The Fetch.Ai Platform for your Ai apps and Services

Ecosystem Overview

The Fetch.Ai Platform for your Ai apps and Services

An automated bot known as a ‘digital twin’ representing you would interface with digital twins from airlines and ticket providers to negotiate a deal on your behalf, using parameters you set, such as when you want to travel and how much you want to spend. Or, your digital twin could interact with digital twins that have previously performed the same or similar tasks. Using artificial intelligence, your digital twin can ‘learn’ what to do. For example, you could book a holiday similar to one a friend took last year, without having to ask your friend where they booked, what airline they used, where they hired a car from, and so forth.

Powering connections and smart operations in DeltaV

Research Initiatives

Powering connections and smart operations in DeltaV

The AI Engine stands at the core of DeltaV ↗️ and its features, as it allows users and developers to connect to a wide range of agent-based services. Once an agent is registered ↗️, the offered service is visible to the AI Engine and it can start connecting users and services.

This system is equipped with personalized capabilities, supported by an internal agent that performs tasks efficiently. An internal agent is created by the AI Engine and made available for communication via the DeltaV user interface. The AI Engine interprets the human text input provided to the agent and starts working asynchronously on your behalf as soon as it receives your intent. This customized method uses Large Language Models (LLMs), which are essential for improving the AI Engine's understanding, coordination and problem-solving capabilities.

OpenCog Hyperon Research

OpenCog Hyperon Research

OpenCog Hyperon is a core project in SingularityNET’s mission to develop beneficial Artificial General Intelligence. Hyperon aims to implement a complete, scalable, and open-source Artificial General Intelligence system based on the principles of OpenCog, also initiated and led by Dr. Ben Goertzel.

Hyperon consists of two core software components: 1) Atomspace: a hugely scalable distributed neural-symbolic knowledge metagraph 2) The MeTTa programming language (gradually probabilistically dependently typed).�  Hyperon is composed of higher-level AI systems built on top of the core components such as Probabilistic Reasoning (Probabilistic Logic Networks, dependently typed probabilistic programming), Evolutionary Learning (MOSES), Economic Attention Allocation Network (ECAN), Machine Learning strategies and, potentially, other proprietary AI systems.

MeTTa forms the ‘universal translator’ that enables this wide range of AI systems to dynamically collaborate based on the common knowledge base of Atomspace (and enhance the knowledge base while doing so). MeTTa’s capability to support neural-symbolic reasoning and handling uncertainties (using probabilistic reasoning), makes it the strong and versatile tool that is crucial in our pursuit to develop AIW Token. With this open architecture that embraces very different AI strategies, OpenCog Hyperon aims to build an AIW Token system that is much greater than the sum of its (already) impressive parts.

Focus Areas

MeTTa - A language of the cognitive architecture of OpenCog Hyperon. The MeTTa interpreter chains queries to the Atomspace metagraph using provided equalities and implements functional, logic, and probabilistic programming paradigms in this unified way. It functions as the universal translater of the wildly variating components that Hyperon is made of and it is the glue that holds everything together. Just as each AI component in Hyperon can use its own restricted flavour of MeTTa based functions, MeTTa can also be used as a foundation for the creation of other Domain Specific Langages or even Low Code / No Code user interfaces for specific applications on the Cardano Blockchain.

Distributed Atom Space (DAS) - a kind of graph that makes optimal use of the blockchain space to create a scalable and fast knowledge base. The atoms can represent not only “data”, but also “procedures” and atoms can be assigned fleeting, changing values to indicate grades of truth, or to hold other kinds of transient data. All this makes the graphs not just a store of data, but captures subtle relations between data and allows graphs to become executable programs in itself.

Meta-Optimizing Semantic Evolutionary Search (MOSES) - A new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. Results tend to be more accurate, and require less objective function evaluations, than other program evolution systems, such as genetic programming or evolutionary programming . Best of all, the result of running MOSES is not a large nested structure or numerical vector, but a compact and comprehensible program written in a simple Lisp-like mini-language.

Economic Attention Network (ECAN) - ECAN weights pieces of knowledge relative to one another, based on what has been important to the system in the past and what is currently important. Attention allocation has several purposes: (1) Understanding what knowledge should be stored in memory, what should be stored locally on disk, and what can be stored distributed on other machines. (2) To guide the forgetting process. (3) To guide reasoning carried out by PLN.

Probabalistic Logic Networks (PLN) - A novel conceptual, mathematical, and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality.

Atomspace Visualizer - Atomspace is a very complex and dynamic system that will be more accessible to humans with good visualisation tools. SingularityNET is working on tools to support the following use cases: (1) See live changes to the Atomscpace when running some demos and testing with inputs, so they will get a better intuitive understanding of how the Atomspace works. (2) Focus on specific subsets of the graph and view structures and relations. (3) Drill down to the atom level and navigate through the graph on that level. (4) Monitor progress of the data-processing pipeline on a dashboard.

