Alexander Naumenko

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Type SingularityNET RFP
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RFP Guidelines Explore novel hardware architectures and computing paradigms for AGI

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Overview

This RFP is for the development of a theory of intelligence on the basis of which it will be possible to develop a novel AI paradigm orthogonal to both GOFAI (logical) and ML (statistical). The preliminary research has been performed and reflected in the form of blog posts at https://alexandernaumenko.substack.com. It is clear that AGI development will require a shift in research focus in multiple key aspects. To streamline the engagement of other researchers, it is necessary to prepare educational materials and provide clear research directives. The areas will include: theory of intelligence, its components and algorithm, application to natural language processing, perception.

RFP Guidelines

Explore novel hardware architectures and computing paradigms for AGI

Proposal Submission (24 days left)

Type SingularityNET RFP
Total RFP Funding $80,000 USD
Proposals 1
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Explore novel hardware architectures and computing paradigms for AGI

Proposal Submission (24 days left)

The purpose of this RFP is to identify, assess, and experiment with novel computing paradigms that could enhance AGI system performance and efficiency. By focusing on alternative architectures, this research aims to overcome computational bottlenecks in recursive reasoning, probabilistic inference, attention allocation, and large-scale knowledge representation. Bids are expected to range from $40,000 - $80,000.

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Proposal Description

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Total Milestones
3
Total Budget
$60,000_USD
Last Updated
17 Apr 2025

Milestone 1 - Adaptation for Perception

Description

So far, the theory of intelligence has been adapted for natural language processing in a top-down manner. Perception requires a different approach in a bottom-up manner. Intricacies of that domain may require familiarizing myself with the current literature and applying the theory to the observed phenomena. It may require updating the theory.

Deliverables

A series of papers demonstrating how the proposed theory explains the data flow of information from sensors to the central processing module and how that information is transformed into symbolic form.

Budget

$20,000 USD

Success Criterion

If the theory with minimum updates explains how perception works and shows how it can be implemented in hardware and software, it will greatly enable further progress on the path to AGI.

Milestone 2 - Adaptation for Actuation

Description

So far, the theory of intelligence has been used to explain how incoming or existing information is processed. It is necessary to investigate how it can explain how the system built on the basis of this theory may interact with the external environment, including other agents, both natural and artificial. Actuation may pose additional challenges. It may require updating the theory.

Deliverables

A series of papers demonstrating how the proposed theory enables the flow of signals to actuators.

Budget

$25,112 USD

Success Criterion

If the theory with minimum updates explains how actuation relies on symbolic signals and shows how it can be implemented in hardware and software, it will greatly enable further progress on the path to AGI.

Milestone 3 - Educational Materials

Description

The biggest challenge for the development of AGI is the natural but misleading assumptions of AI researchers. To facilitate the shift in research focus across many disciplines, it is required not only to provide a description of an alternative approach but also to demonstrate with multiple examples how to apply it and why it promises better performance. With the results from the previous milestones, it will be possible to develop a comprehensive set of materials to facilitate progress in different areas of cognitive science and the development of systems capable of meaningful interaction with their environment and other such systems.

Deliverables

A series of educational papers intended to show how this theory of intelligence will benefit scientific community by facilitating faster progress in their research, technological companies by providing them with reliable AI capable of dealing with novel situations, general public by providing them with efficient and reliable tools and products.

Budget

$14,888 USD

Success Criterion

The adoption of this theory facilitated by this initial set of educational materials.

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Alexander Naumenko

Project Owner

I will be the sole person responsible for the execution of this project.

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