“Collapsing into the Arms of a Pale Hit Man” Posted

I posted the recording of my writing exercise “Collapsing into the Arms of a Pale Hit Man.” It is supposed to be a romantic story. It took longer than I had hoped because of health issues I had to deal with.

“Collapsing Into the Arms of a Pale Hitman”

Image Creator from Microsoft Bing

2024 3:54

Ursula notices a pale man following her. She confronts him.

Character voices by Voice.ai: Olivia-V20 and Arnold

https://soundcloud.com/dynamiclethargy/collapsing-into-the-arms-of-a-pale-hitman

 

“Artificial Intelligence (AI) and Me”

I have a complete draft of part 3 of my “Artificial Intelligence (AI) and Me” post. I am not totally happy with it, so I plan to work on it some more. Someone suggested I have ChatGPT rewrite it.

This post is a mirror from my main blog http://www.dynamiclethargyfilms.ca/blog

Dreaming Up Story Ideas

I don’t often remember my dreams, but on occasion I get an odd dream that sticks in my head long enough for me to write it down. Apparently many writers draw on their dreams for inspiration for their stories. One story I heard was that when he worked as a fiction writer, L. Ron Hubbard would have someone wake him up so he would be able to write down his dreams.

These are a few of my recent dreams that might inspire some story ideas.

Image Creator from Microsoft Bing

The Spy and Jack Nicholson

I was making a movie with Jack Nicholson. Then I got called away to work as a spy. I can’t remember what the goal was, but I wasn’t supposed to kill anyone. But, if I killed four people, they’d let me go back to work on my movie. Jack Nicholson wasn’t happy that I’d left the movie and kept following me around on my spy mission. That threatened to blow my cover.

Later, I was in a car with three other people. The driver was my cousin. He was driving wildly. We drove past a small-town train station in an exceptionally flat part of Saskatchewan. Someone said that Switzerland didn’t exist and that all the pictures showing Switzerland were shot in that part of Saskatchewan.

The Sub Basement

I was in the basement the house I lived in when I was in High School although there were times when it switched to be the basement of the house I live in now. Justin Trudeau was with me, and he wanted to use some tools from my father’s work bench. I couldn't find them.

I noticed that there seemed to be only a partial wall at the west end of the basement, next to the furnace. I went to investigate and discovered a doorway into a corridor that ran along the outside of the basement wall. I looked down it and it was very long, and I could see doors at several points along the walls.

I didn't go down the corridor because I noticed a set of stairs going down, so I went down to what turned out to be a subbasement. As the dream went on the subbasement got larger. At the far end there were two garage doors. When opened they revealed some cars crushed under the weight of the dirt above.

By then, the subbasement had grown to several stories and there were walkways at several levels above me. I could see what looked like people walking around on them. There was an elevator that connected the floors.

The Therapist and the Rioters

In the first part of the dream I went to see a therapist or doctor. There were a bunch of other people in the exam room who watched while she examined me. She gave me a small hot-air balloon. When I was in high school, I made a couple similar hot air balloons out of tissue paper and a metal coat hangar. The metal part was hot, so I needed to be careful with it. Then she took me upstairs to a space with a big rotunda. I want to try flying my hot-air balloon there, but I decided not to.

In the second part of the dream, some one had tricked me into joining a group of people who were going to start a riot. I didn't want to take part. They had given each of the rioters a red bat that didn't look like any bat I ever saw. I had the impression that it was something used in a game women played.

We went to a room with many people standing lined up in rows. These people were not part of the rioters. The rioters lined up like the other people. I was at the front of the line. Then the rioters started running out through an exit at the front of the room. For some reason a section of the line I was in ran out the other end.

That left me at the end of the line instead of the front. I took advantage of that to lag behind, and then stop and then slip away. I wandered through the big building avoiding the rioters.

Eventually I found my way to an office of the man in charge of security. He seemed to be a very proper military guy. He was John Cleese, but he acted unusually serious. I told him about the rioters. He didn't seem to be convinced that there was a problem, until some other people came and reported the same thing. I gave my red bat to a woman there. She seemed to know all about it.

C-Train Ride

I was riding on the C-Train in Calgary. I was having an interesting conversation with a man I didn’t know and my brother. In the dream, my brother had moved to Calgary and was living close to where my other brother had lived. I mentioned that I would be getting off the train at an upcoming stop. The stranger and my brother took that as a sign our conversation was over, so they got up and went to sit elsewhere.

When I reached my stop, I got off. Then realized that I’d taken my pants off while on the train and left them on the train. I went back, but couldn’t get back on the train, and I could see my pants were not there.

Somehow, I realized that a fellow filmmaker had picked up my pants and was looking for me to give them back. It was dark out and I chased him down an unlit back street. He was looking for me, but he didn’t hear me behind him. I grabbed the pants.

