When it comes to artificial intelligence, forget the scary movies about rebellious robots or the dire warnings of a dystopian world of disconnected humanity imagined by some popular writers. AI promises, rather, to change our lives in profound ways we are just beginning to experience, according to a ground-breaking survey produced by Stanford University. Stanford is taking the long view of AI, with a project called One Hundred Study on Artificial Intelligence (AI100). The study, written by a panel of AI experts from multiple fields including healthcare, will continue as an ongoing activity, with periodic reports examining how AI will touch different aspects of daily life. The first of those reports, "Artificial Intelligence and Life in 2030," looks into the effects that AI advancements will have on a typical North American city a little more than a decade from now.

Always wanted to become a Data Scientist or a Machine Learning Engineer? We have come up with a list of top online courses that we know you will surely have fun learning. These specially selected courses will help you get started with data science, machine learning, and deep mining along with learning Python and R programming. The Discounts will be available for a few days only, so make sure to take advantage of them NOW! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within'quant' teams. 'Quants' (development analysts) design and implement complex models and are sought after by banks, fund managers, insurance companies, hedge funds, and financial software and data providers. Programming experience is an advantage but is not mandatory. Relevant work experience is also taken into account. The programme is delivered through a combination of lectures, tutorials, seminars, and project work.

Border control agencies are already using self-service kiosks to manage the crowds of international travelers entering their countries, but a high-tech type of kiosk in development can do more than just scan passports. The AVATAR--which stands for Automated Virtual Agent for Truth Assessments in Real-Time--can detect travelers trying to lie their way through customs, according to Vocativ. The self-service kiosks, created by the National Center for Border Security and Immigration at the University of Arizona in partnership with the Department of Homeland Security [PDF], scan travelers' passports and ask the kinds of questions posed by human agents, such as "Do you have any fruits or vegetables?" Sensors can identify body cues like facial expression, vocal tics, pupil dilation--and even cues that human agents can't see, like cardiorespiratory data--which could indicate that the person is lying and should be subject to additional screening. They can even see that you're curling your toes, according to a press statement from AVATAR researcher Aaron Elkins of San Diego State University, a professor who studies deception. The kiosks can be programmed to display several virtual agents, choosing from a woman or a man and a stern or a friendly face.

India: American technology major Nvidia has launched the Nvidia Inception programme in India, in recognition of the country's budding innovation ecosystem in Artificial Intelligence (AI). Inception is a virtual incubator programme to support startups with revolutionary ideas in AI. Members will receive a custom set of benefits, from hardware grants and marketing support to training with deep learning experts. The Inception Programme was launched in India at the inaugural Nvidia Emerging Companies Summit India, part of the GPU Technology Conference (GTCx), a platform for the brightest minds and greatest ideas in GPU computing. The momentum around AI among Indian innovators is so significant that, at launch, the Inception Programme already has close to 100 Indian startups as members.

P-values for coefficients of cylinders, horsepower and acceleration are all greater than 0.05. This means that the relationship between the dependent and these independent variables is not significant at the 95% certainty level. I'll drop 2 of these variables and try again. High p-values for these independent variables do not mean that they definitely should not be used in the model. It could be that some other variables are correlated with these variables and making these variables less useful for prediction (check Multicollinearity).

Your car may already feel like an extension of your home based on the hodgepodge of stuff collecting in the backseat, but it's apparent that technology will soon make the two more connected than ever. No one has proposed blurring the line more than Hyundai -- which unveiled a futuristic concept car last week that literally connects to the home via a hole in the wall. Hyundai envisions the car becoming a lounge-like extension of the living space that provides air conditioning and entertainment, and acts as a back-up generator. "By seamlessly blending features from the car with home and work environments, the user experience is uninterrupted whether socializing, working at home, or on the move," Hak Su Ha, Hyundai's design center director, said in a news release. But even if cars never plug into the home physically, they will digitally.

In this paper, we develop a new sequential regression modeling approach for data streams. Data streams are commonly found around us, e.g in a retail enterprise sales data is continuously collected every day. A demand forecasting model is an important outcome from the data that needs to be continuously updated with the new incoming data. The main challenge in such modeling arises when there is a) high dimensional and sparsity, b) need for an adaptive use of prior knowledge, and/or c) structural changes in the system. The proposed approach addresses these challenges by incorporating an adaptive L1-penalty and inertia terms in the loss function, and thus called Inertial Regularization and Selection (IRS). The former term performs model selection to handle the first challenge while the latter is shown to address the last two challenges. A recursive estimation algorithm is developed, and shown to outperform the commonly used state-space models, such as Kalman Filters, in experimental studies and real data.

In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred. In recent years, there has been much development for binary classification with a reject option. Yet, little progress has been made for the multicategory case. In this article, we propose margin-based multicategory classification methods with a reject option. In addition, and more importantly, we introduce a new and unique refine option for the multicategory problem, where the class of an observation is predicted to be from a set of class labels, whose cardinality is not necessarily one. The main advantage of both options lies in their capacity of identifying error-prone observations. Moreover, the refine option can provide more constructive information for classification by effectively ruling out implausible classes. Efficient implementations have been developed for the proposed methods. On the theoretical side, we offer a novel statistical learning theory and show a fast convergence rate of the excess $\ell$-risk of our methods with emphasis on diverging dimensionality and number of classes. The results can be further improved under a low noise assumption. A set of comprehensive simulation and real data studies has shown the usefulness of the new learning tools compared to regular multicategory classifiers. Detailed proofs of theorems and extended numerical results are included in the supplemental materials available online.

Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes. However building an fMRI decoder with the typical structure of Convolutional Neural Network (CNN), i.e. learning multiple level of representations, seems impractical due to lack of brain data. As a possible solution, this work presents the first hybrid fMRI and learnt deep features decoding approach: collected fMRI and deep learnt representations of video object classes are linked together by means of Kernel Canonical Correlation Analysis. In decoding, this allows exploiting the discriminatory power of CNN by relating the fMRI representation to the last layer of CNN (fc7). We show the effectiveness of embedding fMRI data onto a subspace related to deep features in distinguishing two semantic visual categories based solely on brain imaging data.