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NEXT-GENERATION AI: Cause-Effect Knowledge, Cumulative Learning, Empirical Reasoning, Reflection, Autonomy, Trustworthiness

Autonomy

What It Is	Autonomy is a key feature of intelligence - the ability of a system to “act on its own”. Autonomous-X is anything that “autonomy” is relevant for or applies to in a system's operation.
Self-Inspection	Virtually no systems exist as of yet that has been demonstrated to be able to inspect (measure, quantify, compare, track, make use of) their own development for use in its continued growth - whether learning, goal-generation, selection of variables, resource usage, or other self-X.
Self-Growth	No System as of yet has been demonstrated to be able to autonomously manage its own self-growth. Self-Growth is necessary for autonomous learning in task-environments with complexities far higher than the controller operating in it. It is even more important where certain bootstrapping thresholds are necessary before safe transition into more powerful/different learning schemes. For instance, if only a few bits of knowledge can be programmed into a controller's seed (“DNA”), because we want it to have maximal flexibility in what it can learn, then we want to put something there that is essential to protect the controller while it develops more sophisticated learning. An example is that nature programmed human babies with an innate fear of heights.
Why It Is Important	This table exists to highlight some really key features of autonomy that any human-level intelligence probably must have. We say “probably” because, since we don't have any yet, and because there is no proper theory of intelligence, we cannot be sure.
Autonomous Learning	We already have machines that learn autonomously, although most of the available methods are limited in that they (a) rely heavily on quality selection of learning material/environments, (b) require careful setup of training, and (c ) careful and detailed specifications of how progress is evaluated.

Three Levels of Autonomy

Category	Description	Uniqueness	Examples	Learning
Level 1: Automation	The lowest level may be called “mechanical”.	Fixed architecture. Baked-in goals. Does its job. Does not create.	Watt's Governor. Thermostats. DNNs.	No “learning” AILL (after it leaves the lab).
Level 1.5: Reinforcement learning	Can change their function at runtime. Cannot accept goal description. Cannot handle unspecified variables. Cannot create sub-goals autonomously.	“Learns” through piecewise Boolean (good/bad) feedback.	Q-learning.	Limited to a handful of predefined variables
Level 2: Cognitive	Handling of novelty. Figures things out. Accepts goal description. Generates goal descriptions. Creates.	Flexible representation of self. High degree of self-modification.	Humans. Parrots. Dogs.	Learns AILL.
Level 3: Biological	Adapts.	Is alive. Subject to evolution. Necessary precursor to lower levels.	Living creatures.	Adapts AILPS (after it leaves the primordial soup).
Source	Thorisson 2020

What is Needed for Cognitive Autonomy

Selection	Autonomous selection of variables. Very few if any existing learning methods can decide for themselves whether, from a set of variables with potential relevance for its learning, any one of them (a) is relevant, (b) and if so how much, and (c ) in what way. Autonomous selection of processes. Very few if any existing learning methods decide what kind of learning algorithms to employ (learning to learn).
Goal-Generation	Very few if any existing learning methods can generate their own (sub-) goals. Of those that might be said to be able to, none can do so freely for any topic or domain.
Control of Resources	By “resources” we mean computing power (think time), time, and energy, at the very least. Few if any existing learning methods are any good at (a) controlling their resource use, (b) planning for it, (c ) assessing it, or (d) explaining it.
Novelty	To handle novelty autonomously a system needs autonomous hypothesis creation related to variables, relations, and transfer functions.

Four Dimensions of Control System Autonomy

“Autonomy comparison framework focusing on mental capabilities.
Embodiment is not part of the present framework, but is included here for contextual completeness.”
From Thorisson & Helgason 2012 source

Cognitive Growth

What it is	Changes in the cognitive controller (the core “thinking” part) over and beyond basic learning: After a growth burst of this kind the controller can learn differently/better/new things, especially new categories of things.
Human example	Piaget's Stages of Development (youtube video)

Reasoning Types

Deduction	Figuring out the implication of facts (or predicting what may come). General → Specific. Producing implications from premises. The premises are given; the work involves everything else. Conclusion is unavoidable given the premises (in a deterministic, axiomatic world).
Abduction	Figuring out how things came to be the way they are (or how particular outcomes could be made to come about, or how particular outcomes could be prevented). The outcome is given; the work involves everything else. Sherlock Holmes is a genius abducer.
Induction	Figuring out the general case. Specific → General. Making general rules from a (small) set of examples, e.g. 'the sun has risen in the east every morning up until now, hence, the sun will also rise in the east tomorrow.
Analogy	Figuring out how things are similar or different. Making inferences about how something X may be (or is) through a comparison to something else Y, where X and Y share some observed properties.

Considerations for Empirical Reasoning

Why Empirical?	The concept 'empirical' refers to the physical world: We (humans) live in a physical world, which is to some extent governed by rules, some of which we know something about.
Why Reasoning?	For interpreting, managing, understanding, creating and changing rules, logic-governed operations are highly efficient and effective. We call such operations 'reasoning'. Since we want to make machines that can operate more autonomously (e.g. in the physical world), reasoning skills is one of those features that such systems should be provided with.
Why Empirical Reasoning?	The physical world is uncertain because we only know part of the rules that govern it. Even where we have good rules, like the fact that heavy things fall down, applying such rules is a challenge, especially when faced with the passage of time. The term 'empirical' refers to the fact that the reasoning needed for intelligent agents in the physical world are - at all times - subject to limitations in energy, time, space and knowledge (also called the “assumption of insufficient knowledge and resources (AIKR)” by AI researcher Pei Wang).
Trustworthy Reasoning

Trustworthiness

What It Is	The ability of a machine's owner to trust that the machine will do what it is supposed to do.
Why It Is Important	Any machine created by humans is created for a purpose. The more reliably it does its job (and nothing else) the more trustworthy it is. Trusting simple machines like thermostats involves mostly durability, since they have very few open variables (unbound variables at time of manufacture).
Human-Level AI	To make human-level AI trustworthy is very different from creating simple machines because so many variables are unbound at manufacture time. What does trustworthiness mean in this context? We can look at human trustworthiness: Numerous methods exist for ensuring trustworthiness (license to drive, air traffic controller training, certification programs, etc.). We can have the same certification programs for all humans because their principles of operation are shared at multiple levels of detail (biology, sociology, psychology). For an AI this is different because the variability in the makeup of the machines is enormous. This makes trustworthiness of AI robots a complex issue.
To Achieve Trustworthiness	Requires reliability, and predictability at multiple levels of operation. Trustworthiness can be ascertained through special certification programs geared directly at the kind of robot/AI system in question (kind of like certifying a particular horse as safe for a particular circumstance and purpose, e.g. horseback riding kids).
Trustworthiness Methods	For AI are in their infancy.

Center for Analysis and Design of Intelligent Agents

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