What is explainable AI?

Despite its recent boom, AI is a relatively young field – and it has experienced its fair share of ups and downs.

Although it wasn’t called “artificial intelligence” at the time, the first mathematical computer model of a single neuron was developed in the early 1940s. This invention was promising, but a lack of computational power prevented even minor real-world application, which led to what is known as the first AI winter.

There was not much progress in the field of AI until thirty years later in the late 1970s. Neural networks – collections of connections of computer models of neurons – became possible by virtue of the first microprocessors. These were called “discovery systems” or “expert systems,” because they were trained to be experts in a particular field to usher in new discoveries. The original applications for discovery systems were, in fact, in life science: Their primary aims were to study hypothesis formation and discovery in molecular chemistry and disease diagnosis(1). But the limitations of the time – the expenses of data storage, hardware, and implementation – outweighed any benefits, which led to the second AI winter.

Today, with the advent of Big Data and more powerful processors, we are experiencing an AI boom. AI models are achieving impressive feats in areas such as gaming (mastering chess, Go, and then Jeopardy), science (predicting the 3D structures of protein), and automating human tasks (delivering packages, driving cars, and having conversations).

For all its short history of fewer than 100 years, AI has both disappointed and surpassed our expectations.

“Artificial intelligence” is a very broad term, which involves using computer systems to make predictions or automate tasks. When people talk about AI, they’re typically referring to what is called Artificial Narrow Intelligence (ANI).

The field of AI is rapidly evolving, and there are still two other categories of AI that don’t exist yet in reality: Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). These categories are characterized by their closeness to or ability to surpass human intelligence and performance.

ANI has several subfields that are often referred to interchangeably, but in reality they have very different use cases in pharmaceuticals. “Machine learning” involves the training of an algorithm with data to make a prediction, “deep learning” is a subfield of machine learning which has a depth requirement of greater than three neural networks, and a “neural network” is a set of algorithms meant to mimic the neurons in a human brain.

Generative AI, the form of artificial intelligence popularized by ChatGPT and DALL-E, although powerful, is still considered ANI. Generative AI is a deep learning algorithm that has been trained on massive amounts of data to accurately predict the next word or pixel.

Machine learning, deep learning, and neural networks are less like a set of Matryoshka dolls and more like a Venn diagram of algorithms. For example, deep learning is a kind of machine learning, but machine learning does not have to be achieved through neural networks. But memorizing these categories is not the crucial item. The most important aspect to understand and evaluate any AI model is how it achieves a prediction.