Summary: Are there large, sustainable career opportunities in AI and if so where? Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI? If you're a data scientist thinking about expanding your career options into AI you've got a forest and trees problem. There's a lot going on in deep learning and reinforcement learning but do these areas hold the best future job prospects or do we need to be looking a little further forward? To try to answer that question we'll have to get out of the weeds of current development and get a higher level perspective about where this is all headed. The roots of AI are actually in the behavioral sciences migrating eventually into biology and neurology.

Summary: Which of these terms means the same thing: AI, Deep Learning, Machine Learning? While there's overlap none of these is a complete subset of the others and none completely explains the others. Which of the following are substantially the same things? For as precise a profession as we data scientists purport to be we are sometimes way too casual with our language. Read several articles about AI, Deep Learning, and Machine learning and you will come away confused whether these are all the same or all different.

Expectations for a match-up between a professional StarCraft player and sophisticated AI ratcheted up last year after an AI program beat a highly ranked human player at Go, one of the world's most difficult board games. Dave Churchill, an assistant professor of computer science at Memorial University of Newfoundland, who has run the AIIDE competition for the past six years, says the contest's AI bots generally play at a "low amateur" level and have never won against a proficient human player. Last November, DeepMind announced it would collaborate with StarCraft publisher Blizzard to create a free, open-source API tool to enable researchers to test AI algorithms in StarCraft II. Around the same time, Facebook's AI Research group described a reinforcement-learning algorithm it made for StarCraft and released its own free, open-source tools to help AI researchers link deep-learning algorithms to an early version of the game.

Summary: Artificial General Intelligence (AGI) is the long sought after'brain' that brings together all the branches of AI into a general purpose platform that can perform with human level intelligence in a broad variety of tasks. For starters, the incremental gains in partial AI represented by deep learning and robotics have bigger money and bigger companies behind them and more close-in applications. However, it's unlikely we could find a single human able to master every single economically important job so perhaps we can declare victory when the AGI can master one or more jobs if not all. Inductive reasoning makes broad generalizations from specific observations.

The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation that allows for other types of deep learning problems to be considered.

The Residual Network (ResNet), proposed in He et al. (2015), utilized shortcut connections to significantly reduce the difficulty of training, which resulted in great performance boosts in terms of both training and generalization error. It was empirically observed in He et al. (2015) that stacking more layers of residual blocks with shortcut 2 results in smaller training error, while it is not true for shortcut of length 1 or 3. We provide a theoretical explanation for the uniqueness of shortcut 2. We show that with or without nonlinearities, by adding shortcuts that have depth two, the condition number of the Hessian of the loss function at the zero initial point is depth-invariant, which makes training very deep models no more difficult than shallow ones. Shortcuts of higher depth result in an extremely flat (high-order) stationary point initially, from which the optimization algorithm is hard to escape. The shortcut 1, however, is essentially equivalent to no shortcuts, which has a condition number exploding to infinity as the number of layers grows. We further argue that as the number of layers tends to infinity, it suffices to only look at the loss function at the zero initial point. Extensive experiments are provided accompanying our theoretical results. We show that initializing the network to small weights with shortcut 2 achieves significantly better results than random Gaussian (Xavier) initialization, orthogonal initialization, and shortcuts of deeper depth, from various perspectives ranging from final loss, learning dynamics and stability, to the behavior of the Hessian along the learning process.

Below is a list of seven open-source platforms that help businesses integrate machine learning into their production process. With these toolkits, businesses, regardless of their size, can get access to the same ML resources developed and used by prestigious companies. In 2015, Amazon's subsidiary AWS (Amazon Web Services) launched Amazon Machine Learning as part of its Cloud-based solutions. AML is a deliberately simplified platform intended for developers of any skill level to walk them through the creation of machine learning predictive models. Google uses TensorFlow toolkit for its own products and services. Since 2015, TensorFlow is an open source software library for deep learning.

Early detection of cancer: Developing NLP classifiers to analyze biomedical literature microRNAs are bio-markers, which may indicate cancer and other diseases even at early stage. Together, we developed a pipeline and an NLP classifier to detect relations between genes and micro-RNAs in medical research documents. The generalized code and leanings are open sourced and shared on Github. Sound and vision: Visual anomalies from audio data using deep learning We walk you through an effort by Sierra Systems with help from Microsoft and the Microsoft Cognitive Toolkit to detect and classify oil pipeline leaks, using audio data from a sensor ball deployed by Pure Technologies. We convert this audio data into images and use state-of-the-art Deep Learning techniques in the realm of image recognition to find'visual' anomalies and tell leaks from everyday events.

Even more concerning, researchers have shown that completely random nonsense images can be misclassified by CNNs with very high confidence as objects recognizable to humans, even though a human would clearly recognize that there was no image there at all (e.g. If those system observations are intentionally tainted with noise designed to defeat the CNN recognition, the system will be trained to make incorrect conclusions about whether a malevolent intrusion is occurring. Adversarial Machine Learning is an emerging area in deep neural net (DNN) research. The current state of AI has advanced to general image, text, and speech recognition, and tasks like steering the car or winning a game of chess.

Computer scientists at the University of Alberta are gearing up to watch a modern-age version of David versus Goliath, where man will battle machine in complex strategy board game, Go. "You could study Go for a lifetime," said professor Martin Mueller, who is travelling to China on Saturday to watch a three-game match between professional Go player Ke Jie and the computer program AlphaGo. Developed at Google DeepMind by a team of scientists -- including U of A alumni David Silver and Aja Huang -- AlphaGo was the first artificial intelligence to defeat professional Go players. In March 2016, the program beat one of the world's best players, Lee Sedol, in Seoul, South Korea, in a historic series that saw AlphaGo win four out of five games. Go is an ancient Chinese game that involves placing pieces, named stones, on an empty board to form territories and surround the other player. The tournament at the upcoming Future of Go Summit, from May 23 to 27, has the makings of an epic contest.