This is our selection of featured articles and resources posted since Monday. Get Ready for Regulations that Restrict Your Analytics Article: What is Data Science? Article: What is Data Science?

Whereas CNNs have demonstrated immense progress in many vision problems, they suffer from a dependence on monumental amounts of labeled training data. On the other hand, dictionary learning does not scale to the size of problems that CNNs can handle, despite being very effective at low-level vision tasks such as denoising and inpainting. Recently, interest has grown in adapting dictionary learning methods for supervised tasks such as classification and inverse problems. We propose two new network layers that are based on dictionary learning: a sparse factorization layer and a convolutional sparse factorization layer, analogous to fully-connected and convolutional layers, respectively. Using our derivations, these layers can be dropped in to existing CNNs, trained together in an end-to-end fashion with back-propagation, and leverage semisupervision in ways classical CNNs cannot. We experimentally compare networks with these two new layers against a baseline CNN. Our results demonstrate that networks with either of the sparse factorization layers are able to outperform classical CNNs when supervised data are few. They also show performance improvements in certain tasks when compared to the CNN with no sparse factorization layers with the same exact number of parameters.

The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated.

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs. We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues. Furthermore, we develop spiking implementations of common CNN operations such as max-pooling, softmax, and batch-normalization, which allow almost loss-less conversion of arbitrary CNN architectures into the spiking domain. Empirical evaluation of different network architectures on the MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.

Secret to GoogleBrain and RankBrain algorithm revealed. One is going to give a historical overview about GoogleBrain and analyse the pattern, then we will conclude our finding about the current situation and future changes in search engine algorithm. Back in 2006 there were some interests in implementing artificial intelligence in Google search engine algorithm. A few years later in 2014, GoogleBrain was established after acquisition of DeepMind, a British artificial intelligence company which was founded in 2010. They worked on how to play video games based on machine learning and artificial neural networks (ANNs).

In the original business plan, one of the stated goals for AWS, a nascent part of Amazon at the time, was to bring technology which had traditionally been a playground for only the largest, most well funded companies, within reach of everyone. Our goal today remains the same: to put futuristic technologies which are cumbersome, complex or capital intensive into the hands of every developer. When it comes to artificial intelligence, we started down this path in 2015 with Amazon Machine Learning - a service which put predictive analytics in the hands of developers with tons of domain knowledge (and tons of data), without requiring deep machine learning expertise. This is the same technology we use internally at Amazon for everything from fraud to counterfeit good detection, now available in a friendly console for everyone. At AWS Re:Invent this year, we brought a new wave of AI services to developers, in the form of Amazon AI; a collection of new artificial intelligence services which, along side Amazon ML, serve to put intelligence at the heart of every app and every business, through sophisticated, high-quality artificial intelligence that is easy to use and cost effective.

Apple will allow its artificial intelligence teams to publish research papers for the first time, marking a significant change in strategy that could help accelerate the iPhone maker's advances in deep learning. When Apple introduced its Siri virtual assistant in 2011, the company appeared to have a head start over many of its nearest competitors. But it has lost ground since then to the likes of Alphabet Inc.'s Google Assistant and Amazon.com Researchers say among the reasons Apple has failed to keep pace is its unwillingness to allow its AI engineers to publish scientific papers, stymieing its ability to feed off wider advances in the field. That policy has now changed, Russ Salakhutdinov, an Apple director of AI research, said last week at the Neural Information Processing Systems conference in Barcelona, Spain, according to Twitter posts from those present.

Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well. This approach is also taken within data science, where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach – collaboration, open-sourcing code – is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains. Dr Tristan Fletcher, research director, Thought Machine, said: "Without this specialisation and domain knowledge, it's very hard to rise above the noise. However, the algorithms themselves are often applicable in many areas or problems, and we are probably seeing decreasing specialisation here. "Fintech lends itself particularly to specialisation because there are many well-packaged problems that need to be solved and can be clearly delineated – KYC, AML, credit checking etc.

AWS (Amazon Web Services) introduced a handful of new artificial intelligence (AI) services at its re:Invent conference last week. Among the new products are Polly, a more lifelike text-to-speech service, Rekognition, an image analysis and face recognition service that can be added to applications and Lex, a standalone version of the technology that powers the company's Alexa AI assistant. "Amazon AI services are fully managed services so there are no deep learning algorithms to build, no machine learning models to train, and no up-front commitments or infrastructure investments required," the company said in a statement. "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." So far, few developers have been able to build, deploy, and broadly scale apps with AI capabilities due to the vast amount of data and specialized expertise in machine learning and neural networks required, Amazon said.