Nvidia announced that AI models can now draw new worlds without using traditional modeling techniques or graphics rendering engines. This new technology uses an AI deep neural network to analyze existing videos and then apply the visual elements to new 3D environments.

Nvidia claims this new technology could provide a revolutionary step forward in creating 3D worlds because the AI models are trained from video to automatically render buildings, trees, vehicles, and objects into new 3D worlds, instead of requiring the normal painstaking process of modeling the scene elements.

But the project is still a work in progress. As we can see from the image on the right, which was generated in real time on a Nvidia Titan V graphics card using its Tensor cores, the rendered scene isn’t as crisp as we would expect in real life, and it isn’t as clear as we would expect with a normal modeled scene in a 3D environment. However, the result is much more impressive when we see the real-time output in the YouTube video below. The key here is speed: The AI generates these scenes in real time.

Nvidia AI Rendering

Nvidia’s researchers have also used this technique to model other motions, such as dance moves, and then apply those same moves to other characters in real-time video. That does raise moral questions, especially given the proliferation of altered videos like deep fakes, but Nvidia feels that it is an enabler of technology and the issue should be treated as a security problem that requires a technological solution to prevent people from rendering things that aren’t real.

The big question is when this will come to the gaming realm, but Nvidia cautions that this isn’t a shipping product yet. The company did theorize that it would be useful for enhancing older games by analyzing the scenes and then applying trained models to improve the graphics, among many other potential uses. It could also be used to create new levels and content in older games. In time, the company expects the technology to spread and become another tool in the game developers’ toolbox. The company has open sourced the project, so anyone can download and begin using it today, though it is currently geared towards AI researchers.

Nvidia says this type of AI analysis and scene generation can occur with any type of processor, provided it can deliver enough AI throughput to manage the real-time feed. The company expects that performance and image quality will improve over time.

Nvidia sees this technique eventually taking hold in gaming, automotive, robotics, and virtual reality, but it isn’t committing to a timeline for an actual product. The work remains in the lab for now, but the company expects game developers to begin working with the technology in the future. Nvidia is also conducting a real-time demo of AI-generated worlds at the AI research-focused NeurIPS conference this week.

Source: Nvidia Uses AI to Render Virtual Worlds in Real Time

Machine learning is everywhere now, including recruiting. Take CV Compiler, a new product by Andrew Stetsenko and Alexandra Dosii. This web app uses machine learning to analyze and repair your technical resume, allowing you to shine to recruiters at Google, Yahoo and Facebook.

The founders are marketing and HR experts who have a combined 15 years of experience in making recruiting smarter. Stetsenko founded Relocate.me and GlossaryTech while Dosii worked at a number of marketing firms before settling on CV Compiler.

The app essentially checks your resume and tells you what to fix and where to submit it. It’s been completely bootstrapped thus far and they’re working on new and improved machine learning algorithms while maintaining a library of common CV fixes.

“There are lots of online resume analysis tools, but these services are too generic, meaning they can be used by multiple professionals and the results are poor and very general. After the feedback is received, users are often forced to buy some extra services,” said Stetsenko. “In contrast, the CV Compiler is designed exclusively for tech professionals. The online review technology scans for keywords from the world of programming and how they are used in the resume, relative to the best practices in the industry.”

Source: CV Compiler is a robot that fixes your resume to make you more competitive | TechCrunch

Earlier this year, researchers tried teaching an AI to play the original Sonic the Hedgehog as part of the The OpenAI Retro Contest. The AI was told to prioritize increasing its score, which in Sonic means doing stuff like defeating enemies and collecting rings while also trying to beat a level as fast as possible. This dogged pursuit of one particular definition of success led to strange results: In one case, the AI began glitching through walls in the game’s water zones in order to finish more quickly.

