AI FUNDAMENTALS
AI FUNDAMENTALS
What is Artificial Intelligence?
What is Artificial
Intelligence?
Why AI’s definition matters before governance, strategy, risk ownership, or responsibility can be useful.
Why AI’s definition matters before governance,
strategy, risk ownership, or responsibility can be useful.
AI FUNDAMENTALS
What is
Artificial Intelligence?
Why AI’s definition matters before governance, strategy, risk ownership,
or responsibility can be useful.
AI FUNDAMENTALS
What is Artificial Intelligence?
Why AI’s definition matters before governance, strategy, risk ownership, or responsibility can be useful.
You may be surprised to learn that the term 'Artificial Intelligence' or 'AI' has been in use for 70 years, since it was coined at the 1956 Dartmouth Conference on Artificial Intelligence by emeritus Stanford Professor John McCarthy. His original definition for AI is “the science and engineering of making intelligent machines”(1). Even more surprising is that the fundamental technology of today's AI was invented only shorter after the conference (2). Since that time, while AI as a technology has been developing and maturing, AI as a mainstream topic of discussion has been largely off the radar... until now. In the past few years, it seems AI has infiltrated every aspect of our lives and yet, there's an undeniable sense that we're often talking at cross-purposes.
So... what exactly is AI? And why is it suddenly EVERYWHERE?
At its most basic level, artificial intelligence is just that: a non-biological intelligence (thanks, Max Tegmark! (3)). The thing is, that's not really what we're trying to put our fingers on when we're constructing a definition of AI. The definition we're looking for has connotations: it's a computer-based technology, it's generally pegged against human intelligence (artificial can be anything non-biological), there has been human involvement to establish it, it subsequently acts with autonomy to produce an output. And while all these characteristics can be addressed by Tegmark's broad definition, it's so broad that we're likely to end up talking at cross-purposes with our customers, colleagues, advisors, with a consequential impact on responsibility-assigning characteristics such as risk ownership and liabilities.
Roight (as the Aussies say), the context is set. So why doesn't someone just define it and put us all out of our misery?
While deliberately oversimplified, that question is a useful entry point. Leading organizations, lawmakers, standards bodies, philosophers, lawyers, technologists, and commercial teams all bring different priorities to the same term. At the macro level there is general alignment, but when the pen is put to paper, aligning on a sufficiently specific definition of AI has thus far eluded us. The reasons for that are where the article really begins.
KEY TAKEAWAYS
Three things to take with you
Three things to take with you
01
There is no single, settled definition of AI. Treating the term as self-evident is where governance, procurement, and regulatory problems begin.
02
How you define AI determines what falls inside your policies, contracts, and regulatory exposure — definitions are commercial decisions, not semantics.
03
Anchor your organisation to a functional, capability-based definition tied to your actual use cases, so terminology drift can’t widen or shrink your obligations.
The age-old question: what actually is intelligence?
Definitionally, intelligence is a mental quality that includes the ability to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment. (1) That may make intuitive sense, but your interpretation will still differ from mine, from a colleague’s, or from a regulator’s. And herein lies our biggest problem: what is the 'intelligence' referred to in 'artificial intelligence'? This has been on the minds of the industry since day dot, and on the minds of philosophers for millennia.
In 1950, Alan Turing devised the Turing Test, named in honour of its creator, which proposed that if an evaluator could not distinguish between the response of a computer and the response of a human, the computer was exhibiting intelligence. We now know that this test is too simple, but for its time, it helped frame the question.
These days, the struggle continues. Pick up most any book on AI and the reader will observe that introducing the field of AI almost always begins by exploring the contributing disciplines, which span the gamut of the sciences and arts disciplines. They don't start with the 'artificial' (i.e. the technology); they start with the 'intelligence' and the complexities of assigning a common understanding or boundary to the term.
So when we finally align on a common understanding of intelligence, Plato would love to know, please and thanks.
Different actors adjust the definition according to need
Our next big hurdle is that each definition of AI carries a slightly different focus depending on the lens of the actor. This is a major
The age-old question: what actually is intelligence?
Definitionally, intelligence is a mental quality that includes the ability to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment. (1) That may make intuitive sense, but your interpretation will still differ from mine, from a colleague’s, or from a regulator’s. And herein lies our biggest problem: what is the 'intelligence' referred to in 'artificial intelligence'? This has been on the minds of the industry since day dot, and on the minds of philosophers for millennia.
