Earlier this week, Facebook updated its iOS app offering voice descriptions of photographs uploaded by its users. A big step forward for accessibility, but it's far from the only company looking to make the world more inclusive to the visually impaired. In fact, rapid advances in artificial intelligence, machine vision and image-recognition technology are opening up the digital world to the blind and visually impaired – and helping them to interact with their surroundings. One interesting example is Austrian start-up BLITAB, which has created the first ever tactile tablet for blind and visually impaired people, dubbed "the iPad for the blind". As Kristina Tsvetanova, co-founder & CEO at BLITAB Technology, explains, the device looks similar to an ebook but displays small physical bubbles instead of using a screen, which means users can view whole pages of braille text at once, without any mechanical elements.

Recently, Microsoft introduced a new AI chatbot called Tay. The company pulled the chatbot after a lot of drama last week. However, as it seems like, the company has big plans for artificial intelligence. According to a new report from Bloomberg, Microsoft is building "an army" of artificial intelligence bots, which will get revealed later today at Build 2016. At the conference, Microsoft CEO Satya Nadella will apparently unveil his new vision, which he is calling "conversation as a platform." The software giant will reveal several bots, which will have different tasks.

This weekend, the world's greatest Go player beat Google's AlphaGo, an AI program developed by Google's DeepMind unit. Lee Se-Dol, the 33-year-old South Korean has been pitted against a machine in a game that is arguably the most technically challenging thing to take place on a board of squares. Our biggest ever edition of TNW Conference is fast approaching! AlphaGo had already won three of the five games in the 1 million series, making Se-Dol's victory somewhat hollow. Machines have already beaten us mere mortals at chess – way back in 1997 when IBM's Deep Blue dispatched Garry Kasparov.

When we were born, the idea of such a small, powerful computer was a sci-fi dream, and now these smart-devices are everywhere, transforming personal health, relationships and business transactions so completely that life without these seems impossible. We're entering a new era of technology that's bound to reshape the lives of our children predominantly. Yes, this is the era of artificial intelligence. Artificial intelligence is one of the most talked subjects these days, and recent advances in technology have made AI even closer to reality than most of us can imagine. In Simplest terms AI is: "The capability of a machine to imitate intelligent human behavior" Artificial intelligence is a program that does a task and its performance gets better every time it does that task.

Originally the term "machine learning" was defined as "... artificial generation of knowledge from experience," and the first studies have been performed with games, i.e., with the game of checkers [1]. Today, machine learning (ML) is the fastest growing technical field, at the intersection of informatics and statistics, tightly connected with data science and knowledge discovery, and health is among the greatest challenges [2, 3]. Particularly, probabilistic ML is extremely useful for health informatics, where most problems involve dealing with uncertainty. The theoretical basis for the probabilistic ML was laid by Thomas Bayes (1701–1761), [4, 5]. Probabilistic inference vastly influenced artificial intelligence and statistical learning and the inverse probability allows to infer unknowns, learn from data and make predictions [6, 7].

Here we show the trickery as it evolves. The most important aspects to pay important to are the final predictions (bottom left) and the loss history (bottom right). While the results might initially seem quite drastic, and it might seem logical to completely distrust any results from neural networks that is probably a bit exaggerated. Since we had access to the complete network and could train as we wanted the results are significantly more successful than they would be on a blackbox network (which is the case for most public image APIs for example). The more important take away message is that the networks trained, even if they have been trained on millions of images, still do not really'understand' the images.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

We will teach machines to learn. But what will be the consequences of them taking an increasing role in teaching? Sam Smith argues that the growing use of machine learning in teaching and marking students' work, risks undervaluing and losing the unquantifiable skills that drive diversity, creativity and innovation. It was a 19th century folly that there was a hierarchy of progress - a canard that placed the aboriginal societies of Australia at the bottom, and the Strand in London at the peak. History may not repeat itself, but it certainly rhymes.

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors' website.

Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as "AI winters." This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as "convolutional neural networks." This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: "Deep Learning." Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties. LeCun was born in France, and retains from his native country a sense of the importance of the role of the "public intellectual." He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events. IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity. IEEE Spectrum: We read about Deep Learning in the news a lot these days.