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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Different Types of Artificial Intelligence and the Names to Watch in 2017

Different types of Artificial Intelligence and the names to watch in 2017
 #AI #NGAHR

  • This ability to have better training and adjustments can let AI write code to improve other AI.
  • Speaking of murky ethical areas, discussion about AI laws will also be a hot topic of 2017.
  • Systems of law will have to figure out who will be responsible for these AI actions, such as the previously discussed autonomous cars and self-learning machines.
  • Hot topics will include lethal autonomous weapons, job losses and how fair those AI algorithms really are.
  • 2017 is going to be a game-changer for AI, and thus a game-changer for the world.

Artificial intelligence is on the rise. Take a look at the chart above and you’ll see that even in a niche corner of the technological world, there is already the makings of a huge industry. Read on to find out some of our predictions for 2017, because this frontier industry will shape our futures and the world as we know it.
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Google releases new TensorFlow Object Detection API

  • Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images.
  • Google is trying to offer the best of simplicity and performance — the models being released today have performed well in benchmarking and have become regularly used in research.
  • The handful of models included in the detection API include heavy duty inception-based convolutional neural networks and streamlined models designed to operate on less sophisticated machines — a MobileNets single shot detector comes optimized to run in real-time on a smartphone.
  • Earlier this week Google announced its MobileNets family of lightweight computer vision models.
  • Google, Facebook and Apple have been pouring resources into these mobile models.

Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying..
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Different Types of Artificial Intelligence and the Names to Watch in 2017

#AI Names to Watch in 2017 @brianpetro_ 
Mass #Adoption vs. Sophistication

  • This ability to have better training and adjustments can let AI write code to improve other AI.
  • Speaking of murky ethical areas, discussion about AI laws will also be a hot topic of 2017.
  • Systems of law will have to figure out who will be responsible for these AI actions, such as the previously discussed autonomous cars and self-learning machines.
  • Hot topics will include lethal autonomous weapons, job losses and how fair those AI algorithms really are.
  • 2017 is going to be a game-changer for AI, and thus a game-changer for the world.

Artificial intelligence is on the rise. Take a look at the chart above and you’ll see that even in a niche corner of the technological world, there is already the makings of a huge industry. Read on to find out some of our predictions for 2017, because this frontier industry will shape our futures and the world as we know it.
Continue reading “Different Types of Artificial Intelligence and the Names to Watch in 2017”

Different Types of Artificial Intelligence and the Names to Watch in 2017

Different types of Artificial Intelligence and the names to watch in 2017
 #AI #NGAHR

  • This ability to have better training and adjustments can let AI write code to improve other AI.
  • Speaking of murky ethical areas, discussion about AI laws will also be a hot topic of 2017.
  • Systems of law will have to figure out who will be responsible for these AI actions, such as the previously discussed autonomous cars and self-learning machines.
  • Hot topics will include lethal autonomous weapons, job losses and how fair those AI algorithms really are.
  • 2017 is going to be a game-changer for AI, and thus a game-changer for the world.

Artificial intelligence is on the rise. Take a look at the chart above and you’ll see that even in a niche corner of the technological world, there is already the makings of a huge industry. Read on to find out some of our predictions for 2017, because this frontier industry will shape our futures and the world as we know it.
Continue reading “Different Types of Artificial Intelligence and the Names to Watch in 2017”

Scikit-Learn Cheat Sheet: Python Machine Learning (Article)

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.
  • >>> from sklearn import neighbors, datasets, preprocessing >>> from sklearn.model_selection import train_test_split >>> from sklearn.metrics import accuracy_score >>> iris = datasets.load_iris() >>> X, y = iris.data[:, :2], iris.target >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) >>> scaler = >>> X_train = scaler.transform(X_train) >>> X_test = scaler.transform(X_test) >>> knn = >>> knn.fit(X_train, y_train) >>> y_pred = knn.predict(X_test) >>> accuracy_score(y_test, y_pred) Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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Different Types of Artificial Intelligence and the Names to Watch in 2017

Different types of Artificial Intelligence and the names to watch in 2017
 #AI #NGAHR

  • This ability to have better training and adjustments can let AI write code to improve other AI.
  • Speaking of murky ethical areas, discussion about AI laws will also be a hot topic of 2017.
  • Systems of law will have to figure out who will be responsible for these AI actions, such as the previously discussed autonomous cars and self-learning machines.
  • Hot topics will include lethal autonomous weapons, job losses and how fair those AI algorithms really are.
  • 2017 is going to be a game-changer for AI, and thus a game-changer for the world.

Artificial intelligence is on the rise. Take a look at the chart above and you’ll see that even in a niche corner of the technological world, there is already the makings of a huge industry. Read on to find out some of our predictions for 2017, because this frontier industry will shape our futures and the world as we know it.
Continue reading “Different Types of Artificial Intelligence and the Names to Watch in 2017”