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We humans are bound by intellectual abilities. All knowledge is far beyond power of any person. The only way to apply knowledge is to build machines able to present it human way but not limited by volume.
Intellect Modeling Kit (IMK) is intended to build knowledge machines (KM) assisting experts on the steps of activity:
* Observation;
* Producing propositions based on knowledge;
* Elimination of impossible propositions;
* Selection and verification of the most appropriate...
CLISP is a portable ANSI Common Lisp implementation and development environment by Bruno Haible. Interpreter, compiler, debugger, CLOS, MOP, FFI, Unicode, sockets, CLX. UI in English, German, French, Spanish, Dutch, Russian, and Danish.
Presage (formerly Soothsayer) is an intelligent predictive text entry system. Presage generates predictions by modelling natural language as a combination of redundant information sources. Presage computes probabilities for words which are most likely to be entered next by merging predictions generated by the different predictive algorithms. Presage's modular and extensible architecture allows its language model to be extended and customized to utilize statistical, syntactic, and semantic...
Stop Cyber Threats with VM-Series Next-Gen Firewall on Azure
Native application identity and user-based security for your Azure cloud
Gain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
This project consists of various implementations of the Graphplan algorithm. At the moment we have two implementations of Graphplan: * Emplan - A C++ implementation for Linux, Windows and MacOS X * JavaGP - A Java implementation
CrfAny is a C++ package for efficient and exact training and inference of Conditional Random Fields over any graphical structure, supporting all feature types (boolean, integer and real) and commandline, C++/Python Lib interfaces.
AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.