- Early artificial intelligence was a mix
of ambition and self-discipline.
Some scientists were quick to over promise
what could be done with early machines.
At the same time, you could see the potential
in these machines to solve complex problems.
In 1956, you had one of the first attempts
to create a machine with general intelligence.
Allen Newell and Herbert Simon created a computer program
they called the General Problem Solver.
This program was designed to solve any problem
that could be presented as mathematical formulas.
One of the key parts of the General Problem Solver
was what Newell and Simon called
the physical symbol system hypothesis.
In their paper, they argued that symbols
were the key to general intelligence.
If you could get a program to connect
enough of these symbols, then you would have
an intelligent machine.
Symbols are a very big part of how you interact
with the world.
When you see a stop sign, you know to look for traffic.
When you see the letter a, you know that the word
will make a certain sound.
When you see a sandwich, you might think of eating.
Newell and Simon argued that if a machine
were trained to understand these symbols,
they could behave more like humans.
They thought a key part of human reasoning
was simply connecting these different symbols.
In one sense, our language, ideas, and concepts
were just broad groupings of interconnected symbols,
but not everyone bought into this idea.
In 1980, philosopher John Sorrell argued
that you could never call
these symbolic connections real intelligence.
To explain, he created something called
the Chinese room argument.
In this experiment, you should imagine yourself
in a windowless room with one narrow slot on the door,
almost like a mail slot.
You can use the slot to communicate with the outside world.
In the room you have a book on a desk,
and bunch of Chinese symbols on the floor.
This book is filled with long lists of matching patterns.
It says if you see this sequence of Chinese symbols,
then respond with that sequence of Chinese symbols.
To start, John Sorrell imagines that someone who speaks
fluent Chinese writes a note and shoves it through the slot.
You pick up the note, but you don't speak a word of Chinese,
so you really have no idea what it says.
Instead, you simply go through the tedious process
of looking through your book and matching the sequence
of Chinese letters.
Once you find the matching sequence of letters,
you look at how the book tells you how to respond.
Then you tape together the response
from the Chinese symbols and push your note
through the same slot on the door.
A native Chinese speaker on the other side
might believe that they're having a conversation.
In fact, they may even assume that the person
in the room is intelligent, but Sorrell argues that
this is far from intelligence since the person
in the room can't speak Chinese and doesn't have
any idea what you're talking about.
It was just simply matching patterns.
You can try a similar experiment with your smart phone.
If you ask Siri or Cortana how they're feeling,
they'll give you a response.
They'll usually say they feel fine,
but that doesn't mean that they really feel fine.
In reality, they also don't really know
what you're asking, they're just matching your question
to a pre-programmed response just like the person
in the Chinese room.
So Sorrell argues that simply matching symbols
is not a true path to intelligence,
that a computer is acting just like the person in the room.
They don't understand the meaning or the content.
They're just matching symbols
from a long list of instructions.
You can also see how the book of Chinese responses
might become larger and larger
as you try to create more matching statements.
This is called combinatorial explosion.
There are so many different combinations
that matching becomes overwhelming.
Think about all the things that people might ask in Chinese,
or imagine all the different responses that Siri
must be prepared to answer.
Even with these challenges, physical symbol systems
were still the cornerstone of AI for 25 years,
yet in the end creating all these matching connections
took up too much time.
It was also difficult for these machines
to match all the different patterns
without running into combinatorial explosion.