IBM is teaching AI to behave more like the human brain

Can a machine make memories? How IBM is exploring neural network learning in #AI:  @engadget

  • Deep learning neural networks — the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems — are the best machine learning systems we’ve developed to date.
  • While neurons use their various connections with each other to recognize patterns, “We are explicitly forcing the network to discover the relationships that exist” between pairs of objects in a given scenario, Timothy Lillicrap, a computer scientist at DeepMind told Science Magazine.When subsequently tasked in June with answering complex questions…
  • In a pair of research papers presented at the 2017 International Joint Conference on Artificial Intelligence held in Melbourne, Australia last week, IBM submitted two studies: one looking into how to grant AI an “attention span”, the other examining how to apply the biological process of neurogenesis — that is,…
  • It’s the same way that your doctor doesn’t tap your knees with that weird little hammer thing when you come in complaining of chest pain and shortness of breath.While the attention system is handy for ensuring that the network stays on task, IBM’s work into neural plasticity (how well memories…
  • Basically the attention model will cover the short term, active, thought process while the memory portion will enable the network to streamline its function depending on the current situation.But don’t expect to see AIs rivalling the depth of human consciousness anytime soon, Rish warns.

Since the days of Da Vinci’s “Ornithoper”, mankind’s greatest minds have sought inspiration from the natural world for their technological creations. It’s no di…
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IBM is teaching AI to behave more like the human brain

Can a machine make memories? How IBM is exploring neural network learning in #AI:  @engadget

  • Deep learning neural networks — the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems — are the best machine learning systems we’ve developed to date.
  • While neurons use their various connections with each other to recognize patterns, “We are explicitly forcing the network to discover the relationships that exist” between pairs of objects in a given scenario, Timothy Lillicrap, a computer scientist at DeepMind told Science Magazine.When subsequently tasked in June with answering complex questions…
  • In a pair of research papers presented at the 2017 International Joint Conference on Artificial Intelligence held in Melbourne, Australia last week, IBM submitted two studies: one looking into how to grant AI an “attention span”, the other examining how to apply the biological process of neurogenesis — that is,…
  • It’s the same way that your doctor doesn’t tap your knees with that weird little hammer thing when you come in complaining of chest pain and shortness of breath.While the attention system is handy for ensuring that the network stays on task, IBM’s work into neural plasticity (how well memories…
  • Basically the attention model will cover the short term, active, thought process while the memory portion will enable the network to streamline its function depending on the current situation.But don’t expect to see AIs rivalling the depth of human consciousness anytime soon, Rish warns.

Since the days of Da Vinci’s “Ornithoper”, mankind’s greatest minds have sought inspiration from the natural world for their technological creations. It’s no di…
Continue reading “IBM is teaching AI to behave more like the human brain”

Learning Deep Learning with Keras

Learning #DeepLearning with #Keras  #NeuralNetworks @pmigdal

  • For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
  • Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
  • See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

    In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

  • Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
  • Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
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How to Create Convolutional Neural Networks Using Java and DL4J

How to Create Convolutional Neural Networks Using #Java and @deeplearning4j

#deeplearning

  • Using Deeplearning4J, you can create convolutional neural networks, also referred to as CNNs or ConvNets, in just a few lines of code.
  • If you don’t know what a CNN is, for now, just think of it as a feed-forward neural network that is optimized for tasks such as image classification and natural language processing.
  • If you want to list all the labels present in the dataset, you can use the following code:

    At this point, if you compile and run your project, you should see the following output:

    It’s now time to start creating the individual layers of our neural network.

  • Another important thing to note in the above code is the call to the method, which specifies that our neural network’s input type is convolutional, with 32×32 images having 3 colors.
  • To start training the convolutional neural network you just created, just call its method and pass the iterator object to it.

In this tutorial, you’ll learn how to use Java and DeepLearning4J(DL4J) to create a convolutional neural network that can classify CIFAR-10 images.
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This dystopian device warns you when AI is trying to impersonate actual humans

This Dystopian Device Warns You When #AI Is Trying to Impersonate Actual Humans

  • The wearable prototype device is designed to identify synthetic speech and alert the user that the voice they’re listening doesn’t belong to a flesh-and-blood individual.
  • As artificial intelligence (AI) and robotic technology rapidly evolve, we’re facing an uncertain future where machines can seemingly do all sorts of things better than people can – from mastering games to working our jobs, and even making new, more powerful forms of AI.
  • A team at Australian creative technology agency DT trained its AI up on a database of synthetic voices, teaching the offline network to recognise artificial speech patterns.
  • If the AI detects an actual human voice (code green), all is fine:

    But if the system picks up on synthetic speech, it has a unique way of subtly letting the human know that they’re talking to a digital clone.

  • “We wanted the device to give the wearer a unique sensation that matched what they were experiencing when a synthetic voice is detected,” the team explains on DT’s R&D blog.

Meet the Anti-AI AI.
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AI can now predict whether or not humans will think your photo is awesome

#AI can now predict whether or not humans will think your photo is awesome

  • The Aesthetics tool, still in beta testing, allows users to upload a photo and get an auto-generated list of tags, as well as a percentage rate on the “chance that this image is awesome.”
  • According to developers, the neural network was trained to view an image much in the same way a human photo editor would, looking at factors such as color, sharpness, and subject.
  • As early users report, the system seems to be fairly good at recognizing factors like whether or not the image is sharp and if the composition is interesting, but it is certainly far from a pair of human eyes.
  • While the results of just how “awesome” a photo is may not be accurate for every image, the auto-tagging tool could prove useful, generating a list of keywords from object recognition as well as less concrete terms, like love, happiness, and teamwork.
  • Clicking on a keyword will bring up an Everypixel search for other images with that same tag, or users can copy and paste the list of keywords.

Can a computer judge art? A new neural network program will rank photos by their probability of being awesome.
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An Introduction to the MXNet Python API 

An Introduction to the MXNet #Python API #DeepLearning #NeuralNetworks

  • This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API.
  • In this series, I will try to give you an overview of the MXnet Deep Learning library: we’ll look at its main features and its Python API (which I suspect will be the #1 choice).
  • In this article, we’re going to work with a pre-trained model for image classification called Inception v3.
  • In part 4, we saw how easy it was to use a pre-trained version of the Inception v3 model for object detection.
  • In part 5, we used three different pre-trained models for object detection and compared them using a couple of images.


This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API. In-depth and descriptive, this is a great guide for anyone looking to start leveraging this powerful neural network library.

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