An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches. This learning problem is complicated by the rise of complex tasks on these platforms, such as web development and product design, that require a team of workers to complete. The success of a job is now a function of the skills and contributions of all workers involved, which may be unknown to both the platform and the client who posted the job. These team matchings result in a structured correlation between what is known about the individuals and this information can be utilized to create better future matches. We analyze two natural settings where the performance of a team is dictated by its strongest and its weakest member, respectively. We find that both problems pose an exploration-exploitation tradeoff between learning the performance of untested teams and repeating previously tested teams that resulted in a good performance. We establish fundamental regret bounds and design near-optimal algorithms that uncover several insights into these tradeoffs.

Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuousaction domains, multiagent coordination domains with continuous actions have received relatively few investigations. In this paper, we propose an independent learner hierarchical method, named Sample Continuous Coordination with recursive Frequency Maximum Q-Value (SCC-rFMQ), which divides the cooperative problem with continuous actions into two layers. The first layer samples a finite set of actions from the continuous action spaces by a re-sampling mechanism with variable exploratory rates, and the second layer evaluates the actions in the sampled action set and updates the policy using a reinforcement learning cooperative method. By constructing cooperative mechanisms at both levels, SCC-rFMQ can handle cooperative problems in continuous action cooperative Markov games effectively. The effectiveness of SCC-rFMQ is experimentally demonstrated on two well-designed games, i.e., a continuous version of the climbing game and a cooperative version of the boat problem. Experimental results show that SCC-rFMQ outperforms other reinforcement learning algorithms. A large number of multiagent coordination domains involve continuous action spaces, such as robot soccer [1] and multiplayer online battle arena game [2]. In such environments, agents not only need to coordinate with other agents towards desirable outcomes efficiently but also have to deal with infinitely large action spaces.

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. In experiments, we construct a real-world civil case dataset for LRC. Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. We will publish all source codes and datasets of this work on \urlgithub.com for further research.

Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you'll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school. Start with a problem tied directly to a specific business outcome. Make sure the subject of your pilot is small enough to tackle and clear enough to measure.

This animation shows 93 detected signals from FRB121102. Among them 21 have previously been reported (denoted by red numbers), and 72 are new (white numbers). Each signal is shown as a spectrogram - the colors indicate the intensity of the signal as a function of frequency from 4.5 to 8.0 GHz (vertical axis) and time (horizontal axis, showing 100 milliseconds around the time of detection of each burst). Researchers at Breakthrough Listen - the program searching for signs of intelligent life in the Universe - detected 72 new radio bursts from distant galaxies using AI. In 2017, the Listen team at UC Berkeley's search for extraterrestrial intelligence (SETI) research center, had detected 21 fast radio bursts (FRB) by analyzing over 400 terabytes of data.

The loss function is the bread and butter of modern Machine Learning; it takes your algorithm from theoretical to practical and transforms neural networks from glorified matrix multiplication into Deep Learning. This post will explain the role of loss functions and how they work, while surveying a few of the most popular of the past decade. At its core, a loss function is incredibly simple: it's a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your loss function will output a higher number. As you change pieces of your algorithm to try and improve your model, your loss function will tell you if you're getting anywhere.

Sebastian Thrun, one of the best known entrepreneurs in Silicon Valley, is taking on a new challenge that's a big shift from his work in autonomous transportation or online education: Thrun, who founded Google's research lab X and its autonomous car project, education start-up Udacity, and electric aircraft company KittyHawk, is a co-founder and chairman of a stealthy enterprise company called Cresta AI. He's the elder statesman of the founding team, which includes Zayd Enam and Tim Shi, who are both in their late 20s. Cresta's artificial intelligence-powered system is designed to help automate mundane office jobs, allowing users to get work done more efficiently. "If we train AI to take over boring repetitive tasks, we can free humanity and reach new heights," Thrun told CNBC. Enam, Cresta's CEO, describes an unusual situation that he counts as a sign of the success.

The door to education is communication, some say. And the big technology companies are opening that door more widely than ever before, not only as their products become more accessible to people with specialized needs, but also as educators find more ways to use those features in the classroom and beyond. The biggest technology companies -- think Apple, Google and Microsoft -- include language accessibility tools in their vast array of products, and those features are available to pretty much any user. And the companies have been actively improving those tools in recent years. Each of the tech giants has a group that promotes accessibility, said Luis Perez, technical assistance specialist at the National Center on Accessible Educational Materials, who himself has a visual disability.

The power of technology upon education has been immense over the past few decades. There was a time when education was allied with currency, but the things have been changed now. Great education for students is no more a dream. There are millions of applications available at the play store, choosing the right one can revolutionize the way a student looks at the process of learning. Educational Apps by fusion Informatics are making things stress free for students to understand.

I very well remember the cold sweat on my forehead when I opened our client's analytics reports in May 2012. I was working for an SEO agency in Germany and traffic of a few of our clients had dropped sharply. It was about a week after Google had rolled out "Penguin". Back then, it was perfectly normal to buy some backlinks as part of your SEO strategy. Instead, we start with keyword research, trying to understand user intent, screen the existing content, and… do a technical SEO audit. The latter has become a stable part of every SEO strategy.