Strange Events in a Garage

This was a long dream but could only the last part. I was hiding in a car in a garage. The lights in the garage were going off and on while the garage door opened and closed by itself. I was expecting something was going to enter the garage and was afraid to even see it.

The Missing Neighbour

Some people started coming to the front door of my neighbour. They had appointments to see the guy who lived there, but he didn’t seem to be at home. Eventually I went to investigate. Some police came, but they didn’t go inside. When I went inside the house was one long corridor with rooms to either side. The power was out, and all the rooms were dark. When we got closer to back of the house some of the rooms had small night lights that were lit up. Eventually we came to a well-lit room at the back of the house. It was set up as an office with a wooden desk. The room looked as if it hadn’t been used recently. I woke up then with the mystery of what happened to my neighbour unsolved.

Trapped in an Apartment

I had a dream where I was trapped in an apartment building with a bunch of other people. The doors would not open. I was cut off from the main part of the building with just a few other people. This went on for a long time (weeks). I have no idea how we got food to eat. Eventually two of my engineering professors and Sir Isaac Newton, who all lived in the building, managed to free everyone. Just before we were freed, a young woman showed up in our part of the building. She claimed she’d always been there. It turned out that she was an accidental time traveller from the future. I saw her years later playing the drums on the street.

This post is a mirror from my main blog http://www.dynamiclethargyfilms.ca/blog

Artificial Intelligence (AI) and Me – Part 2

Almost everyday I see new developments in the field of Artificial Intelligence (AI), and new opinions that people have about AI. I have tried to keep current in my posts, but inevitably, some of what I say will be outdated, possibly even in just a few days.

AI Problems

In this post I will talk about several problems that have impeded the development of neural network-based AI systems in the past. These same problems will likely continue to be problems for AI in the future.

The Training Data Problem

I think that compiling the training data set for AI poses the most formidable obstacle to creating practical AI systems. Large networks needed a large volume of data. The training data set for ChatGPT3 had 300 billion words. Until the Internet matured, it would have been impossible to find that volume of text data needed to train complex AI systems like ChatGPT.

When I took the Artificial Intelligence class back in the 1990s, they warned us about the need to ensure that the training data set was of high quality. Any errors or mistakes in the data would contaminate the AI, leading to poor quality results. A major part of compiling the data would be to check, and, if necessary, clean the data.

The Intellectual Property Problem

The huge demand for training data has started to run into legal challenges. Writers, other creative people, and owners of intellectual property are concerned that the AI companies are compensating them when their work is used to train AI systems. It could be that my own work may well have been used to train AI systems. I have no idea how I would find out if it had.

OpenAI said that they cannot create a functional AI without the use of copyrighted material. https://arstechnica.com/information-technology/2024/01/openai-says-its-impossible-to-create-useful-ai-models-without-copyrighted-material/. It is likely that some kind of limitation on the use of copyrighted material will emerge. This could restrict the development of AI systems and increase the cost. While there is a large volume of material available in the public domain, this material is often old, and outdated.

The Bias Problem

Bias in the training data is part of the issue of how clean the data is. However, bias is something that deserves special consideration. If the data has a bias, so will the AI. In the course I took in the 1990s, they reinforced this issue time and time again. Sadly, bias has been a problem with many AI systems. While this can have humorous results, it can also result in negative outcomes.

In one case an AI created to identify skin cancers, used the fact that images of actual skin cancers happened to include a ruler for scale, while non cancer images did not. https://venturebeat.com/business/when-ai-flags-the-ruler-not-the-tumor-and-other-arguments-for-abolishing-the-black-box-vb-live/.

Many articles have been published about bias in AI models for law enforcement. For example: https://daily.jstor.org/what-happens-when-police-use-ai-to-predict-and-prevent-crime/ The problem of bias is not limited to policing though.

Bias creeps in during the creation of the training data set. If there is bias in how a law is enforced, the data available about that law will contain that bias. It is essential that the data be checked for bias before training. Not only can the original data be biased, but the people checking for bias may have their own biases, which they may be unaware of.

There was a recent case where attempts by Google to correct bias in an AI model resulted in a different bias. https://globalnews.ca/news/10311428/google-gemini-image-generation-pause/.

It is easy to call out the bias in AI systems. However, it is clear that controlling bias is difficult. Discovering the best ways to address bias in AIs will continue to be a major challenge.

As an aside, I suspect that the underlying cause of the biases people have may well be the same as the cause of bias in AI systems. Maybe learning how to deal with bias in AI systems may help us deal with bias in people.

The Black Box Problem

I spent most of my working career developing and applying transportation forecasting models. These were used to predict what traffic will be like in the future, which were then used to plan the transportation system.

Many people criticized the forecasts the model produced because they saw the model as a black box. They couldn’t see how it worked, so they tended to distrust what they predicted. While I felt that the model could be explained, the explanations were complicated. Few people had the time or patience needed to understand the explanations.

