An executive’s guide to machine learning

An Executive’s Guide to Machine Learning #AI #Data

  • This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company.
  • Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
  • The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.
  • By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.
  • For more articles about machine learning, click here.

This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company. Dorian Pyle is a data expert in McKinsey’s Miami office, and Cristin…
Continue reading “An executive’s guide to machine learning”

An executive’s guide to machine learning

An executive’s guide to #MachineLearning:  #abdsc #BigData #AI #DataScience

  • This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company.
  • Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
  • The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.
  • By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.
  • For more articles about machine learning, click here.

This article was written by Dorian Pyle and Cristina San Jose on McKinsey&Company. Dorian Pyle is a data expert in McKinsey’s Miami office, and Cristin…
Continue reading “An executive’s guide to machine learning”

Artificial synapse could be key to brain-like computing

Artificial synapse could be key to brain-like computing  #ai

  • It behaves like a transistor, with one terminal regulating the electricity flowing between two others.
  • While it’s not exactly natural, it’s largely made out of carbon and hydrogen, and should be compatible with a real brain’s chemistry — the voltages are even the same as those that go through real neurons.The ultimate aim is to create neural networks that exhibit more of the properties of their fleshy equivalents, and they’ve achieved some degree of success.
  • There’s only one synapse so far, but the team has shown that a simulated array of them could accomplish real computing tasks with a high degree of accuracy: the network could recognize handwritten numbers after training on three data sets.
  • The biggest challenge is shrinking the synapse so that it achieves true synapse-like efficiency (they’re still using 10,000 times more energy than a real synapse needs to fire).
  • If scientists can get anywhere close to that, though, you could see neural networks that are not only low-power, but are safe enough to interact with real biology — think AI-driven implants.

If you’re going to craft brain-like computers, it stands to reason that you’d want to replicate brain-like behavior right down to the smallest elements, doesn’t…
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