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If we really want to understand artificial intelligence’s power, promise, and peril, we first need to understand the difference between intelligence as it is generally understood and the kind of intelligence we are building now with AI. That is important, because the kind we are building now is really the only kind we know how to build at all — and it is nothing like our own intelligence.
Despite significant progress in artificial intelligence (AI) research and development, there remains a gap between the potential of AI and the reality of what has been delivered.
While early AI researchers sought to emulate human thinking, modern AI is based on machine learning, which uses statistical methods to build associations from data.
Nonetheless, the recent combination of powerful computing and algorithmic advances has led to breakthroughs in natural language recognition, such as ChatGPT, and renewed optimism about the potential of AI.
Through much of the field’s early years, AI researchers tried to understand how thinking happened in humans, then use this understanding to emulate it in machines. This meant exploring how the human mind reasons or builds abstractions from its experience of the world. An important focus was natural language recognition, meaning the ability for a computer to understand words and their combinations (syntax, grammar, and meaning), allowing them to interact naturally with humans.
Over the years, AI went through cycles of optimism and pessimism — these have been called AI “summers” and “winters” — as remarkable periods of progress stalled out for a decade or more. Now we are clearly in an AI summer. A combination of mind-boggling computing power and algorithmic advances combined to bring us a tool like ChatGPT. But if we look back, we can see a considerable gap between what many hoped AI would mean and the kind of artificial intelligence that has been delivered. The mighty ChatGPT, some argue, is nothing but “autocomplete on steroids”.
Modern versions of AI are based on what is called machine learning. These are algorithms that use sophisticated statistical methods to build associations based on some training set of data fed to them by humans. If you have ever solved one of those reCAPTCHA “find the crosswalk” tests, you have helped create and train some machine learning program. Machine learning sometimes involves deep learning, where algorithms represent stacked layers of networks, each one working on a different aspect of building the associations.
Machine learning is a remarkable achievement in computer science, but it relies on statistical models that use enormous amounts of data to find patterns and make predictions.
While machines have become increasingly proficient at these tasks, they lack the complexity and nuance of human intelligence. Thus, while we should celebrate the accomplishments of AI, we should also acknowledge its limitations and recognize that true machine intelligence may remain beyond our reach for some time.
In this way our AI wonder-machines are really prediction machines whose prowess comes out of the statistics gleaned from the training sets. (While this is oversimplifying the wide range of machine learning algorithms, the gist here is correct.) This view does not diminish in any way the achievements of the AI community, but it underscores how little this kind of intelligence (if it should be called such) resembles our intelligence.
Human minds are so much more than prediction machines. What really makes human beings so potent is our ability to discern causes. We do not just apply past circumstances to our current circumstance — we can reason about the causes that lay behind the past circumstance and generalize it to any new situation. It is this flexibility that makes our intelligence “general” and leaves the prediction machines of machine learning looking like they are narrowly focused, brittle, and prone to dangerous mistakes.
One of the most interesting aspects of machine learning is how opaque it can be. Often it is not clear at all why the algorithms make the decisions they do, even if those decisions turn out to solve the problems the machines were tasked with. This occurs because machine learning methods rely on blind explorations of the statistical distinctions between, say, useful email and spam that live in some vast database of emails. But the kind of reasoning we use to solve a problem usually involves a logic of association that can be clearly explained. Human reasoning is never blind.
That difference is the difference that matters. Early AI researchers hoped to build machines that emulated the human mind. They hoped to build machines that thought like people. That is not what happened. Instead, we have learned to build machines that don’t really reason at all. They associate, and that is very different. That difference is why approaches rooted in machine learning never produce the kind of General Artificial Intelligence the founders of the field were hoping for.
That difference may also be why the greatest danger from AI won’t be a machine that wakes up, becomes self-conscious, and then decides to enslave us. Instead, by misidentifying what we have built as actual intelligence, we pose the real danger to ourselves. By building these systems into our society in ways we cannot escape, we may force ourselves to conform to what they can do, rather than discover what we are capable of.
“An idea is something that won’t work unless you do.” - Thomas A. Edison
Machine learning is coming of age, and it is a remarkable and even beautiful thing. But we should not mistake it for intelligence, lest we fail to understand our own.
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