Robert Haas

project owner

Augmenting BMKGs With Cheminformatics And CADD

Biomedical knowledge graphs (BMKG) contain chemical compounds such as drugs, toxins, metabolites, cofactors or signaling molecules. These entities and some of their relations can be richly augmented with qualitative & quantitative properties by methods from cheminformatics, computer-aided drug design (CADD) and related fields. This enables numerical queries and many analyses such as filtering, clustering, embedding, similarity/outlier detection, QSAR modeling, ML, etc. The aim of this project is to make existing but scattered methods available in a Python package with a unified functional API, expose it to OpenCog Hyperon, and apply it in a PoC study to annotate and analyze Hetionet in MORK.

Livia_Zaharia

Project owner

GlucoseDAO

GlucoseDAO is a decentralized organization that enables diabetics and others with continuous glucose monitors (CGMs) to get accurate glucose predictions at least one hour in advance. For millions of diabetic people, such predictions are crucial to planning their everyday lives (when to inject, act, or do sports). It is also helpful for healthy people as it allows them to optimize their diet and exercise routines based on glucose patterns. What we are developing: -Extension of GlucoBench benchmark that will also measure human performance -Open-source ML model to predict glucose and related health outcomes -ML service and app that everybody can use easily

rakeshskybrain

Project owner

Productivity EEG-AI Agents

Many individuals struggle with maintaining focus and interest at work, often feeling overwhelmed due to mental fatigue. Traditional self-assessments of focus levels are unreliable, leading people to believe they are productive when, in reality, their cognitive performance is declining. This results in frustration, decreased efficiency, and an overall drop in workplace well-being. Each individual varies in their optimal working patterns, and our AI agents help tailor focus strategies based on real-time brain activity. By leveraging wearable EEG technology, we ensure a highly personalized and adaptive approach to productivity enhancement.

khasyahfr

Project owner

Sign Language Translator AI (SLTA)

Sign Language Translator AI (SLTA) is an AI service designed to automatically translate sign language from video files into text. Leveraging computer vision and machine learning, SLTA will interpret hand gestures within the sign language videos to provide real-time translations. This service aims to eliminate communication barriers for millions of deaf and hard-of-hearing individuals, improving accessibility for non-signers. SLTA's inclusion in the SingularityNET marketplace will enhance its ecosystem with a socially impactful AI solution, promoting inclusivity and accessibility, in line with BGI's commitment to social good.

Gabriel Axel Montes

project owner

Neurotech Controls for AGI Motivational Framework

With the increasing interest in brain-computer interfaces (BCI), there is as of yet no framework that links brains with AGI systems. In collaboration with key SingularityNET ecosystem stakeholders, neuroscientist Gabriel Axel Montes (Neural Axis), has been developing a multi-faceted AGI motivation framework rooted in cognitive (neuro)science & psychological theories that allows for the adjustment of personality parameters of AI agents for a variety of applications, e.g. robotics, finance, & metaverse characters. With this proposal, a team of neuroscientists aim to identify a mechanism to control mental-emotional parameters using brain signals, setting a foundation for a brain-to-AI pipeline.

rob-freeman

project owner

Neurosymbolics from Chaos in Language Models

An experiment to test whether symbolic cognitive structure can be made to emerge simply as chaotic attractors in a network of observed language. This experiment is an extension of earlier work which: 1) Demonstrated partial structuring of natural language from ad hoc (chaotic?) re-structurings of vectors. 2) Demonstrated spontaneous oscillations in a network of language sequences. The core idea posits neuro-symbolic integration has eluded us because what we perceive as symbolic order in the world, may actually be chaotic attractors on what is a fundamentally multiply interpretable, reality. If that is so, full neuro-symbolic integration may be easy, we just have to embrace the chaos.

rakeshskybrain

Project owner

AI-Enhanced Neurotech for Mental Well-being

SkyBrain Neurotech aims to revolutionize cognitive and mental well-being by integrating EEG-based neurotechnology with advanced AI/ML algorithms, blockchain, and IoMT. Our solutions provide precise assessments and interventions in healthcare, sports, education, and workplace wellness. AI/ML algorithms analyze EEG data for pattern recognition, predictive analytics, and real-time decision-making. IoMT ensures continuous monitoring, while blockchain secures data privacy and integrity. Our project aligns with Singularity NET’s mission to democratize AI, enhancing human cognition and mental health with ethical and innovative technologies.

Dominik Tilman

Project owner

Evidence-based Health Data for AI Longevity Apps

Data and knowledge used in AI-driven health and longevity apps is often inconsistent, contradictory or based on subjective theories. Our goal is to improve the integrity of this data and revolutionise healthcare by providing each individual with an evidence-based, personalised health protocol for wellbeing, longevity and disease prevention. For this ideation proposal, we will combine TrustLevel's work in data integrity and Lennart's expertise in biohacking and focus on market research, concept development and feasibility study for our new approach.

photrek

Project owner

Intelligent Ocular Image Generation

AI applications developed for detection of medical conditions are hindered by lack of available data for training. Photrek contributes to the goals of DF4 with this Miscellaneous Pool proposal by generating synthetic image libraries for diseases of the human vision system and making these capabilities available to the community. Photrek has built a team across three continents (Africa, Asia, North America) at four universities (U of Buea Cameroon, VIT India, KNUST Ghana, and SUNY Poly USA) with a team focused on image processing techniques applied to medical applications in a low cost environment within SNET goals for creation of democratic, decentralized, beneficial AGI.

