Nvidia Corp. announced new processors Monday to try to embed its products in artificial-intelligence systems that are increasingly becoming part of daily life. The chipmaker, which dominates in video gaming, rolled out graphics chips for running software that makes split-second decisions needed when everything from phones to cars to internet search engines respond to inputs such as speech, images and moving objects. The company said its new Tesla P4 chip is for servers used in massive data centers. Based on its Pascal design, the P4 is more than three times as efficient at processing images than its predecessor and 40 times more efficient than Intel server chips, according to Nvidia. Another new chip, called the P40, is designed for more-powerful single computers, such as supercomputers.

Natural language processing (NLP) software algorithms for mammographic imaging characteristics and mammogram reports provide an automated means to aid in data extraction and analysis for clinical decision support, according to a study published in Cancer. Using the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings, researchers developed NLP software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The patients had undergone breast biopsy within three months of the date of their abnormal mammogram, between January 2006 and May 2015. A total of 543 patients who met the inclusion criteria were identified using the NLP algorithm to obtain key characteristics. The results showed that patients with estrogen receptor-positive tumors were more likely to have spiculated margins, and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications.Stephen T. Wong, PhD, PE "This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram. This has the potential to decrease unnecessary biopsies," lead author Stephen T. Wong, PhD, PE, chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute, said in a release.

SAN FRANCISCO--(BUSINESS WIRE)--Today Seal Software unveiled a new version of its leading contract discovery and analytics solution: Version 5.0. The new version introduces "Analyze This Now" (ATN), an add-in for Microsoft Word that allows business users to leverage Seal's powerful contract data extraction and review capabilities from within the familiar MS Word interface in order to shorten contact cycle times, reduce risk and lower processing costs. The release also extends Seal's machine learning framework and interface so Seal's technology can be used by business users to train the system for complex data extractions, improving extraction speed, accuracy and shortening time-to-value. "Seal Version 5.0 was developed with business users in mind, allowing them to easily harness the power of Seal without extensive education or resources. We want those closest to the business objectives in procurement, legal, M&A, and sales to use our system without extensive training and data management skills," said Ulf Zetterberg, co-founder and CEO of Seal Software.

The first introductory textbook on LCS algorithms (authored by Will Browne and myself) will be published by'Springer' this fall: (link will be found here once it's available) To follow research and software developed by Ryan Urbanowicz PhD on rule-based machine learning methods or other topics, check out the following links.

Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception. Imagine you're a doctor tasked with choosing a cancer therapy. You could think hard about the problem and come up with some rules. But these rules would be overly simplistic, not personalized to the patient or customer.

By 2021, robots will have eliminated 6% of all jobs in the US, starting with customer service representatives and eventually truck and taxi drivers. That's just one cheery takeaway from a report released by market research company Forrester this week. These robots, or intelligent agents, represent a set of AI-powered systems that can understand human behavior and make decisions on our behalf. Current technologies in this field include virtual assistants like Alexa, Cortana, Siri and Google Now as well as chatbots and automated robotic systems. For now, they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs like self-driving cars.

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

We are on the cusp of a major breakthrough in how organizations collect, analyze, and act on knowledge. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Artificial intelligence is about to transform management from an art into a combination of art and science.

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.

We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin classifier built on randomly-selected pairs of samples. The locality in VSC is achieved by multiplying the value of the feature by a confidence measure that can be characterized in terms of the Chebichev inequality. The output of the layer is then fed in a output layer of neurons. The weights of the output layer are then determined by a regularized pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of binary classification is carried out. Results on 22 benchmark datasets with fixed parameters show that VSC is competitive with the Multi Layer Perceptron (MLP) and outperforms the other competitors. An exploration of the parameter space shows VSC can outperform MLP.