Milestones in AI
Artificial intelligence has come a long way in a very short time. At HeadshotBooth, we create our images with Flux™. Flux allows us to produce high-quality images through a structured, iterative process that guarantees consistency and quality for our customers. Incredibly powerful, Flux is a recent technological development, capping a deluge of AI innovations over the past few years. While these advancements have been exciting, AI development began much earlier, and we wanted to provide a history of this evolution.
Joey Stein
5 min read
·October 12, 2024
Foundations
AI development has its foundations in the 1940s, when Warren McCulloch and Walter Pitts proposed the first mathematical model of a neural network, laying the groundwork for modern AI concepts. In their paper, “A Logical Calculus of the Ideas Immanent in Nervous Activity,” they demonstrated how networks of artificial neurons could process binary information, essentially mimicking the neural networks of our own brains. This opened the door to developing systems that could simulate human reasoning, influencing AI research and later studies in machine learning, ultimately setting the stage for the AI systems of today.
In 1950, Alan Turing published Computing Machinery and Intelligence, a landmark paper that addressed the question: Can machines think? He proposed the "Turing Test," a way to assess if a program was indistinguishable from a human. This was a revolutionary idea because it brought focus to another important question: Is thinking more defined by behavior or internal cognition? By focusing on behavior, machine learning became more practical and testable. Eight years later, John McCarthy developed the programming language LISP, and the era of AI research began to take off. Before LISP, programming languages lacked the flexibility to handle symbolic computation, which is critical for AI tasks. Researchers were able to rapidly prototype systems and explore AI concepts faster than ever.
A decade later, in 1966, Joseph Weizenbaum created ELIZA, one of the earliest chatbots, which demonstrated the potential for machines to simulate human conversation. While basic compared to the AI chat available today, this development highlighted AI’s possibilities and paved the way for further exploration. ELIZA surprised many with how well it could engage with a user and showed how basic algorithms could simulate a real conversation without needing to understand the true meaning behind it.
The first AI winter
Between 1974 and 1980, a period referred to as the first AI winter occurred. Development of AI systems slowed, and people became frustrated with the limited computing power available at the time. There was a sense that raw computing power needed to be developed before further advancements could be made. Unfortunately, government and research institutions began cutting funding to AI. This era came to an end with the rise of expert systems, a form of AI that used knowledge-based rules to solve specific problems. Expert systems showed that real-world problems in fields like medicine, finance, and engineering could be solved. It led to the creation of Japan’s Fifth Generation Computer Systems project in the 1980s, sparking a new era of global competition and investment in AI research.
AI in the mainstream
A major milestone many remember occurred in 1997 when IBM's Deep Blue defeated chess champion Garry Kasparov. This was a major triumph for AI, as chess requires strategic thinking. Deep Blue demonstrated how brute-force computation combined with algorithms was capable of surpassing a human. This event captured global attention and further increased excitement in AI’s development.
In 2011, IBM marked another AI milestone when IBM's Watson defeated Jeopardy stars Ken Jennings and Brad Rutter. This was a significant development because it showed how natural language processing could allow a system to handle complex, ambiguous questions asked in natural human language. This is much more difficult than chess and showcased AI’s ability not only to retrieve facts but also to make quick decisions in real time. Today, this type of AI has become commonplace—you might not even realize when you’re talking to an AI handling customer support.
Watson defeating Jeopardy champions Ken Jennings and Brad Rutter.
Recent advancements
Closer to the present, in 2018, OpenAI and Google made significant progress in the development of AI by releasing large-scale language models. These models were a breakthrough in AI’s ability to process and generate text. This development serves as the foundation that led to the AI systems we’ve become accustomed to, such as ChatGPT, Anthropic’s Claude, and Google Gemini. Large language models, referred to as LLMs, have also influenced the development of AI image generation models by interpreting text input, which can be used by image generation systems to produce corresponding visuals.
Finally, in 2023, platforms like ChatGPT became fully integrated with commercial applications and entered the mainstream. Today, AI is integrated into healthcare, education, entertainment, customer support, and essentially every software system you use. As we mentioned in the beginning, our image generation is driven by these breakthroughs and the growing availability of tools and data. As AI continues to develop, we’ll be able to deliver higher-quality photos and potentially new options that haven’t yet been imagined.
