The first step ensures that important sentences will be extracted, which can be used to better condition the transformer. The second, abstractive step can thus generate a better summary during the inference phase. The researchers concluded that their extractive step significantly improve the performance of the summarization results.

CHICAGO – PhysIQ, a leader in applying artificial intelligence to wearable sensor data, today announced that it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for their algorithm to continuously determine respiration rate in ambulatory patients. This clearance adds to their expanding portfolio of FDA-cleared cloud-based analytics, which also include QRS detection, heart rate, heart rate variability, atrial fibrillation detection, and their personalized physiology change detection analytic. The latest clearance advances physIQ's strategy to offer a deep portfolio of FDA-cleared analytics that can be applied to wearable sensor data. To enable this, physIQ's platform collects raw telemetry from the device and uploads it to the cloud where FDA-cleared analytics use the raw biosignals to produce vital signs. With this approach physIQ is able to provide vital sign analytics that benefit from the superior computing power of the cloud and fuel the higher-level analytics that further characterize dimensions of human physiology.

Don't miss this opportunity to meet your next big connection! Our events convene the most influential professionals in the area. Our purpose is to help our members forge strong, lasting relationships within the Silicon Valley tech community – combining Fortune 1000s with Entrepreneurs and Startups. We create unique opportunities & connections to help you grow your business and network!

Being able to reason about the predictions of a machine learning system is becoming increasingly important as sophisticated, non-linear predictive models are being adopted across the enterprise and beyond. In this talk we will discuss some requirements and challenges of model explanation algorithms and demo some practical examples using the open-source library Alibi we've developed at Seldon. - What makes an explanation interpretable? - The trade-off between interpretability and fidelity of an explanation algorithm - Practical examples of using some interpretable techniques (e.g.

The United States is at the precipice of a defining moment in history. Throughout the past 5 years, progress in artificial intelligence (AI) has greatly accelerated. From the defeat of Go champion Lee Sedol by DeepMind's AlphaGo program to the first deployments of fully-autonomous vehicles on public roads, recent events are challenging us to re-evaluate what may soon be possible for computerized systems. AI systems have already begun to quietly pervade a growing share of businesses, governments, and individual lives around the world, and we are only just beginning to grasp the impacts that this technological revolution will have on our economy, society, and national security. In our paper, we outline the key elements of a comprehensive national strategy for the United States to promote the safe and responsible development of AI, and to maintain U.S. leadership in AI technology.

Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?

Let's be clear, Artificial Intelligence, in particular in its latest development, deep learning that mimics the way the human mind works, first emerged in America. This gave the U.S. a huge head start over the rest of the world – including China, putting the U.S. firmly in the lead of the race for AI. What Americans didn't develop at home, they bought from Europe. In this respect, two British firms stand out with groundbreaking contributions to AI development: ARM and DeepMind. While all eyes are trained on the AI race between China and America, is there a role left for Europe?

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Artificial intelligence technology is proliferating fast across the world and is being deployed in applications from speech recognition to deepfake videos and monitoring traffic congestion. It's also increasingly being used to monitor and track citizens, according to a new report. At least 75 out of 176 nations surveyed globally are actively using AI technologies for surveillance purposes, according to the Carnegie Endowment for International Peace. These include facial recognition systems, smart policing tools, and the establishment of safe city platforms. The leading vendors of these systems globally are Chinese firms, led by Huawei, which has supplied these technologies to at least 50 states worldwide.

As my colleagues and I attend 3 major data and technology industry events in New York City this coming week – ML Ops, Finovate, and the granddaddy of them all, Strata Data Conference – it is interesting to note how far we have come in a few short decades. It may be hard to imagine today, but there was a time not too very long ago when data analysts, with a few notable exceptions, were relegated to the hidden recesses of most corporations. Better to toil away in the bowels than to be shown the light of day. For many decades, even as information technology (IT) emerged as a critical business function, data was viewed more as something that firms filed away in vaults for the mandatory seven years to comply with regulators, and not a business asset that could be mined to unlock critical business insights. Data was perceived as the purview of those who were sometimes derisively referred to as data geeks or "propeller heads". This was long before Silicon Valley, or Wall Street, embraced the term "geek".