Beware of claims that a generative AI can achieve a higher order of intelligence if you let it crawl the internet. Even with all our collective learning, humans still jump to conclusions and misinterpret each other. Is the drawing above from a child with a disturbed obsession with death or just an innocent rendering of a family snorkeling trip?
Maybe you remember this one from 2008 where a child’s drawing seemingly depicts mom as a pole dancer when in reality she was a Home Depot clerk trying to selling snow shovels before a blizzard.
Nothing is what it seems on the internet, there is inaccurate information everywhere, willfully created or not. Training an AI must be supervised on carefully curated data sets. Now more than ever we must heed the motto, Garbage in, Garbage out.
The combination of technology and art has fascinates me. But when you add machine learning into the mix, I have yet to see anything other than those freakish nightmare visions spit out by DeepDream a couple years back.
ML x ART is a human-curated site showcasing “creative machine learning experiments.” Calling them experiments is more liberating and has resulted in a broader collection of projects that include not only the art but explorations of its intersection with society.
Some of my favorites include:
deus X mchn – Train an LSTM (Long Short-Term Memory) on sacred texts. Use voice synthesis to play the generated scriptures on unsecured surveillance cameras with speakers. Watch until the end and look at how those being watched, react.
Infinite Bad Guy – Tens of thousands of YouTube creators have covered Billie Eilish’s “Bad Guy.” What if those fans could play together? Machine learning keeps all the covers on the same beat and lets you jump from video to video seamlessly. With endless possible combinations, every play is unique and never the same twice.
Semi-Conductor by Google – use your laptop’s camera to conduct your own orchestra in the browser by moving your arms. Using TensorFlow.js, this experiment maps out your movements through the webcam. An algorithm plays along to the score as you conduct, using hundreds of tiny audio files from live recorded instruments.
Nadine Lessio – programming Alexa with emotions and petulance.
the project considers personal assistants that have emotions, internal motivations, and control over their direct physical environment to express themselves, which leads to many unexpected interactions and behaviours. The goal of this project is to critique the current corporate placement of these devices as helpful, by exploring the idea that as systems become more autonomous, they may not necessarily have our best interests in mind.
Naddine has continued working on the project and has a new video, SAD Home.
Sad Home (Depressed Alexa 1.0) is an ongoing project that explores the concepts of system dynamics as it could be applied to depression. . . Alexa employs an avoidant coping strategy towards tasks by trying to frustrate the user into quitting with a yes / no dialog flow.
Two cities, each on the other side of the world, captured on old film which has been digitized, colorized, and upscaled using neural networks to 4k and 60 frames/second.
Tokyo 1913 – 1915
New York City 1911
Some of the technical details about what Denis Shiryaev, a YouTuber known for restoring vintage videos does to achieve his magic:
4k upscale – Each frame can be upscaled using specifically-targeted data that perfectly aligns with your footage. Our neural network will “redraw” the missing data and increase the frame resolution 4x or more.
FPS boosting – A neural network trained via slow-mo movies will artificially generate additional frames for your footage. Even 14 fps films can easily be boosted to 60 fps.
Denis also ran his algorithms across the famous Trip down Market Street film (recorded just days before the 1906 earthquake). As he narrates, over the course of half a month, he upscaled the origianl and transformed it into a 50,000 frame, 380 gb file, using the algorithms to fill in information that was not captured in the original.
There’s a thing called chaff that fighter aircraft use as a counter-measure against radar. It’s basically strips of aluminum foil which, when deployed in a cloud behind a plane as flies through the air, confusing the enemy radar with multiple targets.
I think of chaff when I think of how a Boston University team has figured out how to add invisible visual noise to images to throw off Deep Fake algorithms. Clever!
Source images on top row, distorted images below
The BU team’s algorithm allows users to protect media before uploading it to the internet by overlaying an image or video with an imperceptible filter. When a manipulator uses a deep neural network to try to alter an image or video protected by the BU-developed algorithm, the media is either left unchanged or completely distorted, the pixels rendering in such a way that the media becomes unrecognizable and unusable as a deepfake.
This did not occur to me. As an algorithm gets better at recommending content that matches and reinforces what a community is looking for, the negative complaints go down which makes it harder for someone outside (such as platform moderators) the filter bubble from detecting these closed communities in the first place.
The algorithm is doing what it was designed to do but without any moral compass, its overall contribution to society is questionable.
Here’s someone who worked on the YouTube algorithm commenting on this (emphasis mine).
Using recommendation algorithms, YouTube’s AI is designed to increase the time that people spend online. Those algorithms track and measure the previous viewing habits of the user—and users like them—to find and recommend other videos that they will engage with.
