IBM and the Massachusetts Institute of Technology conducted research into artificial intelligence's impact on employment and found that the emerging technology would result in fewer tasks and more focus on work that requires "soft skills." MIT and IBM's Watson AI Lab analyzed the potential applicability of machine learning in 170M online job postings from 2010 to 2017 as part of the study titled "The Future of Work: How New Technologies Are Transforming Tasks," IBM said Wednesday. According to the study, there was a 3.7 decrease in required work across 18.5K tasks and 46 percent less demand for tasks that are more suited for AI and machine learning. The IBM-MIT team noted that workers were asked to perform 4.3 fewer tasks encompassing activities like scheduling and credential validation. Tasks requiring soft skills like common sense, intuition, judgment and creativity saw 2.9 fewer tasks throughout a seven-year period.

CytoReason's unmatched proprietary and public data, cutting-edge machine learning technologies, unique biological models and unrivalled analysis delivers: Only by understanding what human cells are doing in response to a disease, and its treatment, can we really understand how to better impact that disease. CytoReason's mission is to simulate the cells that can stimulate discovery of: We have developed a unique machine-learning driven approach to "seeing" the cells that can make the difference in patients seeing a better life. The insights our approach generates, enable pharmaceutical and biotech companies to make the right decisions, at the right time, in the drug discovery and development programs that bring better therapies. The immune system is a cell-based system. Gene-based studies can tell us a certain amount, but not the whole story.

Customer experience platform provider, Cerebri AI, recently announced the launch of its new second-generation solution, Cerebri Values. The Cerebri Values CX platform is now in full production and available to customers everywhere. The CV/CX version 2 offering measures customer engagement and uses the metrics around CX to drive financial results for businesses. Cerebri helps customers to manage cross-selling, up-selling and churn solutions. Designed as a new way for companies to apply AI to draw insights from modern customer journeys, CV/CX v2 is a leap forward in business intelligence and analytics.

Face recognition is one of the applications of Deep Learning which has witnessed successful practical implementations within a short span of time. From secure transactions to facial detection, the future of facial recognition looks bright and we're currently at the beginning of the facial recognition revolution. Currently, the applications focus moreover strengthening the security systems but its potential applications can bring a change to many other services. Cloud computing, edge processing, and Artificial Intelligence have played a crucial role in the advancement of computer systems. Here, we will be listing out some prime applications and impact of facial recognition.

Current commercial robots generally contain hard parts that represent a risk to the security of their administrators or there is a point of confinement for their usability. Because of this, soft robots have as of late pulled in impressive consideration, in spite of the fact that their absence of structural inflexibility intensely restricts their utilization in numerous practical applications. In the course of recent years, analysts have tried to make mechanical robotic personal assistants and bionic limbs or prosthetics that consolidate the strength of regular robots with the flexibility of soft robots. More recently, combinations of cellular structures have demonstrated intriguing advancement toward the enhancement of non-trifling abilities, for example, getting a handle on exceptionally shaped articles. In any case, tuning the mechanical properties of the robotic body for custom applications is still exceptionally challenging.

We sat down with Spiros Margaris, Venture Capitalist at Margaris Ventures and AI Influencer to discuss the state of VC funding of AI and Fintech startups and how crazy thoughts could be the start of an AI business. I have spent most of my professional career in the hedge fund and startup world. I also founded my own startups in New York during the dotcom era, so I know what it means when things don't go as planned, and what it can mean personally, with all the bitter consequences. I have been primarily involved in the fintech, insurtech and AI space as an investor and advisor through my firm Margaris Ventures. I have been a keynote speaker at major technology and banking events, where I have been fortunate to meet very interesting people and exchange thoughts and ideas.

The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function's forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions. Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle. Does this project match the characteristics of a typical machine learning problem? Is there a solid foundation of data and experienced analysts?

It seems like every week there's a new survey out detailing the ever-increasing amount of focus that IT shops of all sizes put on the technology. If it's true that data is the new currency, then it's artificial intelligence that mines that data for value. Your C-suite understands that, and its why they continually push to build AI and machine learning capabilities. Nowhere is AI/ML more impactful than in the world of government and government contractors. It's not just the usual suspects of defense and intelligence who demand these capabilities--AI/ML is fast becoming a fact-of-life across the spectrum of government agencies.

Up to 20 million manufacturing jobs could be lost to robots by 2030, according to a new report by Oxford Economics. The study found that robots will lead to twice as many manufacturing job losses in low-skill areas, thereby aggravating income inequality. The report, "How Robots Change the World", estimates that each new industrial robot eliminates 1.6 manufacturing jobs on average, and calls on governments to prepare with policies including better training and welfare programs, and a universal basic income. It suggests that in Australia, South Australia is most vulnerable to the future robot rollout. The state is Australia's most manufacturing intensive but has the slowest-growing economy and low levels of manufacturing productivity, the report argued.

In recent times, the race towards making Autonomous Vehicles (AVs) or self-driving cars have taken a big leap as several automotive players, and tech giants are investing huge capital on it. Many of them even have already begun test trials in countries like the US, Germany, UK, Japan, China, among others. Unlike its global counterparts, the Indian automotive sector seems slowly towards it. It is undoubtedly that the Indian automotive is one of the largest industries in the world. On Global perspective, several large automotive companies, technology firms as well as fleet service providers, such as Daimler, Uber, Tesla, General Motors, Audi, Waymo, Ford, Honda, BMW, Mercedes-Benz, Nissan, Toyota, and much more are investing substantial time, debt and capital on developing AVs. Conversely, in India, the Government is apprehensive of job losses due to automation.