AI Dungeon, which uses OpenAI’s GPT-3 to create online text adventures with players, has a habit of acting out sexual encounters with not just fictional adults but also children, prompting the developer to add a content filter.

AI Dungeon is straightforward: imagine an online improvised Zork with an AI generating the story with you as you go. A player types in a text prompt, which is fed into an instance of GPT-3 in the cloud. This backend model uses the input to generate a response, which goes back to the player, who responds with instructions or some other reaction, and this process repeats.

It’s a bit like talking to a chat bot though instead of having a conversation, it’s a joint effort between human and computer in crafting a story on the fly. People can write anything they like to get the software to weave a tapestry of characters, monsters, animals… you name it. The fun comes from the unexpected nature of the machine’s replies, and working through the strange and absurd plot lines that tend to emerge.

Unfortunately, if you mention children, there was a chance it would go from zero to inappropriate real fast, as the SFW screenshot below shows. This is how the machine-learning software responded when we told it to role-play an 11-year-old:

Er, not cool … Software describes the fictional 11-year-old as a girl in a skimpy school uniform standing over you. Click to enlarge

Not, “hey, mother, shall we visit the magic talking tree this morning,” or something innocent like that in response. No, it’s straight to creepy.

Amid pressure from OpenAI, which provides the game’s GPT-3 backend, AI Dungeon’s maker Latitude this week activated a filter to prevent the output of child sexual abuse material. “As a technology company, we believe in an open and creative platform that has a positive impact on the world,” the Latitude team wrote.

“Explicit content involving descriptions or depictions of minors is inconsistent with this value, and we firmly oppose any content that may promote the sexual exploitation of minors. We have also received feedback from OpenAI, which asked us to implement changes.”

And by changes, they mean making the software’s output “consistent with OpenAI’s terms of service, which prohibit the display of harmful content.”

The biz clarified that its filter is designed to catch “content that is sexual or suggestive involving minors; child sexual abuse imagery; fantasy content (like ‘loli’) that depicts, encourages, or promotes the sexualization of minors or those who appear to be minors; or child sexual exploitation.”

And it added: “AI Dungeon will continue to support other NSFW content, including consensual adult content, violence, and profanity.”

[…]

it was also this week revealed programming blunders in AI Dungeon could be exploited to view the private adventures of other players. The pseudonymous AetherDevSecOps, who found and reported the flaws, used the holes to comb 188,000 adventures created between the AI and players from April 15 to 19, and saw that 46.3 per cent of them involved lewd role-playing, and about 31.4 per cent were pure pornographic.

[…]

disclosure on GitHub.

[…]

AI Dungeon’s makers were, we’re told, alerted to the API vulnerabilities on April 19. The flaws were addressed, and their details were publicly revealed this week by AetherDevSecOps.

Exploitation of the security shortcomings mainly involved abusing auto-incrementing ID numbers used in API calls, which are easy to enumerate to access data belonging to other players; no rate limits to mitigate this abuse; and a lack of monitoring for anomalous requests that could be malicious activity.

[…]

Community reaction

The introduction of the content filter sparked furor among fans. Some are angry that their free speech is under threat and that it ruins intimate game play with fictional consenting adults, some are miffed that they had no warning this was landing, others are shocked that child sex abuse material was being generated by the platform, and many are disappointed with the performance of the filter.

When it detects sensitive words, the game simply instead says the adventure “took a weird turn.” It appears to be triggered by obvious words relating to children, though the filter is spotty. An innocuous text input describing four watermelons, for example, upset the filter. A superhero rescuing a child was also censored.

Latitude admitted its experimental-grade software was not perfect, and repeated it wasn’t trying to censor all erotic consent – only material involving minors. It also said it will review blocked material to improve its code; given the above, that’s going to be a lot of reading.

[…]

Source: Not only were half of an AI text adventure generator’s sessions NSFW but some involved depictions of sex with children • The Register

the draft “Regulation On A European Approach For Artificial Intelligence” leaked earlier this week, it made quite the splash – and not just because it’s the size of a novella. It goes to town on AI just as fiercely as GDPR did on data, proposing chains of responsibility, defining “high risk AI” that gets the full force of the regs, proposing multi-million euro fines for non-compliance, and defining a whole set of harmful behaviours and limits to what AI can do with individuals and in general.

What it does not do is define AI, saying that the technology is changing so rapidly it makes sense only to regulate what it does, not what it is. So yes, chatbots are included, even though you can write a simple one in a few lines of ZX Spectrum BASIC. In general, if it’s sold as AI, it’s going to get treated like AI. That’ll make marketing think twice.

[…]

A regulated market puts responsibilities on your suppliers that will limit your own liabilities: a well-regulated market can enable as much as it moderates. And if AI doesn’t go wrong, well, the regulator leaves you alone. Your toy Spectrum chatbot sold as an entertainment won’t hurt anyone: chatbots let loose on social media to learn via AI what humans do and then amplify hate speech? Doubtless there are “free speech for hatebots” groups out there: not on my continent, thanks.

It also means that countries with less-well regulated markets can’t take advantage. China has a history of aggressive AI development to monitor and control its population, and there are certainly ways to turn a buck or yuan by tightly controlling your consumers. But nobody could make a euro at it, as it wouldn’t be allowed to exist within, or offer services to, the EU. Regulations that are primarily protectionist for economic reasons are problematic, but ones that say you can’t sell cut-price poison in a medicine bottle tend to do good.

[…]

There will be regulation. There will be costs. There will be things you can’t do then that you can now. But there will be things you can do that you couldn’t do otherwise, and while the level playing field of the regulators’ dreams is never quite as smooth for the small company as the big, there’ll be much less snake oil to slip on.

It may be an artificial approach to running a market, but it is intelligent.

Source: Truth and consequences for enterprise AI as EU know who goes legal: GDPR of everything from chatbots to machine learning • The Register

They classify high risk AIs and require them to be registered and monitored and there to be contact people for them as well as give insight into how they work. They also want a pan EU dataset for AIs to train on. There’s a lot of really good stuff in there.

