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

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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

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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

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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

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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)

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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

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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

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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

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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

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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

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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

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