public:t-709-aies-2024:aies-2024:next-gen-ai
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Table of Contents
T-720-AIES-2024 Main
Links to Lecture Notes
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
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| “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 |
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