SRC's software-design HIPS is a Multi-Layered proprietary 
architecture incorporating the best features of Artificial 
Intelligence systems and processes into a single engine. 
This provides the IT industry with the technology capable 
of emulating the actions of humans, which includes 
making decisions in the same way humans would make 
them no matter how complex they may be. The engine 
can learn, reason, solve problems, perceive, and 
interpret any kind of input.  We have added complex 
algorithms to assist in performing difficult tasks, 
and we do this in parallel. The software is very 
small at 100k in size, and has been written in Java 
for maximum portability and adaptability. 
	
       

SRC Technologies Heuristic Artificial Intelligence product (HAI) can determine and select which types of AI need to be used 
based on the task requested, at a specific time, that best fits each particular requirement, whether the process is 
AI based or functional based including exceptions that are not AI.
   
The engine creates end products (or processes) by following large volumes of requirements and satisfying intricate decision 
structures. For example the engine can be used to design computers. It can create its own control rules, using different forms 
of AI rules and structures, to control the way that it works and solves specific problems at a particular time under specific
requirements or events. It can learn that the control rules that it uses can be improved and will improve them based upon the 
knowledge gained through its processing. This makes SRC HAI an evolutionary process. It creates events, controls, and 
different control rule structures. This is part of what differentiates SRC from the rest of the industry. This is the way 
humans create that which really happens on an instinctual level.          

Instinctual reasoning is extremely fast and this is one of the basic processes of the engine.  Deductive reasoning is much 
slower because the human must follow a logical thought pattern to its logical end result. The engine is also capable of 
deductive reasoning. 

The engine: 

Learns

Has instinct 
Creates deductions from instinct 
Creates end products and processes 
Performs conclusions and applications 
Monitors the current state of the engine 
Performs analysis required processing, making determinations based on the other modules 
Maximum interoperability 


Definition of Artificial Intelligence: 

Artificial Intelligence (AI) usually defined a science of making computers do things that require intelligence when done by humans.
The important part of intelligence is the ability to adapt one’s behavior to fit new circumstances. Mainstream thinking in 
psychology regards human intelligence not as a single ability or cognitive process but rather an array of separate components. 
AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and language 
understanding. Today AI is about new ways of connecting people to computers, people to knowledge, people to the physical world, and 
people to people. Today’s AI is enabled in part by technical advances and in part by hardware and infrastructure advances. 


What is the difference between strong AI and weak AI? 

Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans. Strong AI: Claim that 
computers can be made to actually think, just like human beings do. More precisely, the claim that there exists a class of computer 
programs, such that any implementation of such a program is really thinking. (SRC product). Most every one utilizing AI falls into 
the next statement which is weak AI. Weak AI simply states that some "thinking-like" features can be added to computers to make 
them more useful tools... and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition 
software). 


What are the branches of AI? 

There are many, some are 'problems' and some are 'techniques'. 

Automatic Programming - The task of describing what a program should do and having the AI system 'write' the program. 
Bayesian Networks - A technique of structuring and inferring with probabilistic information. 

Natural Language Processing (NLP) - Processing and (perhaps) understanding human ("natural") language Knowledge 

Engineering/Representation - turning what we know about a particular domain into a form in which a computer can understand it. 

Planning - given a set of actions, a goal state, and a present state, decide which actions must be taken so that the present state 
is turned into the goal state. 

Constraint Satisfaction - solving NP-complete problems, using a variety of techniques. 

Machine Learning - Programs that learn from experience. 

Visual Pattern Recognition - The ability to reproduce the human sense of sight on a machine. 

Speech Recognition - Conversion of speech into text. 

Search -The finding of a path from a start state to a goal state. Similar to planning, yet different...

Neural Networks (NN) – This is a computational learning technique to find patterns in data. The name comes from the fact that 
they are loosely modeled on the functional workings of biological neurons. There are numerous different types of neural networks. 
Strengths: good way to automatically find patterns in data. Weaknesses: computationally intensive, and no way for a human to 
interpret how the answer was derived. 

Belief Network (also Bayesian Network): A mechanism for representing probabilistic knowledge. Inference algorithms in belief 
networks use the structure of the network to generate inferences efficiently (compared to joint probability distributions over all 
the variables). Case-based Reasoning: This is a question answering technique where prior example cases (literally from prior 
customer questions) are used to answer new incoming questions. Comparisons between cases are complex, and usually 
customized to the topic domain of the questions.  Cases are usually complex, entities including fields intended to be read by the 
system, not humans. They are stored as question-action pairs. Strengths: can provide very accurate responses to previously seen 
inquires. Weaknesses: Responses are literally individual prior responses, thus more personalized.  

Fuzzy Logic: In Fuzzy Logic, truth values are real values in the closed interval [0..1]. The definitions of the Boolean operators are 
extended to fit this continuous domain. By avoiding discrete truth-values. 

In conclusion we have all of this in one engine and the ability to apply to applications.  This is why we make the claim we do. 
SRC's HIPS product emulates the way a human thinks, makes decisions, and performs applications.