The current boom in artificial intelligence can be traced back to 2012 and a breakthrough during a competition built around ImageNet, a set of 14 million labeled images. In the competition, a method called deep learning, which involves feeding examples to a giant simulated neural network, proved dramatically better at identifying objects in images than other approaches. That kick-started interest in using AI to solve different problems. But research revealed this week shows that ImageNet and nine other key AI data sets contain many errors. Researchers at MIT compared how an AI algorithm trained on the data interprets an image with the label that was applied to it.

Those that climb need to be both fast and stable to avoid predation and find food. A robot made to mimic their movements has now shown how the rotation of their legs and the speed with which they move up vertical surfaces helps them climb efficiently. "Most lizards look a lot like other lizards," says Christofer Clemente at University of the Sunshine Coast, Australia. To find out why, Clemente and his team built a robot based on a lizard's body plan to explore its efficiency. It is about 24 centimetres long, and its legs and feet were programmed to mimic the gait of climbing lizards.

Imagine for a moment that you have suction cups for fingertips--unless you're currently on hallucinogens, in which case you should not imagine that. Each sucker is a different size and flexibility, making one fingertip ideal for sticking onto a flat surface like cardboard, another more suited to a round thing like a ball, another better for something more irregular, like a flower pot. On its own, each digit may be limited in which things it can handle. But together, they can work as a team to manipulate a range of objects. This is the idea behind Ambi Robotics, a lab-grown startup that is today emerging from stealth mode with sorting robots and an operating system for running such manipulative machines.

Adobe Photoshop has got a new AI feature that can quadruple the number of pixels in your photos. The tool, called Super Resolution, is now shipping in Camera Raw 13.2 and will be coming soon to Lightroom and Lightroom Classic. The feature uses a machine learning model trained on millions of photos to enlarge images while preserving their clean edges and fine details. Programmer Eric Chan said it's very simple to use: Press a button and watch your 10-megapixel photo transform into a 40-megapixel photo. It's a bit like how Mario eats a mushroom and suddenly balloons into Super Mario, but without the nifty sound effects.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. There's a growing interest in employing autonomous mobile robots in open work environments such as warehouses, especially with the constraints posed by the global pandemic. And thanks to advances in deep learning algorithms and sensor technology, industrial robots are becoming more versatile and less costly. But safety and security remain two major concerns in robotics. And the current methods used to address these two issues can produce conflicting results, researchers at the Institute of Science and Technology Austria, the Massachusetts Institute of Technology, and Technische Universitat Wien, Austria have found.

Boston Dynamics, the company many people know for its dog robot Spot, has unveiled a new bot targeting warehouses. The company's new robot called Stretch was designed for moving boxes at warehouses and distribution centers. Stretch features a small, omni-directional mobile base, a custom-designed lightweight arm and a smart-gripper with advanced sensing and controls to handle a variety of packages. "Warehouses are struggling to meet rapidly increasing demand as the world relies more on just-in-time delivery of goods," said Robert Playter, CEO of Boston Dynamics, in a statement Monday. "Mobile robots enable the flexible movement of materials and improve working conditions for employees."

"Please listen to our entire menu as our options have changed. Say or press one for product information..." Sometimes, these automated customer service experiences are effective and efficient--other times, not so much. Many organizations are already using chatbots and virtual assistants to help better serve their customers. These intelligent, automated self-service agents can handle frequently asked questions, provide relevant knowledge articles and resources to address customer inquiries, and help customers fill out forms and do other routine procedures. In the case of more complex inquiries, these automated self-service agents can triage those requests to a live human agent.

Even if you can't name the tunes, you've probably heard them: from the iconic "dun-dun-dun-dunnnn" opening of Beethoven's Fifth Symphony to the melody of "Ode to Joy," the German composer's symphonies are some of the best known and widely performed in classical music. Just as enthusiasts can recognize stylistic differences between one orchestra's version of Beethoven's hits and another, now machines can, too. A Duke University team has developed a machine learning algorithm that "listens" to multiple performances of the same piece and can tell the difference between, say, the Berlin Philharmonic and the London Symphony Orchestra, based on subtle differences in how they interpret a score. In a study published in a recent issue of the journal Annals of Applied Statistics, the team set the algorithm loose on all nine Beethoven symphonies as performed by 10 different orchestras over nearly eight decades, from a 1939 recording of the NBC Symphony Orchestra conducted by Arturo Toscanini, to Simon Rattle's version with the Berlin Philharmonic in 2016. Although each follows the same fixed score -– the published reference left by Beethoven about how to play the notes -- every orchestra has a slightly different way of turning a score into sounds.

The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. By training the models using the responses of these cells to 412 cancer drugs listed in drug response repositories, DRUML was able to produce ordered lists based on the effectiveness of the drugs to reduce cancer cell growth. This study represents a significant advancement in artificial intelligence in biomedical research, and demonstrates that machine learning using proteomics and phosphoproteomics data may be an effective way of selecting the best drug to treat different cancer models. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset.

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