87% of companies will add workers this year, despite rise of robots

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More employers than ever before (87%) will either increase or maintain their workforce numbers this year because of automation, according to a ManpowerGroup report released on Friday. The report surveyed 19,000 employers across 44 countries, assessing the effects of automation on job growth in the next couple years. Robots replacing human jobs remains a top concern in the workforce, as automated systems are able to complete manual tasks at a much faster rate than humans. Instead of eliminating or reducing employment opportunities, robots will actually cause organizations to create new jobs, the report found. Other research has come to the same conclusion: Only 8% of 100 business executives from Global 2000 organizations confirmed their companies were cutting jobs after integrating new technology, according to a recent Dun & Bradstreet report.


Amazon built vests to protect warehouse workers from its robots

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We already knew that working at one of Amazon's warehouses could be hazardous. Amazon Robotics' latest product could protect workers -- but its existence is a strange vision of human-robot relations. TechCrunch reports that the online retail giant's in-house design firm had to come up with a solution -- in the form of a vest -- to protect workers from warehouse robots. The vest sends signals to nearby robots telling them to slow down in order to avoid a collision. "In the past, associates would mark out the grid of cells where they would be working in order to enable the robotic traffic planner to smartly route around that region," Amazon Robotics VP Brad Porter told TechCrunch.


Amazon built vests to protect warehouse workers from its robots

#artificialintelligence

We already knew that working at one of Amazon's warehouses could be hazardous. Amazon Robotics' latest product could protect workers -- but its existence is a strange vision of human-robot relations. TechCrunch reports that the online retail giant's in-house design firm had to come up with a solution -- in the form of a vest -- to protect workers from warehouse robots. The vest sends signals to nearby robots telling them to slow down in order to avoid a collision. "In the past, associates would mark out the grid of cells where they would be working in order to enable the robotic traffic planner to smartly route around that region," Amazon Robotics VP Brad Porter told TechCrunch.


From WhatsApp to Alexa : why the ad-free era is over

The Guardian

So-called "native advertising" online, where advertising is presented in a similar way to editorial, has failed to take off. A US study last year from Stanford University found native advertising is no better at getting us to buy than standard online ads. "Consumers are very good at filtering out messages," explains Lisa Du-Lieu, a senior lecturer in marketing at Huddersfield University. "If you don't get their attention within the first couple of seconds, it just bounces off them." For that reason, brands are shifting their attention to platforms and formats that they know we are engaged with.


Driving AI's potential in organizations

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For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."


Signal to Noise: Understanding the State of Machine Learning Services • Filestack Blog

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Getting past the buzzwords to the meat of what is on offer in the machine learning services landscape isn't easy. That's why we've put together a little guide to help you understand how you can start utilizing real and true machine learning to accelerate your business. Lots of buzzwords these days actually reference open-source machine learning platforms and libraries, which often require knowledge of computer science, statistics, linear algebra, calculus and sometimes even more arcane math to fully utilize. These are separate from the technologies built on top of them, but can get conflated in the media landscape. Tensorflow is the (mostly) open-source library that Google maintains and presumably builds their internal and external machine learning services with.


AI, Robotics, and Automation: Keep Humans in the Loop

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Far from stealing jobs, robots and AI are most effective when they work side by side with humans. Organizations' next challenge is redesigning work accordingly. Enterprise adoption of automation, robotics, and artificial intelligence (AI) is accelerating dramatically. Almost half (47 percent) of the 11,070 respondents surveyed for Deloitte's 2018 Global Human Capital Trends report say their organizations are deeply involved in automation projects: 24 percent are using AI and robotics to perform routine tasks, 16 percent to augment human skills, and 7 percent to restructure work entirely. Expectations for the technology are also high: 42 percent of this year's respondents believe AI will be widely deployed at their organizations within three to five years--up from 38 percent last year.


Mountaineer develops new model for environmental and energy uses – Tech Check News

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A new machine-learning model developed by a West Virginia University student has the potential for energy, environmental and even healthcare applications. The model, which can be used to predict the adsorption energies, i.e. adhesive capabilities in gold nanoparticles, was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high technology applications, electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," he said.


The Machine Behind Identity Governance

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That basic fact is the dominant goal of effective risk management and it is, of course, what identity governance is all about: seeing clearly into the complex activity of human and nonhuman actors throughout an organization and implementing appropriate controls to ensure the right actors have access to the right data at the right time. The automation that comes from a next-gen, AI-driven identity governance program permeates many aspects of an enterprise.Getty Enterprise organizations of all sizes rely on identity governance to enforce strict policies that protect data from unwanted access. With the advent of identity and artificial intelligence (AI), it's now easier to find the needles you didn't even know you had in your haystack. Today's generation of identity management learns patterns and delivers advanced insights by ingesting vast amounts of identity, access and event data. This is how organizations of all stripes filter risky anomalies from the non-risky noise--and it's how IT and security teams gain greater clarity and the ability to make smart identity decisions.


Data Science and Machine Learning – MITU Skillologies

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Python Type Conversion, Decisions and Loops – Type Conversion – Decision making -- a) If.. -- b) If..else -- c) Nested if..else -- d) If..elif..else – Loops -- a) For loop -- b) While loop -- c) Nested loops -- d) break, pass and continue