When Vinod Khosla had a skiing accident in 2011 that led to an ACL injury in his knee, doctors gave conflicting opinions over his treatment. Frustrated with the healthcare system, the leading venture capitalist proffered, in a hotly debated article, that AI algorithms could do the job better than doctors. Since then, Khosla's firm has invested in a number of robotics and medtech companies, including Rad AI, a radiology tech company. The self-professed techno-optimist still stands by his assertions a decade later. "Almost all expertise will be free in an AI model, and we'll have plenty of these for the benefit of humanity," he told TIME in an interview in August.

I have a thoroughly delightful chance today to interview Vinod Khosla, who many years ago started Khosla Ventures, one of the most successful venture capital firms in the world. Vinod, I think you started out in engineering in India at one of the most prestigious institutes of technology, then you went to Carnegie Mellon, and then Stanford. But engineering wasn't where you landed long-term, because at some point you started Sun Microsystems, right? When I started out, there wasn't a thing called computer science. That tells you how old I am. In those days, you could pursue any new area if you created your own program because there were not a lot of programs. At the Indian Institute of Technology, we started the computer science program, and then I moved to biomedical engineering. I went on to get a master's degree in biomedical engineering at Carnegie Mellon. That was also a tiny program in the basement.

Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve state-of-the-art performance of relation extraction.

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Boston Dynamics' video of a humanoid robot executing a perfect backflip sparked a lot of dark humor, and now billionaire Elon Musk has responded with dark seriousness about the risks posed by super-intelligent, super-agile bots. Musk suggested that future robots could move so fast they could match the fictional Flash, who eludes his comic-book foes by moving too fast for the eye to see. Compared to that superpower, doing human-level backflips is "nothing," Musk said in a tweet: In a few years, that bot will move so fast you'll need a strobe light to see it. Then Musk added a bit of explanation for those who didn't see where he was going: How would a strobe light help see anything? Otherwise you'd only see a blur The agility of Boston Dynamics' Atlas robot gave Musk another opportunity to touch on a theme he's been emphasizing for months: that the potential risks posed by the rapid rise of artificial intelligence are so great that a regulatory regime is needed: Got to regulate AI/robotics like we do food, drugs, aircraft & cars.

That was the question last week as Element.ai raised this hefty sum in a Series A funding. Investors included Microsoft, Nvidia, and Intel Capital, all of whom have their own AI ambitions. Element aims to make AI easy for businesses to use by connecting them with machine learning experts. And while Element may have brought together competitors Microsoft and Intel, the rivals have been fiercely staking their own claims with a flurry of investments and acquisitions. While these moves point to AI one day becoming ubiquitous for business tasks, Google's release of an academic paper called "One Model to Learn Them All" shows another route for machine learning to become commonplace.

While much of the conversation around AI and jobs is focused on widespread job losses in sectors like trucking, venture capitalist and Sun Microsystems cofounder Vinod Khosla thinks that there's a high-paying job on the chopping block: oncology. "I can't imagine why a human oncologist would add value, given the amount of data in oncology," he said during a panel conversation hosted by MIT in San Francisco today. "They can't possibly comprehend all of the things that are possible." His comments were part of a broader point that education will not be enough to stem the economic upheaval and job loss that comes about as part of the growth of artificial intelligence. He doesn't believe that human radiologists will exist in five years as a result, for example.