Russian Federation

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff&#39;s Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an ``AI kernel&#39;&#39; for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages, which are briefly introduced. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, and intelligent agents.

AGI-20

Omega: An Architecture for AI Unification

openended

geneticalgorithms

machinelearning

induction

solomonoff

architecture

deeplearning

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.

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an ``AI kernel'' for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages, which are briefly introduced. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, and intelligent agents.

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

From Psychonics to Artificial General Intelligence and Hybrid Intelligence

Texts as an unlimited, but indirect trace of human thinking processes are the richest source for AI modelling that we have. How to handle it and where does SuperGLUE come about - let's find out together.

Russian SuperGLUE Creating a Language Understanding Evaluation Benchmark

Panel discussion with authors of papers about 'Cognitive Architectures and Agents' at AGI-20 conference. Held live on Underline, 06/24/2020

Panel Session 1: Cognitive Architectures and Agents

Panel discussion with authors of papers about 'Intelligence measures, AGI Benchmarks and Evaluations' at AGI-20 conference. Held live on Underline, 06/24/2020

Panel Session 2: Intelligence measures, AGI Benchmarks and Evaluations

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

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