Artificial Intelligence

An international team of researchers led by the Swinburne University of Technology has demonstrated the world’s fastest and most powerful optical neuromorphic processor for artificial intelligence (AI), which operates faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large-scale data. World’s fastest optical neuromorphic processor This breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general. Artificial neural networks, a key form of AI, can ‘learn’ and perform complex operations with wide applications to computer vision, natural language processing, facial recognition, speech translation, playing strategy games, medical diagnosis and many other areas. Inspired by the biological structure of the brain’s visual cortex system, artificial neural networks extract key features of raw data to predict properties and behaviour with unprecedented accuracy and simplicity. An optical neuromorphic processor operating more than 1000 times faster than any previous processor, with the system also processing record-sized

The majority of software development teams believe software testing team don’t test well. They understand that the effect of quality defects is substantial, and they invest heavily in quality assurance, but they still aren’t getting the results they want. This is not due to a lack of talent or effort — the technology supporting software testing is simply not effective. The industry has been underserved. There can’t be a successful release until the software has been properly and thoroughly tested, and testing can sometimes take significant resources considering the amount of time and human effort required to get the job done right. This gaping need is just beginning to be filled. Machine learning (ML), which has disrupted and improved so many industries, is just starting to make its way into software testing. Heads are turning, and for good reason: the industry is never going to be the same again. While

A new study shows how training deep-learning models on patient outcomes could help reveal gaps in existing medical knowledge. In the last few years, research has shown that deep learning can match expert-level performance in medical imaging tasks like early cancer detection and eye disease diagnosis. But there’s also cause for caution. Other research has shown that deep learning has a tendency to perpetuate discrimination. With a health-care system already riddled with disparities, sloppy applications of deep learning could make that worse. Now a new paper published in Nature Medicine is proposing a way to develop medical algorithms that might help reverse, rather than exacerbate, existing inequality. The key, says Ziad Obermeyer, an associate professor at UC Berkeley who oversaw the research, is to stop training algorithms to match human expert performance. The paper looks at a specific clinical example of the disparities that exist in the treatment of knee osteoarthritis, an ailment which causes chronic pain.