In 1950, Alan Turing devised the Turing Test, named in honour of its creator, which proposed that if an evaluator could not distinguish between the response of a computer and the response of a human, the computer was exhibiting intelligence. We now know that this test is too simple, but for its time, it helped frame the question.
These days, the struggle continues. Pick up most any book on AI and the reader will observe that introducing the field of AI almost always begins by exploring the contributing disciplines, which span the gamut of the sciences and arts disciplines. They don't start with the 'artificial' (i.e. the technology); they start with the 'intelligence' and the complexities of assigning a common understanding or boundary to the term.
So when we finally align on a common understanding of intelligence, Plato would love to know, please and thanks.
The age-old question: what actually is intelligence?
Definitionally, intelligence is a mental quality that includes the ability to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment. (1) That may make intuitive sense, but your interpretation will still differ from mine, from a colleague’s, or from a regulator’s. And herein lies our biggest problem: what is the 'intelligence' referred to in 'artificial intelligence'? This has been on the minds of the industry since day dot, and on the minds of philosophers for millennia.
FIGURE 1 - SCIENTIFIC DISCIPLINES INFLUENCING AI
FIGURE 1 - SCIENTIFIC DISCIPLINES INFLUENCING AI
reason we struggle to align: a single definition will inevitably advantage some actors and disadvantage others through risk ownership, liability ownership, higher or undue levels of responsibility, or compliance burden.
Determann, for example, is a lawyer and from that lens, his definition of AI is that of a computer system with outputs that are unpredictable. Predictability is, of course, important to lawyers because their role is to help us identify and mitigate our risks. If an output is unpredictable, it's a lot harder to determine who is responsible, and that makes it much harder to protect us from negative outcomes.
By contrast, lawmakers and regulatory bodies like the OECD and the EU, prefer a broad, encompassing definition of AI because that breadth reduces the risk of missing scope, and also allows for laws and regulations to evolve alongside technology. These broad laws and regulations can be uncomfortable for companies trying to comply, because - as we've seen with the EU AI Act - they may not even know how to comply. And if they comply now, will compliance be continued in future? And a definition that works for a technical standard may not be specific enough for a contract, and a legal definition may be too broad to guide product design.
Different actors adjust the definition according to need
Our next big hurdle is that each definition of AI carries a slightly different focus depending on the lens of the actor. This is a major reason we struggle to align: a single definition will inevitably advantage some actors and disadvantage others through risk ownership, liability ownership, higher or undue levels of responsibility, or compliance burden.
Determann, for example, is a lawyer and from that lens, his definition of AI is that of a computer system with outputs that are unpredictable. Predictability is, of course, important to lawyers because their role is to help us identify and mitigate our risks. If an output is unpredictable, it's a lot harder to determine who is responsible, and that makes it much harder to protect us from negative outcomes.
By contrast, lawmakers and regulatory bodies like the OECD and the EU, prefer a broad, encompassing definition of AI because that breadth reduces the risk of missing scope, and also allows for laws and regulations to evolve alongside technology. These broad laws and regulations can be uncomfortable for companies trying to comply, because - as we've seen with the EU AI Act - they may not even know how to comply. And if they comply now, will compliance be continued in future? And a definition that works for a technical standard may not be specific enough for a contract, and a legal definition may be too broad to guide product design.
In 1950, Alan Turing devised the Turing Test, named in honour of its creator, which proposed that if an evaluator could not distinguish between the response of a computer and the response of a human, the computer was exhibiting intelligence. We now know that this test is too simple, but for its time, it helped frame the question.
These days, the struggle continues. Pick up most any book on AI and the reader will observe that introducing the field of AI almost always begins by exploring the contributing disciplines, which span the gamut of the sciences and arts disciplines. They don't start with the 'artificial' (i.e. the technology); they start with the 'intelligence' and the complexities of assigning a common understanding or boundary to the term.
So when we finally align on a common understanding of intelligence, Plato would love to know, please and thanks.
Different actors adjust the definition according to need
Our next big hurdle is that each definition of AI carries a slightly different focus depending on the lens of the actor. This is a major reason we struggle to align: a single definition will inevitably advantage some actors and disadvantage others through risk ownership, liability ownership, higher or undue levels of responsibility, or compliance burden.
Determann, for example, is a lawyer and from that lens, his definition of AI is that of a computer system with outputs that are unpredictable. Predictability is, of course, important to lawyers because their role is to help us identify and mitigate our risks. If an output is unpredictable, it's a lot harder to determine who is responsible, and that makes it much harder to protect us from negative outcomes.