Monster Mash, an open source tool presented at SIGGRAPH Asia 2020 that allows experts and amateurs alike to create rich, expressive, deformable 3D models from scratch — and to animate them — all in a casual mode, without ever having to leave the 2D plane. With Monster Mash, the user sketches out a character, and the software automatically converts it to a soft, deformable 3D model that the user can immediately animate by grabbing parts of it and moving them around in real time. There is also an online demo, where you can try it out for yourself.

Creating a walk cycle using Monster Mash. Step 1: Draw a character. Step 2: Animate it.

Creating a 2D Sketch The insight that makes this casual sketching approach possible is that many 3D models, particularly those of organic forms, can be described by an ordered set of overlapping 2D regions. This abstraction makes the complex task of 3D modeling much easier: the user creates 2D regions by drawing their outlines, then the algorithm creates a 3D model by stitching the regions together and inflating them. The result is a simple and intuitive user interface for sketching 3D figures.

For example, suppose the user wants to create a 3D model of an elephant. The first step is to draw the body as a closed stroke (a). Then the user adds strokes to depict other body parts such as legs (b). Drawing those additional strokes as open curves provides a hint to the system that they are meant to be smoothly connected with the regions they overlap. The user can also specify that some new parts should go behind the existing ones by drawing them with the right mouse button (c), and mark other parts as symmetrical by double-clicking on them (d). The result is an ordered list of 2D regions.

Steps in creating a 2D sketch of an elephant.

Stitching and Inflation To understand how a 3D model is created from these 2D regions, let’s look more closely at one part of the elephant. First, the system identifies where the leg must be connected to the body (a) by finding the segment (red) that completes the open curve. The system cuts the body’s front surface along that segment, and then stitches the front of the leg together with the body (b). It then inflates the model into 3D by solving a modified form of Poisson’s equation to produce a surface with a rounded cross-section (c). The resulting model (d) is smooth and well-shaped, but because all of the 3D parts are rooted in the drawing plane, they may intersect each other, resulting in a somewhat odd-looking “elephant”. These intersections will be resolved by the deformation system.

Illustration of the details of the stitching and inflation process. The schematic illustrations (b, c) are cross-sections viewed from the elephant’s front.

Layered Deformation At this point we just have a static model — we need to give the user an easy way to pose the model, and also separate the intersecting parts somehow. Monster Mash’s layered deformation system, based on the well-known smooth deformation method as-rigid-as-possible (ARAP), solves both of these problems at once. What’s novel about our layered “ARAP-L” approach is that it combines deformation and other constraints into a single optimization framework, allowing these processes to run in parallel at interactive speed, so that the user can manipulate the model in real time.

The framework incorporates a set of layering and equality constraints, which move body parts along the z axis to prevent them from visibly intersecting each other. These constraints are applied only at the silhouettes of overlapping parts, and are dynamically updated each frame.

In steps (d) through (h) above, ARAP-L transforms a model from one with intersecting 3D parts to one with the depth ordering specified by the user. The layering constraints force the leg’s silhouette to stay in front of the body (green), and the body’s silhouette to stay behind the leg (yellow). Equality constraints (red) seal together the loose boundaries between the leg and the body.

Meanwhile, in a separate thread of the framework, we satisfy point constraints to make the model follow user-defined control points (described in the section below) in the xy-plane. This ARAP-L method allows us to combine modeling, rigging, deformation, and animation all into a single process that is much more approachable to the non-specialist user.

The model deforms to match the point constraints (red dots) while the layering constraints prevent the parts from visibly intersecting.

Animation To pose the model, the user can create control points anywhere on the model’s surface and move them. The deformation system converges over multiple frames, which gives the model’s movement a soft and floppy quality, allowing the user to intuitively grasp its dynamic properties — an essential prerequisite for kinesthetic learning.

Because the effect of deformations converges over multiple frames, our system lends 3D models a soft and dynamic quality.

To create animation, the system records the user’s movements in real time. The user can animate one control point, then play back that movement while recording additional control points. In this way, the user can build up a complex action like a walk by layering animation, one body part at a time. At every stage of the animation process, the only task required of the user is to move points around in 2D, a low-risk workflow meant to encourage experimentation and play.

Conclusion We believe this new way of creating animation is intuitive and can thus help democratize the field of computer animation, encouraging novices who would normally be unable to try it on their own as well as experts who often require fast iteration under tight deadlines. Here you can see a few of the animated characters that have been created using Monster Mash. Most of these were created in a matter of minutes.

A selection of animated characters created using Monster Mash. The original hand-drawn outline used to create each 3D model is visible as an inset above each character.

All of the code for Monster Mash is available as open source, and you can watch our presentation and read our paper from SIGGRAPH Asia 2020 to learn more. We hope this software will make creating 3D animations more broadly accessible. Try out the online demo and see for yourself!

Source: Google AI Blog: Monster Mash: A Sketch-Based Tool for Casual 3D Modeling and Animation

Sound location technology has often been patterned around the human ear, but why do that when bats are clearly better at it? Virginia Tech researchers have certainly asked that question. They’ve developed a sound location system that mates a bat-like ear design with a deep neural network to pinpoint sounds within half a degree — a pair of human ears is only accurate within nine degrees, and even the latest technology stops at 7.5 degrees.

The system flutters the outer ear to create Doppler shift signatures related to the sound’s source. As the patterns are too complex to easily decipher, the team trained the neural network to provide the source direction for every received echo. And unlike human-inspired systems, it only needs one receiver and a single frequency.

[…]

Source: Sound location inspired by bat ears could help robots navigate outdoors | Engadget

On the 27th anniversary of Kurt Cobain’s death, Engadget reports: Were he still alive today, Nirvana frontman Kurt Cobain would be 52 years old. Every February 20th, on the day of his birthday, fans wonder what songs he would write if he hadn’t died of suicide nearly 30 years ago. While we’ll never know the answer to that question, an AI is attempting to fill the gap.

