Patent lawsuits are costly and time-consuming. An ability to forecast a patent litigation and time to litigation allows companies to better allocate budget and time in managing their patent portfolios. We develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigation based on both textual and non-textual features. Our work focuses on improving the state-of-the-art by relying on a different set of features and employing more sophisticated algorithms with more realistic data. The rate of patent litigations is very low, which consequently makes the problem difficult. The initial model for predicting the likelihood is further modified to capture a time-to-litigation perspective.

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.

This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in individual patients. As very few measurements are available for each patient, it is difficult to produce good predictors for individuals. A recently proposed clustering-based method enhances the power of prediction using patient data with similar spatiotemporal patterns. Each patient is categorized into a cluster of patients, and a predictive model is constructed using all of the data in the class. Predictions are highly dependent on the quality of clustering, but it is difficult to identify the best clustering method. Thus, we propose a method for aggregating cluster-based predictors to obtain better prediction accuracy than from a single cluster-based prediction. Further, the method shows very high performances by hierarchically aggregating experts generated from several cluster-based methods. We use real datasets to demonstrate that our method performs significantly better than conventional clustering-based and patient-wise regression methods, because the hierarchical aggregating strategy has a mechanism whereby good predictors in a small community can thrive.

Artificial intelligence (AI) is the most promising technology that can change the world, said Facebook's CEO Mark Zuckerberg on Saturday. "Artificial intelligence will understand senses, such as vision and feeling, better than human beings. Its application in daily lives such as autonomous driving will improve the world," Zuckerberg said at the China Development Forum in Beijing. According to him, though it will take a few more years for the cutting-edge technology to be widely used, its potential is huge. They can always maintain their focus.

Gary Marcus March 15, 2016 For nearly half a century, Artificial Intelligence (AI) has been more science fiction than science: exciting, possible, but just out of reach. And despite significant advances, "strong AI" in many ways remains elusive. Best-selling author and entrepreneur Gary Marcus provides a cognitive scientist's perspective on AI. What are we still struggling with? Perhaps most compelling, is there anything programmers of AI can still learn from studying the science of human cognition?

Microsoft's brain is now available for anyone to use in their apps. The company has open sourced the artificial intelligence framework it uses to power speech recognition in its Cortana digital assistant and Skype Translate applications. This means that anyone in the world is now free to view, modify, and use Microsoft's code in their own software. The framework, called, CNTK, is based on a branch of artificial intelligence called deep learning, which seeks to help machines do things like recognize photos and videos or understanding human speech by mimicking the structure and functions of the human brain. Tech giants like Microsoft, Google and Facebook have invested heavily in deep learning research for years, going so far as to hire many of academics who pioneered the field.

When a person's intelligence is tested, there are exams. When artificial intelligence is tested, there are games. But what happens when computer programs beat humans at all of those games? This is the question AI experts must ask after a Google-developed program called AlphaGo defeated a world champion Go player in four out of five matches in a series that concluded Tuesday. Long a yardstick for advances in AI, the era of board game testing has come to an end, said Murray Campbell, an IBM research scientist who was part of the team that developed Deep Blue, the first computer program to beat a world chess champion.

I have voraciously read endless pro and con scenarios about artificial intelligence since first writing about it years ago. At this point, there is no doubt that concerns about the dangers of runaway AI raised by Elon Musk, Stephen Hawking, Bill Gates, Bill Joy and others are genuine. There also is no doubt whatsoever that the new organizations aimed at mitigating the dangers -- OpenAI, The Future of Life Institute, Machine Intelligence Research Institute and others -- are extremely important developments. Clearly, no sane person or organization wants to see, let alone encounter, runaway AI. However, a base problem is that no one knows where the actual crossover point -- the edge or tipping point -- exists, and thus we mortals are unlikely to be able to prevent it from occurring. Said differently, there is a very high probability that we will misjudge where that crossover point is and will thus go beyond the key threshold.

With the advent of Amazon's Alexa and Siri's consistent capacity to take on more chores (and get more and more sassy), many are wondering: what's next for natural language understanding and conversational voice interfaces? There are several companies neck-and-neck in this race. There's Wit.ai, the company Facebook acquired -- you can toy around with demo. Apple has its HomeKit and, with it, is doing what Apple does best -- kicking ass. One company hot on the trail of natural language understanding is MindMeld. MindMeld provides its natural language understanding capabilities to other companies that are looking to add intelligent voice interfaces to their products, services, or devices.

The James Webb telescope will be the world biggest and most powerful telescope when it launches in 2018. Nasa describes it as a'time machine' that can peer back 200 million years after the Big Bang. This week, Nasa engineers in Maryland got a little closer to launch with the completion of testing on its science cameras and the installation of the final flight mirrors. NASA's James Webb Space Telescope completed primary mirror sits in the cleanroom at NASA Goddard Space Flight Center, and supported over it on the tripod is the secondary mirror After over a year of planning, nearly four months of final cold testing and monitoring, the testing on the science instruments module of the observatory was completed. They were removed from a giant thermal vacuum chamber at Nasa Goddard Space Flight Center in Greenbelt, Maryland called the Space Environment Simulator.