Artificial Intelligence (AI) and Me – Part 1

Artificial Intelligence (AI) has been all the rage in 2023 and it looks like it will continue to be a big thing in 2024 as well. I have mixed feelings about AI. I can see its value as well as the dangers and challenges it brings. In this series of blog posts I will try to clarify my own thoughts about AI.

This post will focus on my experiences with the concept of artificial intelligence before the recent developments in AI. I feel this background information will be useful in later posts. In later posts I will look at the technical challenges of AI, the problems of AI in application, and my own use of AI systems.

My exposure to AI began in the 1960s when I began to read Science fiction. Then later, in the 1990s, I took a course in expert systems and neural networks systems.

Artificial Intelligence (AI) in Science Fiction

Artificial Intelligence has long been a feature of Science Fiction stories. That is where I was first exposed to the idea.

You can’t really talk about AI, without referring to the movie “2001: A Space Odyssey.” It is my favorite movie. Although AI was common in earlier science fiction, “2001” exposed the concept to a wider audience. The benefits and dangers of AI are an important aspect of the story.

One comment that I find of particular interest comes at about 1 hour and three minutes into the film. A TV interviewer asks Dave Bowman if HAL, the onboard computer, has real emotions. He replies, “Well, he acts like he has genuine emotions. Um, of course he’s programmed that way to make it easier for us to talk to him. But as to whether he has real feelings is something I don’t think anyone can truthfully answer.”

Many of Isaac Asimov’s robot stories deal with the opportunities and dangers of AI. One story that sticks in my mind nearly 50 years after I last read it is “Galley Slave.” It is about a robotic proofreader who is alleged to have ruined a writer’s reputation by making changes to the galleys of a book an author had written. Since people are now using AI systems for proof reading, this 70-year-old story is still relevant. (  I think people who have experienced frustration with autocomplete will empathize with the main character.

“The Tunnel Under the World” by Frederik Pohl is another story that is relevant to AI. I never read the story, but I heard a radio play based on it. [Spoiler Alert] In the story tiny intelligent robots, who represent real people, live in a model of a city. They are used to test how real people might react to different advertising campaigns as they go about their daily activities.

Expert Systems and Neural Networks

In the mid-1990s I took a night class on expert systems and neural networks. Neural networks are the underlying technology behind today’s artificial intelligence systems.

In the 1990s I worked on the creation and application of computer models to forecast traffic for planning the transportation system. I took the class to better understand a new type of transportation model that, like “The Tunnel Under the World,” used AI simulations of individual people to predict the behavior of real people when faced with changes to the transportation system. These newer models didn’t come into use until after I left the field, so I don’t know how they worked out.

Expert systems were an earlier attempt to develop artificial intelligence. However, by the mid-1990s, neural network technology was beginning to supplant the older expert systems.

Neural networks are based on a simulation of how the human brain works. The brain consists of neurons that are interconnected. It is the strength of these connections that determine how a brain thinks. In neural network systems nodes represent neurons and parameters represent the connections.

A human brain may have 100 billion neurons and about 700 trillion connections. By comparison ChatGpt3 had about 175 billion parameters. The human brain is about 4,000 times as complex as ChatGPT3.

After a neural network is set up, it is trained using a training data set, which gives the network the inputs you have and the outputs that you want it to produce.

This is a simplified explanation. You can get a more detailed explanation here:

How To Create a Neural Network

There are two major steps in creating an AI system using a neural network. The first step is setting up the network. The second step is to train the network.

The neural networks we covered in the class I took were quite simple. These networks had only a matter of a few dozen nodes. The structure consisted of an input layer, an output layer and one or more intermediate layers. The systems we use today are far more complex.

The more interesting and understandable step to me is training the network.

Before a neural network can be trained, you must create the training data set. This consists of observations of the inputs available and outputs you want. An example of this could be images of handwritten letters and the corresponding letters they represent.

Creating a useful training data set is a major challenge. Not only must you find the data, but you will need to ensure the data covers a broad enough range of possibilities. Also, you need to ensure that the data is clean. That is, the outputs must match the inputs. For example, all the images of handwritten “A’s” must be identified in the output as “A’s”. In the course the teacher warned us that any bias in the data will result in a biased AI.

The neural network is calibrated by repeatedly running the input data through the neural network, then compared to the corresponding output. Based on how well the neural network replicates the observed output data, the parameters of the neural network are adjusted, until the outputs match. This can take a long time.

In the 1990s, it was very difficult to assemble a useful training data set. As the Internet grew and more information became available in digital form, it became easier to compile the kind of large data sets that neural networks needed for calibration. There are still major barriers now to preparing a useful training data set.

The computers available in the 1990s were much slower, which limited the size of neural networks. Even the fastest super computers in the 1990s would struggle to calibrate large neural networks. Many modern home computers can outperform the super computers from the 1990s. This has dramatically expanded the size of neural networks we can work with, and consequently the ability of AI systems has also expanded.

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