When we might meet the first intelligent machines

When we might meet the first intelligent machines

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How close are we to living in a world where human-level intelligence is surpassed by machines? Throughout my career, I have regularly engaged in a thought experiment where I try to “think like the computer” to imagine a solution to a programming challenge or opportunity. The gap between human reasoning and software code was always quite clear.

Then, a few weeks ago, after talking to the LaMDA chatbot for months, now “former” Google AI engineer Blake Lemoine said he thought LaMDA was sentient [subscription required]. Two days before Lemoine’s announcement, Pulitzer Prize-winning AI pioneer and cognitive scientist Douglas Hofstadter wrote an article saying [subscription required] that artificial neural networks (the software technology behind LaMDA) are not conscious. He also came to that conclusion after a series of conversations with another powerful AI chatbot named GPT-3. Hofstadter concluded the article by estimating that we are still decades away from machine consciousness.

A few weeks later, Yann LeCun, the chief scientist at Meta’s Artificial Intelligence (AI) Lab and winner of the 2018 Turing Award, released a paper titled “A Path Towards Autonomous Machine Intelligence.” He shares in the paper an architecture that goes beyond consciousness and sensing to propose a path to programming an AI with the ability to reason and plan like humans. Scientists call this Artificial General Intelligence or AGI.

I think we will come to regard LeCun’s paper with the same reverence we reserve today for Alan Turing’s 1936 paper that described the architecture of the modern digital computer. Here’s why.

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Simulation of action using a world model

LeCun’s first breakthrough is imagining a way past the limitations of today’s specialized AIs with his concept of a “world model.” This is made possible in part by the invention of a hierarchical architecture for predictive models that learn to represent the world at multiple levels of abstraction and over multiple time scales.

With this world model, we can predict possible future states by simulating action sequences. In the paper, he notes, “This can enable reasoning by analogy, applying the model configured for one situation to another.”

A configurator module to drive new learning

This brings us to the second major innovation in LeCun’s article. As he notes, “One can imagine a ‘generic’ world model of the environment with a small fraction of the parameters modulated by the configurator for the task at hand.” He leaves the question of how the configurator learns to decompose a complex task into a sequence of subgoals open.. But this is basically how the human mind uses analogies.

For example, imagine if you woke up this morning in a hotel room and had to operate the shower in the room for the first time. Chances are you quickly broke down the task into a series of sub-goals by drawing on analogies you’ve learned from using other showers. First determine how to turn on the water using the handle, then confirm which direction to turn the handle to make the water hotter, etc. You can ignore the vast majority of data points in the room to focus on just a few that are relevant to those goals .

Once started, all intelligent machine learning is self-learning

The third major advance is the most powerful. LeCun’s architecture runs on a self-supervised learning paradigm. This means that AI is capable of learning on its own by watching videos, reading text, interacting with humans, processing sensor data, or processing any other source of input. Most AIs today must be trained on a diet of specially labeled data prepared by human trainers.

Google’s DeepMind has just released a public database produced by their AlphaFold AI. It contains the estimated form of nearly all 200 million proteins known to science. In the past, it took scientists 3-5 years to experimentally predict the shape of just “one” protein. DeepMind’s AI trainers and AlphaFold completed nearly 200 million within the same five-year window.

What will it mean when an AI can plan and reason even without human trainers? Today’s leading AI technologies—machine learning, robotic process automation, chatbots—are already transforming organizations in industries ranging from pharma research labs to insurance companies.

When they arrive, whether in a few decades or a few years, intelligent machines will introduce both great new opportunities and surprising new risks.

Brian Mulconrey is an advisor to the insurtech startup Sureify Labs, a co-founder of Force Diagnostics and a futurist. He lives in Austin, Texas.

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