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“You shall know a word by the company it keeps.”
– Krohn, J.[1]
Definitions are artificial meaning-giving constructs. A definition is a specific linguistic form with a specific function. Definitions are patterns of weighted attributes, handpicked by means of (wanted and unwanted) biases. A definition is then as a category of attributes referring to a given concept and which then, in turn, aims at triggering a meaning of that targeted concept.
Definitions are aimed at controlling such meaning-giving of what it could refer to and of what it can contain within its proverbial borders: the specified attributes, narrated into a set (i.e. a category), that makes up its construct as to how some concept is potentially understood.
The
preceding sentences could be seen as an attempt to a definition of the concept
“definition” with a hint of how some concepts in the field of AI itself are
defined (hint: have a look at the definitions of “Artificial Neural Networks”
or of “Machine Learning” or of “Supervised and Unsupervised Learning”). Let us
continue looking through this lens and expand on it.
Definitions
can be constructed in a number of ways. For instance: they can be constructed
by identifying or deciding on, and giving a description of, the main attributes
of a concept. This could be done, for instance, by analyzing and describing
forms and functions of the concept. Definitions could, for instance, be
constructed by means of giving examples of usage or application; by stating
what some concept is (e.g. synonyms, analogies) and is not (e.g. antonyms); by
referring to a historical or linguistic development (e.g. its etymology,
grammatical features, historical and cultural or other contexts, etc.); by
comparison with other concepts in terms of similarities and differentiators; by
describing how the concept is experienced and how not; by describing its needed
resources, its possible inputs, its possible outputs, intended aims (as a
forecast), actual outcome and larger impact (in retrospect). There are many
ways to construct a definition. So too is it with a definition for the concept of
“Artificial Intelligence”.
For a moment, as another playful side-note, by using our imagination and by trying to make the link between the process of defining and the usage of AI applications stronger: one could imagine that an AI solution is like a “definition machine.”
One could following imagine that this machine gives definition to a data set –by offering recognized patterns from within the data set– at its output. This AI application could be imagined as organizing data via some techniques. Moreover, the application can be imagined to be collecting data as if attributes of a resulting pattern. To the human receiver this in turn could then define and offer meaning to a selected data set . Note, it also provides meaning to the data that is not selected into the given pattern at the output. For instance: the date is labelled as “cat” not “dog” while also some data has been ignored (by filtering it out; e.g. the background “noise” of ‘cat’). Did this imagination exercise allow one to make up a definition of AI? Perhaps. What do you think? Does this definition satisfy your needs? Does it do justice to the entire field of AI from its “birth”, its diversification process along the way, to “now”? Most likely not.
A human designer of a definition likely agrees with the selected attributes (though not necessarily) while, those receiving the designed definition might agree that it offers a pattern but, not necessarily the meaning-giving pattern they would construct. Hence, definitions tend to be contested, fine-tuned, altered, up-dated, dismissed all-together over time and, depending on the perspective, used to review and qualify other yet similar definitions. It almost seems that some definitions have a life of their own while others are, understandably, safely guarded to be maintained over time.
When learning about something and when looking a bit deeper than a surface, one then quickly is presented with numerous definitions of what was thought to be one and the same thing yet, which show variation and diversity in a field of study. This is OK. We, as individuals within our species, are able to handle, or at least live with ambiguities, uncertainties and change. These, by the way, are also some of the reasons why, for instance and to some extent, the fields of Statistics, Data Science and AI (with presently the sub-field of Machine Learning and Deep Learning) exist.
The “biodiversity” of definitions can be managed in many ways. One can manage different ideas at the same time in one’s head. It is as one can think of black and white and a mix of the two, in various degrees and that done simultaneously; while also introducing a plethora of additional colors. This can still offer harmony in one’s thinking. If that doesn’t work, one can put more importance to one definition over another, depending on some parameters befitting the aim of the learning and the usage of the definition (i.e. one’s practical bias of that moment in spacetime). One can prefer to start simple, with a reduced model as offered in a modest definition while (willingly) ignoring a number of attributes. This one could remind oneself to do so by not equating this simplified model / definition with the larger complexities of that what it only initiates to define.
One can
apply a certain quality standard to allow the usage of one definition over
another. One could ask a number of questions to decide on a definition. For instance:
Can I still find out who made the definition? Was this definition made by an
academic expert or not, or is it unknown? Was it made a long time ago or not;
and is it still relevant to my aims? Is it defining the entire field or only a
small section? What is intended to be achieved with the definition? Do some people disagree with the definition;
why? Does this (part of the) definition aid me in understanding, thinking about
or building on the field of AI or does it rather give me a limiting view that
does not allow me to continue (a passion for) learning? Does the definition
help me initiate creativity, grow eagerness towards research, development and
innovation in or with the field of AI? Does this definition allow me to
understand one or other AI expert’s work better? If one’s answer is
satisfactory at that moment, then use the definition until proven inadequate.
