Artificial intelligence (AI) in medicine has a long history (1). AI has been an active subfield of computer science for more than 60 years, while medicine is even a much older field, which can trace back to thousands of years. Researchers from both AI and medicine communities have been interacting to create novel solutions for better patient care and enabling more efficient healthcare systems (2,3). Collaborations between both communities were either technology-driven or problem-driven. In technology-driven research, innovations are mainly the development and validation of new AI algorithms for selected clinical problems where the algorithms are generic and not necessary to be optimal in solving real-world problems.

For many, just mentioning artificial intelligence brings up mental images of sentient robots at war with mankind and man's struggle to avoid the endangered species list. While this may one day be a real scenario for when (perhaps a big if?) mankind ever creates an artificial general intelligence (AGI), the more pressing matter is whether embedded software developers should be embracing or fearing the use of artificial intelligence in their systems. Here are five reasons why you may want to include machine learning in your next project. From an engineering perspective, including a technology or methodology in a design simply because it has marketing buzz is something that every engineer should fight. The fact though is that if there is a buzz around something, odds are it will in the end help to sell the product better.

Hikvision has marketed an AI camera that automatically identifies Uyghurs, on its China website, only covering it up days ago after IPVM questioned them on it. This AI technology allows the PRC to automatically track Uyghur people, one of the world's most persecuted minorities. Hikvision's product description states this camera supports Uyghur recognition (screenshot via Google Translate): Capable of analysis on target personnel's sex (male, female), ethnicity (such as Uyghurs, Han) and color of skin (such as white, yellow, or black), whether the target person wears glasses, masks, caps, or whether he has beard, with an accuracy rate of no less than 90%. By April 2019, Hikvision was well-aware of the human rights issues surrounding Xinjiang; that same month, they disclosed in their ESG report that they had "recently commissioned an internal review" on the matter. The PRC officially recognizes 56 ethnic groups, which the Chinese ambassador recently described as being'part of big family of Chinese nation'.

This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.

In recent years, the efforts of digital marketing companies have taken a significant turn by using artificial intelligence technologies. One of the major problems that marketers are facing is how to personalize the content to users and generate better experience and results. Lately several startups developed AI technologies that aimed to help marketer solving this problem. The vast amount of information collected about users is used by advertising systems dominated by the big players such as: Google and Facebook. However, most businesses are having difficulties to use AI technologies to improve their digital marketing.The great challenge that any marketing manager faces is how to tailor the message to the customer in a personalized way that is fits to each user according to interests, purchase intent and the right timing.What is Artificial Intelligence Marketing?Artificial Intelligence Marketing (AI Marketing) allows you to leverage the data collected on users or customers to tailor the content or messages to the user profile so that the content presented to the user is tailored to their interests at the time relevant to the user and their purchase intent.

AutoAI, a powerful, automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award yesterday at the AIconics AI Summit in San Francisco. Chosen by a panel of 13 independent judges, the AIconics awards recognize breakthroughs in AI for business. To share what went behind the development of AutoAI and how it accelerates time to value with data science projects, I interviewed one of our principal inventors: Jean-Francois Puget, PhD, a distinguished engineer for machine learning and optimization at IBM and a two-time Kaggle Grandmaster. What challenge led you to start developing AutoAI? Jean-Francois Puget: As data scientists, our work is a mix of applying general-purpose recipes and creating domain-specific insights.

Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.

Global "Enterprise AI Market" describe market overview, market opportunities, market driving force product scope, and market risks. Enterprise AI Market competitive situation, sales, revenue and global market share of top manufacturers are analysed emphatically by landscape contrast. The prime objective of this report is to help the user understand the market in terms of its definition, segmentation, market potential, influential trends, and the challenges that the market is facing. Deep researches and analysis were done during the preparation of the report. The readers will find this report very helpful in understanding the market in depth.

Firm leverages AI technology similar to facial recognition to figure out which small molecules can bind most effectively with targeted enzymes. Does the future of drug development lie in a kind of facial-recognition technology for enzymes? That is the hope of X-37 LLC, a drug-development startup that is using artificial intelligence and a deep neural network developed by San Francisco-based Atomwise. "We think that this is an approach and a technology that is really going to transform drug discovery across the board," said Dr. David Collier, CEO of X-37, which is partly owned by Atomwise and is based in South San Francisco, California. It was founded last year.

As Deep Learning (DL) models have been increasingly used in latency-sensitive applications, there has been a growing interest in improving their response time. An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities. However, the current profiling tools lack the highly desired abilities to characterize ideal performance, identify sources of inefficiency, and quantify the benefits of potential optimizations. Such deficiencies have led to slow characterization/optimization cycles that cannot keep up with the fast pace at which new DL models are introduced. We propose Benanza, a sustainable and extensible benchmarking and analysis design that speeds up the characterization/optimization cycle of DL models on GPUs. Benanza consists of four major components: a model processor that parses models into an internal representation, a configurable benchmark generator that automatically generates micro-benchmarks given a set of models, a database of benchmark results, and an analyzer that computes the "lower-bound" latency of DL models using the benchmark data and informs optimizations of model execution. The "lower-bound" latency metric estimates the ideal model execution on a GPU system and serves as the basis for identifying optimization opportunities in frameworks or system libraries. We used Benanza to evaluate 30 ONNX models in MXNet, ONNX Runtime, and PyTorch on 7 GPUs ranging from Kepler to the latest Turing, and identified optimizations in parallel layer execution, cuDNN convolution algorithm selection, framework inefficiency, layer fusion, and using Tensor Cores.