It was a creative solution to the problem laid out in front of the AI, which ended up discovering accidental shortcuts while trying to move right. But it wasn’t quite what the researchers had intended. One of researchers’ goals with machine-learning AIs in gaming is to try and emulate player behavior by feeding them large amounts of player generated data. In effect, the AI watches humans conduct an activity, like playing through a Sonic level, and then tries to do the same, while being able to incorporate its own attempts into its learning. In a lot of instances, machine learning AIs end up taking their directions literally. Instead of completing a variety of objectives, a machine-learning AI might try to take shortcuts that completely upend human beings’ understanding of how a game should be played.

Victoria Krakovna, a researcher on Google’s DeepMind AI project, has spent the last several months collecting examples like the Sonic one. Her growing collection has recently drawn new attention after being shared on Twitter by Jim Crawford, developer of the puzzle series Frog Fractions, among other developers and journalists. Each example includes what she calls “reinforcement learning agents hacking the reward function,” which results in part from unclear directions on the part of the programmers.

“While ‘specification gaming’ is a somewhat vague category, it is particularly referring to behaviors that are clearly hacks, not just suboptimal solutions,” she wrote in her initial blog post on the subject. “A classic example is OpenAI’s demo of a reinforcement learning agent in a boat racing game going in circles and repeatedly hitting the same reward targets instead of actually playing the game.”

Source: AIs Are Getting Better At Playing Video Games…By Cheating

The New York Times doesn’t keep bodies in its “morgue” — it keeps pictures. In a basement under its Times Square office, stuffed into cabinets and drawers, the Times stores between 5 million and 7 million images, along with information about when they were published and why. Now, the paper is working with Google to digitize its huge collection.

The morgue (as the basement storage area is known) contains pictures going back to the 19th century, many of which exist nowhere else in the world. “[It’s] a treasure trove of perishable documents,” says the NYT’s chief technology officer Nick Rockwell. “A priceless chronicle of not just The Times’s history, but of nearly more than a century of global events that have shaped our modern world.”

That’s why the company has hired Google, which will use its machine vision smarts to not only scan the hand- and type-written notes attached to each image, but categorize the semantic information they contain (linking data like locations and dates). Google says the Times will also be able to use its object recognition tools to extract even more information from the photos, making them easier to catalog and resurface for future use.

Source: Google is using AI to help The New York Times digitize 5 million historical photos – The Verge

Source: Welcome to Spinning Up in Deep RL! — Spinning Up documentation

Timely diagnosis of Alzheimer’s disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

[…]

The researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance,” Dr. Sohn said. “It was able to predict every single case that advanced to Alzheimer’s disease.”

Source: Artificial intelligence predicts Alzheimer’s years before diagnosis — ScienceDaily

Each day, Brenda leaves her home here to catch a bus to the east side of Nairobi where she, along with more than 1,000 colleagues in the same building, work hard on a side of artificial intelligence we hear little about – and see even less.

In her eight-hour shift, she creates training data. Information – images, most often – prepared in a way that computers can understand.

Brenda loads up an image, and then uses the mouse to trace around just about everything. People, cars, road signs, lane markings – even the sky, specifying whether it’s cloudy or bright. Ingesting millions of these images into an artificial intelligence system means a self-driving car, to use one example, can begin to “recognise” those objects in the real world. The more data, the supposedly smarter the machine.

She and her colleagues sit close – often too close – to their monitors, zooming in on the images to make sure not a single pixel is tagged incorrectly. Their work will be checked by a superior, who will send it back if it’s not up to scratch. For the fastest, most accurate trainers, the honour of having your name up on one of the many TV screens around the office. And the most popular perk of all: shopping vouchers.

[…]

Brenda does this work for Samasource, a San Francisco-based company that counts Google, Microsoft, Salesforce and Yahoo among its clients. Most of these firms don’t like to discuss the exact nature of their work with Samasource – as it is often for future projects – but it can be said that the information prepared here forms a crucial part of some of Silicon Valley’s biggest and most famous efforts in AI.

[…]

f you didn’t look out of the windows, you might think you were at a Silicon Valley tech firm. Walls are covered in corrugated iron in a way that would be considered achingly trendy in California, but here serve as a reminder of the environment many of the workers come from: around 75% are from the slum.

