Inside a sunny conference room on the Microsoft campus in Redmond, Wash., a small team of employees is describing how technology can save the world. Microsoft's Digital Diplomacy unit consists of two dozen policy experts who work on everything from the ethical use of artificial intelligence to protecting the 2020 presidential election from foreign cyberinterference. Brad Smith, Microsoft's president, sits in the middle of the table, sipping coffee from a mug bearing the name of his hometown, Appleton, Wis. The group updates Smith on a tech-industry initiative co-founded by Microsoft to combat terrorist messaging on the Internet. Smith pushes for more ideas. "We need something that will create a new mold," he says.

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) and paraphrasing datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also establish a new SOTA in HotpotQA, improving answer prediction F1 by 4 F1 points and supporting fact prediction by 1 F1 point. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts find a map minimizing the discrepancy between two graph Laplacian exponential matrices, under norm-preserving and sparsity constraints. Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians. The objective function of this optimization is multimodal and has discontinuous slope, and is hence difficult for univariate optimizers to solve. We demonstrate a novel procedure for efficiently calculating these optima for two of our distance measure variants. We present numerical experiments demonstrating that (a) upper bounds of our distance metrics can be used to distinguish between lineages of related graphs; (b) our procedure is faster at finding the required optima, by as much as a factor of 10^3; and (c) the upper bounds satisfy the triangle inequality exactly under some assumptions and approximately under others. We also derive an upper bound for the distance between two graph products, in terms of the distance between the two pairs of factors. Additionally, we present several possible applications, including the construction of infinite "graph limits" by means of Cauchy sequences of graphs related to one another by our distance measure.

Signal retrieval from a series of indirect measurements is a common task in many imaging, metrology and characterization platforms in science and engineering. Because most of the indirect measurement processes are well-described by physical models, signal retrieval can be solved with an iterative optimization that enforces measurement consistency and prior knowledge on the signal. These iterative processes are time-consuming and only accommodate a linear measurement process and convex signal constraints. Recently, neural networks have been widely adopted to supersede iterative signal retrieval methods by approximating the inverse mapping of the measurement model. However, networks with deterministic processes have failed to distinguish signal ambiguities in an ill-posed measurement system, and retrieved signals often lack consistency with the measurement. In this work we introduce a variational generative model to capture the distribution of all possible signals, given a particular measurement. By exploiting the known measurement model in the variational generative framework, our signal retrieval process resolves the ambiguity in the forward process, and learns to retrieve signals that satisfy the measurement with high fidelity in a variety of linear and nonlinear ill-posed systems, including ultrafast pulse retrieval, coded aperture compressive video sensing and image retrieval from Fresnel hologram.

When New York City introduced its first automated traffic lights in 1924, it was employing an army of 6,000 traffic cops to switch signals manually to keep the Studebakers and Model Ts from jamming intersections. Over the next two years, 92% of those jobs were automated out of existence. Yet most of the officers came out just fine, thanks to a retraining program that let them land new jobs fixing the very stoplights that took their old job. Almost a century later, we're seeing this pattern repeat itself, as it has countless times over the decades. This time the automation is coming from AI and machine learning, related technologies poised to make significant inroads on the 6.3 million jobs in finance.

There is a new race in Silicon Valley involving Artificial Intelligence and no it's not HealthTech, FinTech, Voice Commerce or involve Google, Facebook or Microsoft... this race involves the brain and more specifically brain-computer interfaces. This race also involves technology royalty, the US government, billion dollar defence companies, a big connection to PayPal and years of medical research to better understand the human brain and implant devices that could make a consumer brain-computer interface a reality. The race is called "Neural implants, merging the human brain with AI" So what exactly are neural implants? Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries.[1]

The Louisiana Economic Development department is using a computer model to help target countries for foreign investment in the state, and the agency says it has hit the mark on some projects. LED built the predictive investment computer model to help hone its strategy about two years ago, leveraging a $170,000 federal grant through the U.S. Economic Development Administration in 2017. The agency tapped into a customized database that parses through investments made across the U.S. by companies in industries suited for Louisiana's existing infrastructure. "You go fishing where the fish are biting," said Larry Collins, executive director of the Office of International Commerce. The fishing expedition produced a five-year snapshot of companies ranging from advanced manufacturing to chemical makers.

In the last decade, artificial intelligence ("AI") applications have exploded across various research sectors, including computer vision, communications and medicine. Now, the rapidly developing technology is making its mark in weather prediction. The fields of atmospheric science and satellite meteorology are ideally suited for the task, offering a rich training ground capable of feeding an AI system's endless appetite for data. Anthony Wimmers is a scientist with the University of Wisconsin–Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) who has been working with AI systems for the last three years. His latest research investigates how an AI model can help improve short-term forecasting (or "nowcasting") of hurricanes. Known as DeepMicroNet, the model uses deep learning, a type of neural network arranged in "deep" interacting layers that finds patterns within a dataset.

You probably didn't know this but today, April 10th, 2019 banking execs are being grilled by Congress. As capitalism is blooming in 2019, where FANG stocks alone have gained $600 Billion since December, 2018 (5 months). The Banks are doing well too. It's timely then to mention that a report by IHS Markit predicts the global business value of artificial intelligence in finance will be $300 billion by 2030. Whether it's detecting fraud or helping automate processes, there's no denying AI has become more commercially viable in banking.

New York City Mayor Bill de Blasio joined Fox News' Tucker Carlson for a discussion on automation in the workforce and his new "robot tax." Far-left New York City Mayor Bill de Blasio, whose campaign for the Democratic presidential nomination is getting less than 1 percent support in polls, wants to create a "robot tax" and a massive new government bureaucracy to slow the progress and innovation that have made America the world's economic powerhouse. The unpopular mayor is terrified of robots, computers with artificial intelligence and other advanced machines that will eventually be able to do things only people can do today, eliminating millions of jobs. He neglects to mention the obvious fact that technological advances also create new jobs – like auto workers replacing blacksmiths, airline pilots replacing stagecoach drivers, and photographers replacing portrait painters. Under de Blasio's proposed "robot tax," companies that replace jobs with automation would have to pay the equivalent of five years of payroll taxes for each employee whose job is lost, making cost-saving innovations far less attractive.