In a fourth-floor Tufts lab, a computer program was in the process of convincing a student that she was actually interacting with a human. It was spring 2015, and the student had come to the lab for a study involving a new way of teaching people to play the piano. The beginning of the session had been fairly unremarkable. The researchers--Beste Yuksel, E16, then a Ph.D. candidate in computer science, and Kurt Oleson, A15, a brain science major with a minor in music engineering--put a headband-like contraption on the student's head and sat her down at a piano keyboard. In front of the keyboard was a computer screen that displayed the soprano line of a Bach piano chorale that she was supposed to play.

For the past year, we've ranked nearly 14,500 Machine Learning articles to pick the Top 10 stories (0.069% chance) that can help you advance your career in 2017. This machine learning list includes topics such as: Deep Learning, A.I., Natural Language Processing, Face Recognition, Tensorflow, Reinforcement Learning, Neural Networks, AlphaGo, Self-Driving Car. This is an extremely competitive list and Mybridge has not been solicited to promote any publishers. Mybridge A.I. ranks articles based on the quality of content measured by our machine and a variety of human factors including engagement and popularity. Academic papers were not considered in this batch.

This tutorial series shows how you can create a chatbot that can be deployed on two messaging applications: Facebook and Slack. In this final tutorial, I explain how you can enhance the chatbot by using IBM Watson Services. The news chatbot in this series uses developerWorks content as an example, but you can modify the content source to meet your own needs. In a previous tutorial, I described the importance of artificial intelligence (AI) in your chatbots and explained how it's hard to build your own AI--it requires not only rock stars in data science, but also a massive amount of data to train models. A small company typically does not have these kinds of resources.

Although this may sound a like a story line from a futuristic drama, the reality is this future may be closer than you think. Call any support line or customer service centre today, and you hear a very simple phone bot. It's an awful experience burned by repetitive questions resulting in a less than optimal experience. For most, the first response is to hit 0 and hope to speak to an actual person. Advancements in natural language processing and generation are quickly propelling this kind of experience to a point where you soon won't even know you're speaking to a bot when you hit 0. Instead, the system will adapt to your preferences. The voice is smooth and comforting, a replica of the company spokesman Morgan Freeman.

Cloud mania continues to grow as businesses move more and more workloads to platforms such as Microsoft Azure and Amazon Web Services (AWS). But while public cloud hype is stealing all the headlines, private data centers are quietly plodding along and growing, as well. There is so much data growth today that businesses have to invest in both public clouds and private data centers, hence the high adoption rate of "hybrid" environments. The landscape for public cloud services is set--Azure and Amazon have won that battle--but private data centers are in a state of change. The legacy model of buying best-of-breed components and cobbling the technology together to build a private cloud is a long, complex process that just can't keep up with the needs of a digital organization.

New research indicates that Artificial Intelligence, or AI, as it is defined and practised today, has several limits. New buzzwords only serve to mystify the populace, and it is increasingly clear to me that many technologists and information technology (IT) managers are just groping about in the dark. They throw out terms such as "neural networks", "deep learning", "big data", "black box systems", and so on, hoping to mask the fact that they know very little of how this technology may evolve over the next several years. As an observer, I can't help but think there is an important question in front of us: are the ramblings of these pundits in fact a case of the one-eyed man becoming king in the land of the blind--or, instead, more akin to the parable of the five blind men, who all encountered an elephant and, after inspecting various parts of the elephant by touch, came away with different definitions of what an elephant is like? The vital premise in today's AI is that the computer program itself learns as it goes along, creating a database of information, and then, uses that database to automatically generate additional computer programming codes as it'learns' more--without the need for human programmers.

Given the subtle differences between AI and human intelligence, it's reasonable to expect that an autonomous AI system will generate its own criteria for judging what passes as new [scientific] knowledge. New knowledge which humans no longer experience as something they themselves have produced would shake the foundations of human culture." This will start with dense, urban areas, but over time every single square meter of every part of the globe will be recorded. Advances in computer vision & AI mean this data will be usable at scale, which will revolutionise advertising, law enforcement, and bring us back to a pre-privacy world." "Text Classification Using Neural Networks" submitted by gk_(@gk_).

Light-based (photonic) technologies offer many benefits when it comes to building a computer: they are efficient, have high bandwidths, and deliver fast processing speeds. But progress in photonics-based computing has almost always been outpaced by advances in semiconductor electronics, which make up the logical elements in today's computers. Where photonics does seem to be making headway is with alternative computation schemes, which solve problems differently than conventional transistor-based (binary) computers. One example of an alternative scheme is reservoir computing, which uses interconnected devices to mimic the neuronal architecture of the brain. Laurent Larger, of the French National Center for Scientific Research (CNRS) and the University of Burgundy Franche-Compté, and co-workers [1] have taken a photonics-based reservoir computer design and refitted part of it with optoelectronic components to achieve a threefold increase in processing speed.

Can artificial intelligence make you a better skier? The ski manufacturer teamed up with sports tech company PIQ to put an AI-powered computer -- complete with an LED display -- right on a pair of skis. Called the Rossingol Hero Master, the idea is similar to the PIQ Robot accessory, which attaches to ski boots and analyzes your turns, speed and other data while you ride. This takes that idea to the next level with the technology built right into the skis. Rather than just sending the data to an app on your phone, the Hero Master comes with its own LED display so skiers can see real-time stats and other information.The prototype uses onboard sensors and PIQ's algorithms to analyze speed, turning angles, and other data.

It's easy to forget that even with the fanciest of machine learning models, we still need humans in the trenches cleaning input data. Descartes Labs, a startup that combines satellite imagery with data about our planet to produce insights and forecasts, knows this all too well. The company ended up building its own cloud-based parallel computing infrastructure to clean and process its massive corpus of satellite imagery. Companies like Descartes Labs cannot just throw raw satellite imagery into machine learning models to extract insights. Images captured contain clouds, cloud shadows and other atmospheric aberrations that make it impossible to compare images taken at different times.