Most impressively, Samasource has overcome a problem that most Silicon Valley firms are famously grappling with. Just over half of their workforce is made up of women, a remarkable feat in a country where starting a family more often than not rules out a career for the mother. Here, a lactation room, up to 90 days maternity leave, and flexibility around shift patterns makes the firm a stand-out example of inclusivity not just in Kenya, but globally.

“Like a lot of people say, if you have a man in the workplace, he’ll support his family,” said Hellen Savala, who runs human resources.

“[But] if you have a woman in the workplace, she’ll support her family, and the extended family. So you’ll have a lot more impact.”

Source: Why Big Tech pays poor Kenyans to teach self-driving cars – BBC News

The ability to put our clothes on each day is something most of us take for granted, but as computer scientists from Georgia Institute of Technology recently found out, it’s a surprisingly complicated task—even for artificial intelligence.

As any toddler will gladly tell you, it’s not easy to dress oneself. It requires patience, physical dexterity, bodily awareness, and knowledge of where our body parts are supposed to go inside of clothing. Dressing can be a frustrating ordeal for young children, but with enough persistence, encouragement, and practice, it’s something most of us eventually learn to master.

As new research shows, the same learning strategy used by toddlers also applies to artificially intelligent computer characters. Using an AI technique known as reinforcement learning—the digital equivalent of parental encouragement—a team led by Alexander W. Clegg, a computer science PhD student at the Georgia Institute of Technology, taught animated bots to dress themselves. In tests, their animated bots could put on virtual t-shirts and jackets, or be partially dressed by a virtual assistant. Eventually, the system could help develop more realistic computer animation, or more practically, physical robotic systems capable of dressing individuals who struggle to do it themselves, such as people with disabilities or illnesses.

Putting clothes on, as Clegg and his colleagues point out in their new study, is a multifaceted process.

“We put our head and arms into a shirt or pull on pants without a thought to the complex nature of our interactions with the clothing,” the authors write in the study, the details of which will be presented at the SIGGRAPH Asia 2018 conference on computer graphics in December. “We may use one hand to hold a shirt open, reach our second hand into the sleeve, push our arm through the sleeve, and then reverse the roles of the hands to pull on the second sleeve. All the while, we are taking care to avoid getting our hand caught in the garment or tearing the clothing, often guided by our sense of touch.”

Computer animators are fully aware of these challenges, and often struggle to create realistic portrayals of characters putting their clothes on. To help in this regard, Clegg’s team turned to reinforcement learning—a technique that’s already being used to teach bots complex motor skills from scratch. With reinforcement learning, systems are motivated toward a designated goal by gaining points for desirable behaviors and losing points for counterproductive behaviors. It’s a trial-and-error process—but with cheers or boos guiding the system along as it learns effective “policies” or strategies for completing a goal.

Source: These Animated AI Bots Learned to Dress Themselves, Awkwardly

De initiatiefnemers van AINED ontwikkelen met ondersteuning van de Boston Consulting Group (BCG) en DenkWerk een Nationale Strategie Artificial Intelligence (AI) voor Nederland, geïnitieerd door het Ministerie van Economische Zaken en Klimaat.

Het projectteam doet dit vanuit de overtuiging dat in het Nederlandse landschap de mogelijkheden van AI nog te weinig worden benut, en de randvoorwaarden van AI nog niet voldoende worden meegenomen in de ontwikkeling en toepassing van AI. In een wereld waarin andere landen dit wel doen en de techniek steeds waardevoller en krachtiger wordt, is het van groot belang voor Nederland om nu óók in te zetten op AI.

Voor de ontwikkeling van deze strategie is AINED opgestart: een samenwerking tussen het TopTeam ICT, VNO-NCW, ICAI, NWO en TNO, ondersteund door The Boston Consulting Group en DenkWerk.

