Pioneering GCs are taking control of legal spend, armed with the latest tech. Can the rest of the in-house community keep pace? If conventional law firms have been slow to embrace technology – and they have – their counterparts in-house have been barely moving. But in the last five years signs have emerged of'early adopters' in the bluechip general counsel (GC) community who are willing to do more than apply new tools at the margins. The GCs are turning to technology to reshape the way they work. Aside from the obvious efficiency benefits, the appeal to such GC pioneers is often more potent to the professional soul: control. As Reckitt Benckiser's vice president and GC Claire Debney comments: 'I want the intellectual capital in-house, in my team. I don't want to be outsourcing all of our supply agreements, distribution agreements, or our digital platform agreements. We want our people at the meetings and in the negotiations and have the external back-up if we need it.'

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

With a pocketful of savings, Melbourne entrepreenur Michelle Mannering and three fellow founders are busy building a platform utilising artificial intelligence to make the world a smarter and better place. As a hacker-in-residence at Carlton Connect and a participant in the Melbourne Accelerator Program, Mannering and her team are one of the first residents in Melbourne's new "melting pot" for innovation. "My co-founders and I all met at a bunch of different hackathons," Mannering tells StartupSmart. Just a year after meeting, the team has launched Black AI, a startup offering a platform that is developing machine learning computer vision. "Basically, we just teach computers how to understand and interpret the world," Mannering says.

Kepware Technologies, a software development business focused on industrial connectivity, has donated 10,000 to Acton Elementary School, in Acton, Maine, as part of its third annual school grant contest. With its headquarters in Portland, Kepware is committed to the advancement of Science, Technology, Engineering, and Mathematics (STEM) education and workforce development in Maine and abroad. The donation will enable Acton Elementary School students to hone mechanical and computer skills essential for building and programming autonomous robots. "The solution to Maine's shortage of engineering talent starts with education," said Tony Paine, Platform President, Kepware. "Local programs and institutions aimed at nurturing STEM curiosity can help encourage Maine's youth to explore rewarding careers in science and engineering. We're honored to be able to assist in these efforts by bringing information and technology into the lives of students."

The entire AI system learns your environment and optimizes performance in real-time. The Expert System makes decisions regarding which parameters to tune based on patterns fed by the learning module. The Expert System also creates Client Behavior Profiles and uses fingerprinting to tailor Device-to-Access Point interactions.

It has become the mark of football's great freekick takers - the ability to strike the ball so it moves unpredictably through the air, leaving goalkeepers standing. Players like Christiano Ronaldo and Gareth Bale have become famous for using the so called'knuckleball' shot. Now researchers have built a robot that kick a'knuckleball' just like Ronaldo every time, but without the fear of crying if it loses, or stripping off its shirt if it wins. Researchers have now built a robot (pictured) that can kick the ball in a'knuckleball' way every time, and used a wind tunnel and high-speed camera to work out what forces cause this zigzag trajectory of balls through the air Knuckleball is a technique developed by baseball player Eddie Cicotte. Cicotte's method involved holding the ball between his knuckles with the intention of avoiding the rotational spin created by throwing a ball normally.

Anh Tong and Jaesik Choi Ulsan National Institute of Science and Technology Ulsan, 44919 Korea { anhth,jaesik } @unist.ac.kr Abstract Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute interesting probabilities of various large, real-world problems. When the structure of model is given, constructing a probabilistic program is rather straightforward. Thus, main focus have been to learn the best model parameters and compute marginal probabilities. In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given. The intuition behind of our method is to find a descriptive covariance structure of time series data in nonparametric Gaussian process regression. We report that such descriptive covariance structure efficiently derives a probabilistic programming description accurately.

The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.

We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable.

Many problems in science and engineering can be formulated as a sequential decision-making problem under uncertainty. A common scenario in such problems that occurs in many different fields, such as online marketing, inventory control, health informatics, and computational finance, is to find a good or an optimal strategy/policy, given a batch of data generated by the current strategy of the company (hospital, investor). Although there are many techniques to find a good policy given a batch of data, only a few of them guarantee that the obtained policy will perform well, when it is deployed. Since deploying an untested policy can be risky for the business, the product (hospital, investment) manager does not usually allow it to happen, unless we provide her/him with some performance guarantees of the obtained strategy, in comparison to the baseline policy (e.g., the policy that is currently in use). In this paper, we focus on the model-based approach to this fundamental problem in the context of infinite-horizon discounted Markov decision processes (MDPs). In this approach, we use the batch of data and build a model or a simulator that approximates the true behavior of the dynamical system, together with an error function that captures the accuracy of the model at each state of the system. Our goal is to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as the baseline strategy, using the simulator and error function. Most of the work on this topic has been in the model-free setting, where safe policies are computed directly from the batch of data, without building an explicit model of the system [12, 13]. Another class of model-free algorithms are those that use a batch of data generated by the current policy and return a policy that is guaranteed to perform better.