I want to make one point, that this is on the record. But we're going to have a great time discussing "Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence." So I'll start with simple introductions, and then we'll lay out some definitions about the kinds of terms that will be involved in this conversation. So my name is John Paul Farmer. Very happy to be here with three experts on the topic. Next to me is Dr. James Manyika, who is a recovering roboticist. And his day job is at McKinsey, at the McKinsey Global Institute, where he's been focusing on the future of jobs and the future of work in this new era. In the middle, we have Dr. Daniela Rus. Dr. Rus is a professor and roboticist at MIT, and she is also the director of the Computer Science and Artificial Intelligence Lab there. And at the end, we have Edwin van Bommel. Edwin is formerly of McKinsey, but now he's the chief cognitive officer at IPsoft. So, with that, let me lay out some definitions that are going to be important, I think, to following this conversation. You may have read in Foreign Affairs and elsewhere about this fourth industrial revolution, the changes that are happening in our society today and many more that will be coming down the pike. So as we--as we talk about these things, one, we should all be on the same page in terms of what artificial intelligence is. What do we mean when we say AI? And the definition that many accept is it's the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and even translation between languages. AI is sometimes humorously referred to as whatever computers can't do today. Machine learning is another term you're going to hear a lot, sometimes thought of as a rebranding of AI, of artificial intelligence. But there's one key difference, which is that it takes a much more probabilistic approach as opposed to deterministic. So it looks at not just yes or no; it looks at a 30 percent chance of X, a 10 percent chance of Y, and so on. Big data, a term that I think we've all heard. Data is the raw material. Some people call it the new oil for this new era.

Xiaoice is an artificial intelligence software. She has a'cute' voice, can instantly comment on weather data and big news events, and she has a job on morning TV. The Chinese live program, 'Morning News,' introduced the AI last year (pictured) The'death ray' that can knock out drones from more than a... Meet your housemates: Incredible images show the PARASITES... Is there an ancient civilisation on Mars? UFO hunters claim... Could we soon REGROW our limbs? Worm DNA may unlock the...

In the fall of 2010, I traveled to New Zealand, and one of the places I visited was the small south island city of Christchurch. I was charmed by the tree-lined Avon River, the English-style cathedral in the main square, and the mountains looming in the distance. Inside the cathedral was a stack of poems with a moving message of peace. I saved one to tack on my cork board at home, where it remains to this day. Three months later I turned on the news to see the Christchurch cathedral splintered and broken, its spire crumbled to the ground.

Would you go on a business date at work? Would you think I was weird if I told you I did? Some apps are making this possible, so I decided to try it out. As a family man who has just entered his 40s, I am not going to be arranging romantic liaisons on my smartphone. But maybe I haven't completely missed the boat when it comes to the thrill of swiping, matching and meeting up with strangers. Shapr is an app that works like the dating app Tinder, but it's for making business connections rather than romantic ones.

Brazilian IT services and business process outsourcing firm Tivit has decided to separate its two core service areas in order to drive growth and provide a better customer offering. As a result of the split of the two areas, a new BPO company has been created. Neobpo will have two business streams: one focused on traditional services such as back office, CRM and receivables and another focused on digital services including automation through an artificial intelligence platform provided by Tivit. According to the company's co-founder Luiz Mattar, the split had been under consideration for the last couple of years. As far and fast as cloud computing is embedding itself into the enterprise, there remain many cloud-resistant applications and services.

ORLANDO, Fla., October 18, 2016 View All Press Releases Gartner Reveals Top Predictions for IT Organizations and Users in 2017 and Beyond Analysts Explore the Digital Future at Gartner Symposium/ITxpo 2016, October 16-20 in Orlando Gartner, Inc. today revealed its top predictions for 2017 and beyond. Gartner's top predictions for 2017 examine three fundamental effects of continued digital innovation: experience and engagement, business innovation, and the secondary effects that result from increased digital capabilities. "Gartner's top strategic predictions continue to offer a provocative look at what might happen in some of the most critical areas of technology evolution. At the core of future outcomes is the notion of digital disruption, which has moved from an infrequent inconvenience to a consistent stream of change that is redefining markets and entire industries," said Daryl Plummer, managing vice president, chief of research and Gartner Fellow. "Last year, we said digital changes were coming fast. This year the acceleration continues and may cause secondary effects that have wide-ranging impact on people and technology."

During its wheeled treks on the Red Planet, NASA's Spirit rover may have encountered a potential signature of past life on Mars, report scientists at Arizona State University (ASU). To help make their case, the researchers have contrasted Spirit's study of "Home Plate" -- a plateau of layered rocks that the robot explored during the early part of its third year on Mars -- with features found within active hot spring/geyser discharge channels at a site in northern Chile called El Tatio. The work has resulted in a provocative paper: "Silica deposits on Mars with features resembling hot spring biosignatures at El Tatio in Chile." As reported online last week in the journal Nature Communications, field work in Chile by the ASU team -- Steven Ruff and Jack Farmer of the university's School of Earth and Space Exploration -- shows that the nodular and digitate silica structures at El Tatio that most closely resemble those on Mars include complex sedimentary structures produced by a combination of biotic and abiotic processes. "Although fully abiotic processes are not ruled out for the Martian silica structures, they satisfy an a priori definition of potential biosignatures," the researchers wrote in the study.

The Artificial Intelligence (AI) market is booming, with nearly every major player in the enterprise space vying for a foothold. IBM, Amazon, GE, Microsoft, Google, Cisco, SAP, HPE, Verizon--you name it, anybody and everybody, even folks who used to sell hardware--are actively working on and/or want to stake a claim in a piece of the AI pie. What encompasses the AI market? Machine learning, natural language processing (NLP), voice recognition and image processing, application and geography are what we're looking at, with mobile devices and cloud being the enablers. And just what will the AI market be?

Natural disasters are out of the reach and influence of human beings. However, a lot can be done to minimize loss of lives. Artificial intelligence is one viable option that can potentially prevent massive loss of lives while at the same time make rescue efforts easy and efficient. To learn more, checkout the infographic below created by Eastern Kentucky University's Online Masters in Safety degree program. In the period between 2005 and 2015, a total of 242 natural disasters occurred in the United States of America.

Machine Learning is a subfield of computer science by which computers have the ability to learn without being explicitly programmed. Machine learning is a method used to develop complex models and algorithms that lend themselves to prediction. Machine learning is deployed where the system deals with large scale of data. Deployments of machine learning leads to improved speed and accuracy of the functions performed by the system. Machine learning is majorly deployed for solving classification and regression problems.