When inadequate, reflect, adapt and move on.
With this approach in mind, the text here offers further 10 considerations and “definitions” on the concept of “Artificial Intelligence”. For sure, others and perhaps “better” ones can be identified or constructed.
#1 An AI Definition and its Issues.
The problem with many definitions of Artificial Intelligence (AI) is that they are riddled with what is called “suitcase words”. They are “…terms that carry a whole bunch of different meanings that come along even if we intend only one of them. Using such terms increases the risk of misinterpretations…”.[2] This term, “suitcase words”, was created by a world-famous computer scientist, who is considered one of the leading figures in the developments of AI technologies and the field itself: Professor MINSKY, Marvin.
#2 The Absence of a Unified Definition.
On the global stage or among all AI researchers combined, there is no official (unified) definition of what Artificial Intelligence is. It is perhaps better to state that the definition is continuously changing with every invention, discovery or innovation in the realm of Artificial Intelligence. It is also interesting to note that what was once seen as an application of AI is (by some) now no longer seen as such (and sometimes “simply” seen as statistics or as a computer program like any other). On the other end of the spectrum, there are those (mostly non-experts or those with narrowed commercial aims) who will identify almost any computerized process as an AI application.
#3 AI Definitions and its Attributes.
Perhaps a large number of researchers might agree that an AI method or application has been defined as “AI” due to the combination of the following 3 attributes:
it is made by humans or it is the result of a technological process that was originally created by humans,
it has the ability to operate autonomously (without the support of an operator; it has ‘agency’[3]) and
it has the ability to adapt (behaviors) to, and improve within changing contexts (i.e. changes in the environment); and this by means of a kind of technological process that could be understood as a process of “learning”. Such “learning” can occur in a number of ways. One way is to “learn” by trial-and-error or a “rote learning” (e.g. the storing in memory of a solution to a problem). A more complex way of applying “learning” is by means of “Generalization”. This means the system can “come up” with a solution, by generalizing some mathematical rule or set of rules from given examples (i.e. data), to a problem that was previously not yet encountered. The latter would be more supportive towards being adaptable in changing and uncertain environments.
#4 AI Definitions by Example.
Artificial Intelligence could, alternatively, also be defined by listing examples of its applications and methods. As such some might define AI by listing its methods (which are individual methods in the category of AI methods. Also see here below one of the listing of types and methods towards defining the AI framework): AI than, for instance, includes Machine Learning, Deep Learning and so on.
Others might define AI by means of its applications whereby AI is, for instance, a system that can “recognize”, locate or identify specific patterns or distinct objects in (extra-large, digital or digitized) data sets where such data sets could, for instance, be an image or a video of any objects (within a set), a set or string of (linguistic) sounds, be it prerecorded or in real-time, via a camera or other sensor. These objects could be a drawing, some handwriting, a bird sound, a photo of a butterfly, a person uttering a request, a vibration of a tectonic plate, and so on (note: the list is, literally, endless).
#5 AI Defined by referencing Human Thought.
Other definitions define AI as a technology that can “think” as the average humans do (yet, perhaps, with far more processing power and speed)… These would be “…machines with minds, in the full and literal sense… [such] AI clearly aims at genuine intelligence, not a fake imitation.”[4] Such a definition creates AI research and developments driven by “observations and hypothesis about human behavior”; as it is done in the empirical sciences.[5]. At the moment of this writing, the practical execution of this definition has not yet been achieved.
#6 AI Defined by Referencing Human Actions.
Further definitions of what AI is, do not necessarily focus on the aspect of ability of thought. Rather some definitions for AI focus on the act that can be performed by an AI technology. Then definitions are something like: an AI application is a technology that can act as the average humans can act or do things with perhaps far more power, strength, speed and without getting tired, bored, annoyed or hurt by features of the act or the context of the act (e.g. work inside a nuclear reactor). Rai Kurzweil, a famous futurist and inventor in technological areas such as AI, defined the field of AI as: “ The art of creating machines that perform functions that require intelligence when performed by people.”[6]
#7 Rational Thinking at the Core of AI Definitions.
Different from the 5th definition is that thought does not necessarily have to be defined through a human lens or anthropocentrically. As humans we tend to anthropomorphize some of our technologies (i.e. give a human-like shape, function, process, etc. to a technology). Though, AI does not need to take on a human-like form, function nor process; unless we want it to. In effect, an AI solution does not need to take on any corporal / physical form at all. An AI solution is not a robot; it could be embedded into a robot.
