- Artificial intelligence or AI refers to software
algorithms that attempt to replicate tasks
that have historically required human intelligence.
AI applications are now becoming embedded in almost
all aspects of economic, social, and personal life,
managing mutual funds, helping you find a better
romantic partner, or even personalized counseling.
Early efforts at developing AI focus largely on feeding
highly codified knowledge to computers and then trying
to have them mimic human decision-making.
For example, researchers would interview doctors,
try to codify their decision-making rules,
and then develop expert systems embedded
with the codified knowledge.
This approach was not only time-consuming but also
started reaching a plateau because it became evident
that human decision-making rules could be codified
to only a limited extent.
Over the last decade however, computer scientists
have been tackling the AI problem in a fundamentally
different and far more promising way.
How do toddlers learn to walk?
Certainly not by first learning physics and biology.
Rather they learn via trial and error.
They observe adults walking, eager to mimic.
They try something and fall down.
They see what works and what doesn't.
Learning from this ground-level data, they try again,
perhaps a bit differently.
Eventually, they figure it out.
Could machines be programmed to learn similarly?
This type of machine learning is the core paradigm
in AI today.
Given enough data, when machines learn on their own
they can get smarter very rapidly.
Importantly, they can even learn to make good decisions
in areas where humans may not have any codified
knowledge at all.
Computer vision and natural language processing
are two of the biggest areas where AI technologies
are being applied today.