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) …
July usually sees Amazon's incredibly popular Prime Day sales event. While this epic shopping day may be postponed until to later in the year, you can still grab some incredible discounts on the mega-site. If you're a fan of Amazon's family of devices then you are sure to love the Amazon Echo Spot Alarm Clock. What's more, the Amazon Echo Spot is now on sale on Amazon, reduced to only £79.99- that's a saving of 33 per cent. Get content in one glance- the Amazon Echo Spot allows you to watch video flash briefings, see music lyrics, weather forecasts, to-do and shopping lists- all for £79.99 Shaped like a futuristic alarm clock, the Amazon Echo Spot brings you everything you love about Alexa, in a stylish and compact design.
While her peers reveled in an unprecedented virtual school year, the self-described "technology enthusiast," Harita Suresh, 13, was bored. She decided on an online course and settled on IBM Skills Network's "AI chatbots without programming." She lacked experience with artificial intelligence, but was eager to learn through the self-paced course. Harita is more than a little familiar with tech, "I have been interested in technology since I was 5," she said. "My first coding challenge was the Lightbot Hour of Code. I was fascinated that the code I wrote could control the actions of the characters on screen. Since then, I pursued coding on multiple platforms like code.org, The more I learned about tech, the more I wanted to know. In fifth grade, I took a Python programming course offered by Georgia Tech."
ASAPP founder Gustavo Sapoznik developed software that trains customer-service reps to be "radically" more productive, winning the young startup an $800 million valuation. If you've ever felt your blood boil after sitting on hold for 40 minutes before reaching an agent . . . A customer-service representative for JetBlue, for instance, might have to flip rapidly among a dozen or more computer programs just to link your frequent-flier number to a specific itinerary. "Imagine that cognitive load, while you have someone screaming at you or complaining about some serious problem, and you're swiveling between 20 screens to see which one you need to be able to help this person," says Gustavo Sapoznik, 34, the founder and CEO of ASAPP, a New York City–based developer of AI-powered customer-service software. Sapoznik remembers just such a scene while shadowing a call-center agent at a "very large" company (he won't name names), watching the worker navigate a "Frankenstack" patchwork of software, entering a caller's information into six different billing systems before locating it.
Microsoft's .NET team boasts that the forthcoming .NET 5 development stack will offer major performance improvements. Microsoft started shipping previews of .NET 5 in March and plans for general availability in November. ".NET 5 has already seen a wealth of performance improvements, and even though it's not scheduled for final release until this fall and there's very likely to be a lot more improvements that find their way in by then, I wanted to highlight a bunch of the improvements that are already available now," said Stephen Toub, partner software engineer on Microsoft's .NET team. The sixth and newest preview of .NET 5 from June allowed developers to build and run Windows Forms apps on Windows Arm64 devices, like the Surface Pro X. Microsoft at that stage was still working on adding support for WPF on Windows on Arm. Toub's performance analysis covers the .NET garbage collector, the Just-In-Time compiler, 'hardware intrinsics methods', runtime helpers, text processing, regular expressions, threading and asynchrony, and more.
Cognitive automation is adept in managing unstructured data. Cognitive automation is an extension of Robotic Process Automation (RPA). If RPA exists to simplify and automate repetitive, mundane tasks following a set of pre-defined and programmed rules and procedures, cognitive automation is responsible for knowledge-based tasks that require decision-making skills and judgment. Cognitive functions in this type of automation come from the integration of artificial intelligence (AI) technologies like machine learning (ML) and natural language process (NLP). Powered by AI technology, cognitive automation possesses the capacity to handle complex, unstructured, and data-laden tasks.
Developing policy informed by science and technology is now more complex than ever. Policymakers must address supply chains, climate change, inequality, technological breakthroughs, misinformation and more. Using artificial intelligence (AI) to mine the literature could put policymaking on a sounder footing. Advanced big-data and natural-language-processing models enable decision makers to look beyond conventional indicators and expert discussions. Millions of scientific articles, patents and market reports can be readily analysed to identify megatrends or fading topics, and to provide predictive opportunities (see go.nature.com/31snkp5). Machine learning can create maps of national competencies and centres of excellence of science and technology.
As our intrinsic nature, we humans are likely to form an opinion about a particular commodity or person even before we have shared any real-life experience with them. The same thing happens for a business brand; we tend to have developed some sort of subconscious thoughts regarding the brand prior to using its products and services. This bias severely impacts how businesses perform in the market and their sales figures. So it is safe to assume that how brands present themselves or appeal customers play a decisive role in determining their success. Now one may wonder how companies can figure out what the customers feel or react to their products; well, this is where emotional analytics comes in. Though data-driven Analytics provides a quick shorthand to businesses, without emotional insights, brands are a handicap.
Arvind Gopalakrishnan is a part of the AIM Writers Programme.… Data mining is taking turns in the industry like anything, but have you ever heard of Opinion Mining? Leveraging customer opinion as quantifiable data is a concept of future to a layman but with Natural Language Processing, the world can finally process and completely absorb customer feedback. Often data is associated with quantity-based statistics with numbers and metrics floating around, however, with natural language processing (NLP), qualitative factors like customer feedback can be processed and used as quantifiable data. For example, if a specific mobile phone models witness a higher number of sales in a given year, the manufacturers tend to incorporate features of that mobile phone to increase the sales of other models where they somehow miss to make upgrades properly basis the customer feedback.
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Fifty-three percent of enterprises adopting artificial intelligence have spent more than $20 million over the past year on technology and talent, according to a survey by Deloitte. The State of AI in the Enterprise survey, based on 2,737 information technology and line of business executives, highlights how AI implementations are moving into production at a rapid pace. Deloitte's respondent base included 26% "seasoned adopters," 47% "skilled" adopters and 27% "starters." The respondents were classified based on AI adoption and systems launched into production.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.