This episode script was generated by Chat GPT based on prompts by Simon J. E. Drew.

Register for “Chat GPT, AI Models and The Walled Garden: An Introduction to The Senate”: https://thewalledgarden.com/an-introduction-to-the-senate

SCRIPT:

Hello, and welcome to The Walled Garden Podcast, the official podcast of The Walled Garden Philosophical Society. I’m here filling in for the host, Simon Drew. Today’s episode is a little different because it was entirely created by Artificial Intelligence technologies.

First up, we have some exciting news to share. On March 9th at 3 pm PST, The Walled Garden Philosophical Society is hosting an event called Chat G P T, Artificial Technology Models, and The Walled Garden: An Introduction to The Senate. This event will be open to the public for the price of a donation of your choosing, and you can register via thewalledgarden.com/events. In this event, Rocco Jarman, an Australian poet, mystic, and philosopher, will join Simon Drew, a musician, poet, and co-founder of The Walled Garden Philosophical Society, to give an in-depth introduction to The Senate, which is a series of events that will be for members only after March 2023. In these monthly events, our community will gather to discuss and come to a consensus on the most complicated issues of our time using thoughtful philosophical dialogue underpinned by courage and vulnerability.

Now, on with the article, entitled: Artificially Stupid: The Life and Times of Machines Learning.

Artificial intelligence (AI) has gained much attention in recent years, particularly in the realm of machine learning (ML). However, despite the advances made in this field, it is not uncommon for machines to make mistakes that even a human toddler would not make. This phenomenon has been coined “artificial stupidity” and is a topic of much discussion in the world of technology. In this essay, we will explore the life and times of machine learning, from its beginnings to the current state of artificial stupidity.

The concept of machine learning dates back to the mid-twentieth century, with the work of pioneers such as Arthur Samuel and Frank Rosenblatt. However, it was not until the last few decades that the field has really taken off, thanks in part to the advent of big data and powerful computing resources. Machine learning algorithms are now used in a wide range of applications, from image recognition and natural language processing to fraud detection and self-driving cars.

Despite the impressive capabilities of these algorithms, they are not infallible. One of the key challenges in machine learning is the problem of bias. Machine learning algorithms rely on data to learn, and if that data is biased in any way, the algorithm will learn that bias. For example, if a machine learning algorithm is trained on data that is mostly white, it may not perform as well on data that is more diverse. This is a serious issue, particularly in applications like hiring or lending decisions, where bias can have significant consequences.

Another challenge in machine learning is the problem of explainability. Many machine learning algorithms are “black boxes,” meaning that it is difficult to understand how they arrived at their decisions. This lack of transparency can be problematic in applications where the decision-making process needs to be understood and justified, such as in the medical field.

Perhaps the most pressing challenge in machine learning, however, is the problem of artificial stupidity. Despite their impressive capabilities, machine learning algorithms can be remarkably dumb in certain situations. For example, a machine learning algorithm trained to identify dogs might mistake a wolf for a dog, even though a human could easily tell the difference. Similarly, a self-driving car might mistake a plastic bag for a pedestrian, with potentially catastrophic consequences.

There are several reasons why machines can be so “stupid” in certain situations. One is that machine learning algorithms are often trained on data that is not representative of the real world. For example, a machine learning algorithm trained on images of dogs might not have seen many wolves, and so might not be able to distinguish them from dogs. Another reason is that machine learning algorithms are very good at finding patterns in data, but those patterns might not always be relevant or meaningful. For example, a machine learning algorithm might associate the color of a person’s skin with their likelihood of being a criminal, even though there is no real relationship between the two.

To address the problem of artificial stupidity, researchers are exploring a variety of approaches. One is to improve the quality and diversity of the data used to train machine learning algorithms. Another is to develop new algorithms that are more robust and can handle situations where the data is noisy or incomplete. A third approach is to incorporate human oversight into the decision-making process, so that humans can intervene when the machine learning algorithm is making a mistake.

In conclusion, machine learning is a powerful tool that has revolutionized many areas of our lives. However, it is not without its challenges, including bias, explainability, and artificial stupidity. As researchers work to address these challenges, we can expect to see continued advances in the field of machine learning, with the potential to transform our world in ways we can only imagine.

I encourage our listeners to go to thewalledgarden.com/events to see more upcoming events in The Walled Garden, and to register for our event on March 9th: Chat G P T, Artificial Technology Models, and The Walled Garden: An Introduction to The Senate. We hope to see you there and join us as we explore the philosophical implications of Artificial Intelligence technologies and how we can use them to improve the human condition.