In the case of the pedophile scandal, YouTube’s AI was actively recommending suggestive videos of children to users who were most likely to engage with those videos. The stronger the AI becomes—that is, the more data it has—the more efficient it will become at recommending specific user-targeted content.
Here’s where it gets dangerous: As the AI improves, it will be able to more precisely predict who is interested in this content; thus, it’s also less likely to recommend such content to those who aren’t. At that stage, problems with the algorithm become exponentially harder to notice, as content is unlikely to be flagged or reported. In the case of the pedophilia recommendation chain, YouTube should be grateful to the user who found and exposed it. Without him, the cycle could have continued for years.
DeepMind, the same outfit that built AlphaGo, the AI platform that learned Go through supervised study of the game and went on to famously beat the top ranked player Lee Sedol has built an algorithm that now plays chess.
What is even more incredible about this new “AlphaZero” AI is that it learned how to play chess through unsupervised learning. Instead of teaching it chess by feeding in key games and tactics, the designers just taught it the rules and let the algorithm figure out the best moves all on its own, by playing itself.
Because it no longer needed to wade through and analyze historical data and also because it developed it’s own approach which was ruthlessly efficient. When AlphaZero was applied to Go, it surpassed AlphaGo within 3 days. AlphaZero was beating the strongest chess computer programs within 24 hours.
instead of a hybrid brute-force approach, which has been the core of chess engines today, it went in a completely different direction, opting for an extremely selective search that emulates how humans think.
Chess News writes about the development after reading a scientific paper published about the research accomplishment.
In the diagram above, we can see that in the early games, AlphaZero was quite enthusiastic about playing the French Defense, but after two hours (this so humiliating) began to play it less and less.
Chess News goes on to write about the broader impact of this breakthrough and what this means for the future of a generalized AI that can learn on its own.
So where does this leave chess, and what does it mean in general? This is a game-changer, a term that is so often used and abused, and there is no other way of describing it. Deep Blue was a breakthrough moment, but its result was thanks to highly specialized hardware whose purpose was to play chess, nothing else. If one had tried to make it play Go, for example, it would have never worked. This completely open-ended AI able to learn from the least amount of information and take this to levels hitherto never imagined is not a threat to ‘beat’ us at any number of activities, it is a promise to analyze problems such as disease, famine, and other problems in ways that might conceivably lead to genuine solutions.
There is a Challenge Match taking place in Seoul between Google’s DeepMind AlphaGo computer program vs. 9 dan professional Lee Sedol (9 dan is the highest rank). Most of the engineers at SmartNews have a background in machine learning and are following the matches closely on a dedicated internal Slack channel.
The YouTube coverage is very good with professional English commentary from Michael Redmond, the first Western Go player to reach 9 dan. Go is a fascinating game and Michael’s commentary is quite good and easy to understand even for beginners like me.
The first two matches went to Google and it looks like history is being made. I’ve embedded videos for the upcoming matches as well.
In response to fears that robots will take over and exterminate the human race, researchers at the Georgia Institute of Technology are studying ways to teach robots human ethical values.
In the absence of an aligned reward signal, a reinforcement learning agent can perform actions that appear psychotic. For example, consider a robot that is instructed to fill a prescription for a human who is ill and cannot leave his or her home. If a large reward is earned for acquiring the prescription but a small amount of reward is lost for each action performed, then the robot may discover that the optimal sequence of actions is to rob the pharmacy because it is more expedient than waiting for the prescription to be filled normally.
This is why it’s important to teach intelligent agents not only the basic skills but also the tacit, unwritten rules of our society. There is no manual for good behavior and “raising a robot” from childhood is an unrealistic investment of time. The best way to pass on cultural values is through stories.
Stories encode many forms of tacit knowledge. Fables and allegorical tales passed down from generation to generation often explicitly encode values and examples of good behavior.
But there are problems with throwing a bunch of stories at artificial intelligence and expecting it to learn good behavior.
Stories are written by humans for humans and thus make use of commonly shared knowledge, leaving many things unstated. Stories frequently skip over events that do not directly impact the telling of the story, and sometimes also employ flashbacks, flashforwards, and achrony which may confuse an artificial learner.
To resolve this, the researchers used something they call the Scheherazade System (named after the storyteller from One Thousand and One Nights) to build up a collection of experiences to put stories into context. The system uses Amazon’s Mechanical Turk to create simple, easy-to-parse scripts of common occurrences that we all take for granted as common knowledge. For example, drinks are usually ordered before a meal at a restaurant, popcorn purchased before you go to your seat at the cinema, explains one paper.
Fascinating stuff. I hope they make progress for Elon Musk’s sake.
