Note: The history information is from ChatGPT on february 6, 2023. I added one comment in brackets, and provided the bold and italic formatting. -Ray

A Summary of the History of AI

The history of AI can be traced back to the mid-20th century when the term “Artificial Intelligence” was first coined by John McCarthy. In the 1950s and 60s, AI research was mainly focused on creating algorithms that could perform specific tasks, such as playing chess. However, progress was limited due to the computational power available at the time. [See AI and Exponential Tech Advancement for more insight on the relationship of information technology (IT) advancement and the advancement of AI.]

In the 1980s and 1990s, AI experienced a resurgence, known as the “second AI boom,” as a result of increased computational power and the development of new algorithms. During this time, expert systems and decision-support systems were developed, which demonstrated the practical applications of AI.

In the 21st century, the field of AI continued to advance rapidly with the development of deep learning and the availability of large amounts of data. This led to breakthroughs in image and speech recognition, and the widespread use of AI in various industries, such as finance, healthcare, and retail.

Today, AI is an active area of research, with the goal of creating more advanced and capable AI systems that can perform a wider range of tasks and have a greater impact on society.

AI Keeps Getting Smarter

As explained in AI and Exponential Tech Advancement, the rate at which AI advances occurs at an ever-accelerating pace. I call it “ever-smarter” because the thinking power and capabilities of applying AI keep doubling at an unbelievable pace. AI progress is only limited by the computational power available at any point in time. Another way to look at it is to say that AI advances at a pace that’s related to advances in computing capability – which is advancing at a humanly incomprehensible rate.

However, that’s just part of the picture. There are actually two major driving forces behind AI advancement:

• Self-Advancement of AI. Exponential computing advancement means that self-learning AI keeps advancing itself on ever faster hardware. Plus, AI is used to design better and better computer hardware and to improve computer hardware manufacturing capabilities to AI advancement is now feeding the exponential advancement of computing. AI figures out exactly how computing hardware needs to improve to support the processing and data handling that AI needs to perform, and then helps make that possible in both the design and manufacturing efforts.
• Human Advancement of AI. Human input number one: A growing number of “the smartest data scientists” in the world continue working on AI improvement and on developing more applications for AI. Human input number two: The more we all use AI, the more AI learns. The more we use AI, the more ways we think of to use AI even more. The extent of AI learning is mind-boggling. According to  The Small Business Blog, “The launch of ChatGPT on November 30th was met with overwhelming success, as it amassed an impressive 1 million users within five days. As of January 2023, ChatGPT has over 100 million users.” In just two years ChatGPT gained as many users as Facebook did in six years. That’s 100 million people who are contributing just to the AI language model behind ChatGPT. And ChatGPT is just a small part of that contribution – a contribution that keeps expanding at an exponential rate.