People listen to the radio as the results of the presidential elections are announced in Kireka, Uganda, in February. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. People listen to the radio as the results of the presidential elections are announced in Kireka, Uganda, in February. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. Its omnipresence can make it easy to forget that making this technology has been really, really hard.

GPU's Role in Artificial Intelligence Advances Featured at Conference By Wayne Rash Posted 2016-10-26 Print NEWS ANALYSIS: The confluence of big data, massively powerful computing resources and advanced algorithms is bringing new artificial intelligence capabilities to scientific research. WASHINGTON, D.C.--Massively parallel supercomputing hardware and advanced artificial intelligence algorithms are being harnessed to deliver powerful new research tools in science and medicine, according to Dr. France A. Córdova, director of the National Science Foundation. Córdova spoke Oct. 26 at the GPU Technology Conference organized by Nvidia, a company that got its start making video cards for PCs and gaming systems and now manufactures advanced graphics processors for high-performance servers and supercomputers. Córdova, who is directing long-term research in AI at the NSF, said the research there is being used already in the Cancer Moonshot project currently spearheaded by Vice President Joe Biden, whose son Beau Biden died of brain cancer in 2015 at the age of 46. The Cancer Moonshot is a major effort to focus resources and funding on the fight to cure cancer on a scale similar to the original mission by NASA to land on the moon.

Fueled by the exponential growth in mobile messaging, chatbots -- interactive messaging bots that harness recent advances in artificial intelligence and machine learning -- are the hottest new technology going right now. Facebook opened up its Messenger platform to bot developers earlier this year; messaging app Telegram is offering developers up to $1 million in prizes to develop bots that are fast, useful, and work in inline mode; and over 20 million people chat with the Xiaoice bot on the Chinese micro-blogging service Weibo. Even the White House has gotten into the act with its Obama Facebook chatbot. Chatbot technology is still in its infancy, but it's quickly being embraced by businesses because of its vast potential for sales, marketing, and customer service. Chatbots stand to help organizations build deeper relationships with their customers and improve service quality, while at the same time save money by automating certain administrative tasks.

Earth experienced a near miss on Sunday night as an asteroid passed near our planet. While the object posed no threat, it did get close, dodging us by only 310,000 miles. Astronomers spotted this the Near Earth Object (NEO) thanks to an experimental "intruder alert" NASA program to detect and track potentially harmful space rocks passing close to our planet. The asteroid is one of many recent discoveries of NEOs as NASA and other international programs continue to refine techniques for discovering these potential threats from space. In recent years, scientists have become increasingly concerned with the question of protecting Earth from potential collisions.

The Joint Optimization of Fidelity and Commensurability (JOFC) manifold matching methodology embeds an omnibus dissimilarity matrix consisting of multiple dissimilarities on the same set of objects. One approach to this embedding optimizes the preservation of fidelity to each individual dissimilarity matrix together with commensurability of each given observation across modalities via iterative majorization of a raw stress error criterion by successive Guttman transforms. In this paper, we exploit the special structure inherent to JOFC to exactly and efficiently compute the successive Guttman transforms, and as a result we are able to greatly speed up the JOFC procedure for both in-sample and out-of-sample embedding. We demonstrate the scalability of our implementation on both real and simulated data examples.

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable clustering models. However, for some applications, this assumption is inappropriate. For example, when performing entity resolution, the size of each cluster should be unrelated to the size of the data set, and each cluster should contain a negligible fraction of the total number of data points. These applications require models that yield clusters whose sizes grow sublinearly with the size of the data set. We address this requirement by defining the microclustering property and introducing a new class of models that can exhibit this property. We compare models within this class to two commonly used clustering models using four entity-resolution data sets.

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.

Rai said that his AI system shows that the candidate in each election who had leading engagement data ended up winning the election. "If Trump loses, it will defy the data trend for the first time in the last 12 years since Internet engagement began in full earnest," Rai wrote in a report sent to CNBC. Currently most national polls put Clinton and the Democrats ahead by a strong margin. Rai said his data shows that Clinton should not get complacent. But the entrepreneur admitted that there were limitations to the data in that sentiment around social media posts is difficult for the system to analyze.

Introduction: In the first blog, we decided on the predictors. We knew that different predictive models have different assumptions about their predictors. Random Forest has none, but Logistic Regression requires normality of the continuous variables, and assumes the probability between 2 consecutive unit levels in a series of numbers to stay constant. K Nearest Neighbors requires the predictors to be at least on the same scale. SVM, Logistic Regression, and Neural Networks tend to be sensitive to outliers.

The BoniRob is a multipurpose robotic platform for agricultural applications featuring independently steerable drive wheels and adjustable track width. Field farming is "the world's oldest profession," and not just because food plants have been cultivated for over 10,000 years. Its individual practitioners are old as well, the median age rising rapidly as young people abandon the farming lifestyle (the U.S. Department of Agriculture reports a median age of 58 in 2012, up from 55 in 2002, with other countries showing similar data). Those who remain face the same repetitive work of seeding, weeding, feeding, and harvesting, the tedium of each task increasing as farms grow ever larger. However, today's agricultural robots excel at repetitive tasks, letting farmers tend to more strategic matters.