A new study has revealed that not all are doomed in a robot takeover. Researchers have discovered that people who are more intelligent and who showed an interest in the arts and sciences while in high school are less likely to fall victim to automation. The team concluded that these individuals are more likely to choose jobs that are more creative or have a higher degree of complexity that is not routine - two areas where robots fall short. Researchers at the University of Houston analyzed personality and background factors in order to determine whether a person will select jobs that are more likely to be automated in the future. The team used a dataset of 346,660 people from the American Institutes of Research.

Christopher Manning, a Stanford University professor who specialized in machine translation and has reviewed the paper, calls it an "impressive achievement," particularly because it can train translation models more quickly than existing systems. This past fall, Google unveiled a new translation system driven entirely by neural networks that topped existing models, and many other companies and researchers are pushing in the same direction, most notably Microsoft and Chinese web giant Baidu. "We've seen more improvements over the past two years than we have seen in the past decade," says John Tinsley, the CEO of Iconic Translation Machines, a translation technology company based in Dublin. And others have explored such networks as a basic technique for machine translation, including researchers at DeepMind, a Google AI lab based in London.

TwitterLinkedInGoogle Published on Dec 5, 2015, y que nos presentan así: In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They've mastered the ancient game of Go and thrashed the best human players. "The pace of progress in artificial general intelligence is incredible fast" (Elon Musk – CEO Tesla & SpaceX) leading to an AI that "would be either the best or the worst thing ever to happen to humanity" (Stephen Hawking – Physicist).

A version of this article appeared in the Spring 2017 issue of strategy business. On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health."

We've all heard the alarms sounding over the last few years -- the trifecta of robots, automation and artificial intelligence are marching in lockstep, shiny arms raised, taking not just our factory and mechanical jobs but also our mid-to-high level white collar jobs in finance, marketing and, ahem, journalism. A new survey by Pew Research Center called "The Future of Jobs and Jobs Training" follows a slew of studies, panels, FAQs and articles which herald this disconcerting prospect. But the report offers some hope: with the proper approach to training, learning and education, humans will do just fine in this brave new world. There's a catch: you have to reimagine everything you think you know about training, learning and education. Focusing on learning specific skills such as a particular computer programming language will not be the answer, Lee Rainie, a co-author of the Pew report, told CMSWire.

Every day it seems we are hearing of new advances made by AIs thanks to Machine Learning, from improving healthcare to beating us at poker, it is often easy to forget that, behind every successful robot, there's a clever human. The swift pace of change we are seeing today is due to a concerted effort across industry and academia to find practical uses for the ever-growing amount of data we are generating and collecting. So, in this post I am going to highlight some of the current movers'n' shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self-driving cars, to the industrial internet. Ng has just resigned from his post as chief data scientist at Chinese online giant Baidu. As well as that he is the founder of the online training resource Coursera and associate professor at Stanford University's computer science department.

There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

Every day it seems we are hearing of new advances made by AIs thanks to Machine Learning, from improving healthcare to beating us at poker, it is often easy to forget that, behind every successful robot, there's a clever human. The swift pace of change we are seeing today is due to a concerted effort across industry and academia to find practical uses for the ever-growing amount of data we are generating and collecting. So, in this post I am going to highlight some of the current movers'n' shakers, whose breakthroughs in machine learning are proving to be fundamental to developing the digital tools and technologies making AI possible, from social networks to self-driving cars, to the industrial internet. Ng has just resigned from his post as chief data scientist at Chinese online giant Baidu. As well as that he is the founder of the online training resource Coursera and associate professor at Stanford University's computer science department.

Training strategies have long since stopped being considered as'nice to have' motivational activity and more and more organisations are expecting close alignment of training and business in order to make training strategies effective. Some of the key expectations of the business from training include outcome driven approach, velocity in training delivery, adaptation to the dynamic needs of the business and tuning to the millennial mindsets in the design of the programme. In this context, it is prudent to take advantage of digital capabilities and design the strategy such that role-specific competency road map is built, which in turn is matched with the training modules that the employees are supported with. The HR Information Systems, Performance Management system and the Learning Management Systems should be integrated and provide the bedrock system for talent development for the organisation. The learning paths put in place for the employees should be supported with the right learning ecosystem both offline and online and be able to switch from one world to the other in a seamless fashion.

It's a few years old, but still acts as a great technical introduction to the topic.