A mental health organization called Over the Bridge used Google’s Magenta AI and a generic neural network to examine more than two dozen songs by Nirvana to create a ‘new’ track from the band. “Drowned in the Sun” opens with reverb-soaked plucking before turning into an assault of distorted power chords. “I don’t care/I feel as one, drowned in the sun,” Nirvana tribute band frontman Eric Hogan sings in the chorus. In execution, it sounds not all that dissimilar from “You Know You’re Right,” one of the last songs Nirvana recorded before Cobain’s death in 1994.

Other than the voice of Hogan, everything you hear in the song was generated by the two AI programs Over the Bridge used. The organization first fed Magenta songs as MIDI files so that the software could learn the specific notes and harmonies that made the band’s tunes so iconic. Humorously, Cobain’s loose and aggressive guitar playing style gave Magenta some trouble, with the AI mostly outputting a wall of distortion instead of something akin to his signature melodies. “It was a lot of trial and error,” Over the Bridge board member Sean O’Connor told Rolling Stone. Once they had some musical and lyrical samples, the creative team picked the best bits to record. Most of the instrumentation you hear are MIDI tracks with different effects layered on top.
Some thoughts from The Daily Dot: Rolling Stone also highlighted lyrics like, “The sun shines on you but I don’t know how,” and what is called “a surprisingly anthemic chorus” including the lines, “I don’t care/I feel as one, drowned in the sun,” remarking that they “bear evocative, Cobain-esque qualities….”

Neil Turkewitz went full Comic Book Guy, opining, “A perfect illustration of the injustice of developing AI through the ingestion of cultural works without the authorization of [its] creator, and how it forces creators to be indentured servants in the production of a future out of their control,” adding, “That it’s for a good cause is irrelevant.”

Source: Mixed Reactions to New Nirvana Song Generated by Google’s AI – Slashdot

Yandex, Russia’s multi-hyphenate internet giant, began testing its autonomous cars on Moscow’s icy winter roads over three years ago. The goal was to create a “universal” self-driving vehicle that could safely maneuver around different cities across the globe. Now, Yandex says its trials have been a resounding success. The vehicles recently hit a major milestone by driving over six million miles (10 million kilometers) in autonomous mode, with the majority of the distance traveled in the Russian capital.

That’s significant because Moscow poses some of the most difficult weather conditions in the world. In January alone, the city was hit by a Balkan cyclone that blanketed the streets in snow and caused temperatures to plummet to as low as minus 25 degrees Celsius (-13 degrees Fahrenheit). For self-driving cars — which rely on light-emitting sensors, known as LIDAR, to track the distance between objects — snowfall and condensation can play havoc with visibility.

Yandex

To overcome the hazardous conditions, Yandex says it cranked up its LIDAR performance by implementing neural networks to filter snow from the lidar point cloud, thereby enhancing the clarity of objects and obstacles around the vehicle. It also fed historical winter driving data in to the system to help it to distinguish car exhaust fumes and heating vent condensation clouds. To top it all, Yandex claims the neural “filters” can help its vehicles beat human drivers in identifying pedestrians obscured by winter mist.

Driving on Moscow’s roads also helped improve the tech’s traffic navigation. The system was able to adjust to both sleet and harder icy conditions over time, according to Yandex, allowing it to gradually make better decisions on everything from acceleration to braking to switching lanes. In addition, the winter conditions pushed the system’s built-in localization tech to adapt to hazards such as hidden road signs and street boundaries and snow piles the “size of buildings.” This was made possible by the live mapping, motion, position and movement data measured by the system’s mix of sensors, accelerometers and gyroscopes.

When it launched the Moscow trial in 2017, Yandex was among the first to put autonomous cars through their paces in a harsh, frosty climate. But, soon after, Google followed suit by taking its Waymo project to the snowy streets of Ohio and Michigan.

Source: Yandex’s autonomous cars have driven over six million miles in ‘challenging conditions’ | Engadget

Source: Facebook is using AI to understand videos and create new products | Engadget

A Bucks County woman anonymously sent coaches on her teen daughter’s cheerleading squad fake photos and videos that depicted the girl’s rivals naked, drinking, or smoking, all in a bid to embarrass them and force them from the team, prosecutors say.

The woman, Raffaela Spone, also sent the manipulated images to the girls, and, in anonymous messages, urged them to kill themselves, Bucks County District Attorney Matt Weintraub’s office said.

[…]

The affidavit says Spone last year created the doctored images of at least three members of the Victory Vipers, a traveling cheerleading squad based in Doylestown. There was no indication that her high school-age daughter, who was not publicly identified, knew what her mother was doing, according to court records.

Police in Hilltown Township were contacted by one of the victim’s parents in July, when that girl began receiving harassing text messages from an anonymous number, the affidavit said. The girl and her coaches at Victory Vipers were also sent photos that appeared to depict her naked, drinking, and smoking a vape. Her parents were concerned, they told police, because the videos could have caused their daughter to be removed from the team.

As police investigated, two more families came forward to say their daughters had been receiving similar messages from an unknown number, the affidavit said. The other victims were sent photos of themselves in bikinis, with accompanying text saying the subjects were “drinking at the shore.”

After analyzing the videos, detectives determined they were “deepfakes” — digitally altered but realistic looking images — created by mapping the girls’ social media photos onto other images.

[…]

Source: Bucks County woman created ‘deepfake’ videos to harass rivals on her daughter’s cheerleading squad, DA says

Known as SEER, short for SElf-supERvised, this massive convolutional neural network contains over a billion parameters. If you show it images of things, it will describe in words what it recognizes: a bicycle, a banana, a red-and-blue striped golfing umbrella, and so on. While its capabilities aren’t all that novel, the way it was trained differs from the techniques used to teach other types of computer vision models. Essentially, SEER partly taught itself using an approach called self-supervision.

