Using Artificial Intelligence to Search for Extraterrestrial Intelligence

Using Artificial Intelligence to Search for Extraterrestrial Intelligence

  • #AI The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017.
  • The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017.
  • The ML4SETI project challenged participants to build a machine-learning model to classify different signal types observed in radio-telescope data for the search for extra-terrestrial intelligence (SETI).
  • The models from the top teams, using deep learning techniques, attained nearly 95% accuracy in signals from the test set, which included some signals with very low amplitudes.
  • Deep learning models trained for signal classification may significantly impact how SETI research is conducted at the Allen Telescope Array, where the SETI Institute conducts its radio-signal search.

#AI The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017. Nearly 75 participants, with a wide range of backgrounds from industry and academia, worked in teams on the project. The top team achieved a signal classification accuracy of 95%. The code challenge was sponsored by IBM, Nimbix Cloud, Skymind, Galvanize, and The SETI League. The Machine Learning 4 SETI Code Challenge (ML4SETI), created by the SETI Institute and IBM, was completed on July 31st 2017. Nearly 75 participants, with a wide range of backgrounds from industry and academia, worked in teams on the project. The top team achieved a signal classification accuracy of 95%. The code challenge was sponsored by IBM, Nimbix Cloud, Skymind, Galvanize, and The SETI League.
The ML4SETI project challenged participants to build a machine-learning model to classify different signal types observed in radio-telescope data for the search for extra-terrestrial intelligence (SETI). Seven classes of signals were simulated (and thus, labeled), with which citizen scientists trained their models. We then measured the performance of these models with tests sets in order to determine a winner of the code challenge. The results were remarkably accurate signal classification models. The models from the top teams, using deep learning techniques, attained nearly 95% accuracy in signals from the test set, which included some signals with very low amplitudes. These models may soon be used in daily SETI radio signal research.
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Beware the unintended consequences of a robot revolution

Beware the unintended consequences of a robot revolution  #robotics #AI

  • Ask an economist or a technology expert and they will happily tell you that decades of data reliably show automation has created more jobs than it has destroyed.
  • The problem with this rose-tinted view of automation, however, is its focus on big averages that take little account of individuals’ experiences.
  • As tempting as it may be to pour money into boosting automation in return for the long-awaited boost to productivity and headline economic growth, doing so without having a clear plan for retraining displaced workers would cause untold harm to millions of individuals.
  • As the Institute for Public Policy Research points out , some workers are far more vulnerable than others to automation.
  • If the government fails to act, the result could all too easily be a spike in unemployment and poverty in places with the lowest skilled workers – a very high price to pay for a bit of average productivity growth.

Investment in education and retraining is needed to equip people to adapt as automation shakes up their workplaces
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Machine Learning Research Should Be Clear, Dynamic and Vivid. Distill Is Here to Help.

#MachineLearning #Research Should Be Clear, Dynamic and Vivid. Distill Interactive Journal

The web is a powerful medium to share new ways of thinking. Over the last few years we’ve seen many imaginative examples of such work. But traditional academic publishing remains focused on the PDF, which prevents this sort of communication.
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Machine Learning Research Should Be Clear, Dynamic and Vivid. Distill Is Here to Help.

Machine Learning Research Should Be Clear, Dynamic and Vivid.
Distill Is Here to Help.

The web is a powerful medium to share new ways of thinking. Over the last few years we’ve seen many imaginative examples of such work. But traditional academic publishing remains focused on the PDF, which prevents this sort of communication.
Continue reading “Machine Learning Research Should Be Clear, Dynamic and Vivid. Distill Is Here to Help.”

How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture

How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture

  • This means we need new ways to grow food that are smarter and helps regulate our use of land, water and energy in order to feed the planet and avoid a global food crisis.
  • Researchers at Carnegie Mellon University’s Robotics Institute believe the answer lies in sensors, artificial intelligence (AI) and robots.
  • In a new initiative called FarmView, researchers are working to combine sensors, robotics and artificial intelligence to create a fleet of mobile field robots they hope will improve plant breeding and crop-management practices.

How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture
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Artificial Intelligence Is Changing How We Shop Online

How #AI is changing the way we do our shopping online:

  • These deep learning algorithms have been used in the autonomous driving industry for quite some time, and only now is it beginning to branch out into other industries, such as online shopping.
  • One company that offers machine learning solutions for e-commerce businesses and others is Adobe Marketing Cloud as they recognize the need to make use of AI as early as possible.
  • Andrew Zhai is an engineer working on the visual search side of things at Pinterest, and he said, “For shopping specifically, improvements to online discovery means new ways to find products you’re interested in but may not have the words for.
  • Etsy is also keen to jump onboard with deep learning technology, and just last fall purchased Blackbird Technologies to integrate the firm image recognition and natural language processing into its search function.
  • Some of Adobe’s marketing tools also use deep learning techniques and are used to predict their customer’s shopping behaviors and patterns.

There’s no doubt about it that our future is one that involves artificial intelligence (AI) in a big way. While some companies are faster than others at
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Your Next Beer Might Be Made by Artificial Intelligence

Your next beer might be made by artificial intelligence - but how will it taste?

  • Beer has a rich history in London.
  • Were about using data to listen to our customers, get all that feedback, and then brew something thats more attuned to what they actually want and need.”
  • The beer is made in small batches, and some customers havent loved the taste of the algorithm-crafted recipes.
  • The algorithm produces new recipes every month incorporating the feedback.
  • Four styles have been made and each version is adjusted by the algorithm based on customer feedback.

The world’s first beer brewed with the help of artificial intelligence is on sale in the U-K.  But how does it taste?
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