From machine learning basics articles to interesting ML news and academic papers, this list has something for both beginners and those more well-versed in the field. The articles below should help strengthen your knowledge of basic AI concepts and keep you up to date on trending AI topics. AI Terms Every Beginner Should Know - This article on Towards Data Science introduces and explains 14 abbreviations commonly used in the field of machine learning. From broad AI concepts to specific types of neural networks and specialized fields, this article contains concise explanations, helpful visual aids and links to more detailed explanations of each term. The Difference Between AI, ML, and DL - Artificial intelligence and machine learning are terms often used interchangeably.

After introducing the first inference benchmarks in June of 2019, today the MLPerf consortium released 595 inference benchmark results from 14 organizations. The MLPerf Inference v0.5 machine learning inference benchmark has been designed to measure how well and how quickly various accelerators and systems execute trained neural networks. The initial version of the benchmark, v0.5 currently only covers 5 networks/benchmark, and it doesn't yet have any power testing metrics, which would be necessary to measure overall energy efficiency. In any case, the benchmark has attracted the attention from the major hardware vendors, all of whom are keen to show off what their hardware can do on a standardized test, and to demonstrate to clients why their solution is superior. Of the 595 benchmark results released today, 166 are in the Closed Division intended for direct comparison of systems.

We are starting a series of events to gather the community of AI practitioners.

Take a joyride through a 3D urban neighborhood that looks like Tokyo, or New York, or maybe Rio de Janeiro -- all imagined by AI. We've introduced at this week's NeurIPS conference AI research that allows developers to render fully synthetic, interactive 3D worlds. While still early stage, this work shows promise for a variety of applications, including VR, autonomous vehicle development and architecture. The tech is among several NVIDIA projects on display here in Montreal. Attendees huddled around a green and black racing chair in our booth have been wowed by the demo, which lets drivers navigate around an eight-block world rendered by the neural network.

Facial recognition technology is convenient. Many of us use it numerous times a day to unlock our smartphones. Although people often access their phones with Face ID or fingerprints, many still worry about their privacy when their biometric data are used in the public space. There is a fine line between consensual identity verification and non-consensual surveillance. Delta Airlines opened the nation's first biometric terminal at Atlanta's Hartsfield Jackson International Airport (ATL) in November 2018.

More than nine out of 10 parents in China (95%) say they are likely to use an AI chatbot to diagnose their sick child in the future, up from 85% in 2018, including more than half (53%) saying they are "extremely" likely. Likewise, in India, 87% say they are likely to use an AI chatbot for their child's diagnosis in 2019 with 60% saying they are "extremely" likely, up significantly from 2018 (38%). In the U.K., parents are more likely in 2019 (53%) than in 2018 (50%) to use this technology to diagnose their child, while U.S. parents continue not to be very likely (51 percent in 2019 as compared to 52% in 2018) to use this technology to diagnose their child. More than nine out of 10 parents in China (95%) say they are likely to use an AI chatbot to diagnose their sick child in the future, up from 85% in 2018, including more than half (53%) saying they are "extremely" likely. Likewise, in India, 87% say they are likely to use an AI chatbot for their child's diagnosis in 2019 with 60% saying they are "extremely" likely, up significantly from 2018 (38%).

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that is now working on Graph Neural Network (GNN) based autonomous machine algorithms development, as part of the research to enhance its Avant! AI systems lend themselves to practical applications. AI is a computer hardware and software system that is able to perform tasks that normally require human intelligence; it is based on machine learning technology and is capable of being trained with a data set. GBT is now developing a set of algorithms within Avant! that are leveraging neural nets to process a huge amount of data, specifically for autonomous machines applications.

How often do you receive a message and wonder, "Did a person really write this?" When receiving a message online, it can often be difficult to discern whether it came from a human or a chatbot. Given the rapid rise in automated forms of communication, it would be foolish to assume that words attached to a name and face are in fact human. On the same token, our emails and messages on social platforms are filled with general platitudes, formulaic personalizations, and responses that seem reminiscent of an if this, then that (IFTTT) equation. We typically chalk this up as a win for technologists, who have been busy adding linguistic nuances and flourishes to how chatbots interact with humans.

You are in a pub and trying to order a drink. Someone cuts in front or the bartender simply doesn't see you. In crowded bars, you can end up spending most of your night just trying to order a drink. The Underdog in Central London is claiming to be the first bar in the world to install facial recognition technology. The AI-powered system, which was developed by British company DataSparQ, will help staff see who's next in line to be serviced.

This amendment delays the due date for the request for information to this program element in order to provide the community with more time to respond. The due date has been extended to November 14, 2019. FDL is an applied artificial intelligence (AI) research accelerator leveraging the newest developments in AI and Machine Learning (ML) technologies from academia and the private sector and applying them to challenges relevant to NASA's goals in space and earth sciences. Teams of computer scientists and space and earth scientists work to solve problems important to NASA and humanity's future within a given time frame. Each team is made up of four participants (two computer scientists and two domain scientists).