Causal relationships are concerned with consequences of actions or decisions; thus, understanding these relationships can be the key ingredient in many scientific studies. For instance, medical practitioners need to know whether a treatment is effective to the target disease in clinical trials; econometricians ask whether a particular purchasing behaviour drives a change in Consumer Price Index (CPI); epidemiologists want to understand whether a government intervention policy has a positive effect on the pandemic. While the goal of revealing causal effects remains the same, the focus in causal relationships can differ by applications. To describe different aspects of the causal notion and design statistical procedures for inferring causal effects, various frameworks have been developed including Rubin's potential outcome framework [Rubin, 2004, 2005], counterfactual distributions [Chernozhukov et al., 2013] and Pearl's causal graphical models [Pearl et al., 2000, 2016]. A succinct yet comprehensive introduction can be found in Peters et al. [2017]. Causality has also been an evolving field in machine learning community and machine learning techniques have been considered to improve the statistical procedures for causal discovery. In particular, nonparmetric independence [Gretton et al., 2005] and conditional independence [Fukumizu et al., 2007] measures have been exploited to infer causal graphical models [Colombo

One common symptom that people with autism struggle with is the inability to interpret facial expressions. This can lead to difficulty in reading social cues in their personal lives, school, workplace, and even media like movies and TV shows. However, researchers at MIT have created an AI that helped shed light on why exactly this is. A paper published on Wednesday in The Journal of Neuroscience unveiled research that found that neurotypical adults (those not displaying autistic characteristics) and adults with autism might have key differences in a region of their brain called the IT cortex. These differences could determine whether or not they can detect emotions via facial expressions.

Many of us easily recognize emotions expressed in others' faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. But new research, published June 15 in The Journal of Neuroscience, sheds light on the inner workings of the brain to suggest an answer. And it does so using a tool that opens new pathways to modeling the computation in our heads: artificial intelligence.

MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision. The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain. Led by Kohitij Kar, a postdoc at the McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, the study looked at an area called the ventrolateral prefrontal cortex (vlPFC), which sends feedback signals to the inferior temporal (IT) cortex via a network of neurons. The main goal of this study was to test how the back-and-forth information processing of this circuitry -- that is, this recurrent neural network -- is essential to rapid object identification in primates. The current study, published in Neuron and available via open access, is a followup to prior work published by Kar and James DiCarlo, the Peter de Florez Professor of Neuroscience, the head of MIT's Department of Brain and Cognitive Sciences, and an investigator in the McGovern Institute and the Center for Brains, Minds, and Machines.

While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the Principal Solution Architect at Accenture, took through a talk at CVDC 2020 on how to make AI more resilient to attack. As Kar shared, AI has become the new target for attackers, and the instances of manipulation and adversaries have increased dramatically over the last few years. From companies such as Google and Tesla to startups are affected by adversarial attacks.

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. The transformations of DTCCA are parametric functions, which are very different from implicit mapping in the form of kernel function. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on three multi-view data sets demonstrate the effectiveness of the proposed method.

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there's a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for artificial intelligence developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child's play for primates such as humans. In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

TradeRev-H, an enhanced version of ADESA's TradeRev app for one-hour online auctions, seeks to tap machine learning to change how vehicle inspections are done. KAR Auction Services Inc. unveiled the enhancements to TradeRev in March before the NADA Show. To indicate that the technology is groundbreaking, the "H" is for Grace Hopper, a U.S. Navy rear admiral credited with developing the precursor to the Common Business Oriented Language, or COBOL, computer- programming language. "For any digital remarketing channel, the visual condition report is key," said Peter Kelly, KAR's chief technology officer. Until now, that often has meant the use of third-party inspectors.

The trucks going down the road a decade from now will likely not look drastically different than they do today. And, despite all the hype over autonomous trucks, they probably will still have drivers. It's what's going on behind the dashboard and over the air that is truly exciting as we enter a new generation of smart and connected trucks. Class 8 trucks being introduced today are preloaded with an impressive -- and expandable -- suite of electronics and wireless communication capabilities. Over the next decade, experts say, those capabilities will exponentially expand the efficiency, safety, productivity, and visibility of commercial trucks hauling freight -- in ways that could fundamentally transform trucking and logistics in the 21st century. The three technologies driving these changes are vehicle connectivity, artificial intelligence, and autonomous operating systems. Many of the systems that will enable these changes are already on trucks today.

In group decision making (GDM) problems fuzzy preference relations (FPR) are widely used for representing decision makers' opinions on the set of alternatives. In order to avoid misleading solutions, the study of consistency and consensus has become a very important aspect. This article presents a simulated annealing (SA) based soft computing approach to optimize the consistency/consensus level (CCL) of a complete fuzzy preference relation in order to solve a GDM problem. Consistency level indicates as expert's preference quality and consensus level measures the degree of agreement among experts' opinions. This study also suggests the set of experts for the necessary modifications in their prescribed preference structures without intervention of any moderator.