This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an overview over the field of deep learning, covering fundamental and core concepts. The third part of the series covers sequence learning topics such as recurrent neural networks and LSTM. I wrote this series in a glossary style so it can also be used as a reference for deep learning concepts. The earliest deep-learning-like algorithms that had multiple layers of non-linear features can be traced back to Ivakhnenko and Lapa in 1965 (Figure 1), who used thin but deep models with polynomial activation functions which they analyzed with statistical methods. In each layer, they selected the best features through statistical methods and forwarded them to the next layer. They did not use backpropagation to train their network end-to-end but used layer-by-layer least squares fitting where previous layers were independently fitted from later layers.

BENGALURU: Mumbai-based Arya.ai offers its deep learning algorithms for developers to build intelligent AI systems that can adapt and do multiple things with minimal inputs from humans. From creating a diagnostic assistant for radiologists to a mathematical assistant for science academicians and on to drone image processing abilities, the uses appear to be really diverse. "We have already launched the advanced AI software tools in a closed group beta phase with developers internationally and researchers in select software companies, these developers are using these softwares for building robots that can assist professionals from different fields in their task," said Vinay Sankarapu, cofounder of Arya.ai (in picture). API for developers can be used for four specific categories. From creating custom APIs to use cases within computer vision, this could range from classifying or searching for products on e-commerce platforms by using visual inputs to security based face matching techniques, as well as language and reasoning, where event prediction can take place.

In December 1964, over a single evening session in Englewood Cliffs, New Jersey, John Coltrane and his quartet recorded the entirety of A Love Supreme. This jazz album is considered Coltrane's masterpiece – the culmination of his spiritual awakening – and sold a million copies. What it represents is all too human: a climb out of addiction, a devotional quest, a paean to God. Five decades later and 50 miles downstate, over 12 hours this April and fuelled by Monster energy drinks in a spare bedroom in Princeton, New Jersey, Ji-Sung Kim wrote an algorithm to teach a computer to teach itself to play jazz. Kim, a 20-year-old Princeton sophomore, was in a rush – he had a quiz the next morning.

Talking toys have come a long way since the original Furby. Now they're connected to the Internet, use speech recognition, and are raising a host of new questions about the online privacy and security of children. Hackers have already targeted toys. Late last year, Hong Kong-based digital toy maker Vtech admitted that cybercriminals accessed the personal information of 6.4 million children. Researchers have also shown how hackers can gain control of connected dolls.

Is Honda's walking robot Asimo marrying Pepper, the chattering robot from SoftBank? Automaker Honda Motor Co. and internet company SoftBank said they will work together on artificial intelligence to develop products with sensors and cameras that can converse with drivers. Asimo, first shown in 1996, walks, runs, dances and grips things. Asimo (left), first shown in 1996, walks, runs, dances and grips things. Pepper (right), which went on sale last year, doesn't have legs but is programmed to recognize mood swings in people it interacts with.

It's been a big year for the humanoid robot'Pepper,' who recently landed a job in Pizza Hut locations in Asia, and announced a partnership with Honda's robot Asimo. Now, the ambitious bot can add life insurance sales to its resume as well. Meiji Yasuda Life Insurance Co will be deploying 100 Pepper robots across 80 branches in Japan to help out on the sales floor by 2017. Meiji Yasuda Life Insurance Co will be deploying 100 Pepper robots across 80 branches in Japan to help out on the sales floor by 2017. It's been a big year for the humanoid robot, who recently landed a job in Pizza Hut locations in Asia, and announced a partnership with Honda's robot Asimo According to The Yomiuri Shimbun, Pepper will accompany employees and explain insurance products and services to customers.

Thanks to an abundance of digital data, and powerful GPUs, we are now capable of teaching computers to read, see, and hear. Just this year, a handful of high-profile experiments came into the spotlight, including Microsoft Tay, Google DeepMind AlphaGo, and Facebook M. These experiments all relied on a technique known as deep learning, which attempts to mimic the layers of neurons in the brain's neocortex. This idea – to create an artificial neural network by simulating how the neocortex works – has been around since the 1980s. During the training process, the algorithm learns to discover useful patterns in the digital representation of data, like sounds and images.

There is more to this tiny paper house than meets the eye. When three balls are placed on the roof and do not roll down the sides, one would think the creator has found a way to defy both geometry and gravity. But what's happening is your brain assumes the roof is constructed in a descending motion, when in fact it is ascending – which is revealed once the house's true form is shown. When three balls are placed on the roof and do not roll down the sides, one would think the creator has found a way to defy both geometry and gravity. But what's happening is your brain assumes the roof is constructed in a descending motion, when in fact it is ascending Kokichi Sugihara is famous for building 3D optical illusions that make viewers question the laws of nature, but are then blown away once the structure's true form is revealed.

Since the dawn of manufacturing, designers and engineers have repeatedly run up against limitations to making things. Their ability to execute and capacity to afford bringing their ideas to market were once constrained by the manufacturing facility they had to find--either local or offshore--to build the things they wanted to build. But in a new world of enhanced robotics, factory automation, 3D printing, generative design, and design-make-use convergence, engineers' project limitations will fade away. And it's all because machine learning, computing power, and robots in the workforce are increasingly capable and intelligent. Soon, engineers will be able to design the best thing possible and then hand it to robots to dissect and turn into a series of assembled 3D-printed components.

MedyMatch Technology Ltd., announced today that it has been ranked in the list of "100 Most Promising BigData Solution Providers" by CIOReview. "The companies selected for our 100 Most Promising BigData Solution Providers 2016 list are an elite group of companies whose products and solutions are changing their respective industries," said Jeevan George, Managing Editor of CIOReview. "We are proud to feature MedyMatch Technology in this edition for its effort in helping organizations to easily and quickly adopt BigData analytics as a core part of their business and accelerate conversion of data into valuable business insights." "It is an honor to be recognized by CIOReview for MedyMatch's achievements in cognitive analytics, artificial intelligence and medical imaging," said Robert Mehler, coFounder & COO. "It is a testament to the accomplishments and capability of our product development team in conjunction with our medical big data clinical partnerships," adds Mehler.