Russian Federation

Creating Strong AI means to develop artificial intelligence to the point where the machine&#39;s intellectual capability is in a way equal to a human&#39;s. Science is definitely one of the summits of human intelligence, the other being the art. Scientific research consists in creating hypotheses that are limited applicability models (methods) implying lossy information compression. In this article, we show that this paradigm is not unique to the science and is common to the most developed areas of human activities, like business and engineering. Thus, we argue, a Strong AI should possess a capability to build such models. Still, the known tests to confirm the human-level AI do not address this consideration. Based on the above we suggest a series of six tests of rising complexity to check if AI have achieved the human-level intelligence (Explanation, Problem-setting, Refutation, New phenomenon prediction, Business creation, Theory creation), five of which are new to the AGI literature.

AGI-20

How do you test the strength of AI?

strong ai

turing test

epistemology

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.

Creating Strong AI means to develop artificial intelligence to the point where the machine's intellectual capability is in a way equal to a human's. Science is definitely one of the summits of human intelligence, the other being the art. Scientific research consists in creating hypotheses that are limited applicability models (methods) implying lossy information compression. In this article, we show that this paradigm is not unique to the science and is common to the most developed areas of human activities, like business and engineering. Thus, we argue, a Strong AI should possess a capability to build such models. Still, the known tests to confirm the human-level AI do not address this consideration. Based on the above we suggest a series of six tests of rising complexity to check if AI have achieved the human-level intelligence (Explanation, Problem-setting, Refutation, New phenomenon prediction, Business creation, Theory creation), five of which are new to the AGI literature.

technical paper

"Autonomous knowledge transfer from a known task to a new
one requires discovering task similarities and knowledge generalization
without the help of a designer or teacher. How transfer mechanisms in
such learning may work is still an open question. Transfer of knowledge
makes most sense for learners for whom novelty is regular (other things
being equal), as in the physical world. When new information must be
unied with existing knowledge over time, a cumulative learning mech-
anism is required, increasing the breadth, depth, and accuracy of an
agent's knowledge over time, as experience accumulates. Here we address
the requirements for what we refer to as autonomous cumulative
transfer learning (ACTL) in novel task-environments, including implementation
and evaluation criteria, and how it relies on the process of
similarity and ampliative reasoning. While the analysis here is theoretical,
the fundamental principles of the cumulative learning mechanism in
our theory have been implemented and evaluated in a running system
described priorly. We present arguments for the theory from an empirical
as well as analytical viewpoint."

Autonomous Cumulative Transfer Learning

In recent works, the DSO Cognitive Architecture’s design is enhanced by incorporating the concept of the Global Workspace Theory(GWT). The theory proposes that consciousness is realised through the competition of massive, specialised, parallelised processes and thus parallelised, unsynchronised cognitive processes become sequential through such bottleneck. Due to the concurrent nature of DSO Cognitive Architecture, coordination of the different parallel processes through this competition mechanism can be difficult and if not handled properly, will create inconsistent results. In this work, we propose a preliminary framework to coordinate the different processes by process composition which borrows concepts from automated planning. Processes, its argument signature and its output are abstracted into higher level type abstractions which can be used to compose with other processes based on matching the output types to argument types. This is known as process composition and it represents a sketch of how different process can coordinate with one another. We combined this with the current design of the DSO Cognitive Architecture and illustrate an example in crowd anomaly detection

Towards Dynamic Process Composition in the DSO Cognitive Architecture

This paper discuses attentional control mechanism of several systems in context of Artificial General Intelligence. Attentional control mechanism of OpenNARS, an implementation of Non-Axiomatic Reasoning System for research purposes is being introduced with description of the related functions and demonstration examples. Paper also implicitly compares OpenNARS attentional mechanism with the one found in other Artificial General Intelligence systems.

An Attentional Control Mechanism for Reasoning and Learning

A pragmatic design for a general purpose reasoner incorporating the Non-Axiomatic Logic (NAL) and Non-Axiomatic Reasoning System (NARS) theory. The architecture and attentional control differ in many respects to the OpenNARS implementation. Key changes include; an event driven control process, separation of sensorimotor from semantic inference and a different handling of resource constraints.

‘OpenNARS for Applications’: Architecture and Control

Many benchmarks and challenges for AI and AGI exist, which help to reveal both short- and long-term topics and directions of research. We analyze elementary school Olympiad math tasks as a possible benchmark for AGI that can occupy a certain free niche capturing some limitations of the existing neural and symbolic systems better than other existing both language understanding and mathematical tests. A detailed comparison and analysis of the implications of AGI are provided.

Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) as-sessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” reflected the engineering level of his time. In fact, the Turing “imi-tation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possibility of transition from a complex observable phenom-enon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Turing test. Such AI must support all forms of communica-tion with a human being, and it should be able to comprehend abstract im-ages and specify concepts as well as participate in social practices.

Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

This work is devoted to unresolved problems of Artificial General Intelligence is the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a model-based approach. In the paper we are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Schema Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward. DSN shows strong performance of transfer learning on the classic Atari game environment.

Delta Schema Network in Model-based Reinforcement Learning

A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by trans-former neural network language models to guide symbolic learning processes like clustering and rule induction. This method exploits the learned linguistic knowledge in transformers, without any reference to their inner representations; hence, the technique is readily adaptable to the continuous appearance of more powerful language models.

Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities

"The complex socio-technological debate underlying safety critical and ethically relevant issues pertaining to AI development and deployment extends across heterogeneous research subfields and involves in part conflicting positions. In this context, it seems expedient to generate a minimalistic joint transdisciplinary basis disambiguating the references to specific subtypes of AI properties and risks for an error-correction in the transmission of ideas. In this paper, we introduce a high-level trans-disciplinary system clustering of ethical distinction between antithetical
clusters of Type I and Type II systems which extends a cybersecurity oriented AI safety taxonomy with considerations from psychology. Moreover, we review relevant Type I AI risks, reflect upon possible epistemological origins of hypothetical Type II AI from a cognitive sciences perspective and discuss the related human moral perception. Strikingly, our nuanced transdisciplinary analysis yields the figurative formulation of the so-called AI safety paradox identifying AI control and value alignment as conjugate requirements in AI safety. Against this backdrop, we craft
versatile multidisciplinary recommendations with ethical dimensions tailored to Type II AI safety. Overall, we suggest proactive and importantly corrective instead of prohibitive methods as common basis for both Type I and Type II AI safety."

Error-Correction for AI Safety

The videogame platform Teleport created earlier allows us to study anonymous social interactions among actors of various nature: human and virtual actor, ensuring their indistinguishability, which implies believable behavior of a virtual actor. The present study found a connection between the human player behavior in the Teleport game and her psychological portrait constructed using the sixteen-factor Catell personality test for empathy. Assuming that this connection is extendable to perception of virtual actor behavior, the game sessions data was analyzed to infer behavioral characteristics of virtual actors. Based on this data analysis, we constructed psychological characteristics of models of a virtual actor (a bot). Partner and emotional characteristics of bots were defined, and their psychological portrait was constructed based on the registered bot behavior. Personal characteristics such as courage, sociability, calmness, balance, and loyalty were attributed to bots and compared to analogous characteristics of human players.

How do you test the strength of AI?

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How do you test the strength of AI?

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Next from AGI-20

Autonomous Cumulative Transfer Learning

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The meaning of things as a concept of strong AI architecture

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