It’s free, every week, in your inbox.
Being multidimensional and subjective, human intelligence can be difficult to measure.
Think of every competition as a function that maps a problem to a solution.
You must map it to a solution.
The size of the solution space depends on the problem.
When it comes to us humans, these competitions really test the limits of our intelligence.
Given the computational limits of the brain, we cant brute-force our way through the solution space.
In more specialized fields, such as math or programming, tests take on more practical implications.
This is why many companies use coding challenges as an important tool to evaluate potential hires.
Otherwise said, competitive coding is a good proxy for the effort that goes into making a good programmer.
And this creates the opportunity for shortcuts that the human mind cant achieve.
Chess was once called the drosophila of artificial intelligence.
In 1996, DeepBlue defeated chess grandmaster Garry Kasparov.
But DeepBlue did not have the general cognitive skills of its human opponent.
They were proven wrong in 2016 whenAlphaGo defeated go grandmaster Lee Sedol.
But again, AlphaGo didnt play the game like its human opponent.
It took advantage of advances in machine learning and computation hardware.
AlphaCode is an even more impressive feat.
It then uses filtering and clustering to choose the 10 most-promising solutions proposed by the model.
Impressive as it is, however, AlphaCodes solution-development process is very different from that of a human programmer.
But when viewed in the framework of searching solution spaces, they take on a different meaning.
Some might dismiss this difference as long as the outcome is acceptable.
What does this mean for the future of human intelligence?
They create unique opportunities for very productive cooperation between humans and AI.
Whether these incremental advances in deep learning will eventually lead to AGI remains to be seen.
you’re able to read the original articlehere.
