AI technologies

In order to be useful, AI must be applicable. Its true value can only be realized when it delivers actionable insights. If we think of AI in terms of a human brain, then AI technologies are like the hands, the eyes, and the movements of the body – all that allows the brain’s ideas to be executed. The following are some of the most widely used and rapidly-advancing AI technologies.

Artificial intelligence technologies

Machine learning

Machine learning – and all its components – is a subset of AI. In machine learning, algorithms are applied to different types of learning methods and analysis techniques, which allow the system to automatically learn and improve from experience without being explicitly programmed. For businesses, machine learning can be applied to any problem or goal that requires predictive outcomes, arrived at from complex data analysis.

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What is the difference between AI and machine learning? Machine learning is a component of AI and cannot exist without it. So it’s not so much that they are different – as how they are different. AI processes data to make decisions and predictions. Machine learning algorithms allow AI to not only process that data, but to use it to learn and get smarter, without needing any additional programming.

Natural language processing (NLP)

NLP allows machines to recognize and understand written language, voice commands, or both. This includes the ability to translate human language into a form that the algorithm can understand. Natural language generation (NLG) is a subset of NLP that allows the machine to convert digital language into natural human language. In more sophisticated applications, NLP can use context to infer attitude, mood, and other subjective qualities to most accurately interpret meaning. Practical applications of NLP include chatbots and digital voice assistants such as Siri and Alexa.

Computer vision

Computer vision is the method by which computers understand and “see” digital images and videos – as opposed to just recognizing or categorizing them. Computer vision applications use sensors and learning algorithms to extract complex, contextual information that can then be used to automate or inform other processes. Computer vision can also extrapolate data for predictive purposes which basically means it can see through walls and around corners. Self-driving cars are a good example of computer vision in use.

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Robotics

Robotics is nothing new and has been used for years, especially in manufacturing. Without the application of AI, however, automation must be accomplished through manual programming and calibration. If weaknesses or inefficiencies exist in those workflows, they can only be spotted after the fact – or after something breaks down. The human operator can often never know what led to a problem, or what adaptations could be made to achieve better efficiency and productivity. When AI is brought into the mix – typically via IoT sensors – it brings with it the capacity to greatly expand the scope, volume, and type of robotic tasks performed. Examples of robotics in industry include order-picking robots for use in large warehouses and agricultural robots that can be programmed to pick or service crops at optimum times.