AI ontwikkelt zich snel en heeft een grote belofte van innovatie en vooruitgang in zich. Het doel van de nationale strategie is de ontwikkeling en toepassing van moderne, data-gedreven AI-technologie in Nederland te versnellen, gericht op kansen voor Nederland en mét inachtneming van juridische, ethische en sociale randvoorwaarden. Als vertrekpunt voor de strategie met doelen en acties, wordt een landschapsschets opgesteld met initiatieven en wensen voor AI in het bedrijfsleven, de overheid, de wetenschap, het onderwijs en de non-gouvernementele sector.

De ontwikkeling van het voorstel loopt tot begin oktober. In een volgende fase zullen de initiatiefnemers van AINED met een brede groep aan stakeholders de doelen en acties verder uitwerken en afstemmen hoe zij kunnen bijdragen hieraan. Naast het Ministerie van Economische Zaken en Klimaat, de Ministeries van Onderwijs, Cultuur en Wetenschap, Defensie en Binnenlandse Zaken, zal een belangrijke rol weggelegd zijn voor het bedrijfsleven. In deze volgende fase zullen ook commitments op de doelen en acties behaald worden. Voor interesse om bij te dragen, neem contact op met Daniël Frijters, secretaris AINED op info@ained.nl

Source: dutch digital delta

Machine learning is, at heart, the art of finding patterns in data. That’s why businesses love it. Patterns help predict the future, and predicting the future is a great way to make money. It’s sometimes unclear how these things fit together, but here’s a perfect example from film studio 20th Century Fox, which is using AI to predict what films people will want to see.

Researchers from the company published a paper last month explaining how they’re analyzing the content of movie trailers using machine learning. Machine vision systems examine trailer footage frame by frame, labeling objects and events, and then compare this to data generated for other trailers. The idea is that movies with similar sets of labels will attract similar sets of people.

As the researchers explain in the paper, this is exactly the sort of data movie studios love. (They already produce lots of similar data using traditional methods like interviews and questionnaires.) “Understanding detailed audience composition is important for movie studios that invest in stories of uncertain commercial,” they write. In other words, if they know who watches what, they will know what movies to make.

It’s even better if this audience composition can be broken down into smaller and more accurate “micro segments.” A good example of this is 2017’s Logan. It’s a superhero movie, yes, but it has darker themes and a plot that attracts a slightly different audience. So can AI be used to capture those differences? The answer is: sort of.

To create their “experimental movie attendance prediction and recommendation system” (named Merlin), 20th Century Fox partnered with Google to use the company’s servers and open-source AI framework TensorFlow. In an accompanying blog post, the search giant explains Merlin’s analysis of Logan.

First, Merlin scans the trailer, labeling objects like “facial hair,” “car,” and “forest”:

While this graph only records the frequency of these labels, the actual data generated is more complex, taking into account how long these objects appear on-screen and when they show up the trailer.

As 20th Century Fox’s engineers explain, this temporal information is particularly rich because it correlates with a film’s genre. “For example,” they write, “a trailer with a long close-up shot of a character is more likely for a drama movie than for an action movie, whereas a trailer with quick but frequent shots is more likely for an action movie.” This definitely holds true for Logan, with its trailer featuring lots of slow shots of Hugh Jackman looking bloody and beaten.

By comparing this information with analyses of other trailers, Merlin can try to predict what films might interest the people who saw Logan. But here’s where things get a little dicey.

The graph below shows the top 20 films that people who went to see Logan also watched. The column on the right shows Merlin’s predictions, and the column on the left shows the actual data (collected, one assumes, by using that pre-AI method of “asking people”).

Merlin gets quite a few of the films correct, including other superhero movies like X Men: Apocalypse, Doctor Strange, and Batman v Superman: Dawn of Justice. It even correctly identifies John Wick: Chapter 2 as a bedfellow of Logan. That’s an impressive intuition since John Wick is certainly not a superhero movie. However, it does feature a similarly weary and jaded protagonist with a comparably rugged look. All in all, Merlin identifies all of the top five picks, even if it does fail to put them in the same order of importance.

