For most animals, walking is instinctual and they're on their legs just minutes out of the womb. For humans, and for robots, it's a trickier proposition that takes a little bit of learning. But with deep learning to help out, a software robot can brave all kinds of obstacles after just a little practice, and someday real-life robots might use the same tactics. The DeepLoco project--by Xue Bin Peng, Glen Berseth and Michiel van de Panne of University of British Columbia and KangKang Yin of National University of Singapore--is series of experiments in deep-learning locomotion presented Siggraph 2017, a conference for advanced computer animation. In its simplest terms, the DeepLoco project has two parts.

When it comes to recruiting tech talent, Toyota Motor Corp. is anything but subtle. The Japanese automaker recently launched a marketing campaign targeting information technology specialists and software engineers along Tokyo's suburban Nambu railway line, where the research centers of Japan's signature tech giants are clustered. "We want engineers from Nambu Line area more than from Silicon Valley," declares one poster at Mukaigawara Station, where one of the exits is designated exclusively for NEC Corp. employees. Toyota's talent raid is unusual in a country where lifetime employment is still the norm at many big companies. "It's very unique for a Japanese company as well-known as Toyota to blatantly target specific talent markets or companies with direct advertising in regional locations like this," said Casey Abel, managing director at recruiter HCCR K.K. based in Tokyo.

Describing the dimension reduction (DR) techniques by means of probabilistic models has recently been given special attention. Probabilistic models, in addition to a better interpretability of the DR methods, provide a framework for further extensions of such algorithms. One of the new approaches to the probabilistic DR methods is to preserving the internal structure of data. It is meant that it is not necessary that the data first be converted from the matrix or tensor format to the vector format in the process of dimensionality reduction. In this paper, a latent variable model for matrix-variate data for canonical correlation analysis (CCA) is proposed. Since in general there is not any analytical maximum likelihood solution for this model, we present two approaches for learning the parameters. The proposed methods are evaluated using the synthetic data in terms of convergence and quality of mappings. Also, real data set is employed for assessing the proposed methods with several probabilistic and none-probabilistic CCA based approaches. The results confirm the superiority of the proposed methods with respect to the competing algorithms. Moreover, this model can be considered as a framework for further extensions.

We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems. The algorithm is based on a local quadratic approximation, and hence, allows incorporating curvature information to speed-up the convergence. HAMSI is inherently parallel and it scales nicely with the number of processors. Combined with techniques for effectively utilizing modern parallel computer architectures, we illustrate that the proposed method converges more rapidly than a parallel stochastic gradient descent when both methods are used to solve large-scale matrix factorization problems. This performance gain comes only at the expense of using memory that scales linearly with the total size of the optimization variables. We conclude that HAMSI may be considered as a viable alternative in many large scale problems, where first order methods based on variants of stochastic gradient descent are applicable.

I was talking to one of my friends who happens to be an operations manager at one of the Supermarket chains in India. Over our discussion, we started talking about the amount of preparation the store chain needs to do before the Indian festive season (Diwali) kicks in. He told me how critical it is for them to estimate / predict which product will sell like hot cakes and which would not prior to the purchase. A bad decision can leave your customers to look for offers and products in the competitor stores. The challenge does not finish there – you need to estimate the sales of products across a range of different categories for stores in varied locations and with consumers having different consumption techniques. While my friend was describing the challenge, the data scientist in me started smiling! I just figured out a potential topic for my next article. In today's article, I will tell you everything you need to know about regression models and how they can be used to solve prediction problems like the one mentioned above. Take a moment to list down all those factors you can think, on which the sales of a store will be dependent on. For each factor create an hypothesis about why and how that factor would influence the sales of various products. For example – I expect the sales of products to depend on the location of the store, because the local residents in each area would have different lifestyle. The amount of bread a store will sell in Ahmedabad would be a fraction of similar store in Mumbai. Similarly list down all possible factors you can think of. Location of your shop, availability of the products, size of the shop, offers on the product, advertising done by a product, placement in the store could be some features on which your sales would depend on.

Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, discussed the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

On July 20, China's State Council issued the "New Generation Artificial Intelligence Development Plan" (新一代人工智能发展规划), which articulates an ambitious, three-step agenda for China to lead the world in AI. The Chinese leadership recognizes that AI will be critical to its "comprehensive national power" and competitiveness, including in national defense. Through this new strategic framework, China will undertake a "three in one" (三位一体) agenda in AI: tackling key problems in research and development, pursuing a range of products and applications, and cultivating AI industry. China wants to become a "premier global AI innovation center" by 2030. This plan seeks to redress current shortcomings and build up indigenous capabilities in innovation.

CHENNAI: With a PhD in mathematics, Bharat Ramakrishna was preparing content for school children when suddenly he found a well-paying job in the machine learning & data sciences space. No longer is a mathematics background purely academic. Maths majors are now in demand for a job in artificial intelligence and data sciences. "After graduating from the University of Utah, I was into preparing question banks for students. Now, concepts such as matrices, linear algebra and calculus are being used in artificial intelligence and it is easier for a mathematics graduate to learn coding than vice versa," said Ramakrishna, data scientist at Skillenza.

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies. Today's artificial intelligence (AI) revolution has been made possible by the big data revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today's web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits.

When 16-year-old Kavya Kopparapu wasn't attending conferences, giving speeches, presiding over her school's bioinformatics society, organizing a research symposium, playing piano, and running a non-profit, she worried about what to do with all her free time. It was June 2016, the summer after her junior year in high school, and Kopparapu was looking for a new project that would use her computer science skills. Her thoughts quickly turned to her grandfather, who lives in a small city on India's eastern coast. In 2013 he began showing symptoms of diabetic retinopathy, a complication of diabetes that damages blood vessels in the retina and can lead to blindness. Eventually he was diagnosed and treated, but not before his vision deteriorated.