Artificial intelligence is transforming the traditional delivery of legal services. In general terms, the set of tools broadly called "legal analytics" promises to do two things: increase the efficiency of tasks that once required substantial time and human effort, and mine masses of data to discover new insights that were previously inaccessible. As legal scholars, we're excited about the promise of applying these tools to legal research questions. Students are involved, too, so that we can educate the next generation of lawyers to leverage these tools in their own practices. Suppose that a company wants to forecast which employee complaints lead to lawsuits.

Applying artificial intelligence to self-driving cars to smooth traffic, reduce fuel consumption, and improve air quality predictions may sound like the stuff of science fiction, but researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have launched two research projects to do just that. In collaboration with UC Berkeley, Berkeley Lab scientists are using deep reinforcement learning, a computational tool for training controllers, to make transportation more sustainable. One project uses deep reinforcement learning to train autonomous vehicles to drive in ways to simultaneously improve traffic flow and reduce energy consumption. A second uses deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental sensors to improve air quality predictions. "Thirty percent of energy use in the U.S. is to transport people and goods, and this energy consumption contributes to air pollution, including approximately half of all nitrogen oxide emissions, a precursor to particular matter and ozone – and black carbon (soot) emissions," said Tom Kirchstetter, director of Berkeley Lab's Energy Analysis and Environmental Impacts Division, an adjunct professor at UC Berkeley, and a member of the research team.

Schools have been removing bathroom doors, posting bathroom monitors, and even closing bathrooms in their struggles to handle the surging popularity of vaping among middle school and high school students. That has translated into steady business for a U.S. company offering AI-assisted school surveillance capable of alerting teachers and administrators to suspected vaping or bullying in bathrooms. Soter Technologies originally developed its Fly Sense device with bullying detection in mind. But schools began asking the company about whether it could also help monitor the rise of vaping--also known as Juuling because of the popular Juul vape pens--among students. In response, the company made Fly Sense into a cloud-connected sensor capable of detecting both the telltale signatures of vaping and elevated sound levels that might indicate fighting or bullying.

"I'd rather we spend more time addressing these very real issues than the sort of fantasies, or scenarios leading to evil killer robots," Mr. Ng said in a recent interview. Mr. Ng is the former chief scientist of Baidu Inc., where he started a 1,300-person division that helped create the Chinese tech conglomerate's AI-powered search engine, virtual assistant and other products. Before that, he co-founded Google Brain, Alphabet Inc.'s deep-learning research team. His work on neural networks helped lead to the creation of an AI system capable of identifying images, such as cats, by watching videos. In April 2017, he left Baidu to launch the Palo Alto, Calif.-based online education platform called deeplearning.ai.

Large enterprises, startups and high-performance businesses across industries are increasingly turning to Artificial Intelligence and advanced analytics to make faster, more effective, data-driven decisions. The volume of unstructured and structured data stored by enterprises is growing at an accelerating rate. The demand for skilled data scientists and candidates with AI skills is at an all-time high. Yet developing those skills typically requires significant investments of time, energy and money. Businesses are struggling to successfully deploy and manage AI projects due to lack of resources.

Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pair-wise co-occurrence) in the multiple base clusterings, but generally neglect the multi-scale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association (ECA) matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multi-scale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach.

Human decision-makers are very good at taking decisions under rather imprecise specification of the decision-making problem, both in terms of constraints as well as objective. One 1 might argue that the human decision-maker can pretty reliably learn from observed previous decisions - a traditional learning-by-example setup. At the same time, when we try to turn these decision-making problems into actual optimization problems, we often run into all types of issues in terms of specifying the model. In an optimal world, we would be able to infer or learn the optimization problem from previously observed decisions taken by an expert. This problem naturally occurs in many settings where we do not have direct access to the decision-maker's preference or objective function but can observe his behaviour, and where the learner as well as the decision-maker have access to the same information. Natural examples are as diverse as making recommendations based on user history and strategic planning problems, where the agent's preferences are unknown but the system is observable. Other examples include knowledge transfer from a human planner into a decision support system: often human operators have arrived at finely-tuned "objective functions" through many years of experience, and in many cases it is desirable to replicate the decision-making process both for scaling up and also for potentially including it in large-scale scenario analysis and simulation to explore responses under varying conditions. Here we consider the learning of preferences or objectives from an expert by means of observing his actions.

Deep neural networks have led to a series of breakthroughs in computer vision given sufficient annotated training datasets. For novel tasks with limited labeled data, the prevalent approach is to transfer the knowledge learned in the pre-trained models to the new tasks by fine-tuning. Classic model fine-tuning utilizes the fact that well trained neural networks appear to learn cross domain features. These features are treated equally during transfer learning. In this paper, we explore the impact of feature selection in model fine-tuning by introducing a transfer module, which assigns weights to features extracted from pre-trained models. The proposed transfer module proves the importance of feature selection for transferring models from source to target domains. It is shown to significantly improve upon fine-tuning results with only marginal extra computational cost. We also incorporate an auxiliary classifier as an extra regularizer to avoid over-fitting. Finally, we build a Gated Transfer Network (GTN) based on our transfer module and achieve state-of-the-art results on six different tasks.

OVER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year. The standard route into these jobs has been a PhD in computer science from one of America's elite universities. Earning one takes years and requires a disposition suited to academia, which is rare among more normal folk.

Apple is investigating reports that one of its parts suppliers is illegally using high school students on its assembly line. Hong Kong-based human rights group Sacom alleges that Taiwanese manufacturer Quanta Computer has been skirting labor laws by using teenage "interns" to assemble the Apple Watch Series 4. According to the Financial Times, Sacom interviewed nearly 30 high school students working at Quanta Computer's factory in Chongqing, China and found many of them were working on the assembly line. One high schooler said there were about 120 students working in the plant, performing the same procedures over and over again "like a robot." A number of students reported being made to work overnight and night shifts, which is illegal under Chinese labor laws. The high schoolers were supposedly sent to the factories by teachers, and a number of the students said they were told they would not graduate if they didn't complete the internships.