Machine Learning: Report Launch Tickets, Tue, 25 Apr 2017 at 18:00

Major report on #MachineLearning to be launched @royalsociety on 25 April:

 #AI

  • The Royal Society is launching a major policy report on machine learning.
  • A panel of experts will present and discuss the report findings and their implications for the economy and society.
  • Many services that we use every day – from image recognition on social media to recommender systems in online retail – rely on machine learning, the exciting and rapidly-developing field of science that allows computer systems to learn from examples, data, and experience.
  • In addition to these existing services, machine learning promises potentially transformative advances in a range of fields, including healthcare, transport, education, and more.
  • The Royal Society has been carrying out a major project on machine learning, to investigate its potential, and highlight the opportunities and challenges it creates.

Eventbrite – The Royal Society presents Machine Learning: Report Launch – Tuesday, 25 April 2017 at The Royal Society, London, England. Find event and ticket information.
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Something New Is Coming – AltspaceVR – Medium

Something New Is Coming  #vr #ar #ai

  • Co-authors Robert Scoble and Shel Israel lead a discussion in virtual reality about how technology got us this far and why AR/VR and AI is next
  • Scoble and Israel, looking down the path of the future with AR, VR, and AI, also want to remind us: “Something new is .”
  • With his first reading, Israel introduced us to the book’s theme by way of its opening sentence – one with a leading clause that sounds particularly familiar. “
  • Entrenched in the tech and digital worlds since before DOS was popular, tech evangelist Robert Scoble and prolific writer Shel Israel performed a reading of their newest collaboration, a book entitled The Fourth Transformation: How Augmented Reality (AR) and Artificial Intelligence (AI) Will Change Everything.
  • In the beginning,” Israel started, “there were mainframes.

Co-authors Robert Scoble and Shel Israel lead a discussion in virtual reality about how technology got us this far and why AR/VR and AI is next Entrenched in the tech and digital worlds since before…
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New neural-network algorithm learns directly from human instructions instead of examples

New neural-network algorithm learns directly from human instructions instead of examples  #ai

  • News New neural-network algorithm learns directly from human instructions instead of examples
  • For example, you could train a neural network to identify sky in a photograph by showing it hundreds of pictures with the sky labeled.
  • Abstract of Hair Segmentation Using Heuristically-Trained Neural Networks
  • Humans conventionally “teach” neural networks by providing a set of labeled data and asking the neural network to make decisions based on the samples it’s seen.
  • Applying the method to the binary classification of hair versus nonhair patches, we obtain a 2.2% performance increase using the heuristically trained NN over the current state-of-the-art hair segmentation method.

Conventional neural-network image-recognition algorithm trained to recognize human hair (left), compared to the more precise heuristically trained algorithm
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Google’s ‘DeepMind’ AI platform can now learn without human input

#Google's #DeepMind #AI platform can now learn without human input ▷

  • Much like the brain, the neural network uses an interconnected series of nodes to stimulate specific centers needed to complete a task.
  • Read next: Facebook makes 360 photos much better with one small update
  • The AI is optimizing the nodes to find the quickest solution to deliver the desired outcome.
  • In a significant step forward for artificial intelligence, Alphabet’s hybrid system – called a Differential Neural Computer (DNC) – uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it.
  • Instead of having to learn every possible outcome to find a solution, DeepMind can derive an answer from prior experience, unearthing the answer from its internal memory rather than from outside conditioning and programming.

Bow to your robot overlords. Google’s parent company, Alphabet, now possesses a smart AI capable of learning without the need for human input.
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Google’s ‘DeepMind’ AI platform can now learn without human input

DeepMind AI platform can now learn without human input

  • The AI is optimizing the nodes to find the quickest solution to deliver the desired outcome.
  • Any time is a good time to start a company.
  • TNW uses cookies to personalise content and ads to make our site easier for you to use.
  • Instead of having to learn every possible outcome to find a solution, DeepMind can derive an answer from prior experience, unearthing the answer from its internal memory rather than from outside conditioning and programming.
  • In a significant step forward for artificial intelligence, Alphabet’s hybrid system – called a Differential Neural Computer (DNC) – uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it.

Bow to your robot overlords. Google’s parent company, Alphabet, now possesses a smart AI capable of learning without the need for human input.
Continue reading “Google’s ‘DeepMind’ AI platform can now learn without human input”

GitHub

  • Optional reading material from Michael Nielsen Chapters 1-4 (Do 3-5 of the optional exercises).
  • Optional reading material from Michael Nielsen Chapter 6 (stop when reaching section called Other approaches to deep neural nets).
  • Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
  • Thanks to professor Ole Winther for supervision and sponsoring the labs.
  • Please note that GitHub no longer supports old versions of Internet Explorer.

tensorflow-tutorial – Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
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Classification in the presence of missing data

Learn how to improve prediction accuracy in the presence of missing data!
#machinelearning

  • The misclassification error is much lower when surrogate splits are used with decision trees.
  • The example shows how decision trees with surrogate splits can be used to improve prediction accuracy in the presence of missing data.
  • y_pred1 = predict(RF1,Xtest); confmat1 = confusionmat(Ytest,y_pred1); y_pred2 = predict(RF2,Xtest); confmat2 = confusionmat(Ytest,y_pred2); disp( ‘Confusion Matrix – without surrogates’ ) disp(confmat1) disp( ‘Confusion Matrix – with surrogates’ ) disp(confmat2) Confusion Matrix – without surrogates 67 1 24 13 Confusion Matrix – with surrogates 65 3 4 33
  • Decreasing value with number of trees indicates good performance.
  • There are several ways to improve prediction accuracy when missing data in some predictors without completely discarding the entire observation.

Read the full article, click here.


@MATLAB: “Learn how to improve prediction accuracy in the presence of missing data!
#machinelearning”


Learn about MATLAB support for machine learning. Resources include examples, documentation, and code describing different machine learning algorithms.


Classification in the presence of missing data

GitHub

Deep #ReinforcementLearning for #Keras: state-of-the art #DeepLearning in #Python

  • In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and Theano and TensorFlow and was built with OpenAI Gym in mind.
  • keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras .
  • You can extend keras-rl according to your own needs.
  • The repo provides some weights that were obtained by running (at least some) of the examples that are included in keras-rl .
  • Keras-rl works with OpenAI Gym out of the box.

Read the full article, click here.


@kdnuggets: “Deep #ReinforcementLearning for #Keras: state-of-the art #DeepLearning in #Python”


keras-rl – Deep Reinforcement Learning for Keras.


GitHub