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Meet Your New Boss: An Algorithm

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Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.


Meet Your New Boss: An Algorithm

Wall Street Journal

Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.


Rebuilding the enterprise with A.I. HCL Blogs

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Change is a constant in the world of Business. That is why, at any given point in modern history, Enterprises are dealing with some "transformational trend" or the other. In the 80s and 90s it was computing; in the 2000s it was the Internet followed by Mobility, Cloud, and now the latest mantra – Digitalization. In a way, it is this constant need to evolve, change, and improve on the status quo, with all the tools available to us, that defines us as a human race. The only way is forward and progress is only limited by our own inventiveness.


Energy Data Insights: The Missing "Smart Step" to Better Building Performance

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Following our last article on "Artificial Intelligence in Energy Management Software", we got many responses from vendors and users alike. Over the next couple of weeks and month we will follow them all up and present here what we do believe are relevant and smart new solutions. As EEIP we are having a particular focus on solutions not only delivering a higher return but are adressing (and solving) a specific barrier in the market. Dexma showed us a new solution based on the insight of the "2 steps to energy efficiency". Basically it means that you need to equip someone within a company with some arguments to kick-off an energy audit.


Enterprise growth is changing in the age of AI

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Not long ago, reaching audiences through technologies like email was considered breakthrough. Fast forward to today, a hyper-accelerated existence in which the average human checks their smartphone over 150 times daily. This phone-fiddling translates into the average enterprise dealing with somewhere between 200 and 300 million digital signals per day, yet most can barely handle about 2 percent of that data. This implies companies attempt to make sense of around 4 million signals every single day -- which is still far too many to analyze, interpret, and respond to quickly. As for the 98 percent of the signals organizations can't handle, massive amounts of opportunity and insight are lost.


System of Intelligence: Building an IoT Learning Loop

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Why is your organization building a connected system? Whatever particular story fits your business, industrial investment in IoT is about value creation. It's about collecting data from environments and assets, and transforming raw machine signals and external events into information used to improve performance. These outcomes are made possible when you know the state of your machines and the world around you at each moment in time. These are the outputs of the new Systems of Intelligence created by data-focused, flexible, secure, and scalable IoT solutions.


AI, Deep Learning Enhances Analytics For TV

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The convergence of broadcast television and internet-delivered content services has become a reality -- one that soon will be cemented by the media industry's embrace of ATSC 3.0. In this cross-media environment, broadcast stations face many challenges, a mix of old and new. While ratings are a long-familiar concern for broadcasters, fierce competition for audience share today comes from a variety of providers on platforms ranging from linear channels to over-the-top (OTT) services to social and digital media. The new complexity of content distribution and consumption makes it hard for executives at a station to understand that station's place -- and the value of its media inventory -- within the larger marketplace. Even more difficult is to be proactive in ensuring that the station leverages that inventory to its best advantage.


The ways that AI can change your business

@machinelearnbot

Artificial intelligence now fits in our daily lives and is deployed in more and more business sectors, hustling human expertise. Artificial intelligence should transform one job over two, but does not necessarily represent a threat. In fact, these jobs should be redirected to less repetitive tasks, with more added value, discusses According to a PwC study from March 2017, 70% of the jobs in the energy sector and 65% of the jobs in the consumer sector could be automated through artificial intelligence. This new technology involves a necessary change in the value chain and, if it opens the way to new skills like cybersecurity, it also represents a major challenge and opportunity for these businesses. Managers and Top-Levels are directly involved in the facing of this challenge, by accompanying the teams through this mutation: vanquish fears, embracing innovation, transforming businesses, training teams.


10 imperatives for Europe in the age of AI and automation

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Europe, while making progress, is behind the US and China in capturing the opportunities of artificial intelligence and automation. Digitization is everywhere, but adoption is uneven across companies, sectors, and economies, and the leaders are capturing most of the benefits. Accelerating progress in AI and automation now bring further opportunities for users, businesses, and the economy. Europe, while making progress, is behind the United States and China. This briefing note was prepared for the European Union Heads of State Tallinn Digital Summit, which brought together heads of state and CEOs to discuss the steps needed to enable people, enterprises, and governments to fully tap into the potential of innovative technologies and digitization.


epicenter-of-ecommerce-artificial-intelligence

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In 2016 it got exponential growth over 2012 and 2017 figures till Sept equally critical is delivering this performance with reduced silicon (all grey & thin areas) area and industry power consumption. Machine learning helps in reducing the required efforts bandwidth between the buyer, seller and manufacturers such bandwidth reductions also reduce the cost and required time. In eCommerce AI based technologies like Big Data, Machine Learning, Neural Networks, Data Science, Bots and Deep Learning (mainly for secured online payments) are currently buzzwords. To safe guard the business from anti social elements deep learning helps in fraud detection, prevention, velocity measure and makes better business decisions with deep understanding of entity resolution (avoid multiple accounts of same person), Image recognition and understanding, Concept extraction, sentiment and trend analysis makes buyers life easy to choose and buy.