AI- My Experience Part 2-Getting Creative

 

Now that earlier today I vented a bit  in part 1 of AI-My Experience, I want to concentrate on a couple of things: 1- setting the basis for discussions on AI, so that everyone has the same understanding and  2: how I have been using it - the various tools - to create the stories and postings of late.  Why? Well, it needs to be discussed-it's here -it's everywhere, and the only way it goes away is if someone flips a switch. If that happens, we have a hell of a lot more to worry about. Chaos, mass, would ensue. And maybe I can reach others like me, people who once did something they loved, but illness and disability have taken it from them, and they are left looking at walls. Just maybe this might spark a light in someone like me and we can form a group ;)   (sarcasm, kinda, maybe not, whatever) 

First,  the term AI, artificial intelligence. It is a blanket term -an umbrella. Much like speaking about healthcare, education, and disability - there's an entire flowchart spewing from each, explaining all the paths it takes - and who or what is directly impacted. 

I asked Grok for a summary of that flowchart. I ran Grok's answer through Perplexity, and they made changes to the flow but not the information. Then, I ran what they had through ChatGPT, which checked the information and made slight revisions. Final draft went back through Grok.  Their combined answer is in the blue italics. Below AI's explanation of its flowchart is an example of how I work with it for creativity. You do not want me using my metaphors in describing tech - it's best that they do. 

IF we want to regulate it - debate it - we need to understand it, much like any issue before the public. Our knee jerk responses are where most of our divisions, stereotypes, hate and divide begin. 

Artificial Intelligence (AI) is often talked about as if it were a single thing — one technology, one system, one idea. In reality, AI is much better understood as an umbrella term: a broad field that covers many different approaches, tools, and philosophies for building machines that can perform tasks we normally associate with human intelligence.

Understanding AI this way helps explain why everything from chess-playing computers to voice assistants to image generators are all called “AI,” even though they work very differently under the hood.

What Does “AI” Actually Mean?

At its core, Artificial Intelligence refers to systems designed to perform tasks such as reasoning, learning, perception, language understanding, and decision-making. AI itself is not one algorithm or product. Instead, it includes multiple subfields that have developed over decades, each solving different kinds of problems.

Under the AI umbrella you’ll find:

• Symbolic (rule-based) AI
• Machine Learning (ML)
• Deep Learning (DL)
• Natural Language Processing (NLP)
• Computer Vision
• Robotics

Some of these approaches are old, some are new, and many modern systems combine several of them.

A Brief History of AI: From Ideas to Everyday Tools

AI did not appear overnight. Its history moves in waves — bursts of optimism followed by setbacks, then renewed progress as technology caught up with ideas.

Early Foundations (1950s)

In 1950, mathematician Alan Turing asked the now-famous question: “Can machines think?” His work laid the conceptual groundwork for AI.

A few years later, the 1956 Dartmouth Conference officially coined the term Artificial Intelligence, marking the birth of the field.

Symbolic AI and Early Programs (1960s–1970s)

Early AI focused on symbolic reasoning — using hand-coded rules and logic to mimic human thought. Programs like ELIZA, an early chatbot, and Shakey the Robot showed promise but struggled outside tightly controlled environments.

AI Winters and Setbacks (1970s–1990s)

Expectations outpaced reality. Limited computing power and brittle systems led to major funding cuts known as AI winters. Despite this, important milestones still occurred, including IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997.

Practical AI and the Deep Learning Boom (2000s–2010s)

As computing power and data grew, machine learning — especially deep learning — transformed AI. Systems began outperforming humans in image recognition, speech processing, and strategic games like Go.

The Generative AI Era (2020s)

Recent years have seen the rise of generative models such as GPT, DALL·E, and ChatGPT. These systems can generate text, images, and code, bringing AI into everyday tools used by millions of people.

Two Major Branches Under the AI Umbrella

Symbolic AI (GOFAI)

Symbolic AI, sometimes called Good Old-Fashioned AI (GOFAI), represents knowledge using explicit rules and logic — often in the form of if–then statements.

Strengths:

• Clear, explainable reasoning
• Predictable behavior

Limitations:

• Difficult to scale
• Struggles with messy real-world data like images or speech

Symbolic AI dominated early decades of AI research and is still used today in rule-based systems and decision logic.

Machine Learning and Deep Learning

Machine learning takes a different approach. Instead of hand-written rules, models learn patterns from data. Deep learning uses layered neural networks to handle complex, unstructured information.

Strengths:

• Excellent at vision, language, and audio tasks
• Adapts as more data becomes available

Limitations:

• Less transparent (“black box” models)
• Requires large amounts of data and computing power

Most modern AI applications — from recommendation engines to image generators — rely heavily on this branch.

Types of AI by Capability

AI systems are often grouped by how broadly they can think and adapt.

Artificial Narrow Intelligence (ANI)

• Designed for specific tasks
• This includes all real-world AI today
• Examples: Siri, Alexa, ChatGPT, recommendation systems

Artificial General Intelligence (AGI)

• Hypothetical AI capable of performing any intellectual task a human can
• No true AGI systems currently exist

Artificial Superintelligence (ASI)

• Theoretical AI that surpasses human intelligence in all areas
• Remains speculative and science-fiction-level for now

Types of AI by Functionality

Another way to categorize AI is by how it processes information.

Reactive Machines

• No memory or learning from past experiences
• Example: IBM Deep Blue

Limited Memory AI

• Uses recent data to improve performance
• Examples: ChatGPT, self-driving car perception systems

Theory of Mind AI

• Would understand emotions, intentions, and beliefs
• Still experimental

Self-Aware AI

• Conscious, self-reflective systems
• Entirely hypothetical

Why Thinking of AI as an Umbrella Matters

Seeing AI as a single monolithic technology leads to confusion and unrealistic expectations. Understanding it as an umbrella term helps clarify:

• Why AI systems behave so differently
• Why older rule-based programs and modern neural networks are both “AI”
• How decades of research led to today’s tools

AI is not one thing. It is a collection of methods, shaped by history, trade-offs, and human design choices — and its evolution is still unfolding.

How I use AI for creativity being disabled with chronic illness.