oliko

Project owner

AGIX/ASI Payment Gateway for SaaS products

The AGIX/ASI Payment Gateway aims to provide a simple and secure payment solution for SaaS product developers to collect payment in AGIX/ASI. This ideation phase will focus on researching, assessing feasibility, and designing the architecture for a payment gateway that supports AGIX and ASI tokens. By addressing the community's need for a reliable payment infrastructure, this project will facilitate the monetization of SingularityNETs tokens, expanding the utility and infrastructure possibilities.

clement_umoh

Project owner

AI4M: Enhancing Malaria Predictability using AI

Our project was awarded and completed in the ideation pool of DFR3. It is a ground breaking initiative that harnesses the power of machine learning to create a robust AI model that can accurately forecast malaria outbreaks and their impact on various population groups. This innovation incorporates a viable and round-the-clock database that can provide predictability patterns for other communicable diseases and can be accessed as API's for other novel innovations and tech systems that would require such category of data for other forms of predictive analysis.

Robert Haas

Project owner

Bringing Network Pharmacology to OpenCog Hyperon

Antecedens e.U. proposes a project aimed at developing a tool to integrate well-curated knowledge graphs from the field of network pharmacology into OpenCog Hyperon. This project serves multiple purposes, including supporting OpenCog Hyperon's language MeTTa and Probabilistic Logic Networks (PLN) development, contributing to SingularityNET's web3 initiative, and sharing insights with the Rejuve team in the biomedicine domain. The tool's goal is to enable the derivation of novel pharmacological relationships and hypotheses through OpenCog Hyperon's advanced querying and reasoning capabilities.

The project requests $40,000 in funding, aligning with RFP4's objective of integrating knowledge graphs with Large Language Models (LLMs). It aims to develop a Python package to retrieve, convert, and import up-to-date knowledge graphs from biomedicine, focusing on pharmacology, into OpenCog Hyperon. The project identifies several high-quality knowledge graph projects in biomedicine and plans to bring them into OpenCog Hyperon through this Python package.

The project outlines milestones, including a literature review, design discussions with MeTTa and PLN teams, implementation phases for integrating knowledge graphs, documentation, and finalization. Potential risks are identified, such as changes in MeTTa's API stability, unforeseen issues with knowledge graphs, and potential redundancies with Rejuve. Mitigations are proposed for each risk.

In summary, Antecedens e.U. aims to enhance OpenCog Hyperon's capabilities by integrating knowledge graphs from network pharmacology, contributing to SingularityNET's web3 initiative, and fostering collaboration in the biomedicine domain. The project requests $40,000 in funding to achieve these goals.

MeAI tool for healthcare system

Cardano2vn.io Company seeks $39,000 in funding to develop the Medical AI (MeAI) tool, which aims to address the early diagnosis of stomach diseases, particularly cancer, using endoscopic images. Stomach cancer is a significant health concern globally and in Vietnam, with a growing number of cases, especially among younger individuals. The tool utilizes AI to analyze endoscopic images, identifying abnormalities such as polyps, gastritis, and cancerous lesions, providing users with advice on whether they should undergo further examinations. The project involves several key steps, including data collection, AI model development, training, deployment, and continuous improvement based on user data. MeAI offers a primary API function for users to upload medical images, obtain diagnostic results, and significantly reduce waiting times while ensuring high accuracy. Potential risks include data collection challenges, data accuracy, and model effectiveness, which the project aims to mitigate through careful planning, expert collaboration, and rigorous experimentation. The voluntary revenue sources for MeAI include charge-based requests and in-app advertising, making the tool sustainable in the long run.

Juana Attieh

Project owner

Intelligent Ocular Image Processing for the AI Marketplace

Photrek aims to address the hindrance in developing machine learning and artificial intelligence applications for diagnosing medical conditions, particularly those related to the human vision system, due to the lack of available training data sets. This Deep Funding Round 3 Ideation Pool proposal seeks funding to generate and market image libraries for diseases affecting the human vision system and to develop classifiers for these diseases. Photrek plans to build an international research and development team across three continents, fostering talent development at three separate universities. The project will conduct a literature review to identify the most needed data sets, with an initial focus on Loiasis. The team intends to submit a Phase 4 New Project Pool proposal to generate medical image libraries and create a user-friendly app on the SNET Marketplace for disease probability assessment. The marketing strategy involves collaborating with medical experts and target communities. The project outlines a single milestone: the writing of a New Project proposal for submission in the next round of SingularityNET Deep Funding, with a two-month timeframe for completion. The team includes experts in mathematics, data science, machine learning, and medical imaging analysis, emphasizing a multidisciplinary approach to solving the data set scarcity issue.

clement_umoh

Project owner

AI4M: An AI Model that enhances predictability of malaria occurrence and outcomes across various demographics for proactive and targeted responses.

HIESMEDIC presents a groundbreaking initiative harnessing machine learning to create an AI model for accurately predicting malaria outbreaks and their impact on diverse population groups. This innovation includes a comprehensive database accessible through APIs, allowing integration with other predictive analysis systems for various communicable diseases. Malaria remains a significant global health challenge, and the lack of accurate predictability hampers targeted interventions. HIESMEDIC's AI4M Model aims to provide granular predictions by considering factors like environment, socio-economics, genetics, and healthcare access. Their marketing strategy includes targeting government health agencies, non-profits, healthcare providers, and research institutions. The project milestones involve data acquisition, model development, validation, integration, pilot implementation, regulatory compliance, scaling, monitoring, impact assessment, and market expansion. The budget breakdown ensures a structured approach to achieving their goals and making a meaningful impact in the fight against malaria.