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.

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.

Image: YouTube/Two Minute Papers Read the rest

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

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

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.

Image: YouTube Read the rest

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

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

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

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

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

Next month, I'm giving a keynote talk at The Future of the Future: The Ethics and Implications of AI, an event at UC Irvine that features Bruce Sterling, Rose Eveleth, David Kaye, and many others! Read the rest

These beetles do not exist: Confusing Coleopterists is an AI trained on illustrations from zoological textbooks. The extreme formality of this art genre, and its placement within the public domain, makes it uniquely apt to the medium of generative adversarial networks: "Results were interesting and mesmerising."

I set up a machine at PaperSpace with 1 GPU (According to NVIDIA’s repository, running StyleGan on 256px images takes over 14 days with 1 Tesla GPU) ?

I trained it with 128px images and ran it for 3 days, costing €125.

Results were nice! but tiny. ... I loaded the beetle dataset and trained it at full 1024px, [on top of the FlickrHD model] and after 3000 steps the results were very nice.

No-one below Ph.D. level should ever trust an illustration of a beetle again! Read the rest

Shardcore (previously) writes, "I took Godley & Creme's seminal 1985 video and sent it through a StyleGAN network." Read the rest

Columbia Journalism School's Tow Center has published the results of a longrunning investigation into a network of shadowy "local news" sites whose bulk content is composed of algorithmically generated stories about subjects like local rates of gas tax, or which bridges are most structurally sound. Read the rest

Every year, the AI Now Institute (previously) publishes a deep, thoughtful, important overview of where AI research is and the ethical gaps in AI's use, and makes a list of a dozen urgent recommendations for the industry, the research community, and regulators and governments. Read the rest

The Chinese Supreme People’s Court has just released a report on a "mobile court" pilot program that's been running since March to manage procedures in civil legal disputes through the Wechat social media platform, through which litigants are prompted by an AI chatbot "judge" (with a judicial avatar) to state their cases; the evidence is entered into the blockchain. Read the rest

A team of researchers from Microsoft and Harvard's Berkman Center have published a taxonomy of "Failure Modes in Machine Learning," broken down into "Intentionally-Motivated Failures" and "Unintended Failures." Read the rest