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Researchers at Columbia Engineering have used Artificial Intelligence to teach robots how to make appropriate reactive human facial expressions.

Scientists say the system could be used to find ‘hidden gems’ of research and guide research funding allocations.

An artificial intelligence system trained on almost 40 years of the scientific literature correctly identified 19 out of 20 research papers that have had the greatest scientific impact on biotechnology – and has selected 50 recent papers it predicts will be among the ‘top 5%’ of biotechnology papers in the future.¹

Scientists say the system could be used to find ‘hidden gems’ of research overlooked by other methods, and even to guide decisions on funding allocations so that it will be most likely to target promising research.

Continue reading “Artificial intelligence system can predict the impact of research” »

Between the rolled eyes, shrugged shoulders, jazzed hands and warbling vocal inflection, it’s not hard to tell when someone’s being sarcastic as they’re giving you the business face to face. Online, however, you’re going to need that SpongeBob meme and a liberal application of the shift key to get your contradictory point across. Lucky for us netizens, DARPA’s Information Innovation Office (I2O) has collaborated with researchers from the University of Central Florida to develop a deep learning AI capable of understanding written sarcasm with a startling degree of accuracy.

“With the high velocity and volume of social media data, companies rely on tools to analyze data and to provide customer service. These tools perform tasks such as content management, sentiment analysis, and extraction of relevant messages for the company’s customer service representatives to respond to,” UCF Adjunct Professor of Industrial Engineering and Management Systems, Ivan Garibay, told Engadget via email. “However, these tools lack the sophistication to decipher more nuanced forms of language such as sarcasm or humor, in which the meaning of a message is not always obvious and explicit. This imposes an extra burden on the social media team, which is already inundated with customer messages to identify these messages and respond appropriately.”

As they explain in a study published in the journal, Entropy, Garibay and UCF PhD student Ramya Akula have built “an interpretable deep learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text.”

Amid the rapid digitalisation of China’s economy, the second-biggest in the world, Midea’s factory represents a snapshot of the future – one in which manufacturing processes and employees need to adapt to increased automation and machine-driven learning.

Machines are increasingly taking over China’s assembly lines as manufacturers upgrade and prepare for fewer, higher-skilled workers.

At Google’s workshop on the conceptual understanding of deep learning, award-winning computer scientist Christos Papadimitriou discussed brain assemblies and their correspondence with artificial intelligence.

Researchers from Zurich have developed a compact, energy-efficient device made from artificial neurons that is capable of decoding brainwaves. The chip uses data recorded from the brainwaves of epilepsy patients to identify which regions of the brain cause epileptic seizures. This opens up new perspectives for treatment.

Current neural network algorithms produce impressive results that help solve an incredible number of problems. However, the electronic devices used to run these algorithms still require too much processing power. These artificial intelligence (AI) systems simply cannot compete with an actual brain when it comes to processing sensory information or interactions with the environment in real time.

AI and cloud-based computing are saving billions of dollars in lost commuting time—and keeping you on your way.

In the welding field, however, some argue that a robot takeover might be beneficial, and even necessary.

Columbus startup Path Robotics believes AI is one solution to the shortage of skilled labor that plagues welding. Path boasts the “world’s first truly autonomous robotic welding system.” Conceived after 18 months in the basement of a foundry, its system identifies what needs to be welded, welds it and learns along the way.

Path Robotics CEO Andy Lonsberry said he and his brother, Alex Lonsberry, chief technology officer at Path Robotics, always wanted to start a business.

The US Department of Energy on Thursday is officially dedicating Perlmutter, a next-generation supercomputer that will deliver nearly four exaflops of AI performance. The system, based at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory, is the world’s fastest on the 16-bit and 32-bit mixed-precision math used for AI.

The HPE Cray system is being installed in two phases. Each of Phase 1’s GPU-accelerated nodes has four Nvidia A100 Tensor Core GPUs, for a total of 6159 Nvidia A100 Tensor Core GPUs. Each Phase 1 node also has a single AMD Milan CPU.