Autism Spectrum Condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction and restricted or repetitive behaviors. Extensive research has been conducted to identify distinctions between individuals with ASC and neurotypical individuals. However, limited attention has been given to comprehensively evaluating how variations in image acquisition protocols across different centers influence these observed differences. This analysis focuses on structural magnetic resonance imaging (sMRI) data from the Autism Brain Imaging Data Exchange I (ABIDE I) database, evaluating subjects' condition and individual centers to identify disparities between ASC and control groups. Statistical analysis, employing permutation tests, utilizes two distinct statistical mapping methods: Statistical Agnostic Mapping (SAM) and Statistical Parametric Mapping (SPM). Results reveal the absence of statistically significant differences in any brain region, attributed to factors such as limited sample sizes within certain centers, noise effects and the problem of multicentrism in a heterogeneous condition such as autism. This study indicates limitations in using the ABIDE I database to detect structural differences in the brain between neurotypical individuals and those diagnosed with ASC. Furthermore, results from the SAM mapping method show greater consistency with existing literature.

Last week, on the eve of the New Hampshire primary, some of the state's voters received a robocall purporting to be from President Joe Biden. Unlike the other such prerecorded calls reminding people to vote, this one had a different ask: Don't bother coming out to the polls, the voice instructed. Better to "save your vote for the November election." The message was strange, even nonsensical, but the voice on the line sure did sound like the president's. And caller ID showed that the call came from a former chair of the New Hampshire Democratic Party, according to the Associated Press.

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science.

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, Ph.D., assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."

How many stores that you grew up visiting aren't around anymore? Remember Sears, Zellers, American Apparel, Wet Seal, The Limited, etc. Many other prominent retailers like Aeropostale, Bebe, A&F, Guess, J.C Penney, Payless, Rue2, etc. are closing hundreds of stores nationwide to deal with dwindling sales. Forbes estimates that in last sector of 2017, 21 retailers are closing 3,591 stores. They can't compete with the e-commerce sector as more and more customers resort to shopping online as reduced prices from the comfort of their homes in lieu of physically going to retail stores. Retailers are forced to leverage data in order to upgrade their infrastructure and services to give their customers a better experience. Many job postings at Macy's, Coach, Kate Spade, Nordstrom, etc. show how serious and desperate the retail and fashion giants are in face of competition.

Kyunghyun Cho, an Assistant Professor at NYU's Center for Data Science (CDS) and the Courant Institute of Mathematical Sciences, has been selected as a 2017 CIFAR Azrieli Global Scholar. CIFAR is a global research institute that connects leading scholars through interdisciplinary research programs like child and brain development, cosmology, genetic networks, learning machines and brains, and more. Founded in 1982, as many as 18 Nobel Laureates have since been associated with CIFAR, and its selected researchers and fellows are continually amongst the most highly cited scholars in their fields. Supported by the Azrieli Foundation, CIFAR's two year program offers the opportunity for researchers like Cho be mentored by other experts in his field, as well as exchange ideas with industry leaders outside of academia. He will also receive $100,000 to support his work, which has already significant contributions to fields like medicine and neural machine translation.

On October 14-15, 2016, the NYU Center for Mind, Brain and Consciousness in conjunction with the NYU Center for Bioethics will host a conference on "The Ethics of Artificial Intelligence". Recent progress in artificial intelligence (AI) makes questions about the ethics of AI more pressing than ever. Existing AI systems already raise numerous ethical issues: for example, machine classification systems raise questions about privacy and bias. AI systems in the near-term future raise many more issues: for example, autonomous vehicles and autonomous weapons raise questions about safety and moral responsibility. AI systems in the long-term future raise more issues in turn: for example, human-level artificial general intelligence systems raise questions about the moral status of the systems themselves.