The project kickstarted the field that has become known as artificial intelligence (AI).
More than six decades later, the dream of creating artificial intelligence still eludes us.
What is the difference between general AI and narrow AI?
It should be able to do reasoning and abstraction, and easily transfer knowledge from one domain to another.
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Creating an AI system that satisfies all those requirements is very difficult, researchers have learned throughout the decades.
Scientists, researchers, and thought leaders believe thatAGI is at least decades away.
Narrow AI is the umbrella term that encompasses all these technologies.
Narrow AI systems are good at performing a single task, or a limited range of tasks.
In many cases, they even outperform humans in their specific domains.
But as soon as they are presented with a situation that falls outside their problem space, they fail.
They also cant transfer their knowledge from one field to another.
Your Google Search queries are answered by narrow AI algorithms.
Alexa and Siri, which have become a staple of many peoples lives, are powered by narrow AI.
Symbolic AI requires programmers to meticulously define the rules that specify the behavior of an intelligent system.
Symbolic AI is suitable for applications where the environment is predictable and the rules are clear-cut.
Machine learning, the other branch of narrow artificial intelligence, develops intelligent systems through examples.
Deep learning is especially good at performing tasks where the data is messy, such ascomputer visionandnatural language processing.
Why narrow AI?
Both symbolic AI and machine learning capture parts of human intelligence.
But they fall short of bringing together the necessary pieces to create an all-encompassing human-level AI.
And this is what prevents them from moving beyond artificial narrow intelligence.
Symbol manipulation is a big part of the human thinking process.
But the mind does a lot more than symbol manipulation.
are things we learn by rote.
We can learn them subconsciously and without doing any form of symbol manipulation in our minds.
Symbolic AI systems are very brittle.
This is an area wheredeep neural networks, the structures used in deep learning algorithms, excel at.
They can ingest mountains of data and develop mathematical models that represent the patterns that characterize them.
But again, the minds learning process cannot be simplified into pure pattern-matching.
The lack of symbol manipulationlimits the power of deep learningand other machine learning algorithms.
What comes after narrow AI?
Scientists agree that none of the AI technologies we have today have the required ingredients for artificial general intelligence.
But they dont necessarily agree on what is the next step to move beyond narrow AI.
One of the problems with artificial intelligence is that its a moving target.
As long as a problem remains unsolved, it is considered AI-complete.
This means that a computer that solves it is considered to have true artificial intelligence.
But once it is solved, it is no longer considered to require intelligence.
More recently, there has been a greater focus on measuring an AI systems capability at general problemsolving.
