Google Brain co-founder raises $175 million fund for AI startups

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Ng announced Tuesday that he raised money from venture capital firms New Enterprise Associates, Sequoia Capital and Greylock Partners as well as SoftBank Group Corp. Under Ng, Baidu released a voice-based operating system that users can talk to - much like Amazon's Alexa voice assistant or Apple's Siri - and also started working on self-driving cars and face recognition technology to open things like transit turnstiles when users approach. I think it's a more systematic, repeatable process than most people think," said Ng, who also taught artificial intelligence courses at Stanford University. The first company to receive money from the fund will be Landing.ai,


Voice assistants dominate CES as Google plays catchup with Alexa

New Scientist

This week about 180,000 visitors flocked to the world's biggest technology exhibition, the Consumer Electronics Show in Las Vegas. And while all the usual gadgets made an appearance, from smart fridges to self-driving cars, there was one dominant theme: speech. With nearly half of people in the US using voice-activated digital assistants in their smartphones or tablets, and the ownership of standalone digital assistants, like Google Home and Amazon Echo, expected to double in 2018, every tech company now wants a slice of the pie. Alexa, Amazon's voice assistant, is now available in everything from microwaves to cars, and from TVs to mirrors. Google had more than 350 voice-controlled devices at the show, including speakers, cars, and a giant toy town complete with a railway.


Google Brain Co-Founder Teams With Foxconn to Bring AI to Factories

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Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


Stochastic Divergence Minimization for Biterm Topic Model

arXiv.org Machine Learning

As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed. Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new contents. Unlike conventional topic models such as latent Dirichlet allocation (LDA), a biterm topic model (BTM) was recently proposed for short texts to overcome the sparseness of document-level word co-occurrences by directly modeling the generation process of word pairs. Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and collapsed variational inference have been proposed for BTM. However, they either require large computational complexity, or rely on very crude estimation. In this work, we develop a stochastic divergence minimization inference algorithm for BTM to estimate latent topics more accurately in a scalable way. Experiments demonstrate the superiority of our proposed algorithm compared with existing inference algorithms.


Google Brain co-founder teams with Foxconn to bring AI to factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's