If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Can machines (or computers) think? What did Alan Turing have to say to that question? Well, he believed that the question is too "meaningless to answer". "The original question, 'Can machines think?', I believe to be too meaningless to deserve discussion." In other words, how can we even answer that question if we don't really know what thinking actually is in the first place?
Ted Sergott, EVP, Product Development at PRO Unlimited, is responsible for all aspects of PRO's Vendor Management System, Wand. In just a few months, the coronavirus pandemic has changed the candidate sourcing and talent landscape. According to a Gallup poll, 70% of the workforce was always or sometimes working remotely in April 2020. Workers and organizations have had to adjust to this "new normal," which offers both challenges and opportunities. For savvy organizations, one such opportunity is the ability to source contingent job positions in previously untapped locations, which can open up possibilities to lower costs, reduce fill times, and increase talent levels and diversity.
Today was the release of the second round (version 0.7) of MLPerf Inference benchmark results. Like the latest training results, which were announced in July, the new inference numbers show an increase in the number of companies submitting and an increased number of platforms and workloads supported. The MLPerf inference numbers are segmented out into four categories – Data Center, Edge, Mobile, and Notebook. The number of submissions increased from 43 to 327 and the number of companies submitting increased from just nine to 21. The companies submitting included semiconductor companies, device OEMs, and several test labs.
Google and the National Oceanic and Atmospheric Administration (NOAA) have signed a three-year deal to use the tech giant's artificial intelligence and machine learning to enhance the agency's environmental monitoring, weather forecasting and climate research, according to a joint announcement released Tuesday. Research under the deal initially focused on developing small-scale artificial intelligence and machine learning systems, and based on the results, NOAA and Google Cloud will focus on executing full-scale prototypes the agency could use across its organization. "Strengthening NOAA's data processing through the use of big data, artificial intelligence, machine learning, and other advanced analytical approaches is critical for maintaining and enhancing the performance of our systems in support of public safety and the economy," NOAA acting administrator Neil Jacobs said in the announcement. "I am excited to utilize new authorities granted to NOAA to pursue cutting-edge technologies that will enhance our mission and better protect lives and property," Jacobs added. Google engineers and data scientists have used artificial intelligence research to develop new methods for understanding and predicting weather.
If we didn't have enough to worry about--Covid-19, a nation divided, massive job losses and civil unrest--now we have to be concerned that robots will take our jobs. The World Economic Forum (WEF) concluded in a recent report that "a new generation of smart machines, fueled by rapid advances in artificial intelligence (AI) and robotics, could potentially replace a large proportion of existing human jobs." Robotics and AI will cause a serious "double-disruption," as the coronavirus pandemic pushed companies to fast-track the deployment of new technologies to slash costs, enhance productivity and be less reliant on real-life people. Millions of people have lost their jobs due to the effects of the Covid-19 pandemic and now the machines will take away even more jobs from workers, according to the WEF. The organization cites that automation will supplant about 85 million jobs by 2025.
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. With machine learning becoming increasingly popular, one thing that has been worrying experts is the security threats the technology will entail. We are still exploring the possibilities: The breakdown of autonomous driving systems? Meanwhile, machine learning algorithms have already found their way into critical fields such as finance, health care, and transportation, where security failures can have severe repercussion. Parallel to the increased adoption of machine learning algorithms in different domains, there has been growing interest in adversarial machine learning, the field of research that explores ways learning algorithms can be compromised.
For the first time, lawyers can apply legal analytics to cases heard in New York County Supreme Court ("New York County"). Lex Machina, a subsidiary of RELX, the British information corporate formerly known as Reed Elsevier, is announcing today the publication of data on 119,000 cases. The data is based on both dockets (analogous to the abstracts of academic papers) and documents (the full papers). Numerically, this caseload is not a massive expansion to the 4.5m cases already in Lex Machina's database, but Karl Harris, Lex Machina's CEO, argues it is an important milestone because New York County is such a significant jurisdiction. Lawyers are not renowned for an addiction to statistics and maths.
A new paper published by researchers affiliated with Facebook and Tel-Aviv University investigates whether machine learning language models can understand basic sets of instructions. The researchers propose a test dubbed the Turking Test to examine a model's ability to follow natural language instructions. Despite what the researchers characterize as a lenient evaluation methodology, they observed that a pretrained language model performed poorly across all tasks. One of the fundamental problems in AI is building a model that can generalize to previously unseen tasks. Recent work proposes a few-shot inference approach, in which a language model is conditioned on a few examples of a new task, followed by input for the model to process.
Did you ever wonder how credit card fraud detection is caught in real-time? Do you want to know how to catch an intruder program if it is trying to access your system? This is all possible by the application of the anomaly detection machine learning model. Anomaly detection is one of the most popular machine learning techniques. In this article, we will learn concepts related to anomaly detection and how to implement it as a machine learning model.
Continental AG is taking a minority stake in AEye Inc., a Dublin, California-based developer of LiDAR technology, in order to bring its autonomous vehicle technology to commercial vehicles sooner. Specifically, AEye, founded in 2013, has developed a long-range LiDAR system that can detect vehicles at a distance of more than 300 meters and pedestrians at more than 200 meters. Continental hopes the investment will enhance its current short-range LiDAR technology that is slated to go into production by the end of 2020. Then the AEye system would be deployed in a automotive passenger or commercial vehicle later this decade. "We now have optimum short-range and long-range LiDAR technologies with their complimentary sets of benefits under one roof," said Frank Petznick, head of Continental's advanced driver assistance systems, in a statement.