People are not the reason: What AI was, and what AI is becoming.

From the Author

I was speaking to the industry of AI practitioners (“experts” from almost every tech company, academia, and professionals). I received vertical (up and down and up and down, nonstop) head nods when I defined AI in 2022 as “a large language model to reveal data-informed decisions.”

I did not get into natural language processing specifically, I did not decompose machine learning, and deep learning was not stated once from me.

I recall the first introduction of data science as new professional tradecraft. A decade or so later, ten billion opinions had already been published on how everyone is a data scientist if they are a “citizen data scientist.”

After the introduction of the role, a myriad of people assertively announced that the concept of data science has existed for decades; nonetheless, it was not until recently that it was designated as an established profession.

From the Author

I dealt with individuals that entered university via ivy league and left with AI bachelor’s or master’s degrees. They have spent decades working in technology, and they are still figuring out (even inside some of the largest tech companies) how to enter a certain AI tradecraft (or exit the industry altogether!)

I anticipated that everyone would eventually hear about AI via the news, a coworker, or the introduction of a revolutionary idea (related to AI). As complicated and blurry as it already is, now throw modelOps and MLOps into the mix. How about implementing something AI related in the cloud?

Artificial intelligence is difficult to define because it encompasses a wide range of phenomena and concepts, from simple mathematical algorithms that recognize patterns in data sets to complex systems capable of intelligent behavior such as reasoning, natural communication, problem-solving, and learning.

AI is a subset of artificial intelligence concerned with the development and deployment of intelligent procedures to accomplish specific goals. In a broader sense, however, AI refers to any process or system that performs functions that are often associated with human intelligence, such as problem-solving, decision-making, and learning.

From the Author

Consider the automation of jobs such as data input, machine learning, and navigation as an example. In each instance, we can identify the precise AI approaches (such as statistical modeling) that are being used to accomplish a certain objective (in this case, accuracy and efficiency). However, any process or system that displays a degree of behavior generally associated with human intelligence might also be labeled artificial intelligence. Why shouldn’t the latter instance be considered a definition?

Some link AI with intelligence because they devote their whole lives to developing intelligence via research. AI is not formally connected with intelligence since such a remark would substantially influence people’s acceptance of it.

To clarify what artificial intelligence means, should we analyze what it means for something to be “smarter” than the ordinary person rather than concentrating on particular actions or abilities? Does this imply possessing exceptional intellect as measured by IQ tests? Or does it merely refer to outperforming people in certain activities and processes?

From the Author

It does not matter to me if you have a Ph.D. in AI, have never worked in AI, or are pursuing a graduate degree in AI after earning an undergraduate degree in the field.

It does not matter how deeply ingrained or different your perspective is on the definition of artificial intelligence or the reasons why it is so perplexing.

We may call a spade a spade together. Isn’t a spade a spade?

The fact is that generating uncertainty gives the industry time to choose its own meaning. If the industry defines it, their description will likely also provide a solution (based on their interpretation).

Because the whole landscape of AI is ready for operationalization, it is quite simple to develop diverse definitions, subfields, and areas of specialization within the discipline of AI.

To clarify, there is nothing wrong with industry defining AI on their own terms (and issuing a solution for it along the way in parallel). In reality, I encourage such an approach (from my perspective) because of the titanic reach of AI (and the myriad places it can have a profoundly positive influence).

From the Author

AI is at a crossroads. In this instance, as of this post, I would go so far as to say that it is easier to define AI based on its future trajectory than on its past.

AI is any computing system (or any process) exhibiting human-like behavior.

The observation of this definition will likely be associated immensely with the digitization of AI. We will generate films from non-3D photos in only seconds.

Everyone will have the ability to produce films, newsreels, and derivatives of original pictures in imaginative, nonfictional, and fictitious ways.

Any process that is enhanced by the integration of a computing system will be based on artificial intelligence. In other words, AI will support every computing system that can enhance a situation, program, model, or decision.

Do you see the path we are headed on? It is far simpler than it has been made up to be.

From the Author

Yet, by engineering standards, AI will become so enormously complicated that the finest engineers will establish their own start-ups instead of working for corporations. AI is a field where so many innovative ideas may swiftly transform into so many lucrative possibilities.

Parting Thoughts

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Anil Tilbe