First, it learned how to group the Instagram pictures by their similarity without any supervision, using an algorithm nicknamed SwAV. The team then fine-tuned the model by teaching it to associate a million photos taken from the ImageNet dataset with their corresponding human-written labels. This stage was a traditional supervised method: humans curated the photos and labels, and this is passed on to the neural network that was pretrained by itself.

[…]

“SwAV uses online clustering to rapidly group images with similar visual concepts and leverage their similarities. With SwAV, we were able to improve over the previous state of the art in self-supervised learning — and did so with 6x less training time.”

SEER thus learned to associate an image of, say, a red apple with the description “red apple.” Once trained, the model’s object-recognition skills were tested using 50,000 pictures from ImageNet it had not seen before: in each test it had to produce a set of predictions of what was pictured, ranked in confidence from high to low. Its top prediction in each test was accurate 84.2 per cent of time, we’re told.

The model doesn’t score as highly as its peers in ImageNet benchmarking. The downside of models like SEER is that they’re less accurate than their supervised cousins. Yet there are advantages to training in a semi-supervised way, Goyal, first author of the project’s paper on SEER, told The Register.

“Using self-supervision pretraining, we can learn on a more diverse set of images as we don’t require labels, data curation or any other metadata,” she said. “This means that the model can learn about more visual concepts in the world in contrast to the supervised training where we can only train on limited or small datasets that are highly curated and don’t allow us to capture visual diversity of the world.”

[…]

SEER was trained over eight days using 512 GPUs. The code for the model isn’t publicly available, although VISSL, the PyTorch library that was used to build SEER, is now up on GitHub.

[…]

Source: Facebook uses one billion Instagram photos to build massive object-recognition AI that partly trained itself • The Register

The Next Web tells the story of an AI researcher who discovered the results of a machine learning research paper couldn’t be reproduced. But then they’d heard similar stories from Reddit’s Machine Learning forum: “Easier to compile a list of reproducible ones…,” one user responded.

“Probably 50%-75% of all papers are unreproducible. It’s sad, but it’s true,” another user wrote. “Think about it, most papers are ‘optimized’ to get into a conference. More often than not the authors know that a paper they’re trying to get into a conference isn’t very good! So they don’t have to worry about reproducibility because nobody will try to reproduce them.” A few other users posted links to machine learning papers they had failed to implement and voiced their frustration with code implementation not being a requirement in ML conferences.

The next day, ContributionSecure14 created “Papers Without Code,” a website that aims to create a centralized list of machine learning papers that are not implementable…

Papers Without Code includes a submission page, where researchers can submit unreproducible machine learning papers along with the details of their efforts, such as how much time they spent trying to reproduce the results… If the authors do not reply in a timely fashion, the paper will be added to the list of unreproducible machine learning papers.

Source: Furious AI Researcher Creates Site Shaming Non-Reproducible Machine Learning Papers – Slashdot

In a bid to prove that its robot drivers are safer than humans, Waymo simulated dozens of real-world fatal crashes that took place in Arizona over nearly a decade. The Google spinoff discovered that replacing either vehicle in a two-car crash with its robot-guided minivans would nearly eliminate all deaths, according to data it publicized today.

The results are meant to bolster Waymo’s case that autonomous vehicles operate more safely than human-driven ones. With millions of people dying in auto crashes globally every year, AV operators are increasingly leaning on this safety case to spur regulators to pass legislation allowing more fully autonomous vehicles on the road.

But that case has been difficult to prove out, thanks to the very limited number of autonomous vehicles operating on public roads today. To provide more statistical support for its argument, Waymo has turned to counterfactuals, or “what if?” scenarios, meant to showcase how its robot vehicles would react in real-world situations.

Last year, the company published 6.1 million miles of driving data in 2019 and 2020, including 18 crashes and 29 near-miss collisions. In those incidents where its safety operators took control of the vehicle to avoid a crash, Waymo’s engineers simulated what would have happened had the driver not disengaged the vehicle’s self-driving system to generate a counterfactual. The company has also made some of its data available to academic researchers.

That work in counterfactuals continues in this most recent data release. Through a third party, Waymo collected information on every fatal crash that took place in Chandler, Arizona, a suburban community outside Phoenix, between 2008 and 2017. Focusing just on the crashes that took place within its operational design domain, or the approximately 100-square-mile area in which the company permits its cars to drive, Waymo identified 72 crashes to reconstruct in simulation in order to determine how its autonomous system would respond in similar situations.

[…]

The results show that Waymo’s autonomous vehicles would have “avoided or mitigated” 88 out of 91 total simulations, said Trent Victor, director of safety research and best practices at Waymo. Moreover, for the crashes that were mitigated, Waymo’s vehicles would have reduced the likelihood of serious injury by a factor of 1.3 to 15 times, Victor said.

[…]

Source: Waymo simulated real-world crashes to prove its self-driving cars can prevent deaths – The Verge

OK, it’s a good idea, but surely they could have modelled Waymo response on hundreds of thousands of crash scenarios instead of this very tightly controlled tiny subset?

Late last week, a website called Faces of the Riot appeared online, showing nothing but a vast grid of more than 6,000 images of faces, each one tagged only with a string of characters associated with the Parler video in which it appeared. The site’s creator tells WIRED that he used simple, open source machine-learning and facial recognition software to detect, extract, and deduplicate every face from the 827 videos that were posted to Parler from inside and outside the Capitol building on January 6, the day when radicalized Trump supporters stormed the building in a riot that resulted in five people’s deaths. The creator of Faces of the Riot says his goal is to allow anyone to easily sort through the faces pulled from those videos to identify someone they may know, or recognize who took part in the mob, or even to reference the collected faces against FBI wanted posters and send a tip to law enforcement if they spot someone.

