AI In Medicine Series: A Brief History of AI (Part 1)

In this four-part series of articles, we will take a closer look at AI and its potential applications in healthcare. To begin, let us delve into a concise history of AI to provide context for its current and future uses in this field.

Imagine a world where computers can think like we do, or at least seem to! That is the dream behind Artificial Intelligence (AI), which unlike many would think, is a journey that began long before our smartphones and self-driving cars.

The Early Days of AI

The idea of intelligent machines has been around for centuries, from myths of animated statues to science fiction stories about robot companions.

But it was not until the rise of computers in the mid-20th century that these dreams started to feel possible. In 1950, a mathematician and computer scientist Alan Turing pondered a fundamental question: could a machine ever fool a human into thinking it was one of us? He devised the “Turing Test,” which is a benchmark for measuring machine intelligence that continues to spark debate today. 

During the same era, as chips and computers became increasingly available and more widely used, researchers sought to develop computer programs capable of playing games like chess. I vividly recall reading about the legendary battle between chess grandmaster Garry Kasparov and the Deep Blue Machine when I was younger. Such games demanded strategic thinking and decision-making, abilities once considered exclusively human.

As video games gained popularity, programmers endeavoured to create AI opponents that could challenge human players. Some AI opponents were programmed to be challenging, while others were intended to be easier to defeat. Often, these video games allow us to choose AI opponent difficulty settings ranging from easy to hard.

Beyond games, machines began making programmatic suggestions to us when we use the internet, such as targeted advertisements and recommended articles and videos. Developers can code a simple recommendation algorithm which tracks the last video that we watched, fetches its title and tags, and uses these as keywords to display recommended videos that have the same tags or almost similar titles.

These achievements hinted at a future where computers would follow instructions and make their own choices, representing a significant shift in our understanding of machine capabilities.

Machine Learning: Setting The Scene for AI Revolution

As mentioned earlier, early AI often involved explicitly programming rules and knowledge into a machine. Think of it like a complex flowchart: “IF a patient has symptom X, THEN consider disease Y.” But we know such a rigid algorithmic approach became unwieldy as medical knowledge grew.

Enter machine learning, a fundamental shift in AI development. Instead of hard-coded rules, machine learning algorithms, or called models, are fed massive amounts of data. These models discover patterns and connections on their own, sometimes even surprising their creators. This is the magic behind your email’s spam filter, your streaming service knowing what show to suggest, and those uncannily targeted ads that pop up in your web browser.

Before machine learning was born, machines could perform tasks and make suggestions based on rules programmers created. But that was a pain — it took forever and needed constant updates. Machine learning changed everything. Now, machines are not slaves to instructions. They can “think” on their own. These sophisticated algorithms let machines learn from data, see patterns, and decide what to do. Machines can now give answers that are just as good or even better than the ones if we used to code ourselves.

The release of ChatGPT in early 2023 marked the beginning of an era that many experts have hailed as the next industrial revolution.

ChatGPT is an AI product that utilizes vast amounts of text (language), enabling it to generate text based on prompts provided by the user. Consequently, this model is also known as a large language model (LLM). Developers employ various machine-learning algorithms to create LLMs. ChatGPT utilizes a form of model known as a generative pre-trained transformer, or GPT for short. Apart from ChatGPT, there are also other popular LLMs such as Gemini, Claude, Llama and many others.

Prompting, defined as the act of communicating with these LLMs, is fast becoming an important skill. We will explore key prompting skills in subsequent parts of this four-part article.

Talking Assistants, Self-Driving Cars, Robotic Surgeries and Beyond

Over the last decade or so, AI has woven its way into the fabric of our daily lives.

Even before LLMs came to become mainstream, “old-school” virtual assistants like Siri and Alexa understood our requests with surprising accuracy. From facial recognition in our phones to the algorithms that personalize our social media feeds, AI is subtly shaping our experiences. Its impact extends well beyond consumer gadgets. Self-driving cars, systems that detect financial fraud, and AI-powered robots assisting in complex surgeries are just a few examples of how AI is transforming industries and challenging our preconceptions about what machines can do.

The Brains Behind AI

Beyond LLMs, AI development is a melting pot of disciplines, attracting experts from diverse backgrounds:

  • Computer Science: Provides the foundational knowledge of how to build software, design algorithms, and manage massive datasets. AI is based inside computer systems and therefore the knowledge of computer science undeniably formed the backbone of the science behind modern-day AI.
  • Mathematics: Statistics, probability, and calculus are essential tools in machine learning, allowing AI to analyze patterns and make predictions. AI models today often analyze large amounts of data, resulting in them becoming more performant, efficient and accurate.
  • Neuroscience: By studying the structure and function of the human brain, researchers develop new AI architectures inspired by biological systems. Neural networks were inspired by how the neurons interact in the brain.
  • Philosophy: The ethical considerations of creating intelligent machines are profound, and philosophers play a crucial role in shaping the responsible development and use of AI.

AI is still a young and rapidly evolving field. However, we cannot deny the scare that was caused since the release of the groundbreaking ChatGPT. This led to questions about bias and the potential impact on jobs, raising important challenges for society.

Nevertheless, its potential to revolutionise how we live and work is undeniable. I believe it is best to position ourselves without overly relying on AI or completely denying its benefits. Taking the moderate approach is the way to go.

In the next part of this series, we will dive into how the AI revolution is transforming the world of medicine.

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