Anticipating whether the car in front is going to take the next left or is slowing down is a fundamental part of driving, and key to not totalling your car. While keeping your eyes ahead should be second nature to those on the road, this most basic of tasks is still a challenge for driverless cars. But details have emerged of a patent filed by Google for its autonomous vehicles to detect and track brake and indicator lights of other cars on the road. Google has filed a patent for its autonomous vehicles to detect and track brake and indicator lights of other cars on the road. This will enable the driverless cars to better anticipate the movements of cars on the road. The technology would enable the driverless cars to anticipate the movements of cars on the road ahead using a forward-facing camera.

In theory, chatbots on Messenger would allow businesses to provide customer service without involving human workers. If you wanted to reach out to a business, you could do so via your Messenger app, rather than by looking up a phone number and calling. For example, Facebook showed off a chatbot for 1-800-Flowers.com that automatically takes orders via Messenger. It says things like: "White is a great choice! What is the recipient's name?"

For millions of people with swallowing disorders, preventing potentially deadly aspiration pneumonia requires following prescribed safe eating strategies. But adherence is poor, and caregivers’ ability to encourage adherence is limited by the onerous and socially aversive need to monitoring another’s eating. We have developed an early prototype for an intelligent assistant that monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one strategy called a “chin tuck.” Results indicate that adaptations of current generation machine vision and personal assistant technologies could effectively monitor chin tuck adherence, and suggest the feasibility of a more general assistant that encourages adherence to a wide range of safe eating strategies.

Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification. While the theory of these problems was thoroughly investigated in the 1980s, prior work either did not scale to industrial size instances or gave up correctness guarantees to achieve scalability. Recently, we proposed a novel approach that combines universal hashing and SAT solving and scales to formulas with hundreds of thousands of variables without giving up correctness guarantees. This paper provides an overview of the key ingredients of the approach and discusses challenges that need to be overcome to handle larger real-world instances.

The problem of designing recommender systems for scholarly article citations has been actively researched with more than 200 publications appearing in the last two decades. In spite of this, no definitive results are available about what approaches work best. Arguably the most important reason for this lack of consensus is the dearth of standardised test collections and evaluation protocols, such as those provided by TREC-like forums. CiteSeerX, a "scholarly big dataset" has recently become available. However, this collection provides only the raw material that is yet to be moulded into Cranfield style test collections. In this paper, we discuss the limitations of test collections used in earlier work, and describe how we used CiteSeerX to design a test collection with a well-defined evaluation protocol. The collection consists of over 600,000 research papers and over 2,500 queries. We report some preliminary experimental results using this collection, which are indicative of the performance of elementary content-based techniques. These experiments also made us aware of some shortcomings of CiteSeerX itself.

Retrieval systems for scholarly literature offer the ability for the scientific community to search, explore and download scholarly articles across various scientific disciplines. Mostly used by the experts in the particular field, these systems contain user community logs including information on user specific downloaded articles. In this paper we present a novel approach for automatically evaluating document similarity models in large collections of scholarly publications. Unlike typical evaluation settings that use test collections consisting of query documents and human annotated relevance judgments, we use download logs to automatically generate pseudo-relevant set of similar document pairs. More specifically we show that consecutively downloaded document pairs, extracted from a scholarly information retrieval (IR) system, could be utilized as a test collection for evaluating document similarity models. Another novel aspect of our approach lies in the method that we employ for evaluating the performance of the model by comparing the distribution of consecutively downloaded document pairs and random document pairs in log space. Across two families of similarity models, that represent documents in the term vector and topic spaces, we show that our evaluation approach achieves very high correlation with traditional performance metrics such as Mean Average Precision (MAP), while being more efficient to compute.

In this paper, we present an open-source emotion tracker and its innovative applications. Our tracker, EmoGram, tracks emotion changes for a sequence of textual units. It is versatile in terms of the textual unit (tweets, sentences in discourse, etc.) and also what constitutes the time sequence (timestamps of tweets, discourse nature of text, etc.). We demonstrate the utility of our system through our applications: a sequence of commentaries in cricket matches, a sequence of dialogues in a play, and a sequence of tweets related to the Maggi controversy in India in 2015. That one system can be used for these applications is the merit of EmoGram.

Relation extraction, which learns semantic relations of concept pairs from text, is an approach for mining commonsense knowledge. This paper investigates an approach for relation extraction, which helps expand a commonsense knowledge base with little labor work. We proposed a framework that learns new pairs from Chinese corpora by adopting concept pairs in Chinese commonsense knowledge base as seeds. Multiple instance learning is utilized as the learning algorithm for predicting relation for unseen pairs. The performance of our system could be improved by learning multiple iterations. The results in each iteration are manually evaluated and processed to next iteration as seeds. Our experiments extracted new pairs for relations “AtLocation”, “CapableOf”, and “HasProperty”. This study showed that new pairs could be extracted from text without huge humans work.

As another example, to achieve high accuracy in recommender systems [45], [60], we need to fully understand the content of items (e.g., documents and movies), analyze the profile and preference of users, and evaluate the similarity among users. Deep learning is good at the first subtask while PGM excels at the other two. Besides the fact that better understanding of item content would help with the analysis of user profiles, the estimated similarity among users could provide valuable information for understanding item content in return. In order to fully utilize this bidirectional effect to boost recommendation accuracy, we might wish to unify deep learning and PGM in one single principled probabilistic framework, as done in [60]. Besides recommender systems, the need for Bayesian deep learning may also arise when we are dealing with control of nonlinear dynamical systems with raw images as input. Consider controlling a complex dynamical system according to the live video stream received from a camera. This problem can be transformed into iteratively performing two tasks, perception from raw images and control based on dynamic models. The perception task can be taken care of using multiple layers of simple nonlinear transformation (deep learning) while the control task usually needs more sophisticated models like hidden Markov models and Kalman filters [21], [38]. The feedback loop is then completed by the fact that actions chosen by the control model can affect the received video stream in return.

Emotional intelligence is a cornerstone of human interactions--an essential part of what it means to be human. But now, artificial intelligences are being developed to better read and process human emotions, which is already changing the way we interact with robots. In the early 1990s, psychologists Salovey and Mayer were the first to recognize emotional intelligence as a set of knowledge and skills distinct from other forms of intelligence, defining it as "the ability to monitor one's own and other's feelings and emotions, to discriminate among them, and to use this information to guide one's thinking and actions." Emotional intelligence is something that seems wonderfully and innately human. But it turns out the tenets of emotional intelligence--which we start picking up in infancy and which seem so closely linked to human nature itself--can be quantified and reduced to logical procedures and algorithms.