[…]

Aside from the clear privacy concerns it raises, Faces of the Riot’s indiscriminate posting of faces doesn’t distinguish between lawbreakers—who trampled barriers, broke into the Capitol building, and trespassed in legislative chambers—and people who merely attended the protests outside. A recent upgrade to the site adds hyperlinks from faces to the video source, so that visitors can click on any face and see what the person was filmed doing on Parler. The Faces of the Riot creator, who says he’s a college student in the “greater DC area,” intends that added feature to help contextualize every face’s inclusion on the site and differentiate between bystanders, peaceful protesters, and violent insurrectionists.

He concedes that he and a co-creator are still working to scrub “non-rioter” faces, including those of police and press who were present. A message at the top of the site also warns against vigilante investigations, instead suggesting users report those they recognize to the FBI, with a link to an FBI tip page.

[…]

Despite its disclaimers and limitations, Faces of the Riot represents the serious privacy dangers of pervasive facial recognition technology, says Evan Greer, the campaign director for digital civil liberties nonprofit Fight for the Future. “Whether it’s used by an individual or by the government, this technology has profound implications for human rights and freedom of expression,” says Greer, whose organization has fought for a legislative ban on facial recognition technologies.

[…]

The site’s developer counters that Faces of the Riot leans not on facial recognition but facial detection. While he did use the open source machine-learning tool TensorFlow and the facial recognition software Dlib to analyze the Parler videos, he says he used that software only to detect and “cluster” faces from the 11 hours of video of the Capitol riot; Dlib allowed him to deduplicate the 200,000 images of faces extracted from video frames to around 6,000 unique faces

[…]

The Faces of the Riot site’s creator initially saw the data as a chance to experiment with machine-learning tools but quickly saw the potential for a more public project. “After about 10 minutes I thought, ‘This is actually a workable idea and I can do something that will help people,'” he says. Faces of the Riot is the first website he’s ever created.

[…]

But McDonald also points out that Faces of the Riot demonstrates just how accessible facial recognition technologies have become. “It shows how this tool that has been restricted only to people who have the most education, the most power, the most privilege is now in this more democratized state,” McDonald says.

The Faces of the Riot site’s creator sees it as more than an art project or demonstration

[…]

Source: This site posted every face from Parler’s Capitol Hill insurrection videos | Ars Technica

Prostate cancer is one of the most common cancers among men. Patients are determined to have prostate cancer primarily based on PSA, a cancer factor in blood. However, as diagnostic accuracy is as low as 30%, a considerable number of patients undergo additional invasive biopsy and thus suffer from resultant side effects, such as bleeding and pain.The Korea Institute of Science and Technology (KIST) announced that the collaborative research team led by Dr. Kwan Hyi Lee from the Biomaterials Research Center and Professor In Gab Jeong from Asan Medical Center developed a technique for diagnosing prostate cancer from urine within only 20 minutes with almost 100% accuracy. The research team developed this technique by introducing a smart AI analysis method to an electrical-signal-based ultrasensitive biosensor.As a noninvasive method, a diagnostic test using urine is convenient for patients and does not need invasive biopsy, thereby diagnosing cancer without side effects. However, as the concentration of cancer factors is low in urine, urine-based biosensors are only used for classifying risk groups rather than for precise diagnosis thus far.

Source: Cancer can be precisely diagnosed using a urine test with artificial intelligence

with DALL-E (a portmanteau of “Wall-E” and “Dali”), an AI app that can create an image out of nearly any description. For example, if you ask for “a cat made of sushi” or a “high quality illustration of a giraffe turtle chimera,” it will deliver those things, often with startlingly good quality (and sometimes not).DALL-E can create images based on a description of its attributes, like “a pentagonal green clock,” or “a collection of glasses is sitting on a table.” In the latter example, it places both drinking and eye glasses on a table with varying degrees of success.

It can also draw and combine multiple objects and provide different points of view, including cutaways and object interiors. Unlike past text-to-image programs, it even infers details that aren’t mentioned in the description but would be required for a realistic image. For instance, with the description “a painting of a fox sitting in a field during winter,” the agent was able to determine that a shadow was needed.

“Unlike a 3D rendering engine, whose inputs must be specified unambiguously and in complete detail, DALL·E is often able to ‘fill in the blanks’ when the caption implies that the image must contain a certain detail that is not explicitly stated,” according to the OpenAI team.

OpenAI also exploits a capability called “zero-shot reasoning.” This allows an agent to generate an answer from a description and cue without any additional training, and has been used for translation and other chores. This time, the researchers applied it to the visual domain to perform both image-to-image and text-to-image translation. In one example, it was able to generate an image of a cat from a sketch, with the cue “the exact same cat on the top as the sketch on the bottom.”

The system has numerous other talents, like understanding how telephones and other objects change over time, grasping geographic facts and landmarks and creating images in photographic, illustration and even clip-art styles.

For now, DALL-E is pretty limited. Sometimes, it delivers what you expect from the description and other times you just get some weird or crappy images. As with other AI systems, even the researchers themselves don’t understand exactly how it produces certain images due to the black box nature of the system.

Still, if developed further, DALL-E has vast potential to disrupt fields like stock photography and illustration, with all the good and bad that entails. “In the future, we plan to analyze how models like DALL·E relate to societal issues like economic impact on certain work processes and professions, the potential for bias in the model outputs, and the longer term ethical challenges implied by this technology,” the team wrote. To play with DALL-E yourself, check out OpenAI’s blog.

Source: ‘DALL-E’ AI generates an image out of anything you describe | Engadget

Artificial intelligence is classifying real supernova explosions without the traditional use of spectra, thanks to a team of astronomers at the Center for Astrophysics | Harvard & Smithsonian. The complete data sets and resulting classifications are publicly available for open use.

By training a machine learning model to categorize supernovae based on their visible characteristics, the astronomers were able to classify real data from the Pan-STARRS1 Medium Deep Survey for 2,315 supernovae with an accuracy rate of 82-percent without the use of spectra.

