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Panel discussion with authors of papers about &#39;Learning, Reasoning, and Resource Allocation&#39; at AGI-20 conference. Held live on Underline, 06/24/2020

AGI-20

Panel Session 3: Learning, Reasoning, and Resource Allocation

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

Underline hosted the two main days of AGI 2020 on Wednesday June 24th and Thursday June 25th. Both days were **live-streamed** and you can read the full program bellow. All presentations are available for a **asynchronous viewing**. All presenters are grouped into panels. You can also hear guest talks by **Ben Goertzel** (SingularityNET), **Valery Tarasov** (Semenov Institute of Chemical Physics), **Tatiana Shavrina** (Sberbank) and **Joscha Bach** (MIT Media Lab). 

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

Panel discussion with authors of papers about 'Learning, Reasoning, and Resource Allocation' at AGI-20 conference. Held live on Underline, 06/24/2020

technical paper

A recently developed Functional Modeling Framework suggests that all models of cognition can be represented by a minimally reducible set of functions, and proposes to define the criteria for a model of cognition to have the potential for the general problem solving ability commonly recognized as true human intelligence. This human-centric functional modeling approach is intended to enable different models of AGI to be more easily compared so research can reliably converge on a single understanding, enabling the possibility of massively collaborative interdisciplinary projects to research and implement models of consciousness or cognition where difficulty in communicating very different ideas, particularly in the case of new models without a significant following, has prevented such massive collaboration from in practice having proved possible before. This paper summarizes a model of cognition developed within this framework.

A Model of Artificial General Intelligence

Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a presentation on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and so on forever: that any sequence of organisms (each one a child of the previous) must contain occasional multi-parent organisms, or must terminate. By proving that a certain measure (arguably an intelligence measure) decreases when an idealized parent AGI single-handedly creates a child AGI, we argue that a similar Law holds for AGIs.

AGI and the Knight-Darwin Law

"While it is still unclear if agents with Artificial General
Intelligence (AGI) could ever be built, we can already use
mathematical models to investigate potential safety systems for these
agents. We present work on an AGI safety layer that creates a special
dedicated input terminal to support the iterative improvement of an
AGI agent's utility function. The humans who switched on the agent
can use this terminal to close any loopholes that are discovered in
the utility function's encoding of agent goals and constraints, to
direct the agent towards new goals, or to force the agent to switch
itself off.

An AGI agent may develop the emergent incentive to manipulate the
above utility function improvement process, for example by deceiving,
restraining, or even attacking the humans involved. The safety layer
will partially, and sometimes fully, suppress this dangerous
incentive.

This paper generalizes earlier work on AGI emergency stop buttons. We
aim to make the mathematical methods used to construct the layer more
accessible, by applying them to an MDP model. We discuss two provable
properties of the safety layer, identify still-open issues, and
present ongoing work to map the layer to a Causal Influence Diagram
(CID)."

Towards AGI Agent Safety by Iteratively Improving the Utility Function

It is often useful for one agent to predict what another agent will believe after receiving new information. In fact, in order to appear intelligent in situations involving multiple interacting agents, we fundamentally need to be able to predict how changes in the world will affect the beliefs of others. This process involves two distinct processes. First, we need to devise a model that captures the way that beliefs change in response to new information. Second, we need to observe the behaviour of individual agents to determine their specific beliefs. In the AI literature, these problems have been addressed by distinct communities. In this paper, we bring these two communities together by demonstrating how an agent can learn a model of belief change from observed behaviour. We argue that this process is essential for natural interaction in an AGI setting, but it has not been addressed to date in a unified manner.

Learning to Model Another Agent's Beliefs: A Preliminary Approach

The paper proposes the architecture of dynamically changing hierarchical memory based on compartmental spiking neuron model. The aim of the study is to create biologically-inspired memory models suitable for implementing the processes of features memorizing and high-level concepts. The presented architecture allows us to describe the bidirectional hierarchical structure of associative concepts related both in terms of generality and in terms of part-whole, with the ability to restore information both in the direction of generalization and in the direction of decomposition of the general concept into its component parts. A feature of the implementation is the use of a compartmental neuron model, which allows the use of a neuron to memorize objects by adding new sections of the dendritic tree. This opens the possibility of creating neural structures that are adaptive to significant changes in the environment.

The hierarchical memory based on compartmental spiking neuron model

Artificial intelligence becomes an integral part of human life. At the
same time, modern widely used approaches, which work successfully due to the availability of enormous computing power, based on ideas about the work of the brain, suggested more than half a century ago. The proposed model describes the general principles of information processing by the human brain, taking into account the latest achievements. The neuroscientific grounding of this model and
its applicability in the creation of AGI or Strong AI are discussed in the article.
In this model, the cortical minicolumn is the primary computing processor that works with the semantic description of information. The minicolumn transforms incoming information into its interpretation to a specific context. In this way, a parallel verification of hypotheses of information interpretations is provided when comparing them with information in the memory of each minicolumn of the cortical zone, and, at the same time, determining a significant context is the information transformation rule. The meaning of information is defined as an interpretation that is close to the information available in the memory of a minicolumn. The behavior is a result of modeling of possible situations. Using this approach will allow creating a strong AI or AGI.

The meaning of things as a concept of strong AI architecture

This paper introduces the open-source software project, Brain Simula-tor II, simplifying experimentation into various facets of AGI. The software seamlessly marries spiking neural networks with symbolic AI algorithms. It sup-ports a large array of simple neurons (of various models) and groups of neurons collected into “Modules”, backed by custom software. 3D and 2D simulators al-low a virtual entity to move about, have binocular vision and touch, and merge this information with spoken input. Information is captured in a Universal Knowledge Store module which represents information in links between nodes. Continuing development will enhance these capabilities.

New Brain Simulator II Open-Source Software

This paper compares the various conceptions of “real-time” in the context of AI, as different ways of taking the processing time into consideration when problems are solved. An architecture of real-time reasoning and learning is introduced, which is one aspect of the AGI system NARS. The basic idea is to form problem-solving processes flexibly and dynamically at run time by using inference rules as building blocks and incrementally self-organizing the system’s beliefs and skills, under the restriction of time requirements of the tasks. NARS is designed under the Assumption of Insufficient Knowledge and Resources, which leads to an inherent ability to deal with varying situations in a timely manner.

An Architecture for Real-time Reasoning and Learning

Even with the relative ascendancy of sub-symbolic approaches to AI, the symbol grounding problem presents an ongoing challenge to work in artificial general intelligence. Prevailing ontology design practices typically presuppose the transparency of the relation between semantics and syntax by transcendently stipulating it extrinsic to the system, rather than providing a platform for the internal development of this relation through agents’ interactions endogenous to the system. Drawing on theoretical resources from ecological psychology, dynamical systems theory, and interactive computation, this work suggests an inversion of the symbol grounding problem in order to analyze how the symbolic regime can emerge out of causally embedded dynamical interactions within a system of autonomous intelligent agents. Under this view, syntactic-symbols come to be stabilized from other signs as constraints harnessing the dynamics of agents’ inter-actions, where the functional effects generated by such constrained dynamics give rise to an internal characterization of semantics broadly aligned with Brandom’s semantic pragmatism. Finally, ludics—a protological framework based on interactive computation—provides a formal model to concretely describe this continuity between syntax and semantics arising within and through the regulative dynamics of interactions in a multi-agent system. Accordingly, this bottom-up approach to grounding the symbolic order in dynamics could provide the conditions for artificial agents to engage in autonomous language design, thus equipping themselves with a powerful cognitive technology for intersubjective coordination and (re)structuring ontologies within a community of agents.

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