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) …
In Star Wars: The Empire Strikes Back, Luke Skywalker is rescued from the frozen wastes of Hoth after a near-fatal encounter, luckily to be returned to a medical facility filled with advanced robotics and futuristic technology that treat his wounds and quickly bring him back to health. The healthcare industry could be headed toward yet another high-tech makeover (even as it continues to adapt to the advent of electronic health records systems and other healthcare IT products) as artificial intelligence (AI) improves. Could AI applications become the new normal across virtually every sector of the healthcare industry? Many experts believe it is inevitable and coming sooner than you might expect. AI could be simply defined as computers and computer software that are capable of intelligent behavior, such as analysis and learning.
We live in a world that is becoming more personalized every day. Consumers have come to expect experiences that are tailored for them -- especially when it comes to engaging with brands. When you open your Uber app, it now suggests your home address; online shopping is increasingly personalized, and, of course, so is advertising. You expect to see ads that reflect your interests and buying patterns and, in fact, are more likely to engage with those ads.We have artificial intelligence (AI) to thank for our increasingly personalized world. As the demand for personalization increases, so too does the buzz around AI. AI is a term that is becoming ubiquitous -- and potentially overused -- as an umbrella term relating to any action a machine takes based on a set of rules in order to mimic human intelligence.
A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics. Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study.
Most of us do not have an equal voice or representation in this new world order. Leading the way instead are scientists and engineers who don't seem to understand how to represent how we live as individuals or in groups--the main ways we live, work, cooperate, and exist together--nor how to incorporate into their models our ethnic, cultural, gender, age, geographic or economic diversity, either. The result is that AI will benefit some of us far more than others, depending upon who we are, our gender and ethnic identities, how much income or power we have, where we are in the world, and what we want to do. The power structures that developed the world's complex civic and corporate systems were not initially concerned with diversity or equality, and as these systems migrate to becoming automated, untangling and teasing out the meaning for the rest of us becomes much more complicated. In the process, there is a risk that we will become further dependent on systems that don't represent us.
After decades of a heavy slog with no promise of success, quantum computing is suddenly buzzing! Nearly two years ago, IBM made a quantum computer available to the world. The 5-quantum-bit (qubit) resource they now call the IBM Q experience. It was more like a toy for researchers than a way of getting any serious number crunching done. But 70,000 users worldwide have registered for it, and the qubit count in this resource has now quadrupled.
But the idea of AI -- of machines that can sense, classify, learn, reason, predict, and interact -- has been around for decades. Today, the combination of massive and available datasets, inexpensive parallel computing, and advances in algorithms has made it possible for machines to function in ways that were previously unthinkable.1 While the more obvious examples such as robotics, driverless cars, and intelligent agents such as Siri and Alexa tend to dominate the news, artificial intelligence has much wider implications. Gartner predicts that "by 2020, algorithms will positively alter the behavior of billions of global workers."2 Markets & Markets expects the AI market to reach $5.05B by 2020.3 This report lays out the current state of AI for business, describes primary and emerging use cases, and states the risks, opportunities, and organizational considerations that businesses are facing. It concludes with recommendations for companies thinking about applying AI to their own organizations and a look at some of the business, legal, and technical trends that are likely to shape the future. Executive Summary 1 What is Artificial Intelligence? 2 Use Cases for Artificial Intelligence 8 Implications and Recommendations 13 A Look at the Future 17 End Notes 19 Methodology 22 Acknowledgements 23 About Us 24 TABLE OF CONTENTS 3. www.altimetergroup.com
In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.
When mundane objects such as cords, keys and cloths are fed into a live webcam, a machine-learning algorithm'sees' brilliant colours and images such as seascapes and flowers instead. The London-based, Turkish-born visual artist Memo Akten applies algorithms to the webcam feed as a way to reflect on the technology and, by extension, on ourselves. Each instalment in his Learning to See series features a pre-trained deep-neural network'trying to make sense of what it sees, in context of what it's seen before'. In Gloomy Sunday, the algorithm draws from tens of thousands of images scraped from the Google Arts Project, an extensive collection of super-high-resolution images of notable artworks. Set to the voice of the avant-garde singer Diamanda Galás, the resulting video has unexpected pathos, prompting reflection on how our minds construct images based on prior inputs, and not on precise recreations of the outside world.
Whether that's the everyday life of improbably rich young millionaires like Jake Paul, a high school dropout from Westlake, Ohio, or PewDiePie, a skinny, fast-talking Swede whose real name is Felix Arvid Ulf Kjellberg, YouTube seeks to serve a need. It does so through "the algorithm" -- YouTube's recommendation engine. It's a black box that YouTube introduced to keep us watching, but which has become a thorn in its side as the platform grows at an astronomically grand scale. YouTube's recommendation algorithm is a set of rules followed by cold, hard computer logic. It was designed by human engineers, but is then programmed into and run automatically by computers, which return recommendations, telling viewers which videos they should watch.
U of Alberta created the first Computing Science department in Canada in 1964. It has a long tradition of research in AI (is rated 3rd in the world in machine learning). It has also led in the development of AI for strategy games. The results can be commercialized in non-game applications as well. Among these are Checkers, Chess, Go and Poker, The evening's talks were by Jonathan Schaeffer (computer chess) and Ryan Hayward (the strategy game Hex).