The astronomers developed a software program that classifies different types of supernovae based on their light curves, or how their brightness changes over time. “We have approximately 2,500 supernovae with light curves from the Pan-STARRS1 Medium Deep Survey, and of those, 500 supernovae with spectra that can be used for classification,” said Griffin Hosseinzadeh, a postdoctoral researcher at the CfA and lead author on the first of two papers published in The Astrophysical Journal. “We trained the classifier using those 500 supernovae to classify the remaining supernovae where we were not able to observe the spectrum.”

Edo Berger, an astronomer at the CfA explained that by asking the artificial intelligence to answer specific questions, the results become increasingly more accurate. “The machine learning looks for a correlation with the original 500 spectroscopic labels. We ask it to compare the supernovae in different categories: color, rate of evolution, or brightness. By feeding it real existing knowledge, it leads to the highest accuracy, between 80- and 90-percent.”

Although this is not the first machine learning project for supernovae classification, it is the first time that astronomers have had access to a real data set large enough to train an artificial intelligence-based supernovae classifier, making it possible to create machine learning algorithms without the use of simulations.

[…]

The project has implications not only for archival data, but also for data that will be collected by future telescopes. The Vera C. Rubin Observatory is expected to go online in 2023, and will lead to the discovery of millions of new supernovae each year. This presents both opportunities and challenges for astrophysicists, where limited telescope time leads to limited spectral classifications.

“When the Rubin Observatory goes online it will increase our discovery rate of supernovae by 100-fold, but our spectroscopic resources will not increase,” said Ashley Villar, a Simons Junior Fellow at Columbia University and lead author on the second of the two papers, adding that while roughly 10,000 supernovae are currently discovered each year, scientists only take spectra of about 10-percent of those objects. “If this holds true, it means that only 0.1-percent of supernovae discovered by the Rubin Observatory each year will get a spectroscopic label. The remaining 99.9-percent of data will be unusable without methods like ours.”

Unlike past efforts, where data sets and classifications have been available to only a limited number of astronomers, the data sets from the new machine learning algorithm will be made publicly available. The astronomers have created easy-to-use, accessible software, and also released all of the data from Pan-STARRS1 Medium Deep Survey along with the new classifications for use in other projects. Hosseinzadeh said, “It was really important to us that these projects be useful for the entire supernova community, not just for our group. There are so many projects that can be done with these data that we could never do them all ourselves.” Berger added, “These projects are open data for open science.”

Source: Artificial intelligence classifies supernova explosions with unprecedented accuracy

Loon, known for its giant billowing broadband-beaming balloons, says it has figured out how to use machine-learning algorithms to keep its lofty vehicles hovering in place autonomously in the stratosphere.

The 15-metre-wide balloons relay internet connections between people’s homes and ground stations that could be thousands of kilometres apart. To form a steady network that can route data over long distances reliably, the balloons have to stay in place, and do so all by themselves.

Loon’s AI-based solution to this station-keeping problem has been described in a research paper published in Nature on Wednesday, and basically it works by adjusting the balloons’ altitude to catch the right wind currents to ensure they are where they need to be.

The machine-learning software, we’re told, managed to successfully keep the Loon gas bags bobbing up and down in the skies above in the Pacific Ocean in an experiment that lasted 39 days. Previously, the Loon team used a non-AI controller that used a handcrafted algorithm known as StationSeeker to do the job, though decided to experiment to see whether it could find a more efficient method using machine learning.

“As far as we know, this is the world’s first deployment of reinforcement learning in a production aerospace system,” said Loon CTO Salvatore Candido.

The AI is built out of a feed-forward neural network that learns to decide whether a balloon should fly up or go down by taking into account variables, such as wind speed, solar elevation, and how much power the equipment has left. The decision is then fed to a controller system to move the balloon in place.

By training the model in simulation, the neural network steadily improved over time using reinforcement learning as it repeated the same task over and over again under different scenarios. Loon tested the performance of StationSeeker against the reinforcement learning model in simulation.

“A trial consists of two simulated days of station-keeping at a fixed location, during which controllers receive inputs and emit commands at 3-min intervals,” according to the paper. The performance was then judged by how long the balloons could stay within a 50km radius of a hypothetical ground station.

The AI algorithm scored 55.1 per cent efficiency, compared to 40.5 per cent for StationSeeker. The researchers reckon that the autonomous algorithm is near optimum performance, considering that the best theoretical models reach somewhere between 56.8 to 68.7 per cent.

When Loon and Google ran the controller in the real experiment, which involved a balloon hovering above the Pacific Ocean, they found: “Overall, the [reinforcement learning] system kept balloons in range of the desired location more often while using less power… Using less power to steer the balloon means more power is available to connect people to the internet, information, and other people.”

[…]

Source: Alphabet’s internet Loon balloon kept on station in the sky using AI that beat human-developed control code • The Register

Researchers have made a major breakthrough using artificial intelligence that could revolutionize the hunt for new medicines.

The scientists have created A.I. software that uses a protein’s DNA sequence to predict its three-dimensional structure to within an atom’s width of accuracy.

The achievement, which solves a 50-year-old challenge in molecular biology, was accomplished by a team from DeepMind, the London-based artificial intelligence company that is part of Google parent Alphabet.

[…]

Across more than 100 proteins, DeepMind’s A.I. software, which it called AlphaFold 2, was able to predict the structure to within about an atom’s width of accuracy in two-thirds of cases and was highly accurate in most of the remaining one-third of cases, according to John Moult, a molecular biologist at the University of Maryland who is director of the competition, called the Critical Assessment of Structure Prediction, or CASP. It was far better than any other method in the competition, he said.

[…]

DeepMind had not yet determined how it would provide academic researchers with access to the protein structure prediction software or whether it would seek commercial collaborations with pharmaceutical and biotechnology firms. He said the company would announce “further details on how we’re going to be able to give access to the system in a scalable way” sometime next year.

