Advertisement Datamation > Arti cial Intelligence > What is Arti cial Intelligence? What is Arti cial Intelligence? By Samuel Greengard, Posted May 24, 2019 Learn how arti cial intelligence (AI) uses software-driven systems and intelligent agents to make decisions that approximate human cognitive functions. SHARE Download the authoritative guide: Arti cial Intelligence Leaders: Top AI Vendors The term arti cial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge DATAMATION DAILY NEWSLETTER SUBSCRIBE TO OUR IT MANAGEMENT NEWSLETTER ENTER YOUR EMAIL ADDRESS repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations. Particularly useful are AI advances such machine learning and deep learning. See our list of the top arti cial intelligence companies It’s important to recognize that AI is a constantly moving target. Things that were once considered within the domain of arti cial intelligence – optical character recognition and computer chess, for example – are now considered routine computing. Today, robotics, image recognition, natural language processing, real-time analytics tools and various connected Most Recent Arti cial Intelligence Articles systems within the Internet of Things (IoT) all tap AI in order to deliver more advanced features and capabilities. Bruin: Business Intelligence & IT Asset Management Platform Arti cial Intelligence Trends: Expert Insight on AI and ML Trends 12 Examples of Arti cial Intelligence: AI Powers Business Top 8 Arti cial Intelligence Software Download Cloud Computing 2019: Using the Cloud for Competitive Advantage Download Arti cial Intelligence Jobs in 2019 Helping develop AI are the many cloud companies that offer cloud-based AI services. Statistica projects that AI will grow at an annual rate exceeding 127% through 2025. By then, the market for AI systems will top $4.8 billion dollars. Consulting rm Accenture reports that AI could double annual economic growth rates by 2035 by “changing the nature of work and spawning a new relationship between man and machine.” Not surprisingly, observers have both heralded and derided the technology as it lters into business and everyday life. Also see: AI Jobs Arti cial intelligence has broad applications across many areas of business. History of AI: Duplicating the Human Mind The dream of developing machines that can mimic human cognition dates back centuries. In the 1890s, science ction writers such as H.G. Wells began exploring the concept of robots and other machines thinking and acting like humans. It wasn’t until the early 1940s, however, that the idea of arti cial intelligence began to take shape in a real way. After Alan Turing introduced the theory of computation – essentially how algorithms could be used by machines to produce machine “thinking" – other researchers began exploring ways to create AI frameworks. In 1956, researchers gathering at Dartmouth College launched the practical application of AI. This included teaching computers to play checkers at a level that could beat most humans. In the decades that followed, enthusiasm about AI waxed and waned. In 1997, a chess-playing computer developed by IBM, Deep Blue, beat reigning world chess champion, Garry Kasparov. In 2011, IBM introduced Watson, which used far more sophisticated techniques, including deep learning and machine learning, to defeat two top Jeopardy! champions. Although AI continued to advance over the next few years, observers often cite 2015 as the landmark year for AI. Google Cloud, Amazon Web Services, and Microsoft Azure and others began to step up research and improve natural language processing capabilities, computer vision and analytics tools. Today, AI is embedded in a growing number of applications and tools. These range from enterprise analytics programs and digital assistants like Siri and Alexa to autonomous vehicles and facial recognition. AI Takes Different Forms Arti cial intelligence is an umbrella term that refers to any and all machine intelligence. However, there are several distinct and separate areas of AI research and use – though they sometimes overlap. These include: General AI. These systems typically learn from the world around them and apply data in a cross-domain way. For example, DeepMind, now owned by Google, used a neural network to learn how to play video games similar to how humans play them. Natural Language Processing (NLP). This technology allows machines to read, understand, and interpret human language. NLP uses statistical methods and semantic programming to understand grammar and syntax, and, in some cases, the emotions of the writer or those interacting with a system like a chat bot. Machine perception. Over the last few years, enormous advances in sensors — cameras, microphones, accelerometers, GPS, radar and more — have powered machine perception, which encompasses speech recognition and computer vision used for facial and object recognition. Robotics. Robot devices are widely used in factories, hospitals and other settings. In recent years, drones have also taken ight. These systems — which rely on sophisticated mapping and complex programming—also use machine perception, to navigate through tasks. Social intelligence. Autonomous vehicles, robots, and digital assistants such as Siri and Alexa require coordination and orchestration. As a result, these systems must have an understanding of human behavior along with a recognition of social norms. AI Methodologies There are a number of approaches used to develop and build AI systems. These include: Machine Learning (ML). This branch of AI uses statistical methods and algorithms to discover patterns