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) …
President and Chief Executive Officer at Insight Enterprises, helping clients manage their business today and transform for the future. If 2020 taught us anything about cybersecurity, it's that strengthening corporate defenses against cyberattacks is increasingly dependent on managing user identities of those who access your network. We've gradually moved in that direction since the birth of the bring-your-own-device movement, which created the need to control access to business data from outside the four walls of the office. The rise of the cloud, edge computing and the Internet of Things (IoT) have led us further along the path, requiring new strategies for managing access to resources across increasingly heterogeneous technology environments. Then Covid-19 triggered a work-from-home stampede.
Brooklyn, New York, June 23, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Cement Production Market is projected to grow at a CAGR value of 28.5% during the forecast period [2021 to 2026]. The rising need for automation, as this industry is yet to transform into digitalization, increasing need to the rising cost of manual procedures, and rising need from the end-user industry for high-quality cement will help the AI in cement production market to grow rapidly. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Cement Production Market - Forecast to 2026"
It's no surprise that the coronavirus pandemic has changed the way we shop, especially when it comes to groceries. Grocery delivery apps experienced a record number of downloads in March 2020, and by the following month, Walmart Grocery (which is now integrated into the Walmart app) surpassed Amazon as the No. 1 shopping app on both Google Play and the App Store. But even as pandemic restrictions have eased, consumers are still using ordering groceries for delivery or pickup more frequently than they were pre-pandemic. As Walmart's grocery delivery services have continued to boom, posing competition to companies like Amazon and Instacart, the tech that Walmart uses has expanded too. Today, Walmart shared information about how it's training its AI to make smarter substitutions in online grocery orders.
The various institutions of the EU aim to be the rule makers and standard bearers for artificial intelligence and associated technology ("AI"). One AI use case which has come under particular scrutiny is that of facial recognition. Since we last wrote on the subject, it has become increasingly clear that the European Commission will take a restrictive approach to the use of facial recognition technology, especially when such use is in public areas. Earlier this year in April, the European Commission led the way in this area suggesting a legal framework for the regulation of facial recognition and certain types of AI systems. The draft legislation (also explained in a press release here) looks to create "trustworthy AI" which protects the fundamental rights of citizens while strengthening AI investment and innovation across the EU. The measures would restrict the use of live facial recognition to a very narrow set of scenarios where this would be deemed essential from a public interest perspective; such as the search for missing children or the policing of terrorist incidents.
As I write this blob post, we're a few days away from the opening of the 2021 RoboCup Competitions and Symposium. Running from June 22nd-28th, this event brings together AI and robotics researchers and learners from around the world, for the first (and ideally last!) time in a fully remote format. The first official international RoboCup event occurred 25 years ago, at the IROS 1996 conference in Osaka, Japan. Called "pre-RoboCup" because the first full RoboCup was slated to launch the following year at the 1997 IJCAI conference in Nagoya, the CMUnited team created by myself and my Ph.D. advisor, Manuela Veloso, was the only non-Japanese entry in the simulation competition, which was the only event that year. While RoboCup has indisputably played a huge role in the last quarter-century of AI research, it has also played a leading role in my own personal story.
Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I'm afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.
Machine learning techniques have contributed to progress in science and technology fields ranging from health care to high-energy physics. Now, machine learning is poised to help accelerate the development of stronger alloys, particularly stainless steels, for America's thermal power generation fleet. Stronger materials are key to producing energy efficiently, resulting in economic and decarbonization benefits. "The use of ultra-high-strength steels in power plants dates back to the 1950s and has benefited from gradual improvements in the materials over time," says Osman Mamun, a postdoctoral research associate at Pacific Northwest National Laboratory (PNNL). "If we can find ways to speed up improvements or create new materials, we could see enhanced efficiency in plants that also reduces the amount of carbon emitted into the atmosphere."
Developments in machine learning (ML) and Artificial Intelligence (AI) are having a great impact on the debt collection industry. At its core Machine Learning generates predictive models using algorithms that learn from data. The idea is that if we can input enough useful and reliable data, we can build models which can make predictions on our behalf. There are a number of ways in which machine learning can aid and improve the debt collection process: Reduce Workloads Collections departments place calls, send countless emails, and seek to work out payment plans — and very frequently none of the above activities translate into the successful recovery of debt. With ML this changes. Since tasks are automated, users experience higher productivity and less time spent on labour-intensive tasks. Protecting Your Business Reputation Since ML can automate communication, you know that all your business correspondence will be professional, methodical and unambiguous. LATERAL’S debt collections software provides its users with a non-intrusive, customer-driven point of engagement, which is proven to be highly successful.