DESCRIPTION Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon's customers? Do you want to build advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify?

Last week, deep learning research leader Yann LeCun took part in a Quora Session, during which he answered questions from community members on a wide variety of (mostly machine/deep learning) topics. When will we see a theoretical background and mathematical foundation for deep learning? The answer turned into a very eloquent overview of three particular thoughts on why deep learning works so well. Here is a quick overview. LeCun's first point of explanation, which maps to a good reason why deep learning works so well, is as follows: One theoretical puzzle is why the type of non-convex optimization that needs to be done when training deep neural nets seems to work reliably.

You know what I love on a Monday? Coming into work to be handed a lengthy machine learning paper by my editor and asked to write up its findings by lunchtime. Any human reader out there can probably identify hints of sarcasm in these sentences (as it happens, this is a double sarcasm bluff: the paper's actually pretty darn interesting). A computer, however, takes things literally -- which is exactly the problem. "The goal of my present work is sarcasm detection," Silvio Amir at the University of Lisbon, Portugal, told Digital Trends.

This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Four lessons from IoT early adopters: To paraphrase the late Roy Scheider in one of the greatest of all summer movies, you're gonna need a bigger router. In 2025, Machina Research predicts, the Internet of Things is going to be a 3 trillion market of 27 billion devices generating more than 2 zettabytes of data. Two zettabytes of data is something like twice the total global IP traffic we'll generate this year, according to Cisco. The IoT data deluge is, by the way, the first of four lessons drawn from early IoT adopters by contributing writer Howard Baldwin for his article in Computerworld.

Nils Lenke is the senior director of corporate research at Nuance Communications. There is an array of neural net machine learning approaches that are simply more than just "deep." In a time when neural networks are increasingly popular for advancing voice technologies and AI, it's interesting that many of the current approaches were originally developed for image or video processing. One of those methods, convolutional neural networks (CNNs), makes it easy to see why image-processing neural nets are strikingly similar to the way our brains process audio stimuli. CNNs, therefore, nicely illuminate that our audio and visual processes are connected in more ways than one.

Moore's law, which says computing power doubles every two years, is getting harder to achieve on a single chip. Tech industry heavyweights including IBM, NVIDIA, Google and Mellanox are now working together through the OpenPOWER Foundation to enhance speed at the system level by engineering chips, interconnects, accelerators, memory and other components that work together seamlessly. This will help ensure fast and powerful AI systems that support breakthroughs on numerous key fronts. AI's evolution will continue to drive innovation that makes for smarter cities, improved healthcare, and other advances that will continue to improve our lives.

The idea of automated customer service is nothing new – from speech recognition technology on dreaded call center phone lines, to Apple's groundbreaking Siri personal assistant system, AI has been in our lives and in our pockets for a while now. Yet, young as 2016 still is, it's already defined itself as the year of the Chatbot – from Microsoft's experimental and indeed controversial (more on that later) attempt to engage millennials with the creation of'Tay', a chatbot designed to imitate a teenage girl, in March, to April's recent inundation of announcements from companies eager to trial Chatbots, this year could be the year Chatbots go mainstream. But in a world where the search for authenticity seems increasingly futile and more and more of our meaningful interactions take place via a screen, will the public really welcome Bots? Are they even ready for widespread release? And how are businesses going to capitalize on them? Put simply, a Bot is a piece of software which performs automated tasks and simple, time-consuming or repetitive errands, and this is exactly what a ChatBot does – except (you guessed it), it simulates human interaction.

Artificial intelligence (AI) and machine learning (ML) have huge potential to drive a new generation of creative brand experiences. They are at the forefront of a powerful shift that will bring brands closer to consumer expectations, passions and emotions. Assistive and smart technologies are catching up and we're already facing a new world of possibilities. AI and ML can be applied in many ways. The use of machine learning to power business decisions and product recommendations is becoming widespread.

Artificial intelligence, or A.I., has been around since the start of computing and has had many false starts. The reality did not live up to the expectations set by science fiction. Accordingly, for many years, the majority of people's understanding of A.I. was confined to university laboratories, corporate skunk works, research parks, and that movie with Haley Joel Osment and Jude Law. Attempts to introduce A.I. products and services into the marketplace and for the broader benefits of society were ill-fated. Computing power was insufficient, and the abundance of structured data -- let alone a knowledge of what to do with said data -- was not yet upon us.

Former Acorn Computers and Element 14 chief Stan Boland, pictured above, is heading up Five AI, a start-up that is using artificial intelligence and machine learning to create a control system for autonomous vehicles that would ensure they could travel safely in crowded urban environments. "We will offer a complete software solution for driverless cars, from sensor input at one end to object perception and control of the vehicle at the other," he said. "We're using the most recent innovations in computer vision and artificial intelligence which means we're doing things differently from companies like Google and others already making systems for driverless cars." Indeed, most autonomous vehicles currently in development use complex 3D maps to keep track of their surroundings, so having an AI which could fulfill this role would save firms the hefty job of mapping the world's 37.2 million kilometers of road. "There's quite a big gap between what vehicle OEMs can do and the research that's going on in universities," said Boland.