What’s more revealing, though, are the mismatches. Merlin predicts that The Legend of Tarzan will be a big hit with Logan fans for example. Neither Google nor 20th Century Fox offers an explanation for this, but it could it have something to do with the “forest,” “tree,” and “light” found in Logan — elements which also feature heavily in the Tarzan trailer.

Similarly, The Revenant has plenty of flora and facial hair, but it was drama-heavy Oscar bait rather than a smart superhero movie. Merlin also misses Ant-Man and Deadpool 2 as lure’s for the same audience. These were superhero films with quick-cut trailers, but they also took offbeat approaches to their protagonists, similar to Wolverine’s treatment in Logan.

Source: 20th Century Fox is using AI to analyze movie trailers and find out what films audiences will like – The Verge

Horizon is an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook. Horizon is built in Python and uses PyTorch for modeling and training and Caffe2 for model serving. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, and optimized serving. For more detailed information about Horizon see the white paper here.

Algorithms Supported

• Discrete-Action DQN
• Parametric-Action DQN
• Double DQN, Dueling DQN, Dueling Double DQN
• DDPG (DDPG)
• Soft Actor-Critic (SAC)

If you’re a researcher looking to deepen your exposure to AI, NVIDIA invites you to apply to its new AI Research Residency program.

During the one-year, paid program, residents will be paired with an NVIDIA research scientist on a joint project and have the opportunity to publish and present their findings at prominent research conferences such as CVPR, ICLR and ICML.

The residency program is meant to encourage scholars with diverse academic backgrounds to pursue machine learning research, according to Jan Kautz, vice president of perception and learning research at NVIDIA.

“There’s currently a shortage of machine learning experts, and AI adoption for non-tech and smaller companies is hindered in part because there are not many people who understand AI,” said Kautz. “Our residency program is a way to broaden opportunities in the field to a more diverse set of researchers and spread the benefits of the technology to more people than ever.”

Applicants don’t need a background in AI, and those with doctoral degrees or equivalent expertise are encouraged to apply. Residents will work out of our Santa Clara location.

Source: NVIDIA Launches Year-Long Research Residency Program | The Official NVIDIA Blog

Google.org is issuing an open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges. Selected organizations will receive support from Google’s AI experts, Google.org grant funding from a $25M pool, credit and consulting from Google Cloud, and more.

Source: Impact Challenge – Google AI

Deep learning has a DRAM problem. Systems designed to do difficult things in real time, such as telling a cat from a kid in a car’s backup camera video stream, are continuously shuttling the data that makes up the neural network’s guts from memory to the processor.

The problem, according to startup Flex Logix, isn’t a lack of storage for that data; it’s a lack of bandwidth between the processor and memory. Some systems need four or even eight DRAM chips to sling the 100s of gigabits to the processor, which adds a lot of space and consumes considerable power. Flex Logix says that the interconnect technology and tile-based architecture it developed for reconfigurable chips will lead to AI systems that need the bandwidth of only a single DRAM chip and consume one-tenth the power.

[…]

In developing the original technology for FPGAs, Wang noted that these chips were about 80 percent interconnect by area, and so he sought an architecture that would cut that area down and allow for more logic. He and his colleagues at UCLA adapted a kind of telecommunications architecture called a folded-Beneš network to do the job. This allowed for an FPGA architecture that looks like a bunch of tiles of logic and SRAM.

Distributing the SRAM in this specialized interconnect scheme winds up having a big impact on deep learning’s DRAM bandwidth problem, says Tate. “We’re displacing DRAM bandwidth with SRAM on the chip,” he says.

[…]

True apples-to-apples comparisons in deep learning are hard to come by. But Flex Logix’s analysis comparing a simulated 6 x 6-tile NMAX512 array with one DRAM chip against an Nvidia Tesla T4 with eight DRAMs showed the new architecture identifying 4,600 images per second versus Nvidia’s 3,920. The same size NMAX array hit 22 trillion operations per second on a real-time video processing test called YOLOv3 using one-tenth the DRAM bandwidth of other systems.

The designs for the first NMAX chips will be sent to the foundry for manufacture in the second half of 2019, says Tate.

