Seven Types of Artificial Intelligence Explained
Key Points
- The speaker proposes classifying AI into seven types, grouped under two broad categories: AI capabilities and AI functionalities.
- Among capabilities, only artificial narrow (or “weak”) AI exists today; it excels at specific tasks but cannot operate beyond its trained scope.
- Artificial general intelligence (AGI or “strong” AI) is a theoretical future form that would autonomously apply prior knowledge to learn new tasks without human training.
- Artificial super AI, also theoretical, would surpass human cognition, possessing its own emotions, beliefs, and desires.
- Functionally, narrow AI includes reactive machine AI—systems like IBM’s Deep Blue that use statistical analysis to perform highly specialized, data‑driven tasks.
Sections
- Classifying AI Capabilities Overview - The speaker outlines a seven‑type AI taxonomy, distinguishing today’s narrow (weak) AI from theoretical general (strong) AI within a framework of capabilities and functionalities.
- Types of Narrow AI - The passage outlines narrow AI’s two core categories—reactive machine AI, exemplified by Deep Blue’s chess calculations, and limited‑memory AI, used in generative chatbots that predict text or visuals based on past data.
Full Transcript
# Seven Types of Artificial Intelligence Explained **Source:** [https://www.youtube.com/watch?v=XFZ-rQ8eeR8](https://www.youtube.com/watch?v=XFZ-rQ8eeR8) **Duration:** 00:06:49 ## Summary - The speaker proposes classifying AI into seven types, grouped under two broad categories: AI capabilities and AI functionalities. - Among capabilities, only artificial narrow (or “weak”) AI exists today; it excels at specific tasks but cannot operate beyond its trained scope. - Artificial general intelligence (AGI or “strong” AI) is a theoretical future form that would autonomously apply prior knowledge to learn new tasks without human training. - Artificial super AI, also theoretical, would surpass human cognition, possessing its own emotions, beliefs, and desires. - Functionally, narrow AI includes reactive machine AI—systems like IBM’s Deep Blue that use statistical analysis to perform highly specialized, data‑driven tasks. ## Sections - [00:00:00](https://www.youtube.com/watch?v=XFZ-rQ8eeR8&t=0s) **Classifying AI Capabilities Overview** - The speaker outlines a seven‑type AI taxonomy, distinguishing today’s narrow (weak) AI from theoretical general (strong) AI within a framework of capabilities and functionalities. - [00:03:11](https://www.youtube.com/watch?v=XFZ-rQ8eeR8&t=191s) **Types of Narrow AI** - The passage outlines narrow AI’s two core categories—reactive machine AI, exemplified by Deep Blue’s chess calculations, and limited‑memory AI, used in generative chatbots that predict text or visuals based on past data. ## Full Transcript
I'm going to attempt to classify all of artificial intelligence or AI into seven types. And that's
a tall order. But these seven types of AI can largely be understood by examining two
encompassing categories. There's AI capabilities, and there's AI functionalities. So let's start
with AI capabilities, and there are three. The first of which is known as artificial narrow AI,
which also goes by the rather unflattering name of "weak AI". Now, on its face, that doesn't sound like
a very interesting capability to start us off. But actually, narrow AI is the only type of AI
that exists today--it's all we currently have. Any other form of AI is theoretical. So we can think
of this as realized AI--that's the artificial intelligence we have today. And theoretical AI,
which is the artificial intelligence we may have in the future. And now narrow AI can be trained
to perform a narrow task, which, to be fair to narrow AI, might be something that a human
could not do as well as the AI can. But it can't perform outside of its defined task. It does need
us humans still to train it. So if narrow AI represents all AI capabilities we have today,
well, what else is there? Well, a favorite of memes, science fiction, and betting markets is
artificial general intelligence, also known as AGI. And also known as "strong AI". To be clear,
AGI is currently nothing more than a theoretical concept. But here's the idea: AGI can use previous
learnings and skills to accomplish new tasks in a different context, without the need for
us human beings to train the underlying models. If AGI wants to learn how to perform a new task,
it will figure it out by itself. Which sounds... disconcerting. But, but look,
we haven't even talked about the third type of AI capability yet. And that's artificial "super AI". If
ever realized, super AI would think, reason, learn, make judgments and possess cognitive
abilities that surpass those of human beings. The application's [possessing] super AI capabilities
would have evolved beyond the point of catering to humans sentiments and experiences, and would be
able to feel emotions and have needs and possess beliefs and desires of their own. Yeah. So let's
park that cheery thought for now, and consider the four types of AI based on functionalities.
And we're back in the real world of realized AI here--at least initially. So we can think
of narrow AI as having two fundamental functions. One of those is reactive machine AI. Now reactive
machine AI are systems designed to perform a very specific specialized task. Reactive AI stems from
statistical math, and it can analyze vast amounts of data to produce a seemingly intelligent output.
We've had reactive AI for quite a long time. Back in the late 1990s, IBM's chess playing supercomputer
Deep Blue beat chess grandmaster Garry Kasparov by analyzing the pieces on the board and predicting
the probable outcomes of each move. That's a specialized task with a lot of available data
to create insights. The hallmark of reactive AI. We can think of other narrow AI functionalities
really as being classified as "limited memory AI". Now this form of AI can recall past events
and outcomes and monitor specific objects or situations over time. It can use past and present
moment data to decide on a course of action most likely to help achieve a desired outcome. And as
it's trained on more data over time, limited memory AI can improve in performance. Think of
your favorite generative AI chatbot, which relies on limited memory AI capabilities to predict the
next word or the next phrase or the next visual element within the context it's generating. Okay,
so what about our two theoretical AI capabilities? Well, if we look at AGI, we have to think about
"theory of mind AI". Now, this would understand the thoughts and emotions of other entities,
specifically us, so it could infer human motives and reasoning and personalize its interactions
with individuals based on their unique emotional needs and intentions. And actually, emotion AI
is a theory of mind AI currently in development. AI researchers hope it will have the ability to
analyze voices, images and other kinds of data to understand and respond to human feelings.
Finally! An AI that really understands me. And then finally, under super AI, we have "self-aware
AI". Winning my personal award for the scariest AI of all, it would have the ability to understand
its own internal conditions and traits, leading to its own set of emotions, needs and beliefs. Look,
we've covered seven types of AI, and only three of them actually exist today! There is still so
much to be learned and discovered. But as those advancements happen, at least here we
have a taxonomy of AI types that will tell us how far along we are on our AI journey.