The problem with neural networks is that they truly are black boxes. We can see the inputs and the outputs. We can even look at the parameters inside the AI. But with AI systems that can have 175 billion parameters, it is not practical for people to understand, let alone explain, how the AI got the answer it did.

The black box problem makes it very difficult to fix an AI that isn’t acting the way you want it to. It can’t be debugged in the same way a computer program. It can’t be reasoned with in the same way as you can with a person.

It appears to me that the current approach is to revisit the training data set and modify it before retraining the model. It may be necessary to revise the data and retrain the AI system many times before the users and developers are satisfied.

Almost everyday I see new developments in the field of Artificial Intelligence (AI), and new opinions that people have about AI. I have tried to keep current in my posts, but inevitably, some of what I say will be outdated, possibly even in just a few days.

AI Problems

In this post I will talk about several problems that have impeded the development of neural network-based AI systems in the past. These same problems will likely continue to be problems for AI in the future.

The Training Data Problem

I think that compiling the training data set for AI poses the most formidable obstacle to creating practical AI systems. Large networks needed a large volume of data. The training data set for ChatGPT3 had 300 billion words. Until the Internet matured, it would have been impossible to find that volume of text data needed to train complex AI systems like ChatGPT.

When I took the Artificial Intelligence class back in the 1990s, they warned us about the need to ensure that the training data set was of high quality. Any errors or mistakes in the data would contaminate the AI, leading to poor quality results. A major part of compiling the data would be to check, and, if necessary, clean the data.

The Intellectual Property Problem

The huge demand for training data has started to run into legal challenges. Writers, other creative people, and owners of intellectual property are concerned that the AI companies are compensating them when their work is used to train AI systems. It could be that my own work may well have been used to train AI systems. I have no idea how I would find out if it had.

OpenAI said that they cannot create a functional AI without the use of copyrighted material. https://arstechnica.com/information-technology/2024/01/openai-says-its-impossible-to-create-useful-ai-models-without-copyrighted-material/. It is likely that some kind of limitation on the use of copyrighted material will emerge. This could restrict the development of AI systems and increase the cost. While there is a large volume of material available in the public domain, this material is often old, and outdated.

The Bias Problem

Bias in the training data is part of the issue of how clean the data is. However, bias is something that deserves special consideration. If the data has a bias, so will the AI. In the course I took in the 1990s, they reinforced this issue time and time again. Sadly, bias has been a problem with many AI systems. While this can have humorous results, it can also result in negative outcomes.

In one case an AI created to identify skin cancers, used the fact that images of actual skin cancers happened to include a ruler for scale, while non cancer images did not. https://venturebeat.com/business/when-ai-flags-the-ruler-not-the-tumor-and-other-arguments-for-abolishing-the-black-box-vb-live/.

Many articles have been published about bias in AI models for law enforcement. For example: https://daily.jstor.org/what-happens-when-police-use-ai-to-predict-and-prevent-crime/ The problem of bias is not limited to policing though.

Bias creeps in during the creation of the training data set. If there is bias in how a law is enforced, the data available about that law will contain that bias. It is essential that the data be checked for bias before training. Not only can the original data be biased, but the people checking for bias may have their own biases, which they may be unaware of.

There was a recent case where attempts by Google to correct bias in an AI model resulted in a different bias. https://globalnews.ca/news/10311428/google-gemini-image-generation-pause/.

It is easy to call out the bias in AI systems. However, it is clear that controlling bias is difficult. Discovering the best ways to address bias in AIs will continue to be a major challenge.

As an aside, I suspect that the underlying cause of the biases people have may well be the same as the cause of bias in AI systems. Maybe learning how to deal with bias in AI systems may help us deal with bias in people.

The Black Box Problem

I spent most of my working career developing and applying transportation forecasting models. These were used to predict what traffic will be like in the future, which were then used to plan the transportation system.

Many people criticized the forecasts the model produced because they saw the model as a black box. They couldn’t see how it worked, so they tended to distrust what they predicted. While I felt that the model could be explained, the explanations were complicated. Few people had the time or patience needed to understand the explanations.

The problem with neural networks is that they truly are black boxes. We can see the inputs and the outputs. We can even look at the parameters inside the AI. But with AI systems that can have 175 billion parameters, it is not practical for people to understand, let alone explain, how the AI got the answer it did.

The black box problem makes it very difficult to fix an AI that isn’t acting the way you want it to. It can’t be debugged in the same way a computer program. It can’t be reasoned with in the same way as you can with a person.

It appears to me that the current approach is to revisit the training data set and modify it before retraining the model. It may be necessary to revise the data and retrain the AI system many times before the users and developers are satisfied.

This post is a mirror from my main blog http://www.dynamiclethargyfilms.ca/blog