What are the odds that a no-name eighteenth-century Presbyterian minister held the key to making artificial intelligence a reality?
While ben Franklin is running around flying kites thomas bayes is figuring out how [to] make educated mathematical guesses in
Situations where you don't have that much to go on like possibly the probability that this guy exists?
But then bayes dines before he's had a chance to tell anyone what he's been up to soon after his philosopher buddy Richard [price]
Discovers bayes's notebooks while digging around his apartment looking for free schwag
Price is a pretty smart guy, so he instantly recognizes the genius of his pals theory in
1763 he cleans up the math and publishes it in an oBscure Journal before using it to revolutionize the
insurance business where they need probabilities to figure out how much to rip you off by
Fast forward to 1812 while the us and England are added again a french guy named Pierre Simone laplace
develops Bayes's theory into something more usable which becomes known as
Conditional probability it lets you update your guesses with any new facts that might come your way like this
say the probability of getting dengue fever while visiting Bermuda for a week is 1 in 10,000 or
[0.01%]
I'm totally making this up you go to bermuda. Come home and to be safe. Take a test that it's 99.9%
Accurate in detecting the virus your test comes back positive
You start to freak out saying goodbye to loved ones
Writing out your will and are about to adopt a new religion when you take a second Bayesian look at the results
Remember for every one person with the virus there are
9999 people without it which means that even a robot
Programmed to always spit out a negative test result regardless of who's taking the test would still guess correctly?
99.99% at a time
being accurate
99.9% of the time then isn't really that impressive what you really need to figure out is how
Accurate that test is when it spits out a positive result
when this one person with the virus takes a test chances are
99.9% that, it'll be accurate out of the
9999 people without the virus taking the test
99.9 percent of them will get an accurate test result which translates to nine thousand nine hundred Eighty nine people that
[means] that the remaining 10 people will be inaccurately diagnosed
Testing positive for dengue fever when they don't actually have it
So out of the 11 people testing positive for the dengue virus
Only one of them actually has it
Meaning the chances [your] positive test result is right is only one in 11 or nine percent not
99.9 percent
now swap [Engh] a fever with
Hiv and you're looking at an actual real-life example people have found themselves in
Conditional probability works pretty well and can literally save your life
But the big thinkers of the day aren't impressed with the phrase works pretty well
they're all about
Frequentist probability a more popular approach that claims that the only way you can really know the odds is to have lots of hard
objective Data that backs you up
Let's say you flip a coin once and it lands [heads] based on that you might assume
It's always going to land heads, but that'd be pretty stupid. We all know that the more times. You flip a coin the closer
It's going to get the landing heads just half [the] time
That's the whole point of frequentist probability flip that coin enough times, and you'll have good enough data to take on Vegas
To those guys Bayesian probability is like shooting an arrow half blindfolded and hoping you hit the Target
But most of the time that's exactly where you find yourself needing an answer without having much to go on
that's why world war two hero alan turing that nerdy British guy who dreamed of computers way before Steve jobs did used Bayesian
probability to crack Top-secret transmissions some of them sent from their fuhrer himself
Despite this the frequentist still think Bayesian czar [lane], and the rivalry continues deep into the 20th century
but then the
1980s people had been trying to make artificial intelligence a reality since the 60s
programming computers with basic logic like if x is true then y happens
That approach works fine as long as x is always true
But as you can guess there are too many unknown variables in the real world to bring C-3po to life
but then in 1988
Judea Pearl jump-starts AI with a new approach based on you guessed
It phases theory
so that's why in
2011 when a computer called Watson beat a couple of humans answering questions on Jeopardy [it] was thomas bayes
Running around inside Watson's brain going if this is true, then that might be true, and if that other thing is true
Then I'm gonna buzz in with what is Chicago Alex
250 years after he flipped his last coin it turns out phases odds were [pretty] damn good
you