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I came across several papers by M. Hutter & S. Legg. Especially this one: Universal Intelligence: A Definition of Machine Intelligence, Shane Legg, Marcus Hutter

Given that it was published back in 2007, how much recognition or agreement has it received? Has any other work better formalizing the idea of intelligence been done since? What is considered current standard on the topic in the field?

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    $\begingroup$ Hutter and Legg's definition of intelligence is based on Hutter's AIXI framework, and it's an optimization-based definition: basically, intelligence is a measure of the capability of an agent to optimize with respect to a "wide range of" environments. I don't think that everyone agrees with this definition (for some reason: maybe because not all people like the optizimization-based idea or the part "wide range of environments"), otherwise, my answer here would have more upvotes, but I haven't found any "better" definition of intelligence so far. $\endgroup$
    – nbro
    Mar 23 at 9:37
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    $\begingroup$ And I highly recommend everyone read nbro's blog post on the subject "On the definition of intelligence" $\endgroup$
    – DukeZhou
    May 27 at 1:53
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My sense is that everyone is pretending Intelligence doesn't have a grounded definition, from which all other definitions arise:

  • Intelligence is a measure of utility in an action space μ(υ)

It can be a relative measure, in relation to other rational agents, or absolute in relation to solved games (problems). An action space is any context, and formalized as problems or sets of problems, typically grouped by complexity class.

Hutter and Legg is an explication of this grounded definition which accounts for unlimited contexts (complexity classes/environments) and for increasing utility of a given agent over time (learning/optimization.) Intelligence itself does not require learning or general applicability, but Hutter & Legg does not refute this, merely grades static intelligence and narrow intelligence as more limited.

Even this is subject to context, as more limited rationality can be more optimal.

  • The definition of intelligence is grounded because, while the term "intelligence" is a symbol, intelligence itself is function, the strength of which evaluated by a measurement of some result (utility)

It doesn't require defining the function to understand it as a function: measurement of a result requires mechanism and decision/action.

You will find this natural language definition applies to even emotional intelligence, which relates to the observational capability of the rational agent in context, and allow that rational agent to make more optimal decisions in context.

This is similar in spirit to truth, only grounded in a formal logic context, where it is a condition and result, not an assertion. By contrast, the the conditionality of truth is often obscured in a natural language context, where is is routinely applied to unvalidatable informal statements, and can even be conflated with the statement itself à la: "this is the truth!"

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Just a few commonsensical remarks about why this kind of intelligence definition seems unable to capture the logic of life:

  1. Optimization only makes sense in a stationary environment. When many agents learn and interact, they are building a constantly changing environment.

  2. Survival and reproduction is the only thing that really matters, and it does not require optimization, just good enough solutions.

  3. The survival of individual living organisms heavily depends on adequate hardwired sensory abilities that can slowly change throughout many generations. But smarter individuals can use their brains (or whatever tools they may have endowed with plasticity) to quickly learn and adapt in non-stationary environments. Fast learning, not optimization, is what these agents really need.

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  • $\begingroup$ Welcome to SE:AI! I think Herbert Simon nailed it with satisficing $\endgroup$
    – DukeZhou
    May 27 at 1:50

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