Listen To A Song Written By Artificial Intelligence, Inspired By The Beatles

Listen to a song written by artificial intelligence, inspired by The Beatles:

  • Sony CSL Research Laboratory is releasing an album next year of songs written by Artificial intelligence, and the first hit track may be this uncanny number programmed in the style of The Beatles (in all honesty it sounds a little more like The Beach Boys to me, at least through the intro).
  • French composer Benoît Carré arranged and produced the harmonies for the songs.
  • Using Sony’s Flowmachines system, the team selected a Beatles style and, well, here it is:

Sony CSL Research Laboratory is releasing an album next year of songs written by Artificial intelligence, and the first hit track may be this uncanny number programmed in the style of The Beatles.
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As many as 48 million accounts on Twitter are actually bots, study finds

As many as 48 million accounts on #Twitter are actually #Bots, study finds #AI

  • In February, Twitter announced it had 319 million monthly active users worldwide, or just slightly under the number of every person in the United States.But of those 319 million, as many as 48 million aren’t actually real, according to a study conducted by researchers from the University of Southern California: They’re just software programs, designed to do everything a normal person on Twitter would do, including following other accounts and liking and retweeting certain messages.Those accounts, called “bots,” can range from accounts dedicated to alerting their followers about emergencies to political advocates intended to boost the numbers of a programmer’s preferred candidate.
  • “Many bot accounts are extremely beneficial, like those that automatically alert people of natural disasters … or from customer service points of view,” a Twitter spokesperson told CNBC.However, there are also plenty of fake accounts.
  • Because a person’s number of Twitter followers is often seen as indicative of how popular and powerful that person is, there are services that allow people to buy followers, and quite often those services use bots as part of their service.Twitter has acknowledged the existence of bots in the past and has attempted to crack down on them, suspending accounts they believe are not human.
  • However, as CNBC reports, the USC study goes far beyond what Twitter itself has claimed about the number of bots on its platform.
  • In February, Twitter estimated in a SEC filing that up to 8.5 percent of its users were not human, while the USC study’s authors say even its estimate of 15 percent is “conservative.”

The study, from researchers at the University of Southern California, found that anywhere between nine and 15 percent of active users on the social media site are automated software that can like, retweet and follow just like accounts managed by humans.
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iot infographic

#IoT Tectonics - #SmartCity #SmartMobility #CyberSecurity #AI #BigData #OpenData

  • (You knew we were…
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        Anyone who has ever had to justify social media spend will appreciate that it feels good to have figures to cling to.

  • We know that a lot of the value is relatively intangible – it’s about sentiment, awareness, relationship…
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      Snapchat is, relatively speaking, one of the newbies on the social media block.

  • “Is that the one that people use to send dirty pictures when they’re…
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    When you think of social channels like Facebook, what do you picture?

  • Is it people over sharing feelings and pictures of their children?

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IoT Central covers: Infrastructure, Application Dev, Data Security and more.
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Book: Evaluating Machine Learning Models

Book: Evaluating #MachineLearning Models #abdsc

  • If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming.
  • With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.
  • In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection.
  • With this report, you will:

    Alice is a technical leader in the field of Machine Learning.

  • Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University.

Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data scien…
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An Overview of Python Deep Learning Frameworks

#ICYMI An Overview of #Python #DeepLearning Frameworks

  • 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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You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

  • This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and Victor Lempitsky, that uses deep architecture to move human eyeballs in a still image.
  • The authors of the demo acknowledge that similar projects already exist (like the smile-manipulator FaceApp), but without such a singular, detailed focus.
  • I tried this using images of Keanu Reeves and several dogs, but the demo didn’t work with the dogs.
  • “Our system is reasonably robust against different head poses and deals correctly with the situations where a person wears glasses,” the authors wrote in their study.
  • The authors say they plan to work on making the demo work more quickly in the future.

Another day, another fun internet thing that uses neural networks for facial manipulation. This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and…
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