This is an AI related post on the nature and philosophy of intelligence. In the various fields that study the mind, human or otherwise, there are many definitions (and lack of) for the term 'intelligence'. What is it, how can we measure it, how can we reproduce it? What implications does this have in the fields of AI, machine learning, and data science? A paper [1] by Shane Legg and Marcus Hutter, attempted to survey the definition from these various fields. The following are some sample definitions they collected.
Collective definitions:
“The ability to use memory, knowledge, experience, understanding, reasoning, imagination and judgment in order to solve problems and adapt to new situations.” AllWords Dictionary, 2006
“The capacity to acquire and apply knowledge.” The American Heritage Dictionary, fourth edition, 2000
Psychologist definitions:
“The facet of mind underlying our capacity to think, to solve novel problems, to reason and to have knowledge of the world.” M. Anderson
“Sensation, perception, association, memory, imagination, discrimination, judgement and reasoning.” N. E. Haggerty
AI researcher definitions:
“Achieving complex goals in complex environments” B. Goertzel
“Intelligence is the ability to use optimally limited resources – including time– to achieve goals.” R. Kurzweil
“The ability to solve hard problems.” M. Minsky
Definition by S. Legg and M. Hutter:
“Intelligence measures an agent’s ability to achieve goals in a wide range of environments”
Here is also a definition from the Wikipedia article on an Intelligent Agent:
“An intelligent agent is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals.”
From the given definitions it is clear that intelligence has certain characteristics:
It is an aspect arising from an agent.
The agent exists in an uncertain environment.
The agent can perceive this environment (has percepts).
The agent can take actions in this environment (has actuators).
The agent has some means of computation for decision making, ie think. It has the ability to input information from it’s percepts, process this information to make a decision, and output information to take an action.
The agents actions are goal directed (very important).
The agent can measure how well it is meeting its goal.
Depending on the goals to be achieved by an agent, intelligence can be fairly simple or very complex. Human level intelligence for instance is extremely difficult to measure and define. People certainly have a number of processing abilities including learning, reasoning, adapting online, ability to self analyze, able to process large amounts of data, short and long term memory, etc.
However, based on the above-aggregated definitions, an intelligence can be any black box which has a defined goal, the ability to input percepts, process the percepts (perform a calculation), and output an action related to the goal. For instance this can be a reflex agent and be something as simple as a computer function based on condition-action rules such as using if-else. It can even be more simpler and be a function that takes two variables, x and y, which are its percepts, adds them which is its calculation, and outputs the result which is its goal directed action. Though this would be as low level of an intelligence as possible.
There is currently no universal measure for intelligence (say between a calculator, an ant, and a human) since intelligence is largely based purely on how well a certain goal can be achieved. Thus intelligence, as so far as I understand, can be only applied as a relativistic measure to agents that have similar goals and problem solving capabilities.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed https://www.datasciencecentral.com/profiles/blogs/what-is-intelligence
