Sight Machine provides an artificial intelligence-powered digital manufacturing platform that is unique in its ability to create a digital twin of the entire manufacturing process. The San Francisco-based startup recently raised a $29.4 million Series C funding round led by South Korean conglomerate LS Group, bringing its total funding to roughly $85 million. The company's platform allows manufacturers to quickly create customized analytics and applications from a diverse variety of data sources on the factory floor.

Koios Medical, the leader in ultrasound diagnosis decision support software, announces its second 510(k) clearance from the U.S. Food and Drug Administration (FDA). Koios DS (Decision Support) Breast 2.0 is intended for use to assist physicians analyzing breast ultrasound images and aligns a machine learning generated probability of malignancy with the appropriate BI-RADS category. This milestone is an important step in advancing the company's mission of empowering physicians to improve diagnostic accuracy. Now cleared for use at the point of care (or connected to an image viewer for studies stored on PACS), Koios Medical's advancements represent a huge leap forward in using artificial intelligence in healthcare by bringing the power of deep learning to physicians' fingertips. Koios DS Breast 2.0 represents the most advanced AI-based diagnostic technology for ultrasound image analysis to date.

Montreal-based C2RO today announced that it has secured CAD$2.25 Million in new financing in a round led by Fonds Innovexport, with participation from GCI Capital Inc., Harbor Street Ventures, Tandemlaunch, Ministere de Economie et L'Innovation, and several angel investors in Canada, the U.S. and Europe. The funds will be used to accelerate the commercialization of C2RO's powerful enterprise grade cloud A.I. services. "We led the investment in C2RO because it has an excellent execution team, a significantly expanding Tier1 customer base, and a formidable technology position in the field of real-time machine vision A.I.," said Richard Bordeleau, President at Fonds Innovexport. "C2RO will have a tremendous impact on the industry and we want to support them through this journey." In June of 2018, the company introduced C2RO Engage, the world's first real-time cloud based facial recognition platform.

Nowadays most machine learning (ML) models predict labels from features. In classification tasks, an ML model predicts a categorical value and in regression tasks, an ML model predicts a real value. These ML models thus require a large amount of feature-label pairs. While in practice it is not hard to obtain features, it is often costly to obtain labels because this requires human labor. Can we learn a model without too many feature-label pairs?

Before Siri and Alexa, there was Watson. Appearing as a contestant on "Jeopardy!" made IBM's Watson a household name. But since its debut -- and win -- in 2011, the computer has morphed into something else entirely: An artificial intelligence tool for business. The company opened up Watson in the cloud wars, making the technology available on competitors' clouds last month. Behind the Watson branding are career technologists making the tool work for business customers.

Since robots first taking over industrial manufacturing, people have worried that they'll replace us. But now, with the explosion of artificial intelligence applications, our jobs are more under threat than ever before. Automated technology monitors and control production and manufacturing. Drones and driverless cars are taking over transportation and delivery services. By 2030, between 75 million and 375 millions could be automated.

Technology can be dazzling but also debilitating to real human progress, and when I say "progress" I do not mean advancements in the world of machines but advancements in the world of people, and one does not necessarily lead to the other. First, I fully recognize that whenever anyone attempts to criticize technological innovation they take the risk of being labeled a "crackpot" or an "outdated fossil", a barbaric relic of a foregone era. However, this attitude is an ignorant one. It assumes that the path we are on as a species is one of perpetual improvement as long as we continue to follow the great technology god; but what if this assumption is completely wrong? What if we are actually devolving rather than evolving? I'm not here to grunt and shake my spear at the wheel and the combustion engine and the programmable computer – I like all these things.

Ontario is no latecomer to the artificial intelligence age in Canada. In fact, decades ago, it was here that Geoffrey Hinton and a band of AI pioneers hunkered down in labs in Toronto and Waterloo, sustaining what has become today's hottest discipline. Their perseverance to unlock the potential of neural networks has made the province a leading source of AI talent and a magnet for tech's biggest players. In Ontario, businesses can also tap into our world-renowned AI ecosystem to build a sustainable business model while championing the responsible and ethical use of artificial intelligence. And Canada stands out as a place that remains open to differing points of view, connected to the world and free to work collaboratively in our common quest to meet the challenges of our time.

Technological advancements in AI are leading to the development of a true human robot, but this may not be something we need, writes Paul Budde. RECENTLY, I WENT to a lecture organised by the University of Sydney titled'Why should the perfect robot look and think just like a human?' I was intrigued and perhaps even a bit dismayed about this title as I strongly believe that this is not the best direction for robotics. Furthermore, such a new human species will most likely never be developed, certainly not within the next few generations. Beyond that, humanity might perhaps arrive at a stage that we have gathered sufficient intellect and wisdom to develop robots within the restrictions of what we as a society see fit.

But AI shouldn't conjure images of robots taking over. Instead, the future of work will include machines handling tasks employees don't want to or don't have the resources to do. AI can expand a person's capacity to do the work that really matters and can help companies better understand workers. Here are five things AI tools may notice before employees do. As much as everyone hates to think they're being spied on at work, it's already happening by way of Natural Language Processing (NLP).