TJE Ventures

The Landscape at a Glance

The term “AI” covers a broad family of technologies. In everyday business conversation, people often use it to mean one specific thing --usually the chatbot they just tried. In reality, AI describes a spectrum of approaches, each with distinct capabilities and appropriate use cases. Here is a clear map of the major types.

Narrow AI vs. General AI

Narrow AI (what exists today)

Every AI system in commercial use today is narrow AI --meaning it is designed and trained to perform a specific category of tasks. A model trained to generate text is very good at text and largely useless at, say, controlling a robot arm. A model trained to detect tumors in medical images cannot write an email. Narrow AI can be extraordinarily capable within its domain, but it has no general intelligence or ability to transfer skills across domains the way humans can.

General AI (theoretical)

Artificial General Intelligence (AGI) refers to a hypothetical AI that can learn and perform any intellectual task a human can --switching fluidly between domains, learning from minimal examples, and applying genuine reasoning. AGI does not exist today. It remains an active research goal and a subject of significant debate among researchers about whether and when it might arrive. Business decisions should be made on the basis of what exists, not what is speculated.

The Core Technical Approaches

Machine Learning

Machine learning (ML) is the foundational approach behind nearly all modern AI. Instead of being explicitly programmed with rules, an ML model is trained on data and learns to find patterns on its own. A spam filter that learns to recognize junk mail from millions of examples is machine learning. A system that predicts customer churn from historical data is machine learning. It is less glamorous than generative AI but is quietly embedded in countless business systems already.

Deep Learning

Deep learning is a more powerful subset of machine learning that uses artificial neural networks --loosely inspired by the structure of the human brain --to process data through many layers of analysis. Deep learning is what made modern AI capable: it is the technology that enabled voice recognition, real-time translation, and the large language models behind today’s AI chatbots. When people talk about AI breakthroughs, they are usually talking about deep learning advances.

Natural Language Processing (NLP)

Natural language processing is the branch of AI focused on understanding and generating human language --text and speech. Spell check, autocomplete, sentiment analysis, translation, and summarization are all NLP applications. Modern large language models (LLMs) like GPT-4, Claude, and Gemini are the most advanced form of NLP in use today, capable of sustained, contextually coherent conversation and complex writing tasks.

Computer Vision

Computer vision enables AI to analyze and interpret images and video. Applications include facial recognition, quality control inspection on manufacturing lines, medical image analysis, autonomous vehicle navigation, and document scanning and parsing. Computer vision is quietly embedded in many industries --often performing tasks that would be impractical or too slow for humans to do manually at scale.

The AI Types Getting the Most Attention

Generative AI

Generative AI is AI that creates new content rather than simply classifying or analyzing existing content. It can generate text, images, audio, video, code, and more. Tools like ChatGPT, Claude, DALL-E, Midjourney, and GitHub Copilot are generative AI. This is the category that has captured public and business attention since late 2022, and it is where most current enterprise AI investment is focused. See the Practical Uses: Generative AI article for practical applications.

Agentive AI

Agentive AI --also called agentic AI or AI agents --refers to AI systems that can take autonomous action across multiple steps to accomplish a goal, rather than simply responding to a single prompt. An agent might browse the web, read files, draft a document, send an email, and log the result --all in sequence, without a human prompt at each step. This represents a meaningful shift from AI as a responder to AI as an actor. See the Practical Uses: Agentive AI article for more.

Which Type Do You Actually Encounter?

In most business settings, the AI you interact with directly is generative AI --specifically large language models accessed via a chat interface or API. Behind the scenes in your software products (your CRM, your email platform, your analytics tools), you are almost certainly also encountering narrow ML models doing classification, prediction, and anomaly detection. Agentive AI is emerging rapidly in enterprise tools and will become significantly more prevalent over the next two to three years.

“Knowing which type of AI is doing what --and what its limitations are --is the foundation of using it well.”

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