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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.
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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...
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 ass...
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 ...
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 ...
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 algori...
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 ...
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” ...
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 ...
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 int...
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 general...
“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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