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.
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Build an Autonomous Car with RPi, NAVIO2 and Tensorflow/Keras, Part II: The Software – Unmanned Build

Build and Autonomous Car with RPi, NAVIO2 and Tensorflow/Keras, Part II: The Software

  • In the previous post I’ve outlined the hardware build of a “Robocar”, a simple autonomous car platform using monocular vision and Deep Learning, using a small RC car with few modifications.
  • If you’ve followed the directions in that post, you should be able to customize your RC car with a simple wooden or plastic platform, a Raspberry Pi, a camera and a PWM HAT that can control a motor and a servo.
  • In this post we will be focusing on building a simple software stack on the Raspberry Pi that can control the steering of an autonomous vehicle using a Convolutional Neural Network (CNN).
  • This car build uses the Burro autonomous RC car software, freely available on Github.
  • This is the second post in a series discussing the software aspects of a small scale autonomous vehicle, using vision alone and end-to-end machine learning for control and navigation.

In the previous post I’ve outlined the hardware build of a “Robocar”, a simple autonomous car platform using monocular vision and Deep Learning, using a small RC car with few modifications. The post focused exclusively on the hardware. If you’ve followed the directions in that post, you should be able to customize your RC car with a simple wooden or plastic platform, a Raspberry Pi, a camera and a PWM HAT1 that can control a motor and a servo. For my build I’ve also added an RC receiver, since my NAVIO2 HAT supports decoding of SBUS and PPM signals out of the box. However this is optional, and there are many ways to control your car, depending on what you have available (WiFi, for instance).
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Here’s how often IBM’s Watson agrees with doctors on the best way to treat cancer

A snapshot of how #AI could help doctors better treat cancer:  #IBMWatsonHealth

  • The studies looked at concordance rates, or how often Watson for Oncology reached the same course of treatment as the cancer doctors at different cancer centers around the world.
  • At Manipal Comprehensive Cancer Center in India, for 112 cases of lung cancer, there was 96.4% concordance between Watson and the doctors.
  • The concordance was in line with what IBM expected in those cases: If Watson and the docs agreed all the time, there wouldn’t be much value for adding AI to the picture.
  • Norden said that relates to the guidelines for gastric cancer being different in South Korea than at Memorial Sloan Kettering, the hospital where Watson for Oncology was trained.
  • Andrew Norden, the deputy chief health officer at IBM Watson Health told Business Insider that the concordance data isn’t “the ultimate endpoint we’re interested in,” though it was the first they could get to relatively quickly.

IBM’s Watson for Oncology presented data on how often Watson was able to come up with the same cancer treatment plan as doctors.
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An Overview of Python Deep Learning Frameworks

An Overview of #Python #DeepLearning Frameworks #KDN

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

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The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support

The Microsoft Cognitive Toolkit 2.0 Is Now Generally Available with Keras Support

  • That was the inspiration behind the company’s Cognitive Toolkit (previously CNTK) for deep learning, and on Thursday it got a major upgrade.
  • The Microsoft Cognitive Toolkit 2.0 is now generally available, open-source.
  • Though version 2 of the toolkit has been in beta since October, the full release builds on previous functionality.
  • It improves the performance for neural nets outside of speech recognition and also makes it easier for Microsoft to extend it later.
  • With this release, utilization should only grow, as will the toolkit’s functionality.

The general available of the Microsoft Cognitive Toolkit 2.0 adds a number of new features, including Java language bindings for model evaulation, Keras support, performance improvements, and more.
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Summer Of Machine Learning

A summer of machine learning.

  • For me, a good summer will always mean what they meant during my Ph.D.: four months to grind your heart out — to read 500 journal articles, submit five research papers, or finish that one big project.
  • It is a time when the burden of daily responsibilities and thousand little emergencies lifts and you are free to work on big projects with big goals — to enter the chill of fall better, smarter, and farther than before.
  • I left academia and if there is one thing I miss, it is the summers.
  • However, this summer I am going back to the summers of my Ph.D.: a summer of machine learning.
  • From June 1st to September 30th I will make a four month sprint to become a better data scientist and machine learning engineer, filling the dog days of summer with reading, writing, coding, and running.

