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 raises $175 million fund for AI startups

#artificialintelligence

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,


Amazon Is Building a Voice-Controlled Robot That's Like a 'Mobile Alexa'

TIME - Tech

Amazon is developing a higher quality version of the Echo speaker and ramping up work on its home robot. The company plans to release the new Echo by next year, according to people familiar with the product. Prototypes of the cylindrical speaker are wider than the current Echo to squeeze in additional components including at least four tweeters, said the people, who requested anonymity to discuss an internal matter. The robot, previously reported by Bloomberg, has wheels and can be controlled by Alexa voice commands, the people said. Both devices are being developed by Amazon Lab126, a research and development arm based in Sunnyvale, California.


Explainable Agency for Intelligent Autonomous Systems

AAAI Conferences

As intelligent agents become more autonomous, sophisticated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player's path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more self-reliant, people will require that they explain their behaviors on demand. The more impressive these agents' abilities, the more essential that we be able to understand them.