Artificial intelligence as a discipline consists of hundreds of individual technologies, concepts, and applications. These terms have become increasingly important as STEM education expands and there is a boom in practical household and consumer-facing applications for the technology. Despite that, there is a lack of consistency in how many AI concepts are discussed, not just at the STEM education level, but in popular entertainment, science writing, and even at times in scientific journals. To address this, we need to standardize how we describe AI and its many subsets, and accurately define these terms both in general and specific to individual technologies and applications of those technologies. We discuss some of the most commonly misused and what they really mean.
There has long been a chasm between what we perceive artificial intelligence to be and what it can actually do. Our films, literature, and video game representations of "intelligent machines," depict AI as detached but highly intuitive interfaces. We will find communication re-imagined with emotion AI. As these artificial systems are being integrated into our commerce, entertainment, and logistics networks, we are witnessing emotional intelligence. These smarter systems have a better understanding of how humans feel and why they feel that way.
After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama. And today his research is being used to teach computer systems how to feel.
Tech decision makers are (and should keep) looking for ways to effectively implement artificial intelligence technologies into their businesses and, therefore, drive value. And though all AI technologies most definitely have their own merits, not all of them are worth investing in. If one thing and only one thing happens after you read this article, we hope it is that you are inspired to join the 62% of companies who boosted their enterprises in 2018 by adopting Artificial Intelligence into their workflow. Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy. It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.
Artificial intelligence is strategically important for driving enterprise strategies. Every day, new examples are coming out of new problems being solved and old markets being disrupted by what is collectively called "Artificial Intelligence." Enterprises that do not have an AI strategy would be wise to start working on one straight away. Unfortunately, managers often lack understanding when it comes to AI and it started with the term itself. Artificial General Intelligence is what people think of and see destroying the world in apocalyptic summer blockbuster movies.