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) …
A new study shows that anthropogenic climate change made things worse. The Australian National University (ANU) and Optus announced on Thursday the pair would attempt to develop a national system to detect and extinguish fires using a mixture of satellites, drones, and robotics. The first step of the program, which is due to run until 2024, will be to create an "autonomous ground-based and aerial fire detection system". It will begin with the trial of long-range infra-red sensor cameras placed on towers in fire-prone areas in the ACT, which will allow the ACT Rural Fire Service (RFS) to monitor and identify bushfires. The long-term goal, though, is to put out fires using drones.
Volvo Cars, a Swedish company that's owned by a Chinese billionaire and builds vehicles at a plant in the American South using crucial parts made in Mexico, is a poster child for how globally interconnected the auto industry became in an era of increasing free trade. When Volvo opened its factory in Charleston, S.C., in 2018 with ambitious plans to export cars to China, it was the pinnacle of a push to showcase its reemergence as a global brand with a manufacturing presence on three continents. Then the U.S.-China trade war forced Volvo to abandon its export plans. And this spring, as the coronavirus spread around the globe, the factory was plagued by a shortage of components and had to halt production three times. To make matters worse, U.S. car buyers' preferences have shifted rapidly toward sport utility vehicles and away from the sedans Volvo makes in Charleston.
Since it was unveiled earlier this year, the new AI-based language generating software GPT-3 has attracted much attention for its ability to produce passages of writing that are convincingly human-like. Some have even suggested that the program, created by Elon Musk's OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand or perform any task a human can. This breathless coverage reveals a natural yet aberrant collusion in people's minds between the appearance of language and the capacity to think. Language and thought, though obviously not the same, are strongly and intimately related. And some people tend to assume that language is the ultimate sign of thought.
Once in a while, a young company will claim it has more experience than would be logical -- a just-opened law firm might tout 60 years of legal experience, but actually consist of three people who have each practiced law for 20 years. The number "60" catches your eye and summarizes something, yet might leave you wondering whether to prefer one lawyer with 60 years of experience. There's actually no universally correct answer; your choice should be based on the type of services you're looking for. A single lawyer might be superb at certain tasks and not great at others, while three lawyers with solid experience could canvas a wider collection of subjects. If you understand that example, you also understand the challenge of evaluating AI chip performance using "TOPS," a metric that means trillions of operations per second, or "tera operations per second."
From Amazon to Netflix to Pinterest, recommendation systems are the cornerstone of a majority of the modern-day billion-dollar industries. However, building recommender systems is not a straightforward task. What if we can build them in a few lines? Dropping the nitty-gritty details and concentrating on implementing algorithms with more ease is what any data scientist would like to get their hands on. Abstraction is a common trait amongst popular machine learning libraries or frameworks like TensorFlow.
This article was published as a part of the Data Science Blogathon. Yesterday, my brother broke an antique at home. I began to search for FeviQuick (a classic glue) to put it back together. Given that it's one of the most misplaced items, I began to search for it in every possible drawer and every untouched corner of the house I hadn't been to in the past 3 months. I gave up the search after an hour – the FeviQuick was nowhere to be found.
A study by McKinsey & Company found that AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection. Though machines with automated optical inspection (AOI), powered by machine vision, have replaced most of the manual processes in the modern assembly line, quality control still remains a huge and costly challenge. The European Commission claims that in some industries 50% of production can be abandoned due to defects, and the defect rate can reach up to 90% in complex production environments. The critical limitation with machine learning AOI systems is in disclosing surface defects where even a slight variant (often invisible to the human eye) can hamper the entire production run and render hundreds to thousands of products useless before the defect is discovered. The economic impact can be devastating.
Artificial intelligence in manufacturing is changing the world – not least the manufacturing industry, and AI is set to make the fourth modern insurgency. This will be because of a mix of AI, propelled robots, added substance Artificial intelligence and Machine Learning are changing the world – not least the manufacturing industry, and AI is set to make the fourth modern insurgency. This will be because of a mix of AI, propelled robots, added substance Manufacturing (or 3D printing), and the web of things. The manufacturing industry keeps on finding a way to modernize strategic policies. Trendsetting innovations like the Industrial Internet of Things, community-oriented robots and Artificial Intelligence (AI) have shot Manufacturing into the 21st century.
Deep learning models like Google's BERT and the new OpenAI GPT-3 have brought machines much closer to approximating human understanding. The keyword here is "approximating" because these deep learning models don't actually understand the text they see. While not perfect, they have become much better at predicting what words might come next in a given sentence or search string. Does this mean we're getting close to true artificial intelligence (AI)? Not yet, although machines will soon be able to do the heavy lifting when it comes to data analysis so that all we will have to do is step in and interpret the results.
The first artificial neural networks weren't abstractions inside a computer, but actual physical systems made of whirring motors and big bundles of wire. Here I'll describe how you can build one for yourself using SnapCircuits, a kid's electronics kit. I'll also muse about how to build a network that works optically using a webcam. And I'll recount what I learned talking to the artist Ralf Baecker, who built a network using strings, levers, and lead weights. I showed the SnapCircuits network last year to John Hopfield, a Princeton University physicist who pioneered neural networks in the 1980s, and he quickly got absorbed in tweaking the system to see what he could get it to do. I was a visitor at the Institute for Advanced Study and spent hours interviewing Hopfield for my forthcoming book on physics and the mind. The type of network that Hopfield became famous for is a bit different from the deep networks that power image recognition and other A.I. systems today.