It has been less than two weeks since a White House report warned that automation has the potential to disrupt millions of jobs, and this week a Japanese insurance firm proved how close that future might be. According to Japanese news publication Mainichi Shinbun (via Quartz), Fukoku Mutual Life Insurance Co. will be replacing roughly 30 percent of the employees in its payment assessment department with an artificial intelligence system powered by IBM Watson. Fukoku Mutual plans to lay off 34 employees by the end of March, and the company will not renew contracts for an additional 13 employees once they expire. The new AI will determine how much insurance should be paid out for each claim based on a number of factors, including the insured's medical history, the procedures they have undergone, the doctor's diagnosis and so on. According to Mainichi Shinbun, the types of cases the AI would handled totaled around 132,000 in 2015.

As someone who will become a future Software Engineer (Embedded / Cyber Security / AI most likely), my concern is this: lets say we increase the wages of undesirable jobs so as to compensate, then how do we handle other types of jobs? Certainly, one could argue my profession will have more'relevance' in this robotically automated world, so from my stand point it would seem unfair that wages increase for, say, the grass-cutter or dishwasher, while my job stays where its at. I suppose one could argue that "all jobs are equally important" but this simply isn't how our world works. Engineers make more money than teachers because we build the things that private / government entities need, and teachers make more money than grass-cutters because the future generations need to learn. The follow-up concern with this is that we're right back where we started in a sense: we have a hierarchical system of what is and is not important.

Agriculture is the industry that accompanied the evolution of humanity from pre-historic times to modern days and fulfilled faithfully one of its most basic needs: food supply. Today this still remains its core mission, but it's integrated in a more complex than ever mechanism driven by multiple sociological, economic and environmental forces. This $5 trillion industry representing 10 percent of global consumer spending, 40 percent of employment and 30 percent of greenhouse gas emissions continues to keep pace with world's evolution, changing tremendously over the past years. Digital and technological advancements are taking over the industry, enhancing food production while adding value to the entire farm-to-fork supply chain and helping it make use of natural resources more efficiently. Data generated by sensors or agricultural drones collected at farms, on the field or during transportation offer a wealth of information about soil, seeds, livestock, crops, costs, farm equipment or the use of water and fertilizer.

Today's consumers seek brands that create seamless experiences that feel less automated and more human, less generic and more personal, and less about the brand and more about them. With IBM Watson, Invoca is helping marketers across industries live up to and exceed these consumer expectations. By transforming phone conversations into a source of actionable data, Invoca is using IBM Watson to provide marketers the insights they need to deliver more personalized customer experiences. According to an IBM survey of over 700 CMOs, one of the top four priorities this year is to "inject data-driven insights into every marketing decision." When effectively applied to the customer experience, data has the potential to improve the customer experience, and this applies well beyond workflow automation and customer service bots – it applies to every single customer interaction.

Hi Mimi Yes, but I'm very concerned about the usage of private AI-blackbox as IBM Watson, in medicine, healthcare and any analytics and decisions regarding people-privacy/private citizens life. Generally speaking there is a huge concern, now underrated, about incoming pervasive presence of artificial intelligence systems in our societies (IBM is along with Google, Amazon and few others big players). Specifically, the problem I see with IBM Watson is that it is a closed product of a private company. There is nothing bad in a pure commercial product approach, but problems arise if this product become spread in public domains, public services (universities, hospitals, etc.). Technically speaking, is not clear to me (and some scientists I interviewed) Watson inside algorithms/mechanics, because it's fully closed; I call this AI-blackbox.

The Jeopardy Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researcherss, Watson is performing at human expert levels in terms of precision, confidence, and speed at the Jeopardy quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating, and advancing a wide range of algorithmic techniques to rapidly advance the field of QA. The architecture and methodology developed as part of this project has highlighted the need to take a systems-level approach to research in QA, and we believe this applies to research in the broader field of AI. We have developed many different algorithms for addressing different kinds of problems in QA and plan to publish many of them in more detail in the future.

A large radiology practice in the Miami area is the test bed for the first real-world application of IBM Watson interpreting medical images. Radiology Associates of South Florida, which has more than 75 physicians and handles about 1 million studies per year, is teaming with Baptist Hospital of Miami to apply Watson-powered "cognitive peer review" to medical imaging in an effort to diagnose aortic stenosis earlier. "We want to identify patients at high risk who may have been missed," said Dr. Ricardo Cury, director of cardiac imaging at Baptist Hospital of Miami and chairman and CEO of Radiology Associates. Watson speeds up the peer review process by assisting cardiologists and sonographers in spotting stenosis cases that otherwise might fall through the cracks, Cury explained at the annual meeting of the Radiological Society of North America in Chicago late last month. Watson looks for variations in practice, based on quality metrics and image analytics, explained Jon DeVries, global offering manager for IBM Watson Health Imaging.

At the end of June and beginning of July, I attended Viva Tech's international summit in Paris. Although the event was centred around Fintech and Insurtech, there were humanoid robots, flying drones, VR play stations… One could learn about the latest trends in retail and hospitality, media and medical industries, smart cities… But, the key idea of the organisers was to physically bring together top companies, investors and startups to boost innovation. There was a series of open innovation challenges designed to hack strategic business problems… In one word, a paradise for a guy like me! I learned a lot, met so many interesting people and interviewed some. One of them was David Kenny, General Manager of IBM Watson tasked to build Watson into "artificial intelligence as a service".

When conversation with my non-data scientist friends turns to AI it's almost inevitable that at least one will remark on the wonders of Watson. To many of the uninformed, Watson is synonymous with AI and clearly it's already here. So without getting so technical that their eyes glaze over, and that can happen pretty fast, here's a little bit of explanation you can use if you're caught in the same circumstance. The Watson that lives in the imagination of so many folks is the Watson that won the widely televised contest on Jeopardy in 2011. Fewer people are aware that the month following its televised debut, Watson went to Washington and played an untelevised set of matches against members of the House of Representatives where it also won.

IBM's Watson supercomputer to fight real-world cyber security Seven months after first announcing that it was teaching its Watson cognitive technology platform to fight cybercrime, IBM Corp. has launched it into the real world, at least in test mode. The Watson for Cyber Security platform has been designed to discover behaviour patterns and evidence of hidden cyber attacks and threats that could otherwise be missed by existing security platforms. It does so by using Watson's ability to reason and learn from unstructured data, including the 80 percent of all data on the Internet that traditional security tools cannot process, including blogs, articles, videos, reports, alerts and other information. The software incorporates capabilities such data mining for outlier detection, graphical presentation tools and techniques for finding connections between related data points in different documents, including the ability to identify warnings of new types of malware from even obscure sources. In the initial beta phase, customers are not being charged for the service. Some 40 organisations signed on for the beta test, including Sun Life Financial, the University of Rochester Medical Center, Avnet, SCANA Corp., Sumitomo Mitsui Banking Corp., California Polytechnic State University, the University of New Brunswick and Smarttech.