Source: Flex Logix Says It’s Solved Deep Learning’s DRAM Problem – IEEE Spectrum

In the future, you might talk to an AI to cross borders in the European Union. The EU and Hungary’s National Police will run a six-month pilot project, iBorderCtrl, that will help screen travelers in Hungary, Greece and Latvia. The system will have you upload photos of your passport, visa and proof of funds, and then use a webcam to answer basic questions from a personalized AI border agent. The virtual officer will use AI to detect the facial microexpressions that can reveal when someone is lying. At the border, human agents will use that info to determine what to do next — if there are signs of lying or a photo mismatch, they’ll perform a more stringent check.

The real guards will use handhelds to automatically double-check documents and photos for these riskier visitors (including images from past crossings), and they’ll only take over once these travelers have gone through biometric verification (including face matching, fingerprinting and palm vein scans) and a re-evaluation of their risk levels. Anyone who passed the pre-border test, meanwhile, will skip all but a basic re-evaluation and having to present a QR code.

The pilot won’t start with live tests. Instead, it’ll begin with lab tests and will move on to “realistic conditions” along the borders. And there’s a good reason for this: the technology is very much experimental. iBorderCtrl was just 76 percent accurate in early testing, and the team only expects to improve that to 85 percent. There are no plans to prevent people from crossing the border if they fail the initial AI screening.

Source: Experimental AI lie detector will help screen EU travelers

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space.

The result is an AI bot named ClaRAN that scans images taken by radio telescopes.

Its job is to spot radio galaxies—galaxies that emit powerful radio jets from supermassive black holes at their centres.

ClaRAN is the brainchild of big data specialist Dr. Chen Wu and astronomer Dr. Ivy Wong, both from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR).

Dr. Wong said black holes are found at the centre of most, if not all, galaxies.

“These supermassive black holes occasionally burp out jets that can be seen with a radio telescope,” she said.

“Over time, the jets can stretch a long way from their host galaxies, making it difficult for traditional computer programs to figure out where the galaxy is.

“That’s what we’re trying to teach ClaRAN to do.”

Dr. Wu said ClaRAN grew out of an open source version of Microsoft and Facebook’s object detection software.

He said the program was completely overhauled and trained to recognise galaxies instead of people.

ClaRAN itself is also open source and publicly available on GitHub.

Read more at: https://phys.org/news/2018-10-artificial-intelligence-bot-galaxies.html#jCp

Source: Artificial intelligence bot trained to recognize galaxies

AI can help chemists crack the molecular structure of crystals much faster than traditional modelling methods, according to research published in Nature Communications on Monday.

Scientists from the Ecole Polytechnique Fédérale de Lausanne (EPFL), a research institute in Switzerland, have built a machine learning programme called SwiftML to predict how the atoms in molecules shift when exposed to a magnetic field.

Nuclear magnetic resonance (NMR) is commonly used to work out the structure of compounds. Groups of atoms oscillate at a specific frequencies, providing a tell-tale sign of the number and location of electrons each contains. But the technique is not good enough to reveal the full chemical structure of molecules, especially complex ones that can contain thousands of different atoms.

Another technique known as Density functional theory (DFT) is needed. It uses complex quantum chemistry calculations to map the density of electrons in a given area, and requires heavy computation. SwiftML, however, can do the job at a much quicker rate and can perform as accurately as DFT programmes in some cases.

“Even for relatively simple molecules, this model is almost 10,000 times faster than existing methods, and the advantage grows tremendously when considering more complex compounds,” said Michele Ceriotti, co-author of the paper and an assistant professor at the EPFL.

“To predict the NMR signature of a crystal with nearly 1,600 atoms, our technique – ShiftML – requires about six minutes; the same feat would have taken 16 years with conventional techniques.”

The researchers trained the system on the Cambridge Structural Database, a dataset containing calculated DFT chemical shifts for thousands of compounds. Each one is made up less than 200 atoms including carbon and hydrogen paired with oxygen or nitrogen. 2,000 structures were used for training and validation, and 500 were held back for testing.