By contrast, lawmakers and regulatory bodies like the OECD and the EU, prefer a broad, encompassing definition of AI because that breadth reduces the risk of missing scope, and also allows for laws and regulations to evolve alongside technology. These broad laws and regulations can be uncomfortable for companies trying to comply, because - as we've seen with the EU AI Act - they may not even know how to comply. And if they comply now, will compliance be continued in future? And a definition that works for a technical standard may not be specific enough for a contract, and a legal definition may be too broad to guide product design.
Lothar Determann⁽⁷⁾
AI means computer systems that generate text, images, solutions to problems, and other output, functioning with substantial autonomy and in ways that their developers cannot predict, explain, or control with certainty.
AI means computer systems that generate text, images, solutions to problems, and other output, functioning with substantial autonomy and in ways that their developers cannot predict, explain, or control with certainty.
International Association of Privacy Professionals (IAPP)⁽⁵⁾
AI is a broad term used to describe an engineered system where machines learn from experience, adjusting to new inputs, and potentially performing tasks previously done by humans. More specifically, it is a field of computer science dedicated to simulating intelligent behavior in computers. It may include automated decision-making.
International Standards Organization (ISO)⁽⁶⁾
AI means a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives.
AI means a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives.
Organization for Economic Co-operation and Development (OECD)⁽⁸⁾
AI system means a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.
European Union AI Act (2024)⁽⁹⁾
AI system means a machine-based system designed to operate with varying levels of autonomy, that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
AI system means a machine-based system designed to operate with varying levels of autonomy, that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
Max Tegmark⁽³⁾
AI means a non-biological intelligence.
Lothar Determann⁽⁷⁾
AI means computer systems that generate text, images, solutions to problems, and other output, functioning with substantial autonomy and in ways that their developers cannot predict, explain, or control with certainty.
International Association of Privacy Professionals (IAPP)⁽⁵⁾
AI is a broad term used to describe an engineered system where machines learn from experience, adjusting to new inputs, and potentially performing tasks previously done by humans. More specifically, it is a field of computer science dedicated to simulating intelligent behavior in computers. It may include automated decision-making.
International Standards Organization (ISO)⁽⁶⁾
AI means a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives.
Organization for Economic Co-operation and Development (OECD)⁽⁸⁾
AI system means a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.
European Union AI Act (2024)⁽⁹⁾
AI system means a machine-based system designed to operate with varying levels of autonomy, that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
FIGURE 2 - HOW PURPOSE SHAPES THE AI DEFINITION
FIGURE 2 - HOW PURPOSE SHAPES THE AI DEFINITION
In contrast to the previous examples, a marketing and sales function, too, often applies a broad definition of AI, but they aren't thinking so much about responsibility/liability, rather their focus is on customer acquisition and sales. AI is the hot new thing in the market and customers associate AI with many positive benefits such as being perceived as a market-leader, improved efficiency, cost-reduction, improved decision-making, etc. Applying an 'AI' label captures the attention of potential customers moreso than boring old software and tools, or the enigmatic technological concepts such as machine learning, deep learning, LLMs.
The struggle is real: the OECD secured agreement among its members for a definition of ‘AI system’, but was unable, in six years, to agree a definition of AI. That gap tells us something important about the field. The more the term is used across law, policy, commerce, technology, and public discourse, the more care we need to take before assuming we are all talking about the same thing.
And it's all then compounded by confusion
Intertwined with the definitional challenge is the subset of concepts that sit under the AI umbrella. As we can see from Figure 3, there's a lot of terminology that relates to AI, and this is just the tip of the iceberg.
Drawing on Philosophy 100 (yessssss! I knew Logical Reasoning would come in handy!!!), each of these concepts are a subset of the previous. SO: Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning, and Generative AI is a subset of Deep Learning. Ipso facto, they are all subsets of AI, but they are not synonymous with AI.
This gets pretty confusing when most people are still learning that all these technologies even exist.
So we've got a lack of alignment on the definition of AI and we've got a whole field of ever-advancing technology wherein most people have only just started to recognize the lexicon, never mind the underlying meaning. As one can imagine, the compound effect of these two scenarios makes the AI ecosystem pretty confusing to non-technologists.
Artificial Intelligence
TIER I
Broadly defined as non-biological intelligence. It is the broad field of developing machines that can replicate biological intelligence — reasoning, learning, problem-solving. Artificial General Intelligence (AGI) is human-level intelligence. Superintelligent AGI is intelligence beyond the human level.
