Recent notable applications of deep learning in medicine include automated detection of diabetic retinopathy, classification of skin cancers, and detection of metastatic lymphadenopathy in patients with breast cancer, all of which demonstrated expert level diagnostic accuracy.1,2,3 Recently, a deep-learning model was found to match or outperform human expert radiologists in diagnosing 10 or more pathologies on chest radiographs.4,5 The success of AI in diagnostic imaging has fueled a growing debate6,7,8,9 regarding the future role of radiologists in an era, where deep-learning models are capable of performing important diagnostic tasks autonomously and speculation surrounds whether the comprehensive diagnostic interpretive skillsets of radiologist can be replicated in algorithms. However, AI is also plagued with several disadvantages including biases due to limited training data, lack of cross-population generalizability, and inability of deep-learning models to contextualize.8,10,11,12 Human-in-the-loop (HITL) AI may offer advantages where both radiologists and machine-learning algorithms fall short.13,14
Rev started its own competitor in this realm earlier this year. In Friday's Q. and A., contractors asked if they were being kept around just to train the company's artificial intelligence -- something Mr. Chicola vehemently denied. So far at least, the machine-powered alternatives do not appear to be eating into the work available for skilled transcribers. Paula Kamen, who runs Transcription Professionals from her home near Chicago, said that when she began her company in 1995, she was convinced that Dragon -- the buzzy speech recognition software of that time -- would soon make her business obsolete. But she said she has continued to grow at a steady rate because the advances in speech recognition technology have come alongside the proliferation in recording devices and people wanting to see their words turned into text.
In recent days, Microsoft's improvements to Power BI include the release of the October update for On-premises data gateway, the introduction of new contact lists for reports and dashboards, and plenty more. Earlier this year, the Redmond firm revealed the public preview of Automated Machine Learning (AutoML) for Dataflows in Power BI. Today, AutoML has reached general availability in all public cloud regions that offer Power BI Premium and Embedded services. A bunch of new capabilities have been added to the service ever since its preview version became available in April. For those unaware, AutoML allows business analysts to easily develop machine learning (ML) models.
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.
A man has been able to move all four of his paralysed limbs using a groundbreaking mind-controlled exoskeleton, scientists have said. The tetraplegic 30-year-old, known only as Thibault, said his first steps in the robotic suit felt like being "the first man on the Moon". The system, which works by recording and decoding brain signals, was trialled in a two-year study by French researchers at biomedical research centre Clinatec and the University of Grenoble. Scientists conceded the suit was an experimental treatment far from clinical application but said it had the potential to improve patients' quality of life and autonomy. Wearing the robotic limbs, Thibault was able to walk and move his arms using a ceiling-mounted harness for balance.
Minecraft's open-ended play environment could be ideal for AI research, some researchers say.Credit: Microsoft To see the divide between the best artificial intelligence and the mental capabilities of a seven-year-old child, look no further than the popular video game Minecraft. A young human can learn how to find a rare diamond in the game after watching a 10-minute demonstration on YouTube. Artificial intelligence (AI) is nowhere close. But in a unique computing competition ending this month, researchers hope to shrink the gap between machine and child -- and in doing so, help to reduce the computing power needed to train AIs. Competitors may take up to four days and use no more than eight million steps to train their AIs to find a diamond.
Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas. While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL. By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood. "This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases," Wood told TechCrunch.
To compete in today's global marketplace, retailers need accurate retail sales forecasting software that can respond to supply chain disruptions with fast, data-driven decisions. Smart retailers are powering those decisions with advancements in artificial intelligence--bringing unprecedented precision to the omni-channel supply chain with machine learning-powered retail demand forecasting and management solutions. But choosing the right machine learning solution can be a daunting decision in and of itself. Download this buyer's guide to get the truth about which machine learning solutions are the real deal, and which ones are nothing more than slick marketing campaigns that can't (and won't) hold up.
Robots will soon be everywhere – especially if ordinary objects can be turned into them. A computer program can now use 3D-printing to turn household objects into hand-activated robots. It can be used to turn on the water taps on a bathroom sink with the wave of a hand, or to give a window the ability to shut itself when the weather gets cold. Xiang'Anthony' Chen at the University of California in Los Angeles and colleagues developed the tool, known as Robiot, to automate simple physical tasks.