Artistic engineer Alex Ruben aka artBoffin has created "Deeply Artificial Trees", a brilliantly psychedelic video interpretation of an episode the iconic The Joy of Painting with Bob Ross. Ruben accomplished this trippy kaleidoscopic effect using deep learning architecture (artificial neural network), which also clarified the "unreasonable effectiveness" of itself . This artwork represents what it would be like for an AI to watch Bob Ross on LSD (once someone invents digital drugs). It shows some of the unreasonable effectiveness and strange inner workings of deep learning systems. The unique characteristics of the human voice are learned and generated as well as hallucinations of a system trying to find images which are not there.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. There will be more on this at ICRA next month, and we're hoping for a live demo. OpenAI has created "the world's first Spam-detecting AI trained entirely in simulation and deployed on a physical robot."

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. Fortunately, artificial intelligence can help the healthcare sector to overcome present and future challenges.

Google-stablemate DeepMind is creating a blockchain-like system to show how sensitive medical data passing through its processors will be used, allowing healthcare professionals to check if data has been tampered with. Its healthcare arm, DeepMind Health, is working to improve medical diagnoses with machine learning tools. Large amounts of confidential data are required to develop these tools – something DeepMind hasn't always been trusted to handle. Last year, DeepMind was criticized for gaining access to current and historic patient records for 1.6 million individuals across three London Royal Free NHS Trust hospitals – which extended well beyond the scope of the research they had publicly disclosed. The announcement of Verifiable Data Audit this week is an attempt to gain back some of the lost trust.

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse. Accompanying ICML 2015 in Lille, France, there was another, almost as big, event: the 2015 Deep Learning Workshop. The workshop ended with a panel discussion, and at it, Neil Lawrence said, "NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened." Now that is a remark that the computational linguistics community has to take seriously!

Ever wondered what it would be like for artificial intelligence to trip-out while watching Bob Ross paint a pretty picture? This video was created by Alexander Reben, an engineer turned artist who uses technology to explore how machines are changing the human world and vice versa. It features an episode of the stoner-favorite television show The Joy of Painting with Bob Ross through Google's neural network DeepDream. DeepDream is a convolutional neural network, a style of computing inspired by the brain, that identifies and recognizes images and patterns. Most of the time, it's used to create nightmarish visions like these, but it's also a surprisingly insightful visualization that shows how computers "think" in regards to tasks like image classification and speech recognition.

Most of today's artificial intelligence (AI) systems rely on machine learning algorithms that can predict specific outcomes by drawing on pre-established values, but now researchers from OpenAI, a company funded by no less than Elon Musk and Peter Thiel, who are trying to democratise AI for "human good" just discovered – literally – that a machine learning system they created to predict the next character in the text of reviews from Amazon evolved into an unsupervised learning system that could learn how to read sentiment. That's a pretty big deal, and it's also something that, at the moment, even the researchers themselves can't explain. "We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment," said OpenAI in a blog. According to the post OpenAI's neural network was able to train itself and analyse sentiment accurately by classifying Amazon's reviews as either positive or negative – and it then generated follow on text that fit with the sentiment. The AI the team used was what's known as a multiplicative long short-term memory (LSTM) model that was trained for a month, processing 12,500 characters a second using Nvidia Pascal GPU's – which Nvidia's own CEO gifted to Elon Musk last year – with "4,096 units on a corpus of 82 million Amazon reviews to predict the next character in a chunk of text."

There's currently a shortage of over seven million physicians, nurses and other health workers worldwide, and the gap is widening. Doctors are stretched thin -- especially in underserved areas -- to respond to the growing needs of the population. Meanwhile, training physicians and health workers is historically an arduous process that requires years of education and experience. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.

In this article, we have compiled a list of some of the lesser-known Deep Learning libraries. This is an open source framework for distributed deep learning on big-data clusters. This is a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph. The downhill package provides algorithms for minimizing scalar loss functions that are defined using Theano. Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.