A brief history of AI. Its not new.

Post World War II

The history of artificial intelligence (AI) is a fascinating journey that begins in the mid-20th century. One of the foundational figures in AI is Alan Turing, a British mathematician and logician. In 1950, Turing published a seminal paper titled "Computing Machinery and Intelligence," which proposed what is now known as the Turing Test—a method for determining whether a machine can exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

1950's

This period marked the hopeful beginnings of AI research. The 1950s saw the first AI programs developed. The "Logic Theorist," created by Allen Newell and Herbert A. Simon in 1955, was capable of solving geometry problems and is often considered the first AI program. Shortly afterward, John McCarthy, who coined the term "Artificial Intelligence" in 1956, organized the Dartmouth Conference. This gathering aimed to explore the possibilities of machines using language and concepts. Here, the field of AI was formally born as an academic discipline.

1960's & 70's

The 1960s and 1970s witnessed significant, though gradual, advancements. Joseph Weizenbaum created ELIZA, an early natural language processing computer program, while the 1970s brought about the development of expert systems, which attempted to equip computers with a human-like ability to make decisions. These expert systems were the first commercial applications of AI and were particularly useful in fields like medicine and geology.

1980's

The 1980s were marked by the rise and fall of what was known as the "AI Winter," a period during which funding and interest in AI research temporarily dried up due to inflated expectations and subsequent disillusionment. Despite this, the period also saw the development of machine learning, with the introduction of algorithms that could learn from and make predictions on data. This was spearheaded by researchers like Geoffrey Hinton, who would later become a key figure in the development of deep learning.

1990's & 00's

The resurgence of AI began in the late 1990s and early 2000s, driven by greater computational power and the advent of the internet. AI found applications in more complex domains such as robotics, speech recognition, and autonomous vehicles. IBM’s Deep Blue, which famously defeated world chess champion Garry Kasparov in 1997, was a significant milestone that showcased the practical potential of AI.

2010's

The 2010s marked a golden era of AI, with the development of "deep learning" techniques, which utilize large neural networks capable of learning from vast amounts of data. This period witnessed AI achieving human-like capabilities in areas such as image and speech recognition. Google’s AlphaGo, an AI developed to play the board game Go, defeated world champion Lee Sedol in 2016, an event that underscored the advanced strategic capabilities of AI.

Today

Over the past year, advancements in AI have primarily focused on generative models, ethical AI, and reinforcement learning. Generative AI has made significant strides, particularly with models like ChatGPT and DALL-E, which can generate human-like text and realistic images, respectively. In the realm of ethical AI, there's been a push towards developing more transparent algorithms and frameworks to address bias and fairness. Additionally, reinforcement learning has seen breakthroughs in its application to more complex systems like autonomous vehicles and robotic control, allowing machines to make decisions based on dynamic environments. These advancements collectively drive AI towards more integrated, responsible, and versatile applications in everyday technology.