Data Science for Political and Social Phenomena
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Microsoft releases version 2.0 of its deep learning toolkit

Microsoft releases version 2.0 of its deep learning toolkit  #CompBindTech

  • Microsoft today launched version 2.0 of what is now called the Microsoft Cognitive Toolkit.
  • This open-source toolkit, which was previously known as CNTK, is Microsoft’s competitor to similar tools like TensorFlow, Caffe and Torch, and, while the first version was able to challenge many of its competitors in terms of speed, this second version puts an emphasis on usability (by adding support for Python and the popular Keras neural networking library, for example) and future extensibility, while still maintaining — and improving — its speed.
  • Because it was essentially an internal tool, though, it didn’t support Python for example, even though it’s by far the most popular language among machine learning Microsoft originally built this toolkit for speech recognition systems, it was very good at working with time series data for building recurrent neural nets.
  • Huang stressed that the first version of the Cognitive Toolkit outperformed its competitors pretty easily on a number of standard tests.
  • Unsurprisingly, Microsoft is stressing the fact that the Cognitive Toolkit is a battle-tested system that it uses to power most of its internal AI systems, including Cortana, and that it can train models faster than most of its competitors.

Microsoft today launched version 2.0 of what is now called the Microsoft Cognitive Toolkit. This open-source toolkit, which was previously known as CNTK, is..
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Google Sheets now uses machine learning to help you visualize your data

Google Sheets now uses machine learning to help you visualize your data  #CompBindTech

  • After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions about your data, it’s now expanding this feature to also automatically build charts for you.
  • All of this is backed by the same natural language understanding tech that already powered the “Explore” feature.
  • It’s worth noting that the previous version of “Explore” could already build graphs for you, but those focused on your complete data set.
  • With this new version, Google also is making it easier to keep in sync data from Sheets that you use in Docs or Slides.
  • You could already update charts you copy into Docs and Slides with just a click, but now you also can do the same with tables.

Google Sheets is getting smarter today. After adding the machine learning-powered “Explore” feature last year, which lets you ask natural language questions..
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Experts Predict When Artificial Intelligence Will Exceed Human Performance

  • They surveyed the world’s leading researchers in artificial intelligence by asking them when they think intelligent machines will better humans in a wide range of tasks.
  • And many of the answers are something of a experts that Grace and co coopted were academics and industry experts who gave papers at the International Conference on Machine Learning in July 2015 and the Neural Information Processing Systems conference in December 2015.
  • These are two of the most important events for experts in artificial intelligence, so it’s a good bet that many of the world’s experts were on this list.Grace and co asked them all—1,634 of them—to fill in a survey about when artificial intelligence would be better and cheaper than humans at a variety of tasks.
  • Grave and co then calculated their median responsesThe experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027).
  • It’s easy to think that this gives the lie to these predictions.The experts go on to predict a 50 percent chance that AI will be better than humans at more or less everything in about 45 years.That’s the kind of prediction that needs to be taken with a pinch of salt.

Trucking will be computerized long before surgery, computer scientists say.
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Human-Level AI Is Right Around the Corner—or Hundreds of Years Away

#Human level #ai is in sight

  • Robin Hanson
    Martine Rothblatt
    Ruchir Puri
    Ray Kurzweil
    Carver Mead
    Nick Bostrom
    Rodney Brooks
    Gary Marcus
    Jürgen Schmidhuber

    Robin Hanson
    Author of The Age of Em: Work, Love, and Life When Robots Rule the Earth
    When will we have computers as capable as the brain?

  • Ray Kurzweil
    Cofounder and chancellor, Singularity University
    When will we have computers as capable as the brain?
  • Carver Mead
    Professor emeritus, California Institute of Technology
    Do you have any qualms about a future in which computers have human-level (or greater) intelligence?
  • Since we won’t have intelligent computers like humans for well over 100 years, we cannot make any sensible projections about how they will change the world, as we don’t understand what the world will be like at all in 100 years.
  • Computers are already far more capable than brains in many respects (for example, arithmetic and memory), but I think it could still be 20 to 50 years before machines have the ability to read and comprehend and reason about novel situations as fluently as people can.

Ray Kurzweil, Rodney Brooks, and others weigh in on the future of artificial intelligence
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