Using imitation and reinforcement learning to tackle long-horizon robotic tasks

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Reinforcement learning (RL) is a widely used machine-learning technique that entails training AI agents or robots using a system of reward and punishment. So far, researchers in the field of robotics have primarily applied RL techniques in tasks that are completed over relatively short periods of time, such as moving forward or grasping objects.

A team of researchers at Google and Berkeley AI Research has recently developed a new approach that combines RL with learning by imitation, a process called relay policy learning. This approach, introduced in a paper prepublished on arXiv and presented at the Conference on Robot Learning (CoRL) 2019 in Osaka, can be used to train artificial agents to tackle multi-stage and long-horizon tasks, such as object manipulation tasks that span over longer periods of time.

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Men in creative jobs are described very differently than their female peers

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Machine learning reveals that news coverage of people in creative industries such as design and art is shaped by gender. Can it guide us toward parity?

How long would it take you to review half a million articles? Not just to read, but to tally for particular keywords, such as “he,” “she,” and the words that immediately follow them? Well, let’s just say you’d have to quit your day job.

Undeterred, the Creative Industries Policy and Evidence Centre, which provides independent research and policy recommendations for the U.K.’s creative industry, in partnership with the innovation foundation Nesta, made it their day job. They had some help: AI.

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Researchers tout AI that can predict 25 video frames into the future

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AI video prediction

AI and machine learning algorithms are becoming increasingly good at predicting next actions in videos. The very best can anticipate fairly accurately where a baseball might travel after it has been pitched, or the appearance of a road miles from a starting position. To this end, a novel approach proposed by researchers at Google, the University of Michigan, and Adobe advances the state of the art with large-scale models that generate high-quality videos from only a few frames. All the more impressive, it does so without relying on techniques like optical flows (the pattern of apparent motion of objects, surfaces, or edges in a scene) or landmarks, as previous methods have.

“In this work, we investigate whether we can achieve high-quality video predictions … by just maximizing the capacity of a standard neural network,” wrote the researchers in a preprint paper describing their work. “To the best of our knowledge, this work is the first to perform a thorough investigation on the effect of capacity increases for video prediction.”

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Online dating in a world of deepfakes

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Facebook has teamed up with the Partnership on AI, Microsoft, and academics from Cornell Tech, MIT, University of Oxford, UC Berkeley, University of Maryland, College Park, and University at Albany–SUNY to build the Deepfake Detection Challenge (DFDC).

Deepfake detection is an enduring arms race that will never end. In case you are wondering… no, this technology will not protect the 2020 election from deepfakes. No science is up to that task.

Facebook’s goal is to commission a realistic data set that will use paid actors, with the required consent obtained, to contribute to the challenge. This “benchmark data” will be used to help developers build better tools to detect deepfakes. Everyone should applaud this effort! As I’ve written about recently, deepfakes will be used extensively by both good and bad people.

Facebook also announced it was bringing its dating service to the U.S. after testing it in roughly 20 countries since its launch last year. These two stories may not seem to have much correlation at first glance. But when combined, they present a potential reality as sinister as it is deceitful. Imagine online dating in a world replete with deepfakes.

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Using machine learning to reconstruct deteriorated Van Gogh drawings

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Researchers at TU Delft in the Netherlands have recently developed a convolutional neural network (CNN)-based model to reconstruct drawings that have deteriorated over time. In their study, published in Springer’s Machine Vision and Applications, they specifically used the model to reconstruct some of Vincent Van Gogh’s drawings that were ruined over the years due to ink fading and discoloration.

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An artificial-intelligence first: Voice-mimicking software reportedly used in a major theft

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A fake video featuring former president Barack Obama. A new worry: fake voice recordings that can be used to persuade people that they’re being asked to do something by an authority. (AP/AP)

Thieves used voice-mimicking software to imitate a company executive’s speech and dupe his subordinate into sending hundreds of thousands of dollars to a secret account, the company’s insurer said, in a remarkable case that some researchers are calling one of the world’s first publicly reported artificial-intelligence heists.

