AI is moving fast. It has now become so commonplace that it's become an expectation that systems are intelligent. For example, not too long ago, the technology to compete against a human mind in chess was a groundbreaking piece of AI to be marveled at. Now we don't even give it a second thought. Millions of tactical and strategic calculations a second is now just a simple game that can be found on any computer or played on hundreds of websites.
That seemingly was intelligence… that was artificial. Simple right? With spam blockers, recommendation engines, and the best delivery route, the goalposts keep shifting so much that now, all of what was once thought of as AI is simply now regarded as everyday tools.
What was once considered AI is now just thought of as simply software. It seems that AI just means problems that are still unsolved. As those become normal, day-to-day operations, they can fade away from what we generally think of as AI. This is known as the Larry Tesler Theorem, which states "Artificial intelligence is whatever hasn't been done yet."
For example, if you asked someone what AI is, they would probably talk about autonomous driving, drone delivery, and robots that can perform very complex actions. All of these examples are very much in the realm of unsolved problems, and as (or if) they become solved, they may no longer be thought of as AI as the newer, harder problems take their place.
Before we dive any further, let's make sure we are aligned on a few terms that will be a focal point for the rest of the book.
Defining AI
It's important to call out the fact that there is no universal label as to what AI is, but for the purpose of this book, we will use the following definition:
"Artificial Intelligence (AI) is the development of computer systems to allow them to perform tasks that mimic the intelligence of humans. This can use vision, text, reading comprehension, complex problem solving, labeling, or other forms of input."
Defining a data scientist
Along with the definition of AI, defining what a data scientist is can also lead you to many different descriptions. Know that as with AI, the field of data science can be a very broad category. Josh Wills tweeted that a data scientist is the following:
"A person who is better at statistics than any software engineer and better at software engineering than any statistician."
While there may be some truth to that, we'll use the following definition instead:
"A data scientist is someone who gains insight and knowledge from data by analyzing, applying statistics, and implementing an AI approach in order to be able to answer questions and solve problems."
If you are reading this, then you probably fall into that category. There are many tools that a data scientist needs to be able to utilize to work toward the end goal, and we'll learn about many of those in this book.
Now that we've set a base level of understanding of what AI is, let's take a look at where the state of the world is regarding AI, and also learn about where ML fits into the picture.
United States
Great Britain
India
Germany
France
Canada
Russia
Spain
Brazil
Australia
Singapore
Hungary
Ukraine
Luxembourg
Estonia
Lithuania
South Korea
Turkey
Switzerland
Colombia
Taiwan
Chile
Norway
Ecuador
Indonesia
New Zealand
Cyprus
Denmark
Finland
Poland
Malta
Czechia
Austria
Sweden
Italy
Egypt
Belgium
Portugal
Slovenia
Ireland
Romania
Greece
Argentina
Netherlands
Bulgaria
Latvia
South Africa
Malaysia
Japan
Slovakia
Philippines
Mexico
Thailand
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