Think your dog talks like people? Scientists say you might just be right.

Machine learning, artificial intelligence, deep learning… Unless you've been living under a rock, chances are you've heard these terms before. Indeed, they seem to have become a must for market researchers. Unfortunately, so many precise terms have never meant so little! For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless. My engineers would severely chastise me if I used these words incorrectly--an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.

Many of us know Jack and Jill went up a hill to fetch a pail of water. But did you know that Jill then went to the Georgia Institute of Technology? That's right -- Jill went on to college and is now a teaching assistant in a course on artificial intelligence (AI) in Georgia Tech's computer science program. Jill assists Ashok Goel, professor in the School of Interactive Computing. Jill, implemented on IBM's Watson platform, was first used during the spring 2016 semester to successfully answer frequently asked student questions without the help of humans.

I attended a talk by Andrew Ng at Stanford last week where he discussed the future of artificial intelligence. He reinforced my thinking around the importance of data to the success of any company working on a specific application of A.I. He also confirmed my hypothesis that the large players like Google and Baidu are open sourcing their algorithms and applications in order to gather even more tagged data for their training sets. While there are emerging techniques that may allow for training off of smaller data sets or ones that are completely unstructured in the future, today these techniques are still in their infancy. As a result, to build a defensible business, a company must create a large, proprietary data set that will lead to the best trained algorithms in its field.

The roots of machine and deep learning come out the study of the human brain, but while that may have been a starting point, modern AI doesn't function like a human brain today. As modern machines are structured differently to human brains, it stands to reason that they can learn and process information in different ways as well. In a fascinating blog post, the Google Translate team set out how they can use machine learning to translate between languages, even when the machine has never seen a direct example of this language pairing before. The AI here effectively translates all languages into a brand new language (clustering) it has developed from its learning - an approach to linguistics no human would ever take. Transfer learning of this type, combined with flexible design, has the potential to offer insights you'd never see from a human perspective.

How human vigor and algorithmic rigor are joining forces in the sales function. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. We live in a data-saturated world where a great many of our interactions with other humans happen online. It makes sense then that one of the most human of business activities -- sales -- is currently undergoing a digital renaissance. While the sales function has historically relied on metrics, today there is far more sales-centric data, and far richer data, than ever.

VR/AR and Voice-Activated AI are hot topics at 2017 DEW! The panel will focus on what is available in the market today. What are the available apps, content, devices, and head gear? What is the current state of consumer adoption? Mr. Lelyveld will present an overview and update on the state of art, technology, and business of AR.

Artificial intelligence (AI) research has a long history, but even before computers existed Hollywood has been implanting a fear of intelligent machines into the public psyche, with more than sixty films to date depicting stereotypical threats to humankind. But only in the last few years has AI featured as a regular news story, invariably illustrated by images of suit-wearing robots seated around a board table. As intelligent machines have become mainstream, all kinds of media outlets serve up stories - some of them post-truth - about how robots will replace humans across a wide range of sectors: thanks to their superior speed and intelligence many current jobs will become obsolete. It will happen fast - within the next generation, according to those seeking to grab our attention, leaving humans to find alternative employment, if they can. As soon as 2021, robots will eliminate 6% of all US jobs, according to market research company Forrester while the World Economic Forum (WEF) predicts a loss of 7 million jobs within four years.

Cosmologist Stephen Hawking and Tesla CEO Elon Musk endorsed a set of principles this week that have been established to ensure that self-thinking machines remain safe and act in humanity's best interests. Machines are getting more intelligent every year and researchers believe they could possess human levels of intelligence in the coming decades. Once they reach this point they could then start to improve themselves and create other, even more powerful AIs, known as superintelligences, according to Oxford philosopher Nick Bostrom and several others in the field. In 2014, Musk, who has his own $1 billion AI research company, warned that AI has the potential to be "more dangerous than nukes" while Hawking said in December 2014 that AI could end humanity. But there are two sides to the coin.

All of a sudden, AI is everywhere--in consumer products, in our entertainment, in our consciousness. Every day we hear stories from Google, Uber, Baidu, Microsoft, Amazon and others about unprecedented achievements in language translation, gaming, image recognition, music composition, beer-delivering driverless trucks, and a host of achievements, all powered by AI. But what is AI, really? And, to be fair, do we even understand what intelligence is? In 1983, and long before what we think of now as the Internet, a developmental psychologist named Howard Gardner published what is now a seminal work on the "Theory of Multiple Intelligences".