Machine Learning on Cloud Using OpenCog?

OpenCog is a widely used open-source artificial intelligence framework by developers. Its generic framework is used in broad-based AGI research. OpenCog is an AI platform for robot and virtual embodied cognition that defines a set of interacting components designed to emulate human-equivalent artificial general intelligence as an emergent phenomenon of the whole system. It’s a diverse assemblage of cognitive algorithms. The human brain consists of many subsystems both genera and specialized that work together synergistically to assist instead of working against each other, which is the core idea behind the OpenCog framework written in C++.

What Are the Advantages of Working on OpenCog on Cloud?

Working on OpenCog projects on cloud resources can solve many problems associated with running the application on physical desktop computers.

  • Resources like number CPUs, RAM/memory, and storage, are scalable as per requirements.
  • It provides remote access to powerful resources for running computing-resource intensive AI workloads if project requirement is as such.
  • You can access your OpenCog on cloud from anywhere in the world from any desktop/laptop/mobile device with internet connectivity.
  • It is possible to store large volumes of OpenCog projects/databases in the cloud.

With OpenCog You Get

  • A graph database, also known as the AtomSpace contains atoms along with atomic formulas, sentences, and relationships, together with their values (or interpretations). Atoms are globally unique, immutable, and are indexed, whereas values are variable.
  • Pre-defined atoms, used for generic knowledge representation, for example, conceptual graphs and semantic networks, as well as to represent and store the rules, needed to manipulate such graphs.
  • Generic rule engine which includes a forward chainer and a backward chainer, that can chain together rules. The rules are exactly the graph queries of the graph query subsystem hence, the rule engine vaguely resembles a query planner. It is designed to allow different kinds of inference engines and reasoning systems to be implemented, such as Bayesian inference or fuzzy logic, or practical tasks, such as constraint solvers or motion planners.
  • An implementation of probabilistic logic networks (PLN). The current implementation uses the rule engine to chain together specific rules of logical inference, together with some very specific mathematical formulas assigning probability and confidence to each deduction. This subsystem can be thought of as a certain kind of proof assistant that works with a modified form of Bayesian inference.

Apps4Rent Can Help

Apps4Rent offers virtual desktops on the cloud which are scalable based on your requirement for exploring OpenCog. Since the virtual/remote desktop is on the cloud, it can be accessed from any local PC/laptop, Windows, or Mac, which has internet connectivity. No configuration change is required on your local PC/laptop. Call, chat or email our virtual/remote desktop specialists, available 24/7 for assistance.

Comments are closed.

Submit Your Requirement