Neuroscientist explains differences between AI and human learning Posted on : Jun 06 - 2022

Recently developed artificial intelligence (AI) models are capable of many impressive feats, including recognizing images and producing human-like language. But just because AI can perform human-like behaviors doesn't mean it can think or understand like humans.

As a researcher studying how humans understand and reason about the world, I think it's important to emphasize the way AI systems "think" and learn is fundamentally different to how humans do—and we have a long way to go before AI can truly think like us.

A widespread misconception

Developments in AI have produced systems that can perform very human-like behaviors. The language model GPT-3 can produce text that's often indistinguishable from human speech. Another model, PaLM, can produce explanations for jokes it has never seen before.

Most recently, a general-purpose AI known as Gato has been developed which can perform hundreds of tasks, including captioning images, answering questions, playing Atari video games, and even controlling a robot arm to stack blocks. And DALL-E is a system which has been trained to produce modified images and artwork from a text description.

These breakthroughs have led to some bold claims about the capability of such AI, and what it can tell us about human intelligence.

For example Nando de Freitas, a researcher at Google's AI company DeepMind, argues scaling up existing models will be enough to produce human-level artificial intelligence. Others have echoed this view.

In all the excitement, it's easy to assume human-like behavior means human-like understanding. But there are several key differences between how AI and humans think and learn.

Neural nets vs the human brain

Most recent AI is built from artificial neural networks, or "neural nets" for short. The term "neural" is used because these networks are inspired by the human brain, in which billions of cells called neurons form complex webs of connections with one another, processing information as they fire signals back and forth.

Neural nets are a highly simplified version of the biology. A real neuron is replaced with a simple node, and the strength of the connection between nodes is represented by a single number called a "weight."

With enough connected nodes stacked into enough layers, neural nets can be trained to recognize patterns and even "generalize" to stimuli that are similar (but not identical) to what they've seen before. Simply, generalization refers to an AI system's ability to take what it has learnt from certain data and apply it to new data. View more