The managing director of a British energy company, believing his boss was on the phone, followed orders one Friday afternoon in March to wire more than $240,000 to an account in Hungary, said representatives from the French insurance giant Euler Hermes, which declined to name the company.

The request was “rather strange,” the director noted later in an email, but the voice was so lifelike that he felt he had no choice but to comply. The insurer, whose case was first reported by the Wall Street Journal, provided new details on the theft to The Washington Post on Wednesday, including an email from the employee tricked by what the insurer is referring to internally as “the false Johannes.”

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The future of manufacturing technology

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The global manufacturing market reached $38 trillion in 2018, contributing a 15% increase in global production output. Within this market, a broad range of goods is produced and processed, spanning from consumer goods, heavy industrials to storage and transportation of raw materials and finished products.

To sustain ongoing growth, today’s manufacturers are hyper-focused on three key mandates. First is to improve utilization rates of expensive fixed assets that are below optimal capacity. Second is to fill the current and increasing void of specialized labor. Deloitte estimates that by 2028, the skills gap in the US will result in 2.4 million unfilled seats out of a total of 16 million manufacturing jobs. Lastly, manufacturers must protect operating profit as industry average EBITDA margin continues to decline from 11.2% in 2015 to 8.6% in 2018.

Many startups are now starting to offer tailored products and services to help traditional manufacturers meet these goals. Until recently, hardware components such as sensors were expensive and had unclear ROI. Data was siloed, and no solution to scale insight was available. However, since the AI revolution in the early 2010s, startups are finding ways to overcome these challenges through technical innovation.

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AI is getting more in touch with your emotions

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EmoNet, a neural network model, was accurately able to pair images to 11 emotion categories.

The EmoNet research study demonstrates how AI can measure emotional significance.

Artificial intelligence might one day start communicating our emotions better than we do. EmoNet, neural network model developed by researchers at the University of Colorado and Duke University, was accurately able to classify images into 11 different emotion categories.

A neural network is a computer model that learns to map input signals to an output of interest by learning a series of filters, according to Philip Kragel, one of the researchers on the study. For example, a network trained to detect bananas would learn features unique to them, such as shape and color.

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Microsoft’s tech can make your hologram speak another language

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This exec doesn’t speak Japanese — but it sure looks like she does.

You no longer need to speak another language to look like you’re fluent in it — to anyone, anywhere.

On Wednesday, Microsoft executive Julia White took the stage at the company’s Inspire partner conference to demonstrate how it’s now possible to not only create an incredibly life-like hologram of a person, but to then make the hologram speak another language in the person’s own voice.

This demo was possible thanks to a combination of two existing technologies — mixed reality and neural text-to-speech — and it foreshadows a future in which tech greatly diminishes existing barriers in human communication.

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Simple ‘smart’ glass reveals the future of artificial vision

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From left to right, Zongfu Yu, Ang Chen and Efram Khoram developed the concept for a “smart” piece of glass that recognizes images without any external power or circuits.

The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds — and looks — surprisingly low-tech.

This window to the future is none other than a piece of glass. University of Wisconsin–Madison engineers have devised a method to create pieces of “smart” glass that can recognize images without requiring any sensors or circuits or power sources.

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Machine learning goes beyond theory to beat human poker champs

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How to deal with a breakdown in theoretical support in machine learning? Researchers from Carnegie Mellon and Facebook describe winning many hands against the world’s top poker players by inventing smart search strategies to counter a lack of theoretical math used in most game-playing AI.

Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google’s DeepMind group’s conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute.

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Machine learning has been used to automatically translate long-lost languages

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Some languages that have never been deciphered could be the next ones to get the machine translation treatment.

In 1886, the British archaeologist Arthur Evans came across an ancient stone bearing a curious set of inscriptions in an unknown language. The stone came from the Mediterranean island of Crete, and Evans immediately traveled there to hunt for more evidence. He quickly found numerous stones and tablets bearing similar scripts and dated them from around 1400 BCE.

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