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Learning online is contrasted with the opportunities a physical classroom environment has to demonstrate concepts using all five senses: for instance the color, smell and touch of a flower, the sliminess of a mollusk, the acrid smell of ammonia. The senses play an integral role in learning – one can go so far as to say that from an evolutionary standpoint it is their sole function; we learn through experience best, and the more vivid that experience is, the deeper the learning and retention. Developmental psychology literature (both popular and academic) agrees that external stimuli – particularly in children – grow neural pathways, and exaggerate and enhance learning. Young children have a surfeit of neuroglial cells, and the credo "use it or lose it" applies – neural cells and pathways not used in discovery and learning new things eventually degenerate and die. The most prevalent example is the relative ease with which young children can learn new languages, compared with when they get older.

The landscape of formal education in India is based on a relatively archaic model. Over the last 150 years, not much has evolved. The students still attend brick-and-mortar establishments for schools in order to educate themselves. The system is largely exam-driven, theoretical and impractical. The emphasis is on scoring rather than learning and subsequent application of the knowledge.

Gaining insights about the latest trends and thought leadership in artificial intelligence (AI) is as easy as reading Enterprise Digitalization. Follow these accounts on Twitter to stay up-to-date with the latest AI news. To help you determine which AI leaders to follow on Twitter, Enterprise Digitalization compiled a list of some of the top experts. Here are 21 AI experts on Twitter that you'll want to follow. The names on the list are in no particular order).

Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these problems are difficult or infeasible to obtain. The gradient-free (zeroth-order) method can address these problems because only the objective function values are required in the optimization. Recently, the first zeroth-order proximal stochastic algorithm was proposed to solve the nonconvex nonsmooth problems. However, its convergence rate is $O(\frac{1}{\sqrt{T}})$ for the nonconvex problems, which is significantly slower than the best convergence rate $O(\frac{1}{T})$ of the zeroth-order stochastic algorithm, where $T$ is the iteration number. To fill this gap, in the paper, we propose a class of faster zeroth-order proximal stochastic methods with the variance reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and ZO-ProxSAGA, respectively. In theoretical analysis, we address the main challenge that an unbiased estimate of the true gradient does not hold in the zeroth-order case, which was required in previous theoretical analysis of both SVRG and SAGA. Moreover, we prove that both ZO-ProxSVRG and ZO-ProxSAGA algorithms have $O(\frac{1}{T})$ convergence rates. Finally, the experimental results verify that our algorithms have a faster convergence rate than the existing zeroth-order proximal stochastic algorithm.

The idea sounded good in theory: Rather than giving away full-boat scholarships, colleges could optimize their use of scholarship money to attract students willing to pay most of the tuition costs. So instead of offering a $20,000 scholarship to one needy student, they could divide the same amount into four scholarships of $5,000 each and dangle them in front to wealthier students who might otherwise choose a different school. Luring four paying students instead of one nonpayer would create $240,000 in additional tuition revenue over four years. The widely used practice, called "financial aid leveraging," is a perfect application of machine learning, the form of predictive analytics that has taken the business world by storm. But it turned out that the long-term unintended consequence of this leveraging is an imbalance in the student population between economic classes, with wealthier applicants gaining admission at the expense of poorer but equally qualified peers. Machine learning, a branch of artificial intelligence, applies specialized algorithms to large data sets to discover factors that influence outcomes that might be invisible to humans because of the sheer quantity of data involved. Researchers are using machine learning to tackle a wide variety of tasks of unimaginable complexity, such as determining harmful drug interactions by correlating millions of patient medication records or identifying new factors that contribute to equipment failure in factories. Web-scale giants such as Facebook Inc., Google LLC and Microsoft Corp. have stoked the frenzy by releasing robust machine learning frameworks under open-source licenses.

Episode Summary: One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI.

One of the most contentious debates in technology is around the question of automation and jobs. At issue is whether advances in automation, specifically with regards to artificial intelligence and robotics, will spell trouble for today's workers. This debate is played out in the media daily, and passions run deep on both sides of the issue. In the past, however, automation has created jobs and increased real wages. A widespread concern with the current scenario is that the workers most likely to be displaced by technology lack the skills needed to do the new jobs that same technology will create.

This post was originally published in Innovate My School Inspiration, on January 26th, 2018. With artificial intelligence (AI) on the rise, educators have increasingly reflected on how this might impact teaching in the coming years, with some of the more scary predictions even suggesting that machines could one day replace teachers altogether. This fear is largely unfounded and an unhelpful way to think about AI and education; rather than posing a threat, when used correctly AI could actually be the very best sidekick for teachers in the classroom. The thought of a machine capable of learning with the potential to become smarter than us can often be a scary prospect, particularly because it challenges the common human belief that we are better and brighter than all other species on the planet. A number of rather recent headlines have fueled these fears, such as Facebook's decision to shut down its AI program last year after it invented its own language, or Stephen Hawking's warning that if we get AI wrong it could kill us when it gets too clever.

I recently took Andrew Ng's Machine Learning course on Coursera, and I'm hoping to write a series of blog posts on what I learnt. In these we will look at a variety of machine learning techniques and categories, starting with linear and logistic regression. Machine learning is something of a buzzword at the moment, but underneath all the hype it's a technology that's expected to revolutionise virtually all industries, and have a huge impact on people's lives in the coming decades. Machine learning problems can be split into supervised learning and unsupervised learning. Supervised learning works by giving the algorithm the "right answers", which are used to train the algorithm so that it can fit and predict when given new examples.