SwiftML managed to calculate the chemical shifts for a molecule that had 86 atoms and the same chemical elements as cocaine, but arranged in a different crystal structure. The process took less than a minute of CPU time, compared around 62 to 150 CPU hours typically needed to calculate the chemical shift of a molecule containing 86 atoms using DFT.

The team hopes that SwiftML can be used to supplement NMR experiments to design new drugs. “This is really exciting because the massive acceleration in computation times will allow us to cover much larger conformational spaces and correctly determine structures where it was just not previously possible. This puts most of the complex contemporary drug molecules within reach,” says Lyndon Emsley, co-author of the study and a chemistry professor at EPFL.

Source: AI can predict the structure of chemical compounds thousands of times faster than quantum chemistry • The Register

Facebook announced today that it has removed 8.7 million pieces of content last quarter that violated its rules against child exploitation, thanks to new technology. The new AI and machine learning tech, which was developed and implemented over the past year by the company, removed 99 percent of those posts before anyone reported them, said Antigone Davis, Facebook’s global head of safety, in a blog post.

The new technology examines posts for child nudity and other exploitative content when they are uploaded and, if necessary, photos and accounts are reported to the National Center for Missing and Exploited Children. Facebook had already been using photo-matching technology to compare newly uploaded photos with known images of child exploitation and revenge porn, but the new tools are meant to prevent previously unidentified content from being disseminated through its platform.

The technology isn’t perfect, with many parents complaining that innocuous photos of their kids have been removed. Davis addressed this in her post, writing that in order to “avoid even the potential for abuse, we take action on nonsexual content as well, like seemingly benign photos of children in the bath” and that this “comprehensive approach” is one reason Facebook removed as much content as it did last quarter.

But Facebook’s moderation technology is by no means perfect and many people believe it is not comprehensive or accurate enough. In addition to family snapshots, it’s also been criticized for removing content like the iconic 1972 photo of Phan Thi Kim Phuc, known as the “Napalm Girl,” fleeing naked after suffering third-degree burns in a South Vietnamese napalm attack on her village, a decision COO Sheryl Sandberg apologized for.

Source: Facebook says it removed 8.7M child exploitation posts with new machine learning tech | TechCrunch

In a landmark study, 20 top US corporate lawyers with decades of experience in corporate law and contract review were pitted against an AI. Their task was to spot issues in five Non-Disclosure Agreements (NDAs), which are a contractual basis for most business deals.

The study, carried out with leading legal academics and experts, saw the LawGeex AI achieve an average 94% accuracy rate, higher than the lawyers who achieved an average rate of 85%. It took the lawyers an average of 92 minutes to complete the NDA issue spotting, compared to 26 seconds for the LawGeex AI. The longest time taken by a lawyer to complete the test was 156 minutes, and the shortest time was 51 minutes. The study made waves around the world and was covered across global media.

Source: 20 top lawyers were beaten by legal AI. Here are their surprising responses

AI can translate between languages in real time as people speak, according to fresh research from Chinese search giant Baidu and Oregon State University in the US.

Human interpreters need superhuman concentration to listen to speech and translate at the same time. There are, apparently, only a few thousand qualified simultaneous interpreters and the job is so taxing that they often work in pairs, swapping places after 20 to 30 minute stints. And as conversations progress, the chance for error increases exponentially.

Machines have the potential to trump humans at this task, considering they have superior memory and don’t suffer from fatigue. But it’s not so easy for them either, as researchers from Baidu and Oregon State University found.

They built a neural network that can translate between Mandarin Chinese to English in almost real time, where the English translation lags behind by up to at least five words. The results have been published in a paper on arXiv.

The babble post-Babel

Languages have different grammatical structures, where the word order of sentences often don’t match up, making it difficult to translate quickly. The key to a fast translation is predicting what the speaker will say next as he or she talks.

With the AI engine an encoder converts the words in a target language into a vector representation. A decoder predicts the probability of the next word given the words in the previous sentences. The decoder is always behind the encoder and generates the translated words until it processes the whole speech or text.

