinned men and darker skinned men, which is insane. For reference, usually you don't deploy a system that's performing at less than 95 percent accuracy.

Rachel Thomas: Another example of bias comes from some software that's used in many U.S. courtrooms. It gives people a rating of how likely they are to commit another crime. And it was found that this software has twice as high a false positive rate on black defendants compared to white defendants. So that means it was predicting that people were high risk even though they were not being rearrested. And so this is something that's really impacting people's lives because it was being used in sentencing decisions and bail decisions.

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This AI's only function is to draw penises

DICK-RNN is a recurrent neural network trained with 10,000 doodles of dicks. You start drawing a shape and the AI tries its best to finish it as a penis. Starting with the balls helps a lot.

Try it.

For the technical background and conceptual inspiration: GitHub page for DICK-RNN Read the rest

This robot plays the marimba and writes and sings its own songs

Shimon, the robotic maestro from Georgia Tech’s Center for Music Technology, is releasing an album and going on tour. To write lyrics, the robot employs deep learning combined with semantic knowledge and rhyme and rhythm. Shimon has also had a complete facelift giving it a much more expressive mug for singing. In IEEE Spectrum, Evan Ackerman interviewed Shimon's creators, professor Gil Weinberg and PhD student Richard Savery:

IEEE Spectrum: What makes Shimon’s music fundamentally different from music that could have been written by a human?

Richard Savery: Shimon’s musical knowledge is drawn from training on huge datasets of lyrics, around 20,000 prog rock songs and another 20,000 jazz songs. With this level of data Shimon is able to draw on far more sources of inspiration than than a human would ever be able to. At a fundamental level Shimon is able to take in huge amounts of new material very rapidly, so within a day it can change from focusing on jazz lyrics, to hip hop to prog rock, or a hybrid combination of them all.

How much human adjustment is involved in developing coherent melodies and lyrics with Shimon?

Savery: Just like working with a human collaborator, there’s many different ways Shimon can interact. Shimon can perform a range of musical tasks from composing a full song by itself or just playing a part composed by a human. For the new album we focused on human-robot collaboration so every song has some elements that were created by a human and some by Shimon.

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Neural network turns 24 fps videos into smooth, clear 60 fps

The latest episode of Two Minute Papers discusses a new video enhancement method called "Depth-Aware Video Frame Interpolation" to increase the frame rate of choppy videos. The breakthrough here is the way this neural network smoothly handles objects that appear from behind other objects.

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Watch Billie Eilish interviewed by an A.I.

Creative technologist Nicole He modified OpenAI's GPT-2 language model to generate questions for happy mutant pop star Billie Eilish and also write Eilish-esque lyrics. Vogue Magazine published Eilish's answers to the AI's wonderfully odd questions like: "Who consumed so much of your power in one go?" and "Have you ever seen the ending?" Read the rest

Neural network restores and colorizes old movies

From the excellent "Two Minute Papers" YouTube channel, a discussion of a paper titled "DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement," that demonstrates the results of a neural network that fixes and colorizes aged, blurry, scratchy films. Read the rest

New machine learning algorithm produces "near-perfect" fake human faces

Face-synthesizing algorithms often struggle with facial details like eyes and teeth. These features sometimes get pinned to a fixed spot as a head turns, resulting in an uncanny valley dweller.

A new algorithm, StyleGAN2, fixes this problem and produces "eye-poppingly detailed and correct images." It can also generate never-before-seen cars, churches, and animals.

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Chicago PD's predictive policing tool has been shut down after 8 years of catastrophically bad results

In 2012, Chicago PD collaborated with the RAND Corporation and the Illinois Institute of Technology to automatically generate "risk scores" for people they arrested, which were supposed to predict the likelihood that the person would be a "party to violence" in the future (this program was called "TRAP" -- Targeted Repeat-Offender Apprehension Program" -- seemingly without a shred of irony). Now, that program has been shut down, and the City of Chicago's Office of the Inspector General has published a damning report on its eight-year reign, revealing the ways in which the program discriminated against the people ensnared in it, without reducing violent crime. Read the rest

Gmail's "Smart Compose" feature is terrible at helping freelancers negotiate

I'm a musician. I'm Irish-American, and play Irish music (among other things). And I live in Boston. Naturally, St. Patrick's Day presents me with some potentially lucrative opportunities.

Unfortunately, Gmail is not a very good negotiator:

In case you can't quite tell what's going on in this screenshot: someone asked how much money I wanted in exchange for providing music. Google's "Smart Compose" feature recommended three possible responses I might want give — the first of which was "Free!"

For all the concerns that people might have about machines stealing our jobs, I certainly never expected them to try and trick me into giving my labor away for free as well.

According to Gmail, Smart Compose is "powered by machine learning and will offer suggestions as you type." While I don't typically use the responses that it recommends, the suggestions usually aren't that bad. I have occasionally found them helpful for quick, short responses. I even let Google try its personalization feature on me, which means it should be giving me suggestions that "are tailored to the way [I] normally write, to maintain [my] writing style." In other words, this machine learning mechanism should be based at least somewhat on the actual emails that I send.

But I can assure you: I have never received an email about money or a freelance job of any kind and then immediately replied with, "Free!" (For what it's worth, I have almost certainly answered with "What's your budget?")

Anyway, if you should find yourself in the Boston area on St. Read the rest

"Edge AI": encapsulating machine learning classifiers in lightweight, energy-efficient, airgapped chips

Writing in Wired, Boing Boing contributor Clive Thompson discusses the rise and rise of "Edge AI" startups that sell lightweight machine-learning classifiers that run on low-powered chips and don't talk to the cloud, meaning that they are privacy respecting and energy efficient. Read the rest

The bubbles in VR, cryptocurrency and machine learning are all part of the parallel computing bubble

Yesterday's column by John Naughton in the Observer revisited Nathan Myhrvold's 1997 prediction that when Moore's Law runs out -- that is, when processors stop doubling in speed every 18 months through an unbroken string of fundamental breakthroughs -- that programmers would have to return to the old disciplines of writing incredibly efficient code whose main consideration was the limits of the computer that runs on it. Read the rest

Wireheading: when machine learning systems jolt their reward centers by cheating

Machine learning systems are notorious for cheating, and there's a whole menagerie of ways that these systems achieve their notional goals while subverting their own purpose, with names like "model stealing, rewarding hacking and poisoning attacks." Read the rest

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