Artificial intelligence has largely been an academic and science-based topic for years. What many do not realize is that it is already being used in the real world of business and manufacturing.
By Karen White
Artificial intelligence (AI) is a source of fascination for STEM (science, technology, engineering and mathematics) researchers and professionals, and they have been diligently working in their respective laboratories for many years trying to perfect the technology so it can be applied in the real world. Until recently, AI remained more a futuristic (and fun) possibility than a current reality with practical applications.
In just the last couple of years, that perspective became outdated because AI is moving into the real world in a variety of industries that include vehicle manufacturing, healthcare, transportation and a number of other business sectors. The AI industry remains nascent as researchers work to perfect its design and usefulness, but it show signs of maturing at an accelerating rate. As it grows more productive in terms of practical applications, AI could dramatically change work in every industry. There are real opportunities shaping up for AI applications, including in business.
Esoteric discussions on the future impacts of AI have led many people to believe AI remains a futuristic technology. It is true that the technology has a long way to go before it is shaped into a technology that is fully woven into the fabric of mainstream operations.
However, AI is here, being used in the real world and holding the promise of changing work. It is a value-producing technology that is already being used in a variety of industries, even though consumers may not recognize current utilization, but its applications are on the verge of exploding.
The advertisements for vehicles able to detect obstacles in the road are showcasing real advancements in AI. The vehicle, in effect, is relying on software that mimics human cognitive functioning. Developers present images to a neural net, in the form of large amounts of data of the various kinds of obstacles a vehicle could encounter. The neural net is an example of machine learning in which a system absorbs data and learns from it. Once neural net recognition is refined until it can identify obstacles with excellent accuracy, the vehicle network is capable of recognizing even a greater variety of objects and people during the inference phase.
Training and deep learning inference are the two main steps in AI implementation, but training neural nets currently accounts for most of the workload. As AI advances, training will shorten and machines will learn faster. Deep Learning is a machine learning method in which complex algorithms are used to learn data representations as opposed to algorithms that are oriented to a specific task.
The most fascinating examples of AI are in robots that are being designed to look more and more like humans, but AI offers real opportunities in numerous ways. AI includes a host of advanced technologies that include robotics processing, machine learning and predictive analytics. It is the ultimate harnessing of big data in order to capture the information it holds. Some opportunities apply to common customer service needs, such as chat bots using natural language processing to answer customer inquiries. The bots could also use machine learning to expand the ability to respond, utilizing user feedback.
Smart vehicles and customer services are just two applications. The medical and biotech industries see enormous possibilities for AI. One is to provide speedier and more accurate diagnoses based on unlimited amounts of processed data and to solve problems like reducing sepsis and helping disabled or severely injured people walk. Robots with AI can explain lab results, make suggestions and supplement the work of caregivers. Aetna has implemented a new security system for its Web and mobile apps that uses a behavior-based security system to monitor consumer devices, supplementing password and fingerprint systems. The list of applications already in use and planned for the future is already quite long.
Making Sense of a Complex Emerging Industry Filled with Innovation
AI is innovation and offers value-producing potential to innovative tech companies, their suppliers, and startups. However, there are some aspects of AI that make it one of the most competitive but also most confusing markets.
Since it remains in its nascent stage, the companies involved in research and development of AI-supported applications do not have standard definitions to draw upon, unlike most other technology areas. The result is hundreds of companies have difficulty identifying their specific market, even if they know the industry.
McKinsey & Company tackled this issue by beginning to identify the layers of machine learning and deep learning technology in order to establish a foundation for discussion. Businesses can address multiple layers but usually not all layers which include hardware, interface, platform, training presented to AI, and services or solutions.
These layers address the focus of businesses from which value flows. Consumers are seeing the early advantages of AI in vehicles with autonomous braking, but there is little doubt that they will begin expecting wave upon wave of technology enhancements.
McKinsey & Company identified additional applications for AI that are just beginning to appear in practice. They include traffic control sensors and signals that adapt to traffic flows throughout the day, banks that use AI to detect money laundering based on patterns of transaction, and retail stores that use AI for theft detection and recognizing shoppers with high purchase potential. AI will eventually be essential to providing business solutions in agriculture (sensor processing), credit industry (loan and credit approvals), call centers (audio processing), knowledge representation (Web searching and linking), and getting actionable business insights in a number of other areas.
The stage is set for AI suppliers to begin targeting particular markets. There are other opportunities for suppliers who can assist with AI implementation, employee training and consulting in new work designs. There is a lot of discussion as to how AI will impact work and productivity, and uncertainty is holding back industries from embracing this new technology.
Though AI is value producing, it will also impact IT, product designs, target markets, allocation of resources, investments and Human Resources. Strategically managing those impacts will determine the amount of value produced.