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| public:t_720_atai:atai-18:lecture_notes_ai_architectures [2018/10/06 16:40] – thorisson | public:t_720_atai:atai-18:lecture_notes_ai_architectures [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Complex | Grossly modular architecture (< 30 modules) with multiple relationships at more than one level of control detail (LoC). \\ Examples: Speech-controlled dialogue systems like Siri and Alexa. | | | Complex | Grossly modular architecture (< 30 modules) with multiple relationships at more than one level of control detail (LoC). \\ Examples: Speech-controlled dialogue systems like Siri and Alexa. | |
| | Super-Complex | Large number of modules (> 30) at various sizes, each with multiple relationships to others, at more than one LoC. \\ Example: Subsumption architecture. | | | Super-Complex | Large number of modules (> 30) at various sizes, each with multiple relationships to others, at more than one LoC. \\ Example: Subsumption architecture. | |
| | Bottom Line | Most architectures are of this kind - towards the higher end of this spectrum very few exists. \\ What about complex control systems for power plants, manufacturing plants, etc - don't they contain an equal level of complexity (or greater) than the complex or super-complex end of this spectrum? Answer is: Perhaps so (depending on how it's measured), but bear in mind that none of them learn or adapt (except in some extremely trivial ways that are highly localized and specific). \\ Relying exclusively on constructionist methodologies, it is difficult to put learning into architectures at the high end of this spectrum, and when it's done it's difficult to keep the side effects securely within safe limits. | | | \\ \\ Bottom Line | Most architectures are of this kind - towards the higher end of this spectrum very few exists. \\ What about complex control systems for power plants, manufacturing plants, etc - don't they contain an equal level of complexity (or greater) than the complex or super-complex end of this spectrum? Answer is: Perhaps so (depending on how it's measured), but bear in mind that none of them learn or adapt (except in some extremely trivial ways that are highly localized and specific). \\ Relying exclusively on constructionist methodologies, it is difficult to put learning into architectures at the high end of this spectrum, and when it's done it's difficult to keep the side effects securely within safe limits. | |
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| | | \\ Transversal Self-Inspection | Reflectivity, as it is known, is a fundamental property of knowledge representation. The fact that we humans can talk about the stuff that we think about, and can talk about the fact that we talk about the fact that we can talk about it, is a strong implication that reflectivity is a key property of AGI systems. \\ Reflectivity must be part of the architecture from the beginning - retrofitting this ability into any architecture is virtually impossible! | | | | \\ Transversal Self-Inspection | Reflectivity, as it is known, is a fundamental property of knowledge representation. The fact that we humans can talk about the stuff that we think about, and can talk about the fact that we talk about the fact that we can talk about it, is a strong implication that reflectivity is a key property of AGI systems. \\ Reflectivity must be part of the architecture from the beginning - retrofitting this ability into any architecture is virtually impossible! | |
| | | Transversal Integration | A general-purpose system must tightly and finely coordinate a host of skills, including their acquisition, transitions between skills at runtime, how to combine two or more skills, and transfer of learning between them over time at many levels of temporal and topical detail. | | | | Transversal Integration | A general-purpose system must tightly and finely coordinate a host of skills, including their acquisition, transitions between skills at runtime, how to combine two or more skills, and transfer of learning between them over time at many levels of temporal and topical detail. | |
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| | ====High-Level View of AERA==== |
| | | AERA | The Auto-Catalytic Endogenous Reflective Architecture is an AGI-aspiring self-programming system that combines feedback and feed-forward control in a model-based and model-driven system that is programmed with a seed. | |
| | | {{/public:t-720-atai:aera-high-level-2018.png?700}} || |
| | | High-level view of the three main functions at work in a running AERA system and their interaction with its knowledge store. || |
| | | \\ Models | All models are stored in a central //memory//, and the three processes of //planning//, //attention// (resource management) and //learning// happen as a result of programs that operate on models by matching, activating, and scoring them. Models that predict correctly -- not just "what happens next?" but also "what will happen if I do X?" -- get a success point. Every time a model 'fires' like that it gets counted, so the ratio of success over counts gives you the "goodness" of a model. \\ Models that have the lowest scores are deleted, models with a good score that suddenly fail result in the generation of new versions of itself (think of it as hypotheses for why it failed this time), and this process over time increases the quality and utility of the knowledge of the controller, in other words it //learns//. | |
