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) …
Using the power of artificial intelligence (AI) and semantic language processing capabilities, ExpertFile's new "Inquiry Quarantine" feature employs advanced algorithms to identify potentially harmful or offensive communications before it reaches the end-user. DoD adopts new ethical principles for the use of artificial intelligence – SpaceNews.com
Deep Learning Market report to study and analyses the market size (Consumption, Value, Volume and Production) By Company, Key Regions, Products and End User/Application, Deep Learning market breakdown data from 2014 to 2019, and 6 year forecast from 2020 to 2026. Bedsides Deep Learning industry research report enriched on worldwide competition by topmost prime manufactures (Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, Xilinx, AMD, General Vision, Graphcore, Mellanox Technologies, Huawei Technologies, Fujitsu, Baidu, Mythic, Adapteva, Inc., Koniku) which providing information such as Company Profiles, Gross, Gross Margin, Capacity, Product Picture and Specification, Production, Price, Cost, Revenue and contact information.Deep Learning Market report provide the in-depth analysis of key factors influencing the growth of the market (Growth Potential, Opportunities, Drivers, Industry-Specific Challenges and Risks). The Latest Deep Learning Industry Data Included in this Report: Deep Learning Market Size & Analysis (2014 – 2026); Deep Learning Market Volume & Future Trends (2014 – 2026); Deep Learning Market; By Geography (Volume and Value); 2014 – 2026; Deep Learning Market Opportunity Assessment (2014 – 2026); Deep Learning (Installed Base) Market Share: By Company; Major Deals in Deep Learning Market; Deep Learning Reimbursement Scenario; Deep Learning Current Applications; Deep Learning Competitive Analysis: By Company; Key Market Drivers and Inhibitors; Major Companies Analysis. Scope of Deep Learning Market: The deep learning market has been segmented on the basis of offerings, applications, end-user industries, and geographies. In terms of offerings, software holds the largest share of the deep learning market.
One of the most powerful upcoming concepts which I wrote about in The State of AI in 2020 is Neural Architecture Search(NAS). There is plenty to know about NAS, but to understand this tutorial I will only summarize. In short, NAS is essentially a method to take the limitations of human design out of Neural Network architectures. To accomplish this, many different architectures are considered in parallel, trained, and evaluated. Following this each may be adjusted based on a selected algorithm to try another architecture.
Intel's sale of its consumer 5G modem unit signaled its exit from the smartphone business last year, but the company remains heavily committed to participating in the growing 5G marketplace -- primarily on the carrier and enterprise sides. Today, the company announced three chips built for various types of 5G computers, plus a 5G-optimized network adapter for PCs. Up first is an updated second-generation Xeon Scalable processor, now at a top speed of 3.9GHz and bolstered by additional AI capabilities to aid with inference applications. The new chip promises up to 36% more performance than the first-generation version, with up to 42% more performance per dollar, though early second-generation chips were introduced in April 2019. Intel says the Xeon Scalable is the "only CPU with AI built in" -- a pitch that's not exactly accurate, given the range of existing laptop and mobile CPUs with AI features, but one Intel further explains means "the only CPU on the market that features integrated deep learning acceleration."
We introduce the weightwatcher (ww), a python tool for a python tool for computing quality metrics of trained, and pretrained, Deep Neural Netwworks. This blog describes how to use the tool in practice; see our most recent paper for even more details. The summary contains the Power Law exponent (), as well as several log norm metrics, as explained in our papers, and below. Each value represents an empirical quality metric that can be used to gauge the gross effectiveness of the model, as compared to similar models. We can use these metrics to compare models across a common architecture series, such as the VGG series, the ResNet series, etc. These can be applied to trained models, pretrained models, and/or even fine-tuned models.
Your code is more complicated than you think. One of the first things every software developer learns about is the command-line. At its core, the command-line is a list of strings that are typically broken down into flags (e.g., -- verbose) and arguments (e.g., -- port 80). This is enough for many simple applications. You can define 2 to 3 command-line arguments in a command-line interface (CLI) parsing library, and you are done.
In a test kitchen in a corner building in downtown Pasadena, Flippy the robot grabbed a fryer basket full of chicken fingers, plunged it into hot oil -- its sensors told it exactly how hot -- then lifted, drained and dumped maximally tender tenders into a waiting hopper. A few feet away, another Flippy eyed a beef patty sizzling on a griddle. With its camera eyes feeding pixels to a machine vision brain, it waited until the beef hit the right shade of brown, then smoothly slipped its spatula hand under the burger and plopped it on a tray. The product of decades of research in robotics and machine learning, Flippy represents a synthesis of motors, sensors, chips and processing power that wasn't possible until recently. Now, Flippy's success -- and the success of the company that built it, Miso Robotics -- depends on simple math and a controversial hypothesis of how robots can transform the service economy.
Self-driving cars are one of the high-risk artificial intelligence applications the European Union wants to regulate. The European Commission today unveiled its plan to strictly regulate artificial intelligence (AI), distinguishing itself from more freewheeling approaches to the technology in the United States and China. The commission will draft new laws--including a ban on "black box" AI systems that humans can't interpret--to govern high-risk uses of the technology, such as in medical devices and self-driving cars. Although the regulations would be broader and stricter than any previous EU rules, European Commission President Ursula von der Leyen said at a press conference today announcing the plan that the goal is to promote "trust, not fear." The plan also includes measures to update the European Union's 2018 AI strategy and pump billions into R&D over the next decade.
CUJO AI is using artificial intelligence (AI) and machine learning (ML) for its new online privacy and tracking platform, which is called Incognito. CUJO AI offers "digital life protection" through its AI solutions that are in use by network service providers and their customers. CUJO AI provides network, mobile and public Wi-Fi operators with a full-stack set of cloud and edge software that captures, processes, curates and acts on device-level network data. With Incognito, CUJO AI uses its AI engine, ML analysis and real-time traffic classification to help broadband users and service providers evaluate privacy threats in the data flows and then block elements to provide privacy protection. Incognito uses machine learning to analyze website requests and upstream responses, looking for third-party trackers such as cookies, browser fingerprinting techniques and tracking ads.
Driver assistance systems, such as lane keeping, adaptive cruise control, collision warning, and blind spot warning, have gradually moved from optional to standard features on most high-end vehicles. They are now making their way to all vehicle models. As automated systems assume more and more of the driver burden and take over increasing amounts of responsibility for the driving task, they require both more data and more processing power to augment the decisions that human drivers have made on their own. Sensors will take the place of human senses and artificial intelligence, it is thought, will substitute for human intelligence. This session will gather global experts on the subject to discuss their views on the progress and the prospects for vehicles that drive themselves.