Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the field of study focused on creating systems or machines that can perform tasks that typically require human intelligence. These tasks include:

Perception: Understanding images, speech, or other sensory input (e.g., facial recognition, speech-to-text).
Reasoning: Making logical inferences, solving problems, and drawing conclusions (e.g., expert systems, decision making).
Learning: Improving performance through experience (e.g., machine learning, deep learning).
Language Understanding: Interpreting and generating human language (e.g., chatbots, translation tools).
Agency and Autonomy: Acting on its own initiative, pursuing goals, and making decisions that influence its environment (e.g., agents, multi-agent systems).
Action and Autonomy: Navigating and interacting with the physical world (e.g., robots, autonomous vehicles).

What are the main approaches to AI?

There are two main approaches to Artificial Intelligence: Symbolic AI, inspired by computer science, and Sub-Symbolic AI, inspired by neurobiology.

What is Symbolic AI?

Symbolic AI, also known as Good Old-Fashioned Artificial Intelligence (GOFAI), uses symbolic rule-based structures to process information. It is well-suited to abstract problems and is valuable for making systems explainable and controllable. However, it requires subject matter experts to manually encode knowledge. Symbolic AI is a “not-so-big-data” approach. Trisotech uses the Decision Model and Notation (DMN) standard to model and automate Symbolic AI.

What is Sub-Symbolic AI?

Sub-Symbolic AI, also known as Connectivism, mimics neural activity using nodes, links, activations, and weights. It processes information in parallel through artificial neural networks. This approach is ideal for perceptual problems, requires less expert input, and scales well with data, although it tends to operate as a “black box.” It is a “big data” approach. Techniques such as Deep Neural Networks (DNNs) and Machine Learning (ML) are well-established and proven. Trisotech supports Sub-Symbolic AI integration via the Predictive Model Markup Language (PMML).

What is Generative AI?

Generative AI is a subset of Artificial Intelligence that falls under the sub-symbolic approach that drives much of today’s AI momentum. It focuses on creating new content, such as text, images, audio, video, or code, that resembles human-generated material. These intelligent systems use Large Language Models (LLMs), Large Action Models (LAMs), or Large Reasoning Models (LRMs) trained on vast datasets to learn patterns and generate new, contextually relevant outputs based on prompts. Generative AI leverages deep learning and transformer-based architectures (e.g., GPT, BERT, DALL-E). Trisotech enables organizations to incorporate Generative AI through flexible connectors to leading GenAI model providers.

How Does Trisotech Leverage AI?

Trisotech approaches AI with a principled and pragmatic philosophy. We promote a Bring-Your-Own AI model, allowing organizations to integrate symbolic, sub-symbolic, or generative AI according to their specific needs. Our AI Role Matrix ensures role-based responsibility, aligning AI capabilities with safe, purposeful business operations.

We promote a Neuro-Symbolic AI approach that combines the strengths of symbolic reasoning and sub-symbolic learning, enabling systems that are both intelligent and explainable.

We advocate Augmented Intelligence, where AI supports, rather than replaces, human decision-making. Trisotech TRUST AI enables T raceability, R eflectiveness, U nderstanding and oversight, S eparation of duties, and T ransparency built into every intelligent workflow. Our solutions emphasize Explainable AI (XAI) and Governed AI, allowing decisions to be executed at the edge while governed centrally.

Above all, Trisotech focuses on Knowledge over raw Data. Our SMART Decision Fabric, powered by S emantic M odeling and A I-Augmented R easoning, and T echnology, empowers organizations to make informed, contextual, and governed business decisions.

Conclusion

AI is transforming how we perceive, reason, act, and decide across industries. From symbolic models to generative neural networks, the AI landscape spans explainability, adaptability, and intelligence augmentation. At Trisotech, we believe AI must be democratized, governed, and embedded within a Decision-Centric Orchestration that prioritizes transparency, trust, and human oversight. By combining symbolic, sub-symbolic, and generative approaches with our SMART Decision Fabric, we enable organizations to create intelligent systems that are powerful, understandable, and aligned with strategic goals. The future of AI is not just about automation, it’s about empowering knowledge workers, supporting informed decisions, and ensuring trustworthy outcomes.