One could define the study of AI as a study of “mental faculties through the use of computational models.”[7] Another manner of defining the field in this way is stating that it is the study of the “computations that make it possible to perceive, reason and act.”[8] [9]
The idea of rational thought goes all the way back to Aristotle and his aim to formalize reasoning. This could be seen as a beginning of logic. This was adopted early on as one of the possible methods in AI research towards creating AI solutions. It is, however, difficult to implement. This is the case since not everything can be expressed in a formal logic notation and not everything is perfectly certain. Moreover, not all problems are practically solvable by logic principles, even if via such logic principles they might seem solved.[10]
#8 Rational Action at the Core of AI Definitions.
A system is rational if “it does the ‘right thing’, given what it knows.” Here, a ‘rational’ approach is an approach driven by mathematics and engineering. As such “Computational Intelligence is the study of the design of intelligent agents…”[11] To have ‘agency’ means to have the autonomous ability and to be enabled to act / do / communicate with the aim to perform a (collective) task.[12] Scientists, with this focus in the field of AI, research “intelligent behavior in artifacts”.[13]
Such AI solution that can function as a ‘rational agent’ applies a form of logic reasoning and would be an agent that can act according to given guidelines (i.e. input) yet do so autonomously, adapt to environmental changes, work towards a goal (i.e. output) with the best achievable results (i.e. outcome) over a duration of time and this in a given (changing) space influenced by uncertainties. The application of this definition would not always result in a useful AI application. Some complex situations would, for instance, be better to respond to with a reflex rather than with rational deliberation. Think about a hand on a hot stove…[14]
#9 Artificial Intelligence methods as goal-oriented agents.
Artificial Intelligence methods as goal-oriented agents. “Artificial Intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience and decision theory.”[15]
#10 AI Defined by Specific Research and Development Methods.
We can somewhat understand the possible meaning of the concept “AI” by looking at what some consider the different types or methods of AI, or the different future visions of such types of AI (in alphabetic order)[16]:
Activity Recognition
- a system that knows what you are doing and
acts accordingly. For instance: it senses that you carry many bags, so it
automatically opens the door for you (without you needing to verbalize the
need).
Affective Computing
- a system that can identify the emotion someone
showcases
Artificial Creativity
- A system that can output something that is
considered creative (e.g. a painting, a music composition, a written work, a
joke, etc.)
Artificial Immune System
- A system that functions in the likes of a
biological immune system or that mimics its processes of learning and
memorizing.
Artificial Life
- A system that models a living organism
Artificial Stupidity
- A system that adapts to the intellectual capacity
of the form (life form, human) it interacts with or to the needs in a given
context.
Automation
- The adaptable mechanical acts coordinated by a
system without the intervening of a human
Blockhead
- A “fake” AI that simulates intelligence by
referencing (vast) data repositories and regurgitating the information at the
appropriate time. This system however does not learn.
Bot
- A system that functions as a bodiless robot
ChatBot / ChatterBot
- A system that can communicate with humans via
text or speech giving the perception to the human (user) that it is itself also
human. Ideally it would pass the Turing test.
Committee Machine
- A system that combines the output from various
neural networks. This could create a large-scale system.
Computer Automated Design
- A system that can be put to use in areas of
creativity, design and architecture that allow and need automation and
calculation of complexities
Computer Vision
- A system that via visual data can identify
(specific) objects
Decision Support System
- A system that adapts to contextual changes and
supports human decision making
Deep Learning
- A system operating on a sub-type of Machine Learning methods (see a future blog post for more info)
Embodied Agent
- A system that operates in a physical or
simulated “body”
Ensemble Learning
- A system that applies many algorithms for
learning at once.
Evolutionary Algorithms
- A system that mimics biological evolutionary processes: birth, reproduction, mutation, decay, selection, death, etc. (see a future blog post for more info)
Friendly Artificial Intelligence
- A system that is void of existential risk to
humans (or other life forms)
Intelligence Amplification
- A system that increases human intelligence
Machine Learning
- A system of algorithms that learns from data sets and which is strikingly different from a traditional program (fixed by its code). (see a future blog post for more info)
Natural Language Processing
- A system that can identify, understand and create speech patterns in a given language. (see a future blog post for more info)
Neural Network
- A system that historically mimicked a brain ‘s structure and function (neurons in a network) though now are driven by statistical and signal processing. (see another of my blog post for more info here)
Neuro Fuzzy
- A system that applies a neural network to
operate in a or fuzzy logic as a non-linear logic, or a non-Boolean logic
(values between 0 or 1 and not only 0 or 1). It allows for further interpretation of vagueness and uncertainty
Recursive Self-Improvement
- A system that allows for software to write its
own code in cycles of self-improvement.