and “train” systems to make predictions or decisions without explicit programming. It may consist of supervised and semi-supervised ML (which includes classi cations and labels) and unsupervised ML (using only data inputs and no human applied labels). Deep Learning. This approach relies on arti cial neural networks (ANNs) to approximate the neural pathways of the human brain. Deep learning systems are particularly valuable for developing computer vision, speech recognition, machine translation, social network ltering, video games and medical diagnosis. Bayesian Networks. These systems rely on probabilistic graphical models that use random variables and conditional independence to better understand and act on the relationships between things, such as a drug and side effects or darkness and a light switch turning on. Genetic Algorithms. These search algorithms tap a heuristic approach modeled after natural selection. They use mutation models and crossover techniques to solve complex biological challenges and other problems. AI in the Real World There is no shortage of compelling use cases for AI. Here are some leading examples: Healthcare Arti cial intelligence in healthcare can play a leading role. It enables health professionals to understand risk factors and diseases at a deeper level. It can aid in diagnosis and provide insight into risks. AI also powers smart devices, surgical robots and Internet of Things (IoT) systems that support patient tracking or alerts. Agriculture AI is now widely used for crop monitoring. It helps farmers apply water, fertilizer and other substances at optimal levels. It also aids in preventative maintenance for farm equipment and it is spawning autonomous robots that pick crops. Finance Few industries have been transformed by AI more than nance. Today, quants (algorithms) trade stocks with no human intervention, banks make automated credit decisions instantly, and nancial organizations use algorithms to spot fraud. AI also allows consumers to scan paper checks and make deposits using a smartphone. Retail A growing number of consumer-facing apps and tools support image recognition, voice and natural language processing and augmented reality (AR) features that allow consumers to preview a piece of furniture in a room or o ce or see what makeup looks like without heading to a physical store. Retailers are also using AI for personalized marketing, managing supply chains, and cybersecurity. Travel, Transportation and Hospitality Airlines, hotels, and rental car companies use AI to forecast demand and adapt pricing dynamically. Airlines also rely on AI to optimize the use of aircraft for routes, factoring in weather conditions, passenger loads and other variables. They can also understand when aircraft require maintenance. Hotels are using AI, including image recognition, for deploying robots and security monitoring. Autonomous vehicles and smart transportation grids also rely on AI. AI Benefits and Risks For businesses, it’s not a question of whether to use AI — many organization already taps into it on a daily basis —it’s a question of how to maximize the bene ts and minimize the risks. As starting point, it's essential to know how and where AI can improve business processes and build a workforce that understands what arti cial intelligence is, where it ts in and what opportunities it offers. This may require workers to have new knowledge and skills – and AI salaries are competitive – along with a rethinking of service providers, work ows and internal processes. Arti cial intelligence serves up other challenges. One of the biggest stumbling points for AI, including machine learning and deep learning, is poorly constructed frameworks. When users train models with bad data or construct awed statistical models, incorrect and even dangerous outcomes often follow. AI tools, while increasingly easy to use, require data science expertise. Other important factors include: ensuring there’s enough computing power and the right cloud-based infrastructure in place, and, mitigating fears about job loss. In any case, arti cial intelligence is introducing bold opportunities to create smarter and more powerful machines. In the years ahead, AI will certainly further transform business and life. SEE ALL Related White Papers and Webcasts ARTIFICIAL INTELLIGENCE ARTICLES Sponsored content Modernizing Enterprise IT: Motivators and Challenges Join the discussion! By Okta October 3, 2019 Recommend Many organizations are embracing an approach to modernizing IT. And the benefits of replacing legacy infrastructure are clear, including reduced costs and increased business agility. But modernizing IT can mean different things to different IT leaders, and the reasons behind it can vary from business to business. Share 2 Comments Newest First Newest Oldest First Oldest M N RAO - 4 months ago Updates pl. Reply Max - 4 months ago How long until we reach Artiļ¬cial General Intelligence? Continue reading... Reply Many organizations are embracing an approach to modernizing IT. And the benefits of replacing legacy infrastructure are clear, including reduced costs and increased business agility. Continue reading... Sponsored by Okta An eWEEK Property TERMS OF SERVICE LICENSING AND REPRINTS PRIVACY POLICY CONTACT US ADVERTISE SITEMAP ABOUT US Copyright 2019 Quinstreet Inc. All Rights Reserved. Advertiser Disclosure: Some of the products that appear on this site are from companies from which QuinStreet receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. QuinStreet does not include all companies or all types of products available in the marketplace.