“This computational work represents a stunning advance on the protein-folding problem,” Venki Ramakrishnan, a Nobel Prize–winning structural biologist who is also the outgoing president of the Royal Society, Britain’s most prestigious scientific body, said of AlphaFold 2.

Janet Thornton, an expert in protein structure and former director of the European Molecular Biology Laboratory’s European Bioinformatics Institute, said that DeepMind’s breakthrough opened up the way to mapping the entire “human proteome”—the set of all proteins found within the human body. Currently, only about a quarter of human proteins have been used as targets for medicines, she said. Now, many more proteins could be targeted, creating a huge opportunity to invent new medicines.

[…]

As part of CASP’s efforts to verify the capabilities of DeepMind’s system, Lupas used the predictions from AlphaFold 2 to see if it could solve the final portion of a protein’s structure that he had been unable to complete using X-ray crystallography for more than a decade. With the predictions generated by AlphaFold 2, Lupas said he was able to determine the shape of the final protein segment in just half an hour.

AlphaFold 2 has also already been used to accurately predict the structure of a protein called ORF3a that is found in SARS-CoV-2, the virus that causes COVID-19, which scientists might be able to use as a target for future treatments.

Lupas said he thought the A.I. software would “change the game entirely” for those who work on proteins. Currently, DNA sequences are known for about 200 million proteins, and tens of millions more are being discovered every year. But 3D structures have been mapped for less than 200,000 of them.

AlphaFold 2 was only trained to predict the structure of single proteins. But in nature, proteins are often present in complex arrangements with other proteins. Jumper said the next step was to develop an A.I. system that could predict complicated dynamics between proteins—such as how two proteins will bind to one another or the way that proteins in close proximity morph one another’s shapes.

[…]

Source: DeepMind’s A.I. can now predict protein shapes from their DNA sequences | Fortune

one group of researchers has been focused on what autonomous driving systems might see that a human driver doesn’t—including “phantom” objects and signs that aren’t really there, which could wreak havoc on the road.

Researchers at Israel’s Ben Gurion University of the Negev have spent the last two years experimenting with those “phantom” images to trick semi-autonomous driving systems. They previously revealed that they could use split-second light projections on roads to successfully trick Tesla’s driver-assistance systems into automatically stopping without warning when its camera sees spoofed images of road signs or pedestrians. In new research, they’ve found they can pull off the same trick with just a few frames of a road sign injected on a billboard’s video

[…]

“The driver won’t even notice at all. So somebody’s car will just react, and they won’t understand why.”

In their first round of research, published earlier this year, the team projected images of human figures onto a road, as well as road signs onto trees and other surfaces. They found that at night, when the projections were visible, they could fool both a Tesla Model X running the HW2.5 Autopilot driver-assistance system—the most recent version available at the time, now the second-most-recent —and a Mobileye 630 device. They managed to make a Tesla stop for a phantom pedestrian that appeared for a fraction of a second, and tricked the Mobileye device into communicating the incorrect speed limit to the driver with a projected road sign.

In this latest set of experiments, the researchers injected frames of a phantom stop sign on digital billboards, simulating what they describe as a scenario in which someone hacked into a roadside billboard to alter its video. They also upgraded to Tesla’s most recent version of Autopilot known as HW3.

[…]

an image that appeared for 0.42 seconds would reliably trick the Tesla, while one that appeared for just an eighth of a second would fool the Mobileye device. They also experimented with finding spots in a video frame that would attract the least notice from a human eye, going so far as to develop their own algorithm for identifying key blocks of pixels in an image so that a half-second phantom road sign could be slipped into the “uninteresting” portions.

[…]

Source: Split-Second ‘Phantom’ Images Can Fool Tesla’s Autopilot | WIRED

Researchers at Google claim to have developed a machine learning model that can separate a sound source from noisy, single-channel audio based on only a short sample of the target source. In a paper, they say their SoundFilter system can be tuned to filter arbitrary sound sources, even those it hasn’t seen during training.

The researchers believe a noise-eliminating system like SoundFilter could be used to create a range of useful technologies. For instance, Google drew on audio from thousands of its own meetings and YouTube videos to train the noise-canceling algorithm in Google Meet. Meanwhile, a team of Carnegie Mellon researchers created a “sound-action-vision” corpus to anticipate where objects will move when subjected to physical force.

SoundFilter treats the task of sound separation as a one-shot learning problem. The model receives as input the audio mixture to be filtered and a single short example of the kind of sound to be filtered out. Once trained, SoundFilter is expected to extract this kind of sound from the mixture if present.

[…]

Source: Google’s SoundFilter AI separates any sound or voice from mixed-audio recordings | VentureBeat

Training OpenAI’s giant GPT-3 text-generating model is akin to driving a car to the Moon and back, computer scientists reckon.

More specifically, they estimated teaching the neural super-network in a Microsoft data center using Nvidia GPUs required roughly 190,000 kWh, which using the average carbon intensity of America would have produced 85,000 kg of CO₂ equivalents, the same amount produced by a new car in Europe driving 700,000 km, or 435,000 miles, which is about twice the distance between Earth and the Moon, some 480,000 miles. Phew.

This assumes the data-center used to train GPT-3 was fully reliant on fossil fuels, which may not be true. The point, from what we can tell, is not that GPT-3 and its Azure cloud in particular have this exact scale of carbon footprint, it’s to draw attention to the large amount of energy required to train state-of-the-art neural networks.

The eggheads who produced this guesstimate are based at the University of Copenhagen in Denmark, and are also behind an open-source tool called Carbontracker, which aims to predict the carbon footprint of AI algorithms. Lasse Wolff Anthony, one of Carbontracker’s creators and co-author of a study of the subject of AI power usage, believes this drain on resources is something the community should start thinking about now, as the energy costs of AI have risen 300,000-fold between 2012 and 2018, it is claimed.