Self-replicating Systems
- A system that can copy itself (hardware and or
software copies). This is researched for (interstellar) space exploration.
Sentiment Analysis
- A system that can identify emotions and
attitudes imbedded into human media (e.g. text)
Strong Artificial Intelligence
- A system that has a general intelligence as a human does. This is also referred to as AGI or Artificial General Intelligence. This does not yet exist and might, if we continue to pursuit it, take decades to come to fruition. When it does it might start a recursive self-improvement and autonomous reprogramming, creating an exponential expansion in intelligence well beyond the confines of human understanding. (see a future blog post for more info)
Superhuman
- A system that can do something far better than
humans can
Swarm Intelligence
- A system that can operate across a large
number of individual (hardware) units and organizes them to function as a
collective
Symbolic Artificial Intelligence
- An approach used between 1950 and 1980 that
limits computations to the manipulation of a defined set of symbols, resembling
a language of logic.
Technological Singularity
- A hypothetical system of super-intelligence
and rapid self-improvement out of the control and beyond the understanding of
any human.
Weak Artificial Intelligence
- A practical system of singular or narrow applications, highly focused on a problem that needs a solution via learning from given and existing data sets. This is also referred to as ANI or Artificial Narrow Intelligence.
Mini
Project #___ : An
Application of a Definition
Do you know any program or technological system that (already) fits this 5th definition?
How would you try to know whether or not it does?
Mini Project #___: Some Common Definitions of Ai with Examples
Team work + Q&A:
What is your team’s definition of AI?
What seems to be the most accepted definition in your daily-life community and in a community of AI experts closest to you?
Reading + Q&A:: Go through some popular and less popular definitions with examples
Discussion: which definition of AI feels more acceptable to your team; why? Which definition seems less acceptable to you and your team? Why? Has your personal and first definition of Ai changed? How?
Objectives: The learner can bring together the history, context, types and meaning of AI into a number of coherent definitions.
[1] Krohn, J., et al.(2019, p.102) the
importance of context in meaning-giving; NLP through Machine Learning and Deep
Learning techniques
[2] Retrieved from Ville Valtonen at Reaktor and
Professor Teemu Roos at the University of Helsinki’s “Elements of AI”, https://www.elementsofai.com/ , on December 12, 2019
[3] agent’ is
from Latin ‘agere’ which means ‘to manage’, ‘to drive’, ‘to conduct’, ‘to do’. To
have ‘agency’ means to have the autonomous ability and to be enabled to act /
do / communicate with the aim to perform a (collective) task.
[4] Haugeland, J. (Ed.). (1985). Artificial Intelligence: The Very Idea. Cambridge, MA: The MIT Press. p. 2 and footnote
#1.
[5] Russell, S. and Peter Norvig. (2016). Artificial Intelligence: A Modern Approach. Third Edition. Essex: Pearson Education. p.2
[6] Russell. (2016). pp.2
[7] Winston, P.
H. (1992). Artificial Intelligence (Third edition). Addison-Wesley.
[8] These are two definitions respectively from
Charniak & McDermott (1985) and Winston (1992) as quoted in Russel, S. and
Peter Norvig (2016).
[9] Charniak, E. and McDermott, D. (1985). Introduction to Artificial Intelligence. Addison-Wesley
[10] Russell (2016). pp.4
[11] Poole, D., Mackworth, A. K., and Goebel, R.
(1998). Computational intelligence: A logical
approach. Oxford University Press
[12]
‘agent’ is from Latin ‘agere’ which means ‘to manage’, ‘to drive’, ‘to
conduct’, ‘to do’
[13] Russell. (2016). pp.2
[14] Russell (2016). pp.4
[15] Maini, V. (Aug 19, 2017). Machine Learning for Humans. Online: Medium.com. Retrieved November 2019 from e-Book https://www.dropbox.com/s/e38nil1dnl7481q/machine_learning.pdf?dl=0 or https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 https://www.dropbox.com/s/e38nil1dnl7481q/machine_learning.pdf?dl=0
[16] Spacey, J. (2016, March 30). 33 Types of Artificial Intelligence. Retrieved from https://simplicable.com/new/types-of-artificial-intelligence on February 10, 2020
Header image caption, credits & licensing:
Depicts the node connections of an artificial neural network
LearnDataSci / CC BY-SA (https://creativecommons.org/licenses/by-sa/4.0)
source: https://www.learndatasci.com/
retrieved on May 6, 2020 from here
