The SETI Institute of Mountain View is inviting all citizen data scientists and technologists to join us as collaborators in our mission to find intelligent radio signals from beyond our solar system. We are issuing a worldwide, public code challenge and accompanying hackathon in San Francisco for the purpose of expanding our radio-telescope signal classification tools using the latest developments available in machine- and deep-learning.

So you've decided it's time to add some of this hot new machine learning or deep learning stuff into your app… Great! But what are your options? Let's say you want to make a "celebrity match" app that tells people which famous person they most look alike. You need to first gather a lot of photos of celebrities' faces. Then you train a deep learning network on these photos to teach it what each celebrity looks like. The model you're using would be some kind of convolutional neural network and you've trained it for the specific purpose of comparing people's faces with the faces of celebrities. Training is a difficult and expensive process.

In the history of London's Underground rail network, the underground fire hazard incident of 1987 at the King's cross station was one the biggest disasters. It claimed 31 lives and also severely scarred the reputation of London Underground. After multiple inquiries, it was concluded that the fire had started because someone dropped a lit match onto a wooden escalator. But how did such a trivial incident snowball into a full-blown disaster? Pulitzer prize-winning author and journalist Charles Duhigg analyzed this question in his international bestseller The Power of Habit.

The black box in aviation, otherwise known as a flight data recorder, is an extremely secure device designed to provide researchers or investigators with highly factual information about any anomalies that may have led to incidents or mishaps during a flight. The black box in Artificial Intelligence (AI) or Machine Learning programs1 has taken on the opposite meaning. The latest approach in Machine Learning, where there have been'important empirical successes,'2 is Deep Learning, yet there are significant concerns about transparency. Developers acknowledge that the inner working of these'self-learning machines' adds an additional layer of complexity and opaqueness concerning machine behavior. Once a Machine Learning algorithm is trained, it can be difficult to understand3 why it gives a particular response to a set of data inputs.

Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist. The cancer finding, reported in Nature, was part of a stream of reports this year offering an early glimpse into what could be a new era of "diagnosis by software," in which artificial intelligence aids doctors--or even competes with them. Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures. Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics.

I always enjoy these industry-spanning infographics. They sometimes point me to companies I want to understand in greater depth. At the same time the reader needs to beware of how these are categorized or miscategorized and what companies may be missing. The inclusion of SAS for example as a BI enterprise system and the total absence of IBM SPSS from the data science category are huge red flags. These two companies alone control what is at least 1/3rd of the data science platform market among the global 8,000 companies with more than $1 Billion in revenue.

Stores are large and have complex layouts that are confusing to navigate. The hummus you want could be in the dairy section, the deli section, or somewhere else entirely. Efficiently navigating a store can be a daunting task. At Instacart, our customers can order millions of products from hundreds of retail partners. Our fleet of tens of thousands of personal shoppers must find these items at thousands of store locations.

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents" – any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futurism. Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed.

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree-structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.