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Artificially Intelligent Painters: can deep learning AI create the next Mona Lisa?

Neural Style

If you have ever used Instagram or Snapchat, you are familiar with using filters that alter the brightness, saturation, contrast, and so on of your images. Neural style, a deep learning algorithm, goes beyond filters and allows you to transpose the style of one image, perhaps Van Gogh’s “Starry Night,” and apply that style onto any other image.  

Neural style, one of many models available on Somatic.io, uses a deep neural network in order to separate and recombine content and style of any two images. It is one of the first artificial neural networks (ANNs) to provide an algorithm for the creation of artistic imagery.

convolutional neural network

How Does it Work?

The model is given two input images, one that will be used for styling, the other for content. At each processing stage in the convolutional neural network’s (CNN) hierarchy, the images are broken into a set of filtered images. While the number of different filters increases along the processing hierarchy, the overall size of the filtered images is reduced, leading to a decrease in the total number of units per layer of the network.

The above figure visualizes the information at different processing stages in the CNN. The  content reconstructions from lower layers (a,b,c) are almost exact replicas of the original image. In the higher layers of the network however, the detailed pixel information is lost while the high-level structures and details remain the same (d,e). Meanwhile, the model captures the style of the other input image on top of the content CNN representations. Then, the style representation draws connections between the different features in different layers of the CNN. The model then reconstructs the style of the input image on top of the content representations within each of the CNN layers. This creates images that match the style on an increasing scale as you move through the network’s hierarchy.

convolutional neural network layers

Try It Out!

Experiment with the model for yourself. All you need to do is select an image you want to use for style and anther one for the content. Here are some creations of the latest creations the model has generated.

 

YouTube Adds Machine Learning To Comments, Rebuilds Its Desktop Creator Studio

YouTube Adds Machine Learning To Comments #machinelearning #ML #datascience #youtube

  • Capping off the slew of updates it unveiled this year at VidCon, YouTube announced at a session on Saturday evening that it is rebuilding its Creator Studio on desktops and revamping comments with new machine learning technology.
  • The all-new YouTube Creator Studio — which the company is renaming YouTube Studio — will still feature a suite of channel management tools aimed to make creators’ lives easier.
  • YouTube says it’s rebuilding the Studio with creator input, and is inviting interested parties to sign up for a beta test right here.
  • YouTube said that updates to comments, including the ability for creators to bestow hearts and pin comments, are seeing great success.
  • The technology will also parse through comments to look for common themes and topics, giving creators a sense of what viewers are talking about at a glance.

YouTube announced it is rebuilding its Creator Studio on desktops and revamping comments with new machine learning technology.
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The dawn of the augmented writing era – Textio Word Nerd

Product genius @jensenharris @textio writes how machine learning is fundamental to writing.

  • It has the potential to be a fundamental breakthrough in human communication.Surprisingly, because of the slow pace of innovation in writing software, augmented writing is really only the third disruption in nearly half a century of computer-assisted writing.Let’s take a brief trip down memory lane.A brief history of writing softwareThe text mode eraThough there was once something sold called a “word processing machine” (basically a glorified typewriter with a small screen so that you didn’t have to use as much gunky Liquid Paper to correct mistakes), the true beginning of mass market word processing started with the advent of IBM’s personal computer and the subsequent PC revolution.The IBM Personal Computer.
  • The idea was that you could see on the screen exactly what a document would look like when it was printed.Microsoft had a purpose-built applications team founded with the mission to build great WYSIWYG word processing software, and they were quick to market with a little product called Microsoft Word that took full advantage of this new technology.Microsoft Word let everyone create garish documents with just the click of a button!For the first time, the printed page was right there on the screen!
  • Once people had Word, they never ever wanted to go back.WordPerfect didn’t have WYSIWYG in their blood… the team and software hadn’t been purpose-built for the graphical era.
  • Even Quip, the biggest writing exit of the last few years, is fully of this collaborative era.The missing linkSo we’ve seen writing software evolve from text mode through WYSIWYG editing to today’s focus on collaborative editing.What does all the software from all of these eras have in common?It doesn’t make your writing better.There are features galore to decorate text… to make it red and bold, or to encapsulate it in fancy bulleted lists or surround it with ornate borders.
  • There are features to quickly get back to earlier revisions of your document.But these products miss the highest, most important potential of writing software — the capability to make the human a more successful writer.The augmented writing eraAnd so now, we stand at the precipice of the next era of writing software — the era of augmented writing.Each era in writing software was fueled by a disruptive technology that changed people’s expectations of what writing software was capable ofThe rise of machine intelligenceAugmented writing builds on an incredibly disruptive technology: machine intelligence.The core tech now exists to be able to quantitatively predict with a high degree of accuracy whether a document or email you’re writing will get the outcome you want.This predictive power is paired with a new kind of writing user interface which x-rays your document in real-time.

