THE AI HYPERBOLE
It will not be exaggeration to say that one of the most misused and most misinterpreted words in common as well as in business context is AI. I recently came across a quote in an article by Cassie Kozyrkov which aptly and ingeniously drives home the point!
If it is written in Python, it’s probably machine learning. If it is written in PowerPoint, it’s probably AI.
Today in the tech world using the term “artificial intelligence” or “A.I” has become increasingly fashionable for any piece of software or system that uses a simple neural network which is typically a pattern recognition system just capable enough to analyze and sort “certain” things into categories. There are even more shameful cases of decision tree based systems being marketed under the guise of AI. AI has also become a fashion for corporate strategy. The Bloomberg Intelligence economist Michael McDonough tracked mentions of “artificial intelligence” in earnings call transcripts, noting a huge uptick in recent years. Jerry kaplan says, “Artificial intelligence, it seems, has a PR problem. While it’s true that today’s machines can credibly perform many tasks (playing chess, driving cars) that were once reserved for humans, that doesn’t mean that the machines are growing more intelligent and ambitious. It just means they’re doing what we built them to do. The robots may be coming, but they are not coming for us—because there is no “they.” Machines are not people, and there’s no persuasive evidence that they are on a path toward sentience.” (Source)
Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”
THERE ARE 3 TYPES OF ARTIFICIAL INTELLIGENCE (AI):
- Narrow or weak AI: face/speech recognition, voice assistants, driving a car etc. Key examples include, Apple’s Siri, IBM’s Watson, etc. It is only Narrow AI which we have almost successfully achieved.
- General or strong AI: This is the capability by which machines can mimics human intelligence or behaviours, with the ability to learn & solve a problem. The very lack of understanding of human brain itself is the biggest deterrent in developing and mimicking itself and modern science till today is far from it.
- Artificial superintelligence: This is a hypothetical AI that surpasses human intelligence and behaviours; ASI is where machines become self-aware… MATRIX!
SCIENTISM ASSOCIATED WITH AI
The usage of these buzzwords and the associated scientism must be stopped and for the businesses, the use of the term must be well thought & defined in the context of the goals and data strategy. It is a sure shot sign of poorly defined goals & strategies if you are encountering these terms more often in business meetings and presentations. The story however doesn’t end at AI, quite like it, the term big data, analytics have also become a cliche and ubiqutous on PPTs, however, this is perhaps a subject of another article!
SOME DILBERT ON AI IN THE CONTEXT