TIER II
Machine Learning
Algorithms detect patterns in large data sets and learn to make predictions by processing data, rather than by receiving explicit programming instructions. E.g. medical imaging analysis.
TIER III
Deep Learning
A subset of machine learning (ML) using neural networks — inspired by neurons interacting in the human brain — to ingest and process data through multiple iterations, making increasingly sophisticated predictions as the machine learns. E.g. an early layer may recognize a specific shape; building on this knowledge, a later layer might identify the shape as a stop sign.
TIER IV
Generative AI
A subset of Deep Learning; an AI model that uses large language models (LLMs) to generate content in response to a prompt. E.g. ChatGPT. While Gen AI is in its infancy, it has potential to create significant disruption in the coming years as it becomes more mature and reliable.
FIGURE 3 · TERMINOLOGY IN THE AI FIELD
And it's all then compounded by confusion
Intertwined with the definitional challenge is the subset of concepts that sit under the AI umbrella. As we can see from Figure 3, there's a lot of terminology that relates to AI, and this is just the tip of the iceberg.
Drawing on Philosophy 100 (yessssss! I knew Logical Reasoning would come in handy!!!), each of these concepts are a subset of the previous. SO: Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning, and Generative AI is a subset of Deep Learning. Ipso facto, they are all subsets of AI, but they are not synonymous with AI.
This gets pretty confusing when most people are still learning that all these technologies even exist.
So we've got a lack of alignment on the definition of AI and we've got a whole field of ever-advancing technology wherein most people have only just started to recognize the lexicon, never mind the underlying meaning. As one can imagine, the compound effect of these two scenarios makes the AI ecosystem pretty confusing to non-technologists.
And it's all then compounded by confusion
Intertwined with the definitional challenge is the subset of concepts that sit under the AI umbrella. As we can see from Figure 3, there's a lot of terminology that relates to AI, and this is just the tip of the iceberg.
Drawing on Philosophy 100 (yessssss! I knew Logical Reasoning would come in handy!!!), each of these concepts are a subset of the previous. SO: Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning, and Generative AI is a subset of Deep Learning. Ipso facto, they are all subsets of AI, but they are not synonymous with AI.
This gets pretty confusing when most people are still learning that all these technologies even exist.
So we've got a lack of alignment on the definition of AI and we've got a whole field of ever-advancing technology wherein most people have only just started to recognize the lexicon, never mind the underlying meaning. As one can imagine, the compound effect of these two scenarios makes the AI ecosystem pretty confusing to non-technologists.
Sooooo… now what?
Two things should now be clear. First, the confusion surrounding AI is not incidental; it reflects genuine differences in technology, use case, risk and accountability. Second, defining AI is not an academic exercise. If you are investing in, procuring, deploying or governing an AI-enabled product, the definition you use determines what falls within scope, what must be tested and where responsibility sits.Without that clarity, teams can believe they are aligned while working from materially different assumptions. The result is often a weak business case, incomplete diligence, poorly framed requirements or controls that do not fit the system they are intended to govern. The question is not simply whether something is called AI. It is whether the definition being used is precise enough for the decision at hand.
We can help
Orelia helps leaders establish that clarity early—before capital is committed, products are procured or governance is designed—so decisions reflect the system’s actual characteristics, risks and implications, rather than the label attached to it.
References
1. Stanford University Human-Centered Artificial Intelligence. 2. Generative Artificial Intelligence: What It Is, What It Is Not and What It Can Be for the United Nations, Jeongki Lim. 3. Life 3.0, Max Tegmark. 4. Definition of Intelligence, Britannica. 5. Glossary, International Association of Privacy Professionals. 6. ISO/IEC 22989:2022, International Standards Organization. 7. Determann’s Field Guide to Artificial Intelligence Law, Lothar Determann. 8. OECD AI Principles overview, Organization for Economic Co-operation and Development. 9. European Union AI Act (2024).
You may be surprised to learn that the term 'Artificial Intelligence' or 'AI' has been in use for 70 years, since it was coined at the 1956 Dartmouth Conference on Artificial Intelligence by emeritus Stanford Professor John McCarthy. His original definition for AI is “the science and engineering of making intelligent machines”(1). Even more surprising is that the fundamental technology of today's AI was invented only shorter after the conference (2). Since that time, while AI as a technology has been developing and maturing, AI as a mainstream topic of discussion has been largely off the radar... until now. In the past few years, it seems AI has infiltrated every aspect of our lives and yet, there's an undeniable sense that we're often talking at cross-purposes.
