Productivity For Humans.
Ai-based systems can completely automate narrow tasks but deep analysis and critical decisions need human experts.
Engineers do not always know what businesses require that is why we jump start conversations by listening & discovering.
Explaining what happens inside software is a challenge, that is why assurance and trust are important mechanisms for stability.
The disruption to business processes and personnel need steady hands to guide a transformation journey.
Neural Networks include all variants of machine learning or deep learning. These techniques are cognitive inspired and are based on purely mathematical (implicit) techniques.
- Identifying Data (images)
- Matching Data
- Counting Data
- Searching Data
Engineering representations of human thinking, human tasks, or domain concepts, often described as rules or knowledge (explicit), can be compiled or interpreted as software code.
- Functional Models
- Rule Models
- Ontology Graphs
- Knowledge Graphs
These types of systems are not suitable for important business functions at this time. There exists numerous opportunities to further research or discover new use cases.
- Genetic Algorithms
- Evolutionary Algorithms
- Neuro Evolutionary
Graph engines are tightly bound micro services designed to read, process, or manipulate software models. Attempts to mimic functional neuroscience provides the most valuable results.
- Recommendation Engines
- Decision Engines
- Case-Based Engines
- Cognitive Engines