February 2026

January 2026

AI Presentation

Pasadena Village members were the stars of the recent presentation on AI coordinated by Dave Folz and Sally Asmundson of the Educational Programs Team. The organizers also distributed a handout about AI that listed the general purpose chatbots and the positives and negatives of AI.

Dick Myers, Sue Addelson, Dan Guerrero, Bruce Christensen, and Pablo Ortiz-Morales each told us about how they have used a large language generative AI program such as ChatGPT, Google Gemini or Perplexity for organizing, learning, and writing. Because they each have used a chatbot to solve a different problem, the broad potential was clearly illustrated. Most had used ChatGPT or Perplexity and found them easy to use. All reminded Villagers that information generated by a chatbot should always be checked and even generated by different chatbots for accuracy.

Bruce, our resident Apple specialist who hosts the Tech Time gathering once a month, now uses Perplexity to generate and organize the handout materials each month. He begins by listing the topic and then saying what information he wants to talk about. For the latest Tech Time, he asked Perplexity how you can put your passport into an iPhone and what you can and can’t use it for. 

Dick uses chatGPT with mostly voice commands and loves that it can answer questions that would be difficult for him to research because of his low vision. It allows him to continue his lifelong interest in learning new things.

Sue also has used ChatGPT in several ways. She has used it to generate a unique book as a birthday present for her grandchildren. She first asked them what sort of story they wanted, and she used that as a prompt to get an outline of the story which she could then write, organize, and add illustrations and photos. Her husband also uses AI to organize and categorize his extensive library of photographs.

Dan uses AI to keep track of Village activities and also to enhance his cooking skills. He thinks a paid subscription to ChatGPT is worth it as he can keep a permanent record for future use. He has come up with new and delicious recipes using foods on hand and can organize recipes he likes and makes often.

Pablo, who we know has lived in China and is still perfecting his Chinese language skills, has come up with a personal language program which functions like a test of his skills, all with proper Chinese characters. 

We hope that attendees are inspired to now open ChatGPT or another AI program and start asking questions about whatever is their interest. This is a great way of discovering just what these new ways of learning can and cannot do.

What follows is the material in the handout.

What does AI stand for?

AI stands for "artificial intelligence." Artificial intelligence is the simulation of human intelligence processes by machines, such as computer systems. AI powers many technology-driven industries, such as health care, finance, transportation, and much more. AI in the workforce Artificial intelligence is prevalent across many industries. Automating tasks that don't require human intervention saves money and time and can reduce the risk of human error. 

Here are a couple of ways AI could be employed in different industries:

Finance industry. Fraud detection is a notable use case for AI in the finance industry. AI's capability to analyze large amounts of data enables it to detect anomalies or patterns that signal fraudulent behavior.

Health care industry. AI-powered robotics could support surgeries close to highly delicate organs or tissue to mitigate blood loss or risk of infection.

Kinds of AI

As researchers attempt to build more advanced forms of artificial intelligence, they must also begin to formulate more nuanced understandings of what intelligence or even consciousness precisely mean. In their attempt to clarify these concepts, researchers have outlined four types of artificial intelligence. Here’s a summary of each AI type, according to Professor Arend Hintze of the University of Michigan:

• Reactive machines: Reactive machines don’t possess any knowledge of previous events, but instead only “react” to what is before them in a given moment. As a result, they can only perform specific tasks within a very narrow scope, such as playing chess and are incapable of performing tasks outside of their limited context.
• Limited memory machines: Limited memory machines possess a limited understanding of past events. They can interact more with the world around them than reactive machines can, but cannot form a complete understanding of the world. For example, self-driving cars use a form of limited memory to make turns, observe approaching vehicles, and adjust their speed.
• Theory of mind machines: Machines that possess a “theory of mind” are able to create representations of the world and possess an understanding of other entities that exist within the world. Right now, these machines are only theoretical.
• Self-aware machines: Machines with self-awareness are the theoretically most advanced kind of AI and would possess an understanding of the world, others, and themselves. This is what most people mean when they talk about "true AI." Currently, this is a far-off concept.

What is generative artificial intelligence?

Generative AI is a kind of artificial intelligence capable of producing original content, such as written text or images, in response to user inputs or "prompts." Generative models are also known as large language models (LLMs) because they're essentially complex, deep learning models trained on vast amounts of data that can be interacted with using normal human language rather than technical jargon.

Generative AI is becoming increasingly common in everyday life, powering tools such as ChatGPT, Google Gemini, and Microsoft Copilot. While other kinds of machine learning models are well suited for performing narrow, repetitive tasks, generative AI is capable of responding to user inputs with  unique outputs that allow it to respond dynamically in real-time. This makes it particularly useful for powering interactive programs like virtual assistants, chatbots, and recommendation systems. That said, while generative AI may produce responses that make it seem like self-aware AI, the reality is that its responses are the result of statistical analysis rather than sentience, the ability to experience feelings and sensations (Wikipedia).

AI benefits and dangers

AI has a range of applications with the potential to transform how we work and live. While many of these transformations are exciting – like self-driving cars, virtual assistants, or wearable devices in the healthcare industry –they also pose many challenges. It’s a complicated picture that often summons competing images: a utopia for some, a dystopia for others. The reality is likely to be much more complex. Here are a few of the possible benefits and dangers AI may pose:

Potential Benefits / Potential Dangers

Greater accuracy for certain repeatable tasks, such as assembling vehicles or computers. 

Job loss due to increased automation.

Decreased operational costs due to greater efficiency 

Potential for bias or discrimination as a result of the data machines set on which the AI is trained.

Increased personalization within digital services and products. 

Possible cybersecurity concerns.

Improved decision-making in certain situations.

Lack of transparency over how decisions are arrived at, resulting in less than optimal solutions.

Ability to quickly generate new content, such as text 

Potential to create misinformation, as well as inadvertently images.

Violate laws and regulations.