So... what exactly is AI? And why is it suddenly EVERYWHERE?
At its most basic level, artificial intelligence is just that: a non-biological intelligence (thanks, Max Tegmark! (3)). The thing is, that's not really what we're trying to put our fingers on when we're constructing a definition of AI. The definition we're looking for has connotations: it's a computer-based technology, it's generally pegged against human intelligence (artificial can be anything non-biological), there has been human involvement to establish it, it subsequently acts with autonomy to produce an output. And while all these characteristics can be addressed by Tegmark's broad definition, it's so broad that we're likely to end up talking at cross-purposes with our customers, colleagues, advisors, with a consequential impact on responsibility-assigning characteristics such as risk ownership and liabilities.
Roight (as the Aussies say), the context is set. So why doesn't someone just define it and put us all out of our misery?
While deliberately oversimplified, that question is a useful entry point. Leading organizations, lawmakers, standards bodies, philosophers, lawyers, technologists, and commercial teams all bring different priorities to the same term. At the macro level there is general alignment, but when the pen is put to paper, aligning on a sufficiently specific definition of AI has thus far eluded us. The reasons for that are where the article really begins.
KEY TAKEAWAYS
Three things to take with you
01
There is no single, settled definition of AI. Treating the term as self-evident is where governance, procurement, and regulatory problems begin.
02
How you define AI determines what falls inside your policies, contracts, and regulatory exposure — definitions are commercial decisions, not semantics.
03
Anchor your organisation to a functional, capability-based definition tied to your actual use cases, so terminology drift can’t widen or shrink your obligations.
Determann, for example, is a lawyer and from that lens, his definition of AI is that of a computer system with outputs that are unpredictable. Predictability is, of course, important to lawyers because their role is to help us identify and mitigate our risks. If an output is unpredictable, it's a lot harder to determine who is responsible, and that makes it much harder to protect us from negative outcomes.
By contrast, lawmakers and regulatory bodies like the OECD and the EU, prefer a broad, encompassing definition of AI because that breadth reduces the risk of missing scope, and also allows for laws and regulations to evolve alongside technology. These broad laws and regulations can be uncomfortable for companies trying to comply, because - as we've seen with the EU AI Act - they may not even know how to comply. And if they comply now, will compliance be continued in future? And a definition that works for a technical standard may not be specific enough for a contract, and a legal definition may be too broad to guide product design.
International Association of Privacy Professionals (IAPP)⁽⁵⁾
AI is a broad term used to describe an engineered system where machines learn from experience, adjusting to new inputs, and potentially performing tasks previously done by humans. More specifically, it is a field of computer science dedicated to simulating intelligent behavior in computers. It may include automated decision-making.
Max Tegmark⁽³⁾
AI means a non-biological intelligence.
Organization for Economic Co-operation and Development (OECD)⁽⁸⁾
AI system means a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.
European Union AI Act (2024)⁽⁹⁾
AI system means a machine-based system designed to operate with varying levels of autonomy, that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
Lothar Determann⁽⁷⁾
AI means computer systems that generate text, images, solutions to problems, and other output, functioning with substantial autonomy and in ways that their developers cannot predict, explain, or control with certainty.
International Standards Organization (ISO)⁽⁶⁾
AI means a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives.
FIGURE 2 - HOW PURPOSE SHAPES THE AI DEFINITION
In contrast to the previous examples, a marketing and sales function, too, often applies a broad definition of AI, but they aren't thinking so much about responsibility/liability, rather their focus is on customer acquisition and sales. AI is the hot new thing in the market and customers associate AI with many positive benefits such as being perceived as a market-leader, improved efficiency, cost-reduction, improved decision-making, etc. Applying an 'AI' label captures the attention of potential customers moreso than boring old software and tools, or the enigmatic technological concepts such as machine learning, deep learning, LLMs.
The struggle is real: the OECD secured agreement among its members for a definition of ‘AI system’, but was unable, in six years, to agree a definition of AI. That gap tells us something important about the field. The more the term is used across law, policy, commerce, technology, and public discourse, the more care we need to take before assuming we are all talking about the same thing.
We can help
Orelia helps leaders establish that clarity early—before capital is committed, products are procured or governance is designed—so decisions reflect the system’s actual characteristics, risks and implications, rather than the label attached to it.
The age-old question: what actually is intelligence?
