It’s official: AI is better than humans at naming craft beers

#ai AI is better than humans at naming craft beers  via @thenextweb

  • I often tell people not to worry about robots taking our jobs, but if you’re in the business of branding craft beer, your career might well be nearing its end, thanks to a clever new AI that’s figured out how to do it on its own.
  • Researcher and electrical engineer Janelle Shane, who’s previously taught AI to conjure up pick-up lines and christen guinea pigs, developed a neural network system to name craft brews by learning from an extensive dataset spanning 90 types of beer culled from BeerAdvocate.com.
  • Here are some of my favorites, which I’d happily consider drinking if I spotted these names on a tap or label:

    Do those sound good or what?

  • There’s more where that came from over at Shane’s blog, and you can also hand over your email address for a PDF with 100 more great names.
  • Craft beer names, invented by neural network on Lewis and Quark

If you’re in the business of branding craft beer, your career might well be nearing its end, thanks to a new AI that’s figured out how to do it on its own.
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Elon Musk’s Neuralink: Potential to Transform the World

Elon Musk's Neuralink: Potential to Transform the World #AI #DataScience

  • Neuralink is the perfect marriage between the human brain and a computer.
  • Elon Musk announced the hypothetical Brain Computer Interface (BCI), the Neuralink technology, in March this year.
  • Once integrated, the technology will work in a similar manner to how humans control and move their limbs and process external stimuli and information.
  • With electrodes processing human brain functions, technology will be more perceptive to human emotions.
  • The experience that a computer integrated directly with the human brain can provide will be far greater than any gaming module.

Elon Musk’s Neuralink possesses the potential to revolutionize technology and life. Mind and machine joining as one will open the doors to a new world.
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Denoising Autoencoders explained – Dominic Monn – Medium

Denoising Autoencoders explained. #BigData #DeepLearning #MachineLearning #DataScience #AI

  • Autoencoders are Neural Networks which are commonly used for feature selection and extraction.
  • However, when there are more nodes in the hidden layer than there are inputs, the Network is risking to learn the so-called “Identity Function”, also called “Null Function”, meaning that the output equals the input, marking the Autoencoder useless.Denoising Autoencoders solve this problem by corrupting the data on purpose by randomly turning some of the input values to zero.
  • It depends on the amount of data and input nodes you have.When calculating the Loss function, it is important to compare the output values with the original input, not with the corrupted input.
  • That way, the risk of learning the identity function instead of extracting features is eliminated.A great implementation has been posted by opendeep.org where they use Theano to build a very basic Denoising Autoencoder and train it on the MNIST dataset.
  • So even if you don’t have too much experience with Neural Networks, the article is definitely worth checking out!Original input, corrupted data and reconstructed data.

Last month, I wrote about Variational Autoencoders and some of their use-cases. This time, I’ll have a look at another type of Autoencoder: The Denoising Autoencoder, which is able to reconstruct…
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The Infinite Drum Machine

The Infinite Drum Machine: Thousands of everyday sounds, organized using machine learning

  • This experiment uses machine learning to organize thousands of everyday sounds.
  • The computer wasn’t given any descriptions or tags – only the audio.
  • Using a technique called t-SNE, the computer placed similar sounds closer together.
  • You can use the map to explore neighborhoods of similar sounds and even make beats using the drum sequencer.

AI Experiments is a showcase for simple experiments that let anyone play with artificial intelligence and machine learning in hands-on ways, through pictures, drawings, language, music, and more.
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Wimbledon serves up a new take on journalism in the age of AI

#ai Wimbledon serves up a new take on journalism in the age of AI  via @thenextweb

  • But that’s not to say that it’s stuck in the past — thanks to a partnership with IBM, Wimbledon is using Watson to automate key parts of the iconic tennis event.
  • But this year round, the job is placed in the hands of the Watson AI.
  • Watson can generate highlight packages without any human input.
  • One IBM representative said that eventually, the company will be able to reduce this time to just 30 minutes

    Watson is also tasked with tagging photos.

  • But surely the argument could be made that Watson is essentially automating entry-level journalism jobs?

Wimbledon is 150 years old. That’s not to say it’s stuck in the past.
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What is Artificial Intelligence?

What is Artificial Intelligence? 
  [via @Accenture]
#AI #ML #DL #NLP #Robots
@ipfconline1

  • In this series, I will try to provide an overview of what constitutes AI, how it is already being applied to banking, insurance and capital markets today, and how organizations can craft a value-driven AI strategy.
  • We view AI as a collection of multiple technologies that enable machines to sense, comprehend and act—and learn—either on their own or to augment human activities.
  • And unlike conventional capital, thanks to its self-learning capabilities, AI can actually improve over time.
  • Today’s definition of AI refers to multiple technologies that can be combined in different ways to:

    All three capabilities are underpinned by the ability to learn from experience and adapt over time.

  • To learn more, take a look at Accenture’s Top 5 Technology Trends 2017, and at how we envision AI to change the Future of Business.

The pace of change in digital technology is accelerating exponentially, particularly in the field of artificial intelligence (AI). All major technology companies are reorganizing around this red-hot sector, and it dominated discussions and presentations at the recent World Economic Forum 2017 in Davos. In this series, I will try to provide an overview of what constitutes AI, how it is already being applied to banking, insurance and capital markets today, and how organizations can craft a value-driven AI strategy.
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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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