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John Giannandrea, Vice President of Engineering with responsibility for Google’s Computer Science Research and Machine Intelligence groups; leading teams in Machine Learning, Machine Intelligence, Computer Perception, Natural Language Understanding, and Quantum Computing, “I’m definitely not worried about the AI apocalypse, I just object to the hype and soundbites that some people are making” said at the TechCrunch Disrupt conference in San Francisco.

Google’s John Giannandrea sits down with Frederic Lardinois to discuss the AI hype/worry cycle and the importance, limitations, and acceleration of machine learning.

A computational model could improve the selection of tumor antigens for personalized cancer vaccines that are now in early-stage clinical trials.

Every cell in the human body is coated with fragments of proteins called antigens that tell the immune system what’s inside the cell. Antigens presented on cells that are infected by foreign invaders or have become rogue cancers prompt an immune attack. Such antigens are often used in vaccines to spur immune responses against, for example, viruses like the flu. But to make vaccines that effectively stimulate attack against cancer, researchers need to predict exactly which tumor-specific antigens will be displayed on tumor cells and hence would be the best ones to put in a cancer vaccine.

Now, scientists at the Broad Institute of MIT and Harvard, Dana-Farber Cancer Institute, and Massachusetts General Hospital have developed a new computational tool that could help with this task. The researchers turned to machine learning to analyze a diverse set of more than 185,000 human antigens that they discovered, and generated a new set of rules that predict which antigens are presented on the surface of a person’s cells. The findings, published today in Nature Biotechnology, could aid in the development of new treatments that stimulate the immune system to attack cancer as well as viruses and bacteria.

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Given that opportunity, the acquisition of Habana is only a component of a wide attack on the market and that it’s not clear how it fits with the other acquisitions and projects, the initial response to the Habana acquisition should be a shrug. Intel is like a VC firm in that it only needs one of the multiple initiatives to hit in order to end up in the black.

(Reuters) — Nvidia Corp on Wednesday said it has won a series of deals in which some of China’s biggest technology companies are using its chips to make product recommendations and to develop self-driving vehicles.

AI doesn’t know when it creates unintended consequences. As builders try to fix this, they may actually contribute to the problem.

As the cars we drive become increasingly sophisticated, the technology that underpins them poses a unique set of challenges.

“Currently, technology is more likely to create distractions in vehicles than it is to combat it,” Alain Dunoyer, SBD Automotive’s head of autonomous research and consulting, said in a statement sent to CNBC via email earlier this month.

“These days, cars have a shopping list of features which has led to tasks that were historically quite simple becoming drastically more complicated and distracting,” he added.

Cerebras Systems has built an AI-processing enormity. The company’s new wafer-scale processor packs 1.2T transistors — yes, with a “T” — and 400,000 processing cores.

Rather than representing an entirely different take on the flying car, the helicopter’s new brains are more equivalent to the “self-driving” features you see in contemporary cars — an effort to make vehicles safer and more accessible than ever before.

“Today, we design our lives around traffic and make decisions about where we live and work based on how hard it is to get there,” Mark Groden, Skyryse CEO and founder, said in a statement. “To get there, we need to make urban flying as safe as riding an elevator and as accessible and affordable as riding a bus.”

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