Why Cognitive Technology May Be A Better Term Than Artificial Intelligence
In general, most people would agree that the fundamental goals of AI are to enable machines to have cognition, perception, and decision-making capabilities that previously only humans or other intelligent creatures have. Max Tegmark simply defines AI as “intelligence that is not biological”. Simple enough but we don’t fully understand what biological intelligence itself means , and so trying to build it artificially is a challenge.
At the most abstract level, AI is machine behavior and functions that mimic the intelligence and behavior of humans. Specifically, this usually refers to what we come to think of as learning, problem solving, understanding and interacting with the real-world environment, and conversations and linguistic communication. However the specifics matter, especially when we’re trying to apply that intelligence to solve very specific problems businesses, organizations, and individuals have.
Saying AI but meaning something else
There are certainly a subset of those pursuing AI technologies with a goal of solving the ultimate problem: creating artificial general intelligence (AGI) that can handle any problem, situation, and thought process that a human can. AGI is certainly the goal for many in the AI research being done in academic and lab settings as it gets to the heart of answering the basic question of whether intelligence is something only biological entities can have. But the majority of those who are talking about AI in the market today are not talking about AGI or solving these fundamental questions of intelligence. Rather, they are looking at applying very specific subsets of AI to narrow problem areas. This is the classic Broad / Narrow (Strong / Weak) AI discussion.
Since no one has successfully built an AGI solution, it follows that all current AI solutions are narrow. While there certainly are a few narrow AI solutions that aim to solve broader questions of intelligence, the vast majority of narrow AI solutions are not trying to achieve anything greater than the specific problem the technology is being applied to. What we mean to say here is that we’re not doing narrow AI for the sake of solving a general AI problem, but rather narrow AI for the sake of narrow AI. It’s not going to get any broader for those particular organizations. In fact, it should be said that many enterprises don’t really care much about AGI, and the goal of
AI for those organizations is not AGI.
If that’s the case, then it seems that the industry’s perception of what AI is and where it is heading differs from what many in research or academia think. What interests enterprises most about AI is not that it’s solving questions of general intelligence, but rather that there are specific things that humans have been doing in the organization that they would now like machines to do. The range of those tasks differs depending on the organization and the sort of problems they are trying to solve. If this is the case, then why bother with an ill-defined term in which the original definition and goals are diverging rapidly from what is actually being put into practice?
What are cognitive technologies?
Perhaps a better term for narrow AI being applied for the sole sake of those narrow applications is cognitive technology. Rather than trying to build an artificial intelligence, enterprises are leveraging cognitive technologies to automate and enable a wide range of problem areas that require some aspect of cognition. Generally, you can group these aspects of cognition into three “P” categories, borrowed from the autonomous vehicles industry:
Perceive – Understand the environment around you and input coming from sensors.