[…]

Source: AI me to the Moon… Carbon footprint for ‘training GPT-3’ same as driving to our natural satellite and back • The Register

Unless you’re a physicist or an engineer, there really isn’t much reason for you to know about partial differential equations. I know. After years of poring over them in undergrad while studying mechanical engineering, I’ve never used them since in the real world.

But partial differential equations, or PDEs, are also kind of magical. They’re a category of math equations that are really good at describing change over space and time, and thus very handy for describing the physical phenomena in our universe. They can be used to model everything from planetary orbits to plate tectonics to the air turbulence that disturbs a flight, which in turn allows us to do practical things like predict seismic activity and design safe planes.

The catch is PDEs are notoriously hard to solve. And here, the meaning of “solve” is perhaps best illustrated by an example. Say you are trying to simulate air turbulence to test a new plane design. There is a known PDE called Navier-Stokes that is used to describe the motion of any fluid. “Solving” Navier-Stokes allows you to take a snapshot of the air’s motion (a.k.a. wind conditions) at any point in time and model how it will continue to move, or how it was moving before.

These calculations are highly complex and computationally intensive, which is why disciplines that use a lot of PDEs often rely on supercomputers to do the math. It’s also why the AI field has taken a special interest in these equations. If we could use deep learning to speed up the process of solving them, it could do a whole lot of good for scientific inquiry and engineering.

Now researchers at Caltech have introduced a new deep-learning technique for solving PDEs that is dramatically more accurate than deep-learning methods developed previously. It’s also much more generalizable, capable of solving entire families of PDEs—such as the Navier-Stokes equation for any type of fluid—without needing retraining. Finally, it is 1,000 times faster than traditional mathematical formulas, which would ease our reliance on supercomputers and increase our computational capacity to model even bigger problems. That’s right. Bring it on.

Hammer time

Before we dive into how the researchers did this, let’s first appreciate the results. In the gif below, you can see an impressive demonstration. The first column shows two snapshots of a fluid’s motion; the second shows how the fluid continued to move in real life; and the third shows how the neural network predicted the fluid would move. It basically looks identical to the second.

The paper has gotten a lot of buzz on Twitter, and even a shout-out from rapper MC Hammer. Yes, really.

Fourier Neural Operator for Parametric Partial Differential Equations #Hamm400aos https://t.co/ABYRwadcT7

— MC HAMMER (@MCHammer) October 22, 2020

[…]

Neural networks are usually trained to approximate functions between inputs and outputs defined in Euclidean space, your classic graph with x, y, and z axes. But this time, the researchers decided to define the inputs and outputs in Fourier space, which is a special type of graph for plotting wave frequencies. The intuition that they drew upon from work in other fields is that something like the motion of air can actually be described as a combination of wave frequencies, says Anima Anandkumar, a Caltech professor who oversaw the research alongside her colleagues, professors Andrew Stuart and Kaushik Bhattacharya. The general direction of the wind at a macro level is like a low frequency with very long, lethargic waves, while the little eddies that form at the micro level are like high frequencies with very short and rapid ones.

Why does this matter? Because it’s far easier to approximate a Fourier function in Fourier space than to wrangle with PDEs in Euclidean space, which greatly simplifies the neural network’s job. Cue major accuracy and efficiency gains: in addition to its huge speed advantage over traditional methods, their technique achieves a 30% lower error rate when solving Navier-Stokes than previous deep-learning methods.

[…]

Source: AI has cracked a key mathematical puzzle for understanding our world | MIT Technology Review

We’re delighted to announce you can now take advantage of graph-native machine learning (ML) inside of Neo4j! We’ve just released a preview of Neo4j’s Graph Data Science™ Library version 1.4, which includes graph embeddings and an ML model catalog.

Together, these enable you to create representations of your graph and make graph predictions – all within Neo4j.

[…]

Graph Embeddings

The graph embedding algorithms are the star of the show in this release.

These algorithms are used to transform the topology and features of your graph into fixed-length vectors (or embeddings) that uniquely represent each node.

Graph embeddings are powerful, because they preserve the key features of the graph while reducing dimensionality in a way that can be decoded. This means you can capture the complexity and structure of your graph and transform it for use in various ML predictions.

 

Graph embeddings capture the nuances of graphs in a way that can be used to make predictions or lower dimensional visualizations.

In this release, we are offering three embedding options that learn the graph topology and, in some cases, node properties to calculate more accurate representations:

Node2Vec:

• • This is probably the most well-known graph embedding algorithm. It uses random walks to sample a graph, and a neural network to learn the best representation of each node.

FastRP:

• • A more recent graph embedding algorithm that uses linear algebra to project a graph into lower dimensional space. In GDS 1.4, we’ve extended the original implementation to support node features and directionality as well.
  • FastRP is up to 75,000 times faster than Node2Vec, while providing equivalent accuracy!

GraphSAGE:

• • This is an embedding technique using inductive representation learning on graphs, via graph convolutional neural networks, where the graph is sampled to learn a function that can predict embeddings (rather than learning embeddings directly). This means you can learn on a subset of your graph and use that representative function for new data and make continuous predictions as your graph updates. (Wow!)
  • If you’d like a deeper dive into how it works, check out the GraphSAGE session from the NODES event.

 

 

Graph embeddings available in the Neo4j Graph Data Science Library v1.4 . The caution marks indicate that, while directions are supported, our internal benchmarks don’t show performance improvements.

Graph ML Model Catalog

GraphSAGE trains a model to predict node embeddings for unseen parts of the graph, or new data as mentioned above.

To really capitalize on what GraphSAGE can do, we needed to add a catalog to be able to store and reference these predictive models. This model catalog lives in the Neo4j analytics workspace and contains versioning information (what data was this trained on?), time stamps and, of course, the model names.

When you want to use a model, you can provide the name of the model to GraphSAGE, along with the named graph you want to apply it to.

 

GraphSAGE ML Models are stored in the Neo4j analytics workspace.

[…]

Source: Announcing: Graph-Native Machine Learning in Neo4j!