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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Deep Learning Research Review: Natural Language Processing

#ICYMI #DeepLearning Research Review: Natural Language Processing  #NLP

  • Since deep learning loves math, we’re going to represent each word as a d-dimensional vector.
  • Extracting the rows from this matrix can give us a simple initialization of our word vectors.
  • The above cost function is basically saying that we’re going to add the log probabilities of ‘I’ and ‘love’ as well as ‘NLP’ and ‘love’ (where ‘love’ is the center word in both cases).
  • One Sentence Summary: Word2Vec seeks to find vector representations of different words by maximizing the log probability of context words given a center word and modifying the vectors through SGD.
  • Bonus: Another cool word vector initialization method: GloVe (Combines the ideas of coocurence matrices with Word2Vec)


This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don’t have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

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Facebook taps deep learning for customized feeds

#Facebook taps deep learning for customized feeds  #MMA

  • The social network does machine learning one better, applying advanced computer learning techniques to…
  • Deep learning is a very generic technique Tulloch said.
  • The company must also deal with content posted in more than 100 languages daily complicating classic machine learning, Tulloch said.
  • But deep learning has pushed the state of the art forward in computer vision tasks, Tulloch said, including with classifying videos.
  • High-level understanding of content helps Facebook surface visual memories.

The social network does machine learning one better, applying advanced computer learning techniques to cater to users’ interests
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Intel is paying more than $400 million to buy deep-learning startup Nervana Systems

Intel is paying at least $350 million to buy deep-learning startup Nervana Systems

  • The chip giant is betting that machine learning is going to be a big deal in the data center.
  • “There is far more data being given off by these machines than people can possibly sift through,” Waxman said.
  • The chip giant isn’t actually saying how much it is paying, but a source with knowledge of the deal said it is valued at around $408 million.
  • “There’s always a next wave,” Waxman said, noting that corporate computing has already gone from mainframes to client-server and now on to cloud computing. “
  • Intel is snapping up deep learning startup Nervana Systems in a huge bet that artificial intelligence represents the next big shift inside corporate data centers.

Read the full article, click here.


@Recode: “Intel is paying at least $350 million to buy deep-learning startup Nervana Systems”


The chip giant is betting that machine learning is going to be a big deal in the data center.


Intel is paying more than $400 million to buy deep-learning startup Nervana Systems

Demystifying Machine Learning Part 4: Image and Video Applications

Demystifying #MachineLearning Part 4: Image and Video Applications from @AnandSRao:  #AI

  • Deep learning requires large volumes of data in order for a system to learn the features and should not be attempted where data is sparse.
  • , deep learning is suitable only for certain classes of problems and cannot be seen as a panacea to solve all problems.
  • In the previous post in our Machine Learning series, we dived into the inner workings of deep learning .
  • Previous post: Sportifying STEM through Robotics to Stimulate Learning
  • Demystifying Machine Learning Part 4: Image and Video Applications

Read the full article, click here.


@PwCAdvisory: “Demystifying #MachineLearning Part 4: Image and Video Applications from @AnandSRao: #AI”


How are companies using deep learning to drive business goals? Improved image and video recognition, audio recognition, and language understanding.


Demystifying Machine Learning Part 4: Image and Video Applications

Apple brings Google-style machine learning to ‘Photos’

Apple brings Google-style machine learning to 'Photos' #WWDC2016

  • Apple brings Google-style machine learning to ‘Photos’
  • Photos now offers up new ways to re-surface forgotten photos too – much like Google Photos.
  • The features borrow some of the best features from Google Photos, like re-surfacing memorable events, creating albums based on events, people and places, and using deep learning to help find images in a more intuitive way.
  • It’s essentially facial recognition that works on places and objects as well.
  • Instead of periodically sorting through old photos, Apple brings these memories to life with a new feature called… wait for it… ‘Memories.’

Read the full article, click here.


@TheNextWeb: “Apple brings Google-style machine learning to ‘Photos’ #WWDC2016”


  Today at WWDC, Apple brought machine learning to Photos to help you find, discover and share your images in a more intuitive way than ever before. T


Apple brings Google-style machine learning to ‘Photos’