Humans have long imagined language superpowers. Imagine if you could know — in advance — exactly how other people would react to your words. This is augmented writing, and it’s here already.
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9 Artificial Intelligence Startups in Medical Imaging

9 Artificial Intelligence Startups in Medical Imaging

  • Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold ‘Em tournament played out over 20 days.
  • The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before.
  • It’s no secret that AI is now performing certain medical imaging tasks better than human doctors.
  • Pundits say “well, people will always trust a human doctor over an AI” and the answer we’d have to that is “not if the AI is going to give a more accurate answer “.
  • It’s only a matter of time before every X-ray machine is connected to the cloud and one human doctor per hospital puts his hand on your shoulder when he reads you the output from the AI algorithm.

You don’t have to be a gambler to appreciate the complexities of the card game Texas Hold ‘Em. It involves a strategy that needs to evolve based on the players around the table, it takes a certain amount of intuition, and it doesn’t require the player to win every hand. Just a few days ago, an artificial intelligence (AI) algorithm named Libratus beat four professional poker players at a no-limit Texas Hold ‘Em tournament played out over 20 days.
If you have even the slightest understanding of how to write code, you would realize that it is impossible to actually code a software program to do that with such “imperfect information”. The AI algorithm did exceptionally well and was utilizing strategies that humans had never used before. Professional poker players are in no danger of losing their jobs, but the incredible capabilities of what AI is mastering these days should make everyone wonder just how safe their jobs actually are.
Let’s take the $3 billion medical imaging market. It’s no secret that AI is now performing certain medical imaging tasks better than human doctors. Pundits say “well, people will always trust a human doctor over an AI” and the answer we’d have to that is “not if the AI is going to give a more accurate answer “. It’s only a matter of time before every X-ray machine is connected to the cloud and one human doctor per hospital puts his hand on your shoulder when he reads you the output from the AI algorithm. Kind of like this:
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Get ready for 5G connectivity with your autonomous car

Get ready for #5G connectivity with your autonomous car   MT @UCStrategies #IoT #M2M #AI

  • Autonomous cars are going to need to send and receive mountains of data, and the current 4G wireless networks simply won’t cut it.
  • Super-fast transfer speeds will be required by self-driving cars to enable them to communicate with a wide range of systems such as navigation services, traffic signals as part of connecting to smart city infrastructure, car-to-car communication and even to close-proximity mobile phone users – all in the interest of pilotless vehicle safety.
  • Using 5G wireless networks will enable driverless cars to avoid hitting pedestrians by a direct connection between the car and the person’s handheld device as they approach an intersection.
  • While 5G standards are currently being worked out, expect to see everything up and running by 2020, when Volkswagen has promised to bring its first semi-autonomous electric car to market.
  • That said, electric car company Tesla will have fully-autonomous vehicles ready by 2018, Toyota, General Motors and Volkswagen by 2020, while Ford and BMW claim they will have autonomous cars on the road by 2021.

Autonomous cars are going to need to send and receive mountains of data, and the current 4G wireless networks simply won’t cut it….
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Concise Visual Summary of Deep Learning Architectures

Concise Visual Summary of #DeepLearning Architectures #abdsc

  • With new neural network architectures popping up every now and then, it’s hard to keep track of them all.
  • RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks.
  • That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants.
  • Many abbreviations also vary in the amount of “N”s to add at the end, because you could call it a convolutional neural network but also simply a convolutional network (resulting in CNN or CN).
  • Composing a complete list is practically impossible, as new architectures are invented all the time.

This article was written by Fjodor Van Veen. 
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing…
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50 Companies Leading the Artificial Intelligence Revolution

Here are 50 companies leading the AI revolution

  • We know that artificial intelligence will soon reshape our world.
  • But which companies will lead the way?
  • To help ­answer that question, research firm CB Insights recently selected the “AI 100,” a list of the 100 most promising artificial intelligence startups ­globally.
  • The private companies were chosen (from a pool of over 1,650 candidates) by CB Insights’ Mosaic algorithm, based on factors like financing history, investor quality, business category, and momentum.
  • A look at the 50 largest startups on the list, ranked by total funds raised, shows that investment in AI is surging worldwide.

A look at the most promising global startups working with artificial intelligence.
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Different Types of Artificial Intelligence and the Names to Watch in 2017

Different types of Artificial Intelligence and the names to watch in 2017
 #AI #NGAHR

  • 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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