| | | \\ Attention | Attention is nothing more than resource management, in the case of cognitive controllers it typically involves management of knowledge, time, energy, and computing power. Attention in AERA is the set of functions that decides how the controller uses its compute time, how long it "mulls things over", and how far into the future it allows itself to "think". It also involves which models the system works with at any point in time, how much it explores models outside of the obvious candidate set at any point in time. | |
| | | \\ Planning | Planning is the set of operations involved with looking at alternative ways of proceeding, based on predictions into the future and the quality of the solutions found so far, at any point in time. The plans produced by AERA are of a mixed opportunistic (short time horizon)/firm commitment (long time horizon) kind, and their stability (subject to change drastically over their course) depend solely on the dependability of the models involved -- i.e. how well the models represent what is actually going on in the world (including the controllers "mind"). | |
| | | Learning | Learning happens as a result of the accumulation of models; as they increasingly describe "reality" better (i.e. their target phenomenon) they get better for planning and attention, which in turn improves the learning. | |
| | | Memory | AREA's "global knowledge base" is in some ways similar to the idea of blackboards: AERA stores all its knowledge in a "global workspace" or memory. Unlike (Selfridge's idea of) blackboards, the blackboard contains executive functions that manage the knowledge dynamically, in addition to "the experts", which in AERA's case are very tiny and better thought of as "models with codelet helpers". | |
| | | Pervasive Use of Codelets | A //codelet// is a piece of code that is smaller than a typical self-contained program, typically a few lines long, and can only be executed in particular contexts. Programs are constructed on the fly by the operation of the whole system selecting which codelets to run when, based on the knowledge of the system, the active goals, and the state it finds itself in at any point in time. | |
| | | \\ No "Modules" | Note that the diagram above may imply the false impression that AERA consists of these four software "modules", or "classes", or the like. Nothing could be further from the truth: All of AERA's mechanism above are a set of functions that are "welded in with" the operation of the whole system, distributed in a myriad of mechanisms and actions. \\ Does this mean that AERA is spaghetti code, or a mess of a design? On the contrary, the integration and overlap of various mechanisms to achieve the high-level functions depicted in the diagram are surprisingly clean, simple, and coherent in their implementation and operation. \\ This does not mean, however, that AERA is easy to understand -- mainly because it uses concepts and implements mechanisms and relies on concepts that are //very different// from most traditional software systems commonly recognized in computer science. | |
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| | ====Autonomous Model Acquisition==== |
| | | What it is | The ability to create a model of some target phenomenon //automatically//. | |
| | | Challenge | Unless we know beforehand which signals cause perturbations in <m>o</m> and can hard-wire these from the get-go in the controller, the controller must search for these signals. \\ In task-domains where the number of available signals is vastly greater than the controller's resources available to do such search, it may take an unacceptable time for the controller to find good predictive variables to create models with. \\ <m>V_te >> V_mem</m>, where the former is the total number of potentially observable and manipulatable variables in the task-environment and the latter is the number of variables that the agent can hold in its memory at any point in time. | |
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| | ====Model Acquisition Function==== |
| | | {{public:t-720-atai:agent-with-model-gen-function1.png?300}} || |
| | | The agent has a model generation function <m>P_M</m> implemented in its controller. The role of the function is to take observed chains of events and produce models intended to capture the events' causal relationships. || |
| | | {{public:t-720-atai:causal-chain_agent1.png?400}} || |
| | | A learning agent is situated so as to perceive the effects of the relationships between variables. \\ The agent observes the interaction between the variables for a while, rendering some data about their relations (but not enough to be certain about it, and certainly not enough to create a complete model of it). \\ This generates hypotheses about the relation between variables, in the form of candidate relational models of the observed events. || |
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| | ==== Model Generation & Evaluation ==== |