Definitionally, intelligence is a mental quality that includes the ability to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment. (1) That may make intuitive sense, but your interpretation will still differ from mine, from a colleague’s, or from a regulator’s. And herein lies our biggest problem: what is the 'intelligence' referred to in 'artificial intelligence'? This has been on the minds of the industry since day dot, and on the minds of philosophers for millennia.
In 1950, Alan Turing devised the Turing Test, named in honour of its creator, which proposed that if an evaluator could not distinguish between the response of a computer and the response of a human, the computer was exhibiting intelligence. We now know that this test is too simple, but for its time, it helped frame the question.
These days, the struggle continues. Pick up most any book on AI and the reader will observe that introducing the field of AI almost always begins by exploring the contributing disciplines, which span the gamut of the sciences and arts disciplines. They don't start with the 'artificial' (i.e. the technology); they start with the 'intelligence' and the complexities of assigning a common understanding or boundary to the term.
So when we finally align on a common understanding of intelligence, Plato would love to know, please and thanks.
Different actors adjust the definition according to need
Our next big hurdle is that each definition of AI carries a slightly different focus depending on the lens of the actor. This is a major reason we struggle to align: a single definition will inevitably advantage some actors and disadvantage others through risk ownership, liability ownership, higher or undue levels of responsibility, or compliance burden.
FIGURE 1 - SCIENTIFIC DISCIPLINES INFLUENCING AI
FIGURE 3 · TERMINOLOGY IN THE AI FIELD
Artificial Intelligence
TIER I
Broadly defined as non-biological intelligence. It is the broad field of developing machines that can replicate biological intelligence — reasoning, learning, problem-solving. Artificial General Intelligence (AGI) is human-level intelligence. Superintelligent AGI is intelligence beyond the human level.
TIER II
Machine Learning
Algorithms detect patterns in large data sets and learn to make predictions by processing data, rather than by receiving explicit programming instructions. E.g. medical imaging analysis.
TIER III
Deep Learning
A subset of machine learning (ML) using neural networks — inspired by neurons interacting in the human brain — to ingest and process data through multiple iterations, making increasingly sophisticated predictions as the machine learns. E.g. an early layer may recognize a specific shape; building on this knowledge, a later layer might identify the shape as a stop sign.
TIER IV
Generative AI
A subset of Deep Learning; an AI model that uses large language models (LLMs) to generate content in response to a prompt. E.g. ChatGPT. While Gen AI is in its infancy, it has potential to create significant disruption in the coming years as it becomes more mature and reliable.
And it's all then compounded by confusion
Intertwined with the definitional challenge is the subset of concepts that sit under the AI umbrella. As we can see from Figure 3, there's a lot of terminology that relates to AI, and this is just the tip of the iceberg.
Drawing on Philosophy 100 (yessssss! I knew Logical Reasoning would come in handy!!!), each of these concepts are a subset of the previous. SO: Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning, and Generative AI is a subset of Deep Learning. Ipso facto, they are all subsets of AI, but they are not synonymous with AI.
This gets pretty confusing when most people are still learning that all these technologies even exist.
So we've got a lack of alignment on the definition of AI and we've got a whole field of ever-advancing technology wherein most people have only just started to recognize the lexicon, never mind the underlying meaning. As one can imagine, the compound effect of these two scenarios makes the AI ecosystem pretty confusing to non-technologists.
Sooooo… now what?
Two things should now be clear. First, the confusion surrounding AI is not incidental; it reflects genuine differences in technology, use case, risk and accountability. Second, defining AI is not an academic exercise. If you are investing in, procuring, deploying or governing an AI-enabled product, the definition you use determines what falls within scope, what must be tested and where responsibility sits.
Without that clarity, teams can believe they are aligned while working from materially different assumptions. The result is often a weak business case, incomplete diligence, poorly framed requirements or controls that do not fit the system they are intended to govern. The question is not simply whether something is called AI. It is whether the definition being used is precise enough for the decision at hand.
References
1. Stanford University Human-Centered Artificial Intelligence. 2. Generative Artificial Intelligence: What It Is, What It Is Not and What It Can Be for the United Nations, Jeongki Lim. 3. Life 3.0, Max Tegmark. 4. Definition of Intelligence, Britannica. 5. Glossary, International Association of Privacy Professionals. 6. ISO/IEC 22989:2022, International Standards Organization. 7. Determann’s Field Guide to Artificial Intelligence Law, Lothar Determann. 8. OECD AI Principles overview, Organization for Economic Co-operation and Development. 9. European Union AI Act (2024).
