Oil & Gas


The Difference Between Artificial Intelligence And Machine Learning

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Artificial Intelligence and Machine Learning, or AI and ML for short respectively, are two terms which get thrown around quite often these days. Most people think that they share the same meaning, and while they are quite closely associated, one cannot be used instead of the other. Both of them crop up frequently when discussing analytics, data or any sort of technological change, so I think it's important we settle what they actually mean once and for all. In short, artificial intelligence is the general concept where machines are able to carry out tasks in a "smart" way. Or, at least, in a way which we would consider smart.


Ask the AI experts: What are the applications of AI?

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Automotive, financial services, utilities--in these and many other industries, businesses are already applying artificial intelligence to core business processes and to innovating products. Business adoption of artificial intelligence is picking up steam, but still today only 20 percent of organizations that are aware of AI actually use this rapidly advancing technology. One reason: many executives are still wondering, "What can AI do for my business?" Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to find out about current and future applications of AI. An edited version of the experts' remarks follows the video.


Deloitte: 5 Trends That Will Drive Machine Learning Adoption - InformationWeek

@machinelearnbot

Companies across industries are experimenting with and using machine learning, but the actual adoption rates are lower than it might be seem. According to a 2017 SAP Digital Transformation Study, fewer than 10% of 3,100 executives from small, medium and large companies said their organizations were investing in machine learning. That will change dramatically in the coming years, according to a new Deloitte report, because researchers and vendors are making progress in five key areas that may make machine learning more practical for businesses of all sizes. There is a lot of debate about whether data scientists will or won't be automated out of a job. It turns out that machines are far better at doing rote tasks faster and more reliably than humans, such as data wrangling.


What You Missed in The IoT: Week of 12/4 - Harbor Research

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What do tomorrow's automakers have to do with net-zero buildings? Why it's important: This will transform the design and technology requirements for buildings in order to accommodate personal EVs and even electric fleets What It Is: Drillinginfo, a SaaS provider for the energy industry, has acquired Pattern Recognition Technologies (PRT), an energy forecasting software player. Why It Matters: Adding PRT's machine learning capabilities to predict energy consumption will allow Drillinginfo to enter horizontal markets in energy data analytics. This maneuver also bolsters Drillinginfo's North American customer base, particularly in clean energy data analytics. Why It Matters: Incumbents are reacting to the transition towards smart products by picking up smart home specialists.


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.


The Surprising Truth about Humans and Artificial Intelligence - Greater Phoenix In Business Magazine

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Artificial intelligence is not new but, suddenly, everyone seems to be talking about it. We have hit an inflection point with computing power and data that is finally allowing for commercial applications of this technology, and that's what all the excitement is about. It's only going to get faster and better from here on out. Along with talk about the new possibilities, there is also a lot of fear about people possibly losing their job to a robot, or even becoming irrelevant. Despite the wow factor of being able to shout a command at Siri or Alexa and have a task performed, when you get right down to it the tasks they are performing are rudimentary.


Lenovo Brings AI to Life

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Artificial intelligence (AI) and machine learning technologies have turned the IT industry on its head, offering enterprises the ability to transform their approach to business strategy and customer insights, and research institutions to pursue humanity's biggest challenges. Once considered an abstract technology that was primarily championed by hyperscale companies (like Google, Microsoft, Baidu etc), it is encouraging to now see startups and larger enterprises alike across a variety of industries explore unique AI applications to solve business problems and scientific challenges. From assisting in healthcare diagnoses, to predicting when things like a jet engine is in need of maintenance, and assisting in crime prevention, the potential for innovation with AI is nearly endless. It's even touching the average consumer's daily life, as well: Facebook's suggested photo tagging feature, for example, uses AI to recognize who's who in your pictures. Of course, AI does have its pain points and a key challenge for businesses today is the ability to differentiate between what's hype and what's reality.


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.


Spain Tests Artificial Intelligence to Manage Fly

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For the second consecutive year, Spain's agricultural ministry has launched a pilot experiment using artificial intelligence to predict the evolution of the olive fly. The experiment uses data collected on the olive fly by the Andalusian Plant Protection and Information Network (RAIF), a project of the Ministry of Agriculture, Fisheries and Rural Development. The data are analyzed and fed into an artificial intelligence model that can predict the fly's behavior up to four weeks in advance by using machine learning techniques. This method provides a valuable tool for olive farmers to better manage the pest by revealing the areas and dates of the greatest risk of infestation. This also allows for the more efficient planning and designing of measures to control the pest.


The Surprising Truth about Humans and Artificial Intelligence - Greater Phoenix In Business Magazine

#artificialintelligence

Artificial intelligence is not new but, suddenly, everyone seems to be talking about it. We have hit an inflection point with computing power and data that is finally allowing for commercial applications of this technology, and that's what all the excitement is about. It's only going to get faster and better from here on out. Along with talk about the new possibilities, there is also a lot of fear about people possibly losing their job to a robot, or even becoming irrelevant. Despite the wow factor of being able to shout a command at Siri or Alexa and have a task performed, when you get right down to it the tasks they are performing are rudimentary.