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BigData News Sunday, July 22

Register yourself for Global Artificial Intelligence Conference in Boston|GlobalBigDataConference

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Register yourself for Global Artificial Intelligence Conference in Boston|GlobalBigDataConference

Statistical machine learning reveals deep brain activity

  • Now, an international team including A*STAR researchers has shown that magnetoencephalography (MEG) and electroencephalography (EEG) can be used to characterize fast timescale activity in these deep brain structures.
  • In a recent breakthrough1, A*STARs Pavitra Krishnaswamy, in a research team spanning the United States, Sweden, and Finland, developed a statistical machine learning approach to resolve deep brain activity with high temporal and spatial resolution.
  • The researchers used simulated test cases and experimental MEG/EEG recordings from healthy volunteers to demonstrate that their approach accurately maps out this deep brain activity amidst concurrent activity in cortical structures.
  • Rather than solely relying on how loud the brainwaves are, the team leveraged the fact that deep brain activity generates distinct spatial patterns across multiple MEG/EEG sensors positioned over the head.
  • When just a limited portion of the cortex is active, even though it appears louder than ongoing deep brain activity, it is possible to transform the data into a space where the deeper signals also have a distinct voice.

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Statistical machine learning reveals deep brain activity

Getting True ROI from Big Data: The Insights on Demand Model

  • The number one benefit of gathering Big Data is in deriving useful information from the massive datasets youve assembled.
  • Big Data analytics tools like Splunk help organizations do just that.
  • However, it’s important to create a budget and know how much you want to spend to solve a particular problem.
  • Understanding your return on investment (ROI) is critical.
  • The questions that can be answered by Big Data analysis are vast, and you’ll need to know what it’s worth to determine the answers to the specific questions you’re interested in answering.

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Getting True ROI from Big Data: The Insights on Demand Model

Most of AI’s Business Uses Will Be in Two Areas

  • As they shouldone estimate suggests that 40% of all the potential value that can created by analytics today comes from the AI techniques that fall under the umbrella deep learning, (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of…
  • As they shouldwe estimate that 40% of all the potential value that can created by analytics today comes from the AI techniques that fall under the umbrella , (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of the human…
  • After all, embedding AI across the business requires significant investment in talent and upgrades to the tech stack as well as sweeping change initiatives to ensure AI drives meaningful value, whether it be through powering better decision-making or enhancing consumer-facing applications.
  • We found that the greatest potential for AI to create value is in use cases where neural network techniques could either provide higher performance than established analytical techniques or generate additional insights and applications.
  • Even as we see economic potential in the use of AI techniques, we recognize the tangible obstacles and limitations to implementing AI.

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Most of AI’s Business Uses Will Be in Two Areas

Data Analyst – Lockheed Martin – Video Hosted by Digi-Me

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Data Analyst – Lockheed Martin – Video Hosted by Digi-Me

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8 Real-Life Applications of Artificial Intelligence in eCommerce

  • Amazon is every online Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales.
  • The technologys most popular subsetmachine learningcanmake sense of all data that online shops collect,drive insights that improve customer experience, boost internal business processes, and fight fraud.
  • Customer reviews have played and still play an important role in helping people make purchasing of people read online reviews regularly and 87 percent trust them just as much as personal connections.
  • Last years controversiesaround fake content have impacted the way customers perceive information they find online, even if its seemingly written by peers.
  • Now,their self-improving, machine learning-based system picks one review it thinks to be true according to different factors such as upvotes, recency, and whether it was written by a verified user.

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8 Real-Life Applications of Artificial Intelligence in eCommerce

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#iot OR “internet of things”_2018-07-20_13-38-07.xlsx

  • The graph represents a network of 2,727 Twitter users whose tweets in the requested range contained #iot OR internet of things, or who were replied to or mentioned in those tweets.
  • The network was obtained from the NodeXL Graph Server on Friday, 20 July 2018 at 20:39 UTC.
  • The tweets in the network were tweeted over the 13-day, 0-hour, 11-minute period from Thursday, 05 July 2018 at 07:32 UTC to Wednesday, 18 July 2018 at 07:43 UTC.
  • Additional tweets that were mentioned in this data set were also collected from prior time periods.
  • These tweets may expand the complete time period of the data.

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#iot OR “internet of things”_2018-07-20_13-38-07.xlsx

Creating Your First Machine Learning Classifier with Sklearn

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Creating Your First Machine Learning Classifier with Sklearn