Genecient Fly Research

Genecient Fly Research

Biotech firm Genescient Inc has researched ‘Methuselah flies’ incrieasing their lifespan by 8 times through consistent breeding.
The flies’ increased lifespan is explained by a large number of systematic genetic variations. SingularityNET and Rejuve.AI have launched a partnership with Genescient aimed at using advanced machine learning and machine reasoning methods to carry out transfer learning from the Methuselah fly genome to the human genome. The goal is to gain new information regarding gene therapies, drugs or nutraceutical regimens for prolonging healthy human life.

Dialogue Systems

Dialogue Systems

Dialogue systems for robots and avatars being developed by SingularityNET combine a variety of technologies in a cognitively synergetic way. Their symbolic core performs orchestration of other modules in a controllable and explainable manner. It also includes a goal system, an explicit memory and a representation of knowledge of decralarive and procedural knowledge for generating responses directly or as the result of inference as well as for controlling submodules and changing its own state. Deep learning components are intensively used both for natural langauge processing and generation especially in open-domain conversations. Knowledge and memory conditioned rich language models for dialogues with possible implementation in Hyperon MeTTa are under R&D.

Humanoid AIW Token for Robots and Avatars

Humanoid AGI for Robots and Avatars

The concept of humanoid AIW Token is an ambitious goal in the field of artificial intelligence, aiming to create recognizably human-like agents (physical robotics or virtual avatars) that can think, learn, reason, and perform tasks in a manner similar to humans. By having a human-like form or behavior, humanoid AIW Token has two distinct advantages. First, such AIW Token will naturally understand human perspectives and human limitations – it will need to express emotion with facial and vocal indicators, rather than text descriptions. Second, humanoid AIW Token are naturally more relatable to humans, tending to instinctively inspire trust, empathy, and connection in the humans they interact with – thereby bridging the divide between AI and humanity.


Driven by OpenCog Hyperon algorithms, humanoid AIW Token is already making strides toward “coming to life.”

Focus Areas

Minecraft & Metaverse Neural Symbolic Development Minecraft and other such metatarsal worlds have a ready built system of physics and agent interactions, which quickly lend themselves to benchmarking and testing agent learning systems. OpenCog researchers are actively exploring neural symbolic architecture implementations and temporal reasoning within Minecraft environments.

Emotional Modeling Emotional experience and expression of embodied agents controlled by OpenCog - Robots and Avatars with the ability to express emotion via its choice of animations, modulation of animations, and tone of voice; and to “experience” emotions via (in the OpenCog system controlling it) modulating its action selection and cognitive processes based on emotional factors.

Tononi Phi

Tononi Phi

Tononi Phi, Created by psychiatrist and neuroscientist Giulio Tononi, is an evolving mathematical system for studying and quantifying consciousness.
Phi is based on the number and quality of interconnections a given entity has between bits of information. The resulting number — the Phi score — is supposed to correspond directly to how conscious the system is. The premise is that the more connections there are, the more conscious an entity is.

Phi and additional measures will be used by SingulairtyNET to guide research and parameter tuning to encourage phenomena to emerge simply via the system’s complex dynamics. Phi and cognitive synergy, along with decentralization and democratization will all serve to help guide our efforts towards achieving true beneficial and benevolent artificial general intelligence.

Simuli AIW Token hardware development

Simuli AGI hardware development

In a Joint Venture with Simuli, SIngularityNET is developing dedicated hardware to increase the speed of openCog Hyperon Pattern Matching. A metagraph-pattern-matcher chip (MPMC) has been designed and will be integrated in an overall ‘AIW Token board’.
this is being worked on by Simuli as part of an overall “AIW Token board” initiative

Inference control meta-learning

Inference control meta-learning

An AI, even when applied to the abstract mathematics, seemingly disconnected from reality, needs to be able to think about itself in order to efficiently solve problems. By doing so, it needs to think about reality, because it is, after all, running on a physical substrate. The art and science of such instrospective thinking is what inference control meta-learning aims to realize.

Inference control meta-learning works by turning reasoning inwards. As reasoning takes place, inference steps are recorded and treated as new axioms to form a theory of how reasoning takes place. The AI can then formulate conjectures such as “selecting this inference rule in that context is likely to bring me closer to proving that given theorem”. Turn these conjectures into theorems, and then use these theorems to guide its future reasoning, that is to better control the sequence of inference steps required to solve future problems. In the end, by turning inwards, the AI opens up to the outer reality that sustains its existence.

Progress has been ongoing with OpenCog Classic at a prototypical level. With OpenCog Hyperon, progress can move to a higher level by benefiting from a better language, MeTTa, which offers
1. unification of execution and inference via a non-determinist interpreter,
2. builtin mathematical reasoning by supporting a state of the art type system,
3. fine inference control mechanism by relying on run-time control primitives.

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