Collaborating Authors

AI Will have Robot Judges Soon. What about Human Judges?


Just like In numerous enterprises, AI provides extraordinary benefits as well as risks for the legal industry. In the court framework, however, the stakes are uncommonly high. Utilizing a predictive algorithm to decide your kid's custody terms isn't exactly equivalent to Netflix recommending which film you should watch next. Most specialists in AI report that in the future AI will turn into a replacement for human jobs. Xiaofa stands in Beijing No 1 Intermediate People's Court, offering legal guidance and assisting general society with getting hold of legal terminology.

How artificial intelligence is shaping online retail in 2017


In just a few years, Artificial intelligence (AI) has successfully maneuvered itself into the daily vernacular of people worldwide. It takes countless shapes and forms, and has altered the way that we view the world and technological possibilities. There's no way of telling just how much development will take place in the next decade. But, one of the places in which AI has been truly impressive has been the world of online retail. We've seen a huge surge of companies using AI to increase their bottom line while providing enriched customer experience.

Deterministic Single-Pass Algorithm for LDA

Neural Information Processing Systems

We develop a deterministic single-pass algorithm for latent Dirichlet allocation (LDA) in order to process received documents one at a time and then discard them in an excess text stream. Our algorithm does not need to store old statistics for all data. The proposed algorithm is much faster than a batch algorithm and is comparable to the batch algorithm in terms of perplexity in experiments. Papers published at the Neural Information Processing Systems Conference.

PAC-Bayes bounds for stable algorithms with instance-dependent priors

Neural Information Processing Systems

PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.

Processing Algorithms: A Reporter's Guide


All of these are the result of algorithms meant to make our lives better. But what happens when those algorithms aren t fair? And what happens when it s government agencies that are using artificial intelligence to conduct the people s business? Government agencies are also increasingly turning to artificial intelligence, and this might sound like a good thing. Relying on data-based algorithms can remove the potential for human biases when making critical decisions or allocating resources.