“In one of the examples, the Chinese sentence ‘Bush President in Moscow…’ would suggest the next English word after ‘President Bush’ is likely ‘meets’”, Liang Huang, principal scientist at Baidu Research, explained to The Register.

“This is possible because in the training data, we have a lot of “Bush meeting someone, like Putin in Moscow” so the system learned that if “Bush in Moscow”, he is likely “meeting” someone.

You can also listen to other examples here.

The problem with languages

The difficulty depends on the languages being translated, Huang added. Languages that are closely related, such as French and Spanish for example, have similar structures where the order of words are aligned more.

Japanese and German sentences are constructed with the subject at the front, the object in the middle, and the verb at the end (SOV). English and Chinese also starts with the subject, but the verb is in the middle, followed by the object (SVO).

Translating between Japanese and German to English and Chinese, therefore, more difficult. “There is a well-known joke in the UN that a German-to-English interpreter often has to pause and “wait for the German verb”. Standard Arabic and Welsh are verb-subject-object , which is even more different from SVO,” he said.

The new algorithm can be applied to any neural machine translation models and only involves tweaking the code slightly. It has already been integrated to Baidu’s internal speech-to-text translation and will be showcased at the Baidu World Tech Conference next week on 1st November in Beijing.

“We don’t have an exact timeline for when this product will be available for the general public, this is certainly something Baidu is working on,” Liang said.

“We envision our technology making simultaneous translation much more accessible and affordable, as there is an increasing demand. We also envision the technology [will reduce] the burden on human translators.”

Source: Linguists, update your resumes because Baidu thinks it has cracked fast AI translation • The Register

Boffins have devised a way to make eavesdropping smartwatches, computers, mobile devices, and speakers with endearing names like Alexa better aware of what’s going on around them.

In a paper to be presented today at the ACM Symposium on User Interface Software and Technology (UIST) in Berlin, Germany, computer scientists Gierad Laput, Karan Ahuja, Mayank Goel, and Chris Harrison describe a real-time, activity recognition system capable of interpreting collected sound.

In other words, a software that uses devices’ always-on builtin microphones to sense what exactly’s going on in the background.

The researchers, based at Carnegie Mellon University in the US, refer to their project as “Ubicoustics” because of the ubiquity of microphones in modern computing devices.

As they observe in their paper, “Ubicoustics: Plug-and-Play Acoustic Activity Recognition,” real-time sound evaluation to classify activities and and context is an ongoing area of investigation. What CMU’s comp sci types have added is a sophisticated sound-labeling model trained on high-quality sound effects libraries, the sort used in Hollywood entertainment and electronic games.

As good as you and me

Sound-identifying machine-learning models built using these audio effects turn out to be more accurate than those trained on acoustic data mined from the internet, the boffins claim. “Results show that our system can achieve human-level performance, both in terms of recognition accuracy and false positive rejection,” the paper states.

The researchers report accuracy of 80.4 per cent in the wild. So their system misclassifies about one sound in five. While not quite good enough for deployment in people’s homes, it is, the CMU team claims, comparable to a person trying to identify a sound. And its accuracy rate is close to other sound recognition systems such as BodyScope (71.5 per cent) and SoundSense (84 per cent). Ubicoustics, however, recognizes a wider range of activities without site-specific training.

Alexa to the rescue

Alexa, informed by this model, could in theory hear if you left the water running in your kitchen and might, given the appropriate Alexa Skill, take some action in response, like turning off your smart faucet or ordering a boat from Amazon.com to navigate around your flooded home. That is, assuming it didn’t misinterpret the sound in the first place.

The researchers suggest their system could be used, for example, to send a notification when a laundry load finished. Or it might promote public health: By detecting frequent coughs or sneezes, the system “could enable smartwatches to track the onset of symptoms and potentially nudge users towards healthy behaviors, such as washing hands or scheduling a doctor’s appointment.”

Source: Alexa heard what you did last summer – and she knows what that was, too: AI recognizes activities from sound • The Register