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| | | {{public:t-720-atai:three-models-1.png?400}} | |
| | | Based on prior observations, of the variables and their temporal execution in some context, the controller's model generation function <m>P_M</m> may have captured their causal relationship in three alternative models, <m>M_1, M_2, M_3</m>, each slightly but measurably different from the others. Each can be considered a //hypothesis of the actual relationship between the included variables//, when in the context provided by <m>V_5, V_6</m>. | |
| | | {{public:t-720-atai:agent-with-models-1.png?300}} | |
| | | The agent's model generation mechanisms allow it to produce models of events it sees. Here it creates models (a) <m>M_1</m> and (b) <m>M_2</m>. The usefulness / utility of these models can be tested by performing an operation on the world (c ) as prescribed by the models. (Ideally, when one wants to find on which one is best, the most efficient method is an (energy-preserving) intervention that can only leave one as the winner.) | |
| | | {{public:t-720-atai:model-m2-prime-1.png?150}} | |
| | | The result of feedback (reinforcement) may result in the deletion, rewriting, or some other modification of the original model selected for prediction. Here the feedback has resulted in a modified model <m>M{prime}_2</m>. | |
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| | ====Demo Of AERA In Action==== |
| | | Demos | The most complex demo of an AERA system was the S1 agent learning to do an interview (in the EU-funded HUMANOBS research project). [[http://www.mindmakers.org/projects/humanobs/wiki/HUMANOBS_Videos|Main HUMANOBS page]] | |
| | | TV Interview | In the style of a TV interview, the agent S1 watched two humans engaged in a "TV-style" interview about the recycling of six everyday objects made out of various materials. | |
| | | Data | S1 received realtime timestamped data from the 3D movement of the humans (digitized via appropriate tracking methods at 20 Hz), words generated by a speech recognizer, and prosody (fundamental pitch of voice at 60 Hz, along with timestamped starts and stops). | |
| | | Seed | The seed consisted of a handful of top-level goals for each agent in the interview (interviewer and interviewee), and a small knowledge base about entities in the scene. | |
| | | What Was Given | * actions: grab, release, point-at, look-at (defined as event types constrained by geometric relationships) \\ * stopping the interview clock ends the session \\ * objects: glass-bottle, plastic-bottle, cardboard-box, wodden-cube, newspaper, wooden-cube \\ * objects have properties (e.g. made-of) \\ * interviewee-role \\ * interviewer-role \\ * Model for interviewer \\ * top-level goal of interviewer: prompt interviewee to communicate \\ * in interruption case: an imposed interview duration time limit \\ * Models for interviewee \\ * top-level goal of interviewee: to communicate \\ * never communicate unless prompted \\ * communicate about properties of objects being asked about, for as long as there still are properties available \\ * don’t communicate about properties that have already been mentioned | |
| | | What Had To Be Learned | GENERAL INTERVIEW PRINCIPLES \\ * word order in sentences (with no a-priori grammar) \\ * disambiguation via co-verbal deictic references \\ * role of interviewer and interviewee \\ * interview involves serialization of joint actions (a series of Qs and As by each participant) \\ \\ MULTIMODAL COORDINATION & JOINT ACTION \\ * take turns speaking \\ * co-verbal deictic reference \\ * manipulation as deictic reference \\ * looking as deictic reference \\ * pointing as deictic reference \\ \\ INTERVIEWER \\ * to ask a series of questions, not repeating questions about objects already addressed \\ * “thank you” stops the interview clock \\ * interruption condition: using “hold on, let’s go to the next question” can be used to keep interview within time limits \\ \\ INTERVIEWEE \\ * what to answer based on what is asked \\ * an object property is not spoken of if it is not asked for \\ * a silence from the interviewer means “go on” \\ * a nod from the interviewer means “go on” | |
| | | Result | After having observed two humans interact in a simulated TV interview for some time, the AERA agent S1 takes the role of interviewee, continuing the interview in precisely the same fasion as before, answering the questions of the human interviewer (see videos HH.no_interrupt.mp4 and HH.no_interrupt.mp4 for the human-human interaction that S1 observed; see HM.no_interrupt_mp4 and HM_interrupt_mp4 for other examples of the skills that S1 has acquired by observation). In the "interrupt" scenario S1 has learned to use interruption as a method to keep the interview from going over a pre-defined time limit. \\ \\ The results are recorded in a set of three videos: \\ [[https://www.youtube.com/watch?v=SH6tQ4fgWA4|Human-human interaction]] (what S1 observes) \\ [[https://www.youtube.com/watch?v=SH6tQ4fgWA4|Human-S1 interaction]] (S1 interviewing a human) \\ [[https://www.youtube.com/watch?v=x96HXLPLORg|S1-Human Interaction]] (S1 being interviewed by a human) | |
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