The Industrial Internet of Things

  • Today, the industrial internet of things has already had an impact on how effectively a factory is run and how its equipment runs.
  • With a network of devices directly linked in the industrial internet of things, there are a few benefits.
  • If there is a jam in machinery, a device connected to the internet of things can halt production around it.
  • If there is a global internet outage, how does that impact factory equipment and other items that are attached through the network that is running the industrial internet of things?
  • While there are some concerns, that doesn’t mean a company shouldn’t consider the industrial internet of things as a solution.

The Industrial Internet of Things – Bill McCabe. The technology in a factory today is far different from what was there even a decade ago.
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Artificial Intelligence at Next Gen 2017 and beyond

#AI is poised to become the defining technology of #finance. Find out how.

  • It is clear that Artificial Intelligence (AI) is the next big wave in computing, but it’s even bigger than that.
  • It’s poised to usher in a better world: Accelerating solutions to large-scale problems, unleashing new scientific discovery, extending our human senses and capabilities, and automating undesirable tasks.
  • To unleash the next wave of AI, there is much work to be done as an industry to move past challenges in performance, accessibility, and wariness of the technologies.
  • Financial institutions will have to navigate a complex array of techniques, overcome cultural challenges, and embrace new technology decisions.
  • This research paper brings together the views of AI experts across the financial industry, on the key opportunities and challenges you are likely to face as you embark on your AI journey.

It is clear that Artificial Intelligence (AI) is the next big wave in computing, but it’s even bigger than that. It’s poised to usher in a better world: Accelerating solutions to large-scale problems, unleashing new scientific discovery, extending our human senses and capabilities, and automating undesirable tasks.
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Stupid TensorFlow tricks – Towards Data Science – Medium

A new take on an old (Thomson) problem using #TensorFlow

  • I wanted to see how far I could push this idea.Electrostatic charge configuration for N=625 in equilibrium.
  • Probably not.The Thomson problem is a classical physics question, “What configuration of N positive charges on the unit sphere minimizes the energy?”
  • N=11 puts the charges in a configuration that completely breaks the symmetry — while the charges are in equilibrium, they are distributed in such a way that there are more on one side than the other; it has a net dipole moment!Solving this in TF is surprisingly easy.
  • For any value of N, we can converge to a stable solution energy minima in a matter of seconds, and we can refine that to the full floating point precision in a matter of minutes by tapering down the learning rate.
  • That’s an impressive 10x speedup!Minimal energy for N=100 charges, prettified.Visualizing the configurations illustrates the regularity and the apparent symmetry, even if we are content knowing that it might not be the global minimum.

Is Google’s machine intelligence library TensorFlow (TF) good for something beyond deep learning? How well can it tackle a classic physics problem?
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The Science of AI and the Art of Social Responsibility

The @HuffingtonPost take a look at the science of #AI and the art of social responsibility:

  • But the transformational nature of artificial intelligence requires new metrics of success for our profession.
  • This year alone at least 1 billion people will be touched in some way by artificial intelligence, which is transforming everything from financial services to transportation, energy, education and retail.
  • And why IBM is a founding member of the Partnership on AI, a collaboration among Google, Amazon, Facebook, Microsoft, Apple and many scientific and nonprofit organizations charged with guiding the development of artificial intelligence to the benefit of society.
  • Opportunity: Developers of AI applications should accept the responsibility of enabling students, workers and citizens to take advantage of every opportunity in the new economy powered by cognitive systems.
  • They should help them acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.

By Guru Banavar, IBM’s Chief Science Officer for Cognitive Computing
I am a computer scientist and engineer, inspired by the art of the possible an…
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12 Statistical and Machine Learning Methods that Every Data Scientist Should Know

12 Statistical and #MachineLearning Methods that Every #DataScientist Should Know #abdsc

  • Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.
  • From my experience in the data science industry for 4 years, I think that currently these 12 methods are the most popular, useful and suitable for various problems requiring data science.
  • As far as I’ve known, there have been not a few lists of “representative methods in data science” ever.
  • Thus I made this list as the one by business person, who knows practical matters and solutions with data science, including statistics and machine learning in the industry.
  • In addition to the list itself, I showed R or Python scripts of an experiment on sample datasets for each method, in order to enable readers to try it easily.

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.

Statistical Hypothesis Testing (t-test, c…
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The Science of AI and the Art of Social Responsibility

The science of #AI and the art of social responsibility:  via @HuffingtonPost

  • But the transformational nature of artificial intelligence requires new metrics of success for our profession.
  • This year alone at least 1 billion people will be touched in some way by artificial intelligence, which is transforming everything from financial services to transportation, energy, education and retail.
  • And why IBM is a founding member of the Partnership on AI, a collaboration among Google, Amazon, Facebook, Microsoft, Apple and many scientific and nonprofit organizations charged with guiding the development of artificial intelligence to the benefit of society.
  • Opportunity: Developers of AI applications should accept the responsibility of enabling students, workers and citizens to take advantage of every opportunity in the new economy powered by cognitive systems.
  • They should help them acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.

By Guru Banavar, IBM’s Chief Science Officer for Cognitive Computing
I am a computer scientist and engineer, inspired by the art of the possible an…
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Probabilistic Machine Learning with PyMC3

Probabilistic Machine Learning with #PyMC3  #ODSC #ML #MachineLearning #DataScience

  • After the overview, Dr. Wiecki’s focused on Probabilistic Programming with PyMC3.
  • Introduction: Dr. Thomas Wiecki of Quantopian spoke at the recent ODSC UK conference compared traditional Machine Learning with Probabilistic Programming.
  • It was a perfect way to cap off a riveting display of the power of Probabilistic Programming.
  • PyMC3 is a popular Python library which leverages this power.
  • Learning R programming by reading books: A book list #rstats Posted 24 November 2016 | 10:41 am

Abstract: Probabilistic Programming is a powerful set of tools which is barely sampled from by the traditional Machine Learning toolbox. PyMC3 is a popular
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