Crucial time and tremendous amounts of resources are lost every day in the world's healthcare systems. Misdiagnoses cost unnecessary additional tests, result in delayed treatment plans and diminished survival or remission rates from what would have transpired had it been caught and identified correctly earlier. Trials, treatments and research completed in silos so there's no leveraging the insights across the country or the world. Some healthcare and technology innovators are collaborating and trying to change our current reality by experimenting with artificial intelligence (AI) and machine learning. Computers and the algorithms they run can scrub colossal amounts of data--much faster and more accurately than human scientists or medical professionals--to unearth patterns and predictions to enhance disease diagnosis, inform treatment plans and enhance public health and safety.

Senator Maria Cantwell, D-Wash., just drafted forward-looking legislation that aims to establish a select committee of experts to advise agencies across the government on the economic impact of federal artificial intelligence. The move is an early step toward formalizing the exploration of AI in a government context. But it could ultimately contribute to jump-starting AI-focused programs that help stimulate the United States economy, benefit citizens, uphold data security and privacy, and eventually ensure America is successful during the initial introduction of this important technology to U.S. consumers. The presence of legislation could also lend legitimacy to the prospect of near-term government investment in AI innovation -- something that may even sway Treasury Secretary Steve Mnuchin and others away from their belief that the impact of AI won't be felt for years to come. Indeed, other than a few economic impact and policy reports conducted by the Obama Administration -- led by former U.S. Chief Data Scientist DJ Patil and other tech-minded government leaders -- this is the first policy effort toward moving the U.S. public sector past acknowledging its significance, and toward fully embracing AI technology.

To understand today's headlines about China's approach to issues such as trade, foreign policy or internet censorship, turn to its past. The country is perhaps more aware of its own history than any other major society on earth. That remembering is certainly partial - events like Mao's Cultural Revolution are still very difficult to discuss within China itself. But it is striking how many echoes of the past can be found in its present. China remembers a time when it was forced to trade against its will.

It seems there are few jobs robots can't do these days, even the most delicate jobs, like picking asparagus or potting plant seedlings. But they're only needed because humans can't - or won't - do the work, farmers say. Marc Vermeer had a problem. He was struggling to attract workers to pick his white asparagus crop in the Netherlands. The workers he did hire moved on quickly, so he was always training new people.

If you think about it, we are all part of a supply chain -- as producers, consumers or distributors, often playing multiple roles. It should not surprise anyone that Artificial Intelligence has a role here too. Now, supply chain management is nothing new, even if the term itself was coined relatively recently, in the 1980s. Supply chain management traces its roots to the early 20th century, when the development of the assembly line necessitated a new system to manage the flow of raw materials and finished goods. Although the science (and art) of supply chain management remains primarily concerned with this, the business landscape has evolved significantly. The increase in international trade, shift towards just-in-time production, and rise of e-commerce are just some of the factors that have changed how the game is played.

As you read this essay, you understand each word based on your understanding of previous words. You don't throw everything away and start thinking from scratch again. Traditional neural networks can't do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue.

The ability to stratify patients by cardiovascular risk is essential for identifying those likely to suffer a heart attack, stroke, or other heart disease in the future. High-risk patients can then take steps to improve their cardiovascular health. Doctors typically take into account a variety of risk factors: demographics such as age, sex and ethnicity; daily behaviors like exercise, smoking status and diet; as well as results from blood pressure and cholesterol tests. As a simple alternative to the traditional patient questionnaire and blood tests, a team of researchers from Google Research and the Stanford School of Medicine have developed deep learning models to predict cardiovascular risk factors from photographs of the back of the retina. Since these retinal fundus images are already collected for diabetic eye disease screening, this initial study suggests that deep learning could uncover additional information that could be further leveraged for preventative health.

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task-specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available.

Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion. Proposed Autoencoder Structure In machine learning, an AE is a neural network (NN) which consists of two parts: an encoder maps an input s (e.g., an image) to a lowerdimensional representation or code and a decoder attempts to reconstruct the input from the code. The AE structure used in this paper is shown in Figure 1 and will be described in the following. The goal is to transmit a message s chosen from a set of M possible messages {1, 2,..., M} M. Following 9 In this paper, we develop an AE for a simplified memoryless fiber channel model.