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The term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. It refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

Today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And it’s driving applications—such as medical image analysis—that help skilled professionals do important work faster and with greater success.


Here are just a few of the most common examples of AI:

  • Speech recognition: Also called speech to text (STT), speech recognition is AI technology that recognizes spoken words and converts them to digitized text. Speech recognition is the capability that drives computer dictation software, TV voice remotes, voice-enabled text messaging and GPS, and voice-driven phone answering menus.

  • Natural language processing (NLP): NLP enables a software application, computer, or machine to understand, interpret, and generate human text. NLP is the AI behind digital assistants (such as the aforementioned Siri and Alexa), chatbots and other text-based virtual assistance. Some NLP uses sentiment analysis to detect the mood, attitude, or other subjective qualities in language.

  • Image recognition (computer vision or machine vision): AI technology that can identify and classify objects, people, writing, and even actions within still or moving images. Typically driven by deep neural networks, image recognition is used for fingerprint ID systems, mobile check deposit apps, video and medical image analysis, self-driving cars, and much more.

  • Real-time recommendations: Retail and entertainment web sites use neural networks to recommend additional purchases or media likely to appeal to a customer based on the customer’s past activity, the past activity of other customers, and myriad other factors, including time of day and the weather. Research has found that online recommendations can increase sales anywhere from 5% to 30%.

  • Virus and spam prevention: Once driven by rule-based expert systems, today’s virus and spam detection software employs deep neural networks that can learn to detect new types of virus and spam as quickly as cybercriminals can dream them up.

  • Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

  • Ride-share services: Uber, Lyft, and other ride-share services use artificial intelligence to match up passengers with drivers to minimize wait times and detours, provide reliable ETAs, and even eliminate the need for surge pricing during high-traffic periods.

  • Household robots: iRobot’s Roomba vacuum uses artificial intelligence to determine the size of a room, identify and avoid obstacles, and learn the most efficient route for vacuuming a floor. Similar technology drives robotic lawn mowers and pool cleaners.

  • Autopilot technology: This has been flying commercial and military aircraft for decades. Today, autopilot uses a combination of sensors, GPS technology, image recognition, collision avoidance technology, robotics, and natural language processing to guide an aircraft safely through the skies and update the human pilots as needed. Depending on who you ask, today’s commercial pilots spend as little as three and a half minutes manually piloting a flight.


Achieving AI goals can be challenging since it requires that the right systems are in place.  Data management is not easy and includes many facets such as storage, cleansing, design, controls, etc before the build process can actually begin. your data for the construction of learning algorithms. Marketware OneWorld AI/ Data Analytics partners each have capabilities that are invaluable to help clients evaluate, prepare and implement solutions for their businesses. Contact us for a conversation about how we can help.



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