Week 1 - Video 4 - The terminology of AI

you might have heard terminology from AI

such as machine learning or data science

or neural networks or deep learning what

do these terms mean in this video you

see what is this terminology of the most

important concepts of AI so that you

will speak with others about it and

start thinking how these things could

apply in your business let's get started

let's say you have a housing data set

like this with the size of house number

bedrooms and Rabab rooms what the house

is newly renovated as well as the price

if you want to build a mobile app to

help people priced houses so this would

be the input a and this would be the

outputs B then this would be a machine

learning system in particular it'd be

one of those machine learning systems

that learns inputs to outputs or a to be

mappings so machine learning often

results in a running AI system so it's a

piece of software that any time of day

any time of night you can automatically

input a these properties of a house and

I'll press B so if you have an AI system

running serving dozens or hundreds of

thousands of millions of users that's

usually a machine learning system in

contrast here's something else you might

want to do which is to have a team

analyze your data set in order to gain

insights so a team might come up with a

conclusion like hey did you know if you

have two houses of a similar size of a

similar square footage if the house has

three bedrooms then they cost a lot more

than the house of two bedrooms even if

the square footage is the same or did

you know that newly renovated homes have

a fifteen percent premium and this could

help you make decisions such as given a

similar square footage do you want to

build a two bedroom or a three bedroom

size in order to maximize value or is it

worth in investments to renovate a home

in the hope that the renovation

increases the price you can sell a house

for so these would be examples of data

science projects where the output of a

data science project is a set of

insights that can help you make business

decisions such as what type of house to

build or whether to invest

in renovation the boundaries between

these two terms machine learning and

data science are actually little bit

fuzzy and these terms are not used

consistently even in industry today but

what I'm giving here is maybe the most

commonly used definitions of these terms

but you will not find Universal

adherence to these definitions so

formalize these two notions a bit more

machine learning is the field of study

that gives computers the ability to

learn without being explicitly

programmed this is a definition by

author Samuel many decades ago after

Samuel was one of the pioneers of

machine learning who was famous for

building a checkers playing program that

could play checkers even better than he

himself the inventor could play the game

so a machine learning project will often

result in a piece of software that runs

that outputs be given a in contrast data

science is the science of extracting

knowledge and insights from data and so

the output of a data science project is

often a slide deck the PowerPoint

presentation that summarizes conclusions

for executives to take business actions

or that summarizes conclusions for a

product team to decide how to improve a

website let me give an example of

machine learning Bursar's data science

in the online advertising industry today

the large high platforms all have a

piece of AI that quickly tells them

what's the ad you are most likely to

click on so that's a machine learning

system and this turns out to be

incredibly lucrative AI system the

inputs information about you and about

the ad and outputs will you click on

this or not these systems are running

24/7 and these are machine learning

systems that drive ad revenue for these

companies so there's a piece of software

that runs in contrast I've also done

data science projects in the online

advertising industry if analyzing data

tells you for example that the travel

industry is not buying a lot of ads but

if you send more sales people to sell

ads the travel companies you could

convince them to use more advertising

then that would be an example of a data

science project the data science

conclusion

the results in the executives deciding

to ask the sales team to spend more time

reaching out to the travel industry so

even in one company you may have

different machine learning and data

science projects both of which can be

incredibly valuable you have also heard

of deep learning so what does deep

learning let's say want to predict

housing prices you want to price houses

so you have an input that tells you the

size of house number of bedrooms and

bathrooms and where this newly renovated

one of the most effective ways to priced

houses given this input a will be

defeated this thing here in order to

have it output the price this big thing

in the middle is called a neural network

and sometimes we also call it an

artificial neural network and that's the

distinguish it from the neural network

that is in your brain so the human brain

is made up of neurons and so when we say

artificial neural network that's just

emphasize that this is not the

biological brain but it says a piece of

software and what a neural network does

we're not official neural network does

is take this input a which is all of

these whole things and then output B

which is the estimated price of the

house now in a later optional video this

week I'll show you more what this

artificial neural network really is but

all of human cognition is made up of

neurons in your brain

passing electrical impulses pass these

little messages each other and when we

draw a picture of an artificial neural

network there's a very loose analogy to

the brain and these little circles are

called artificial neurons or just

neurons for short there are also passes

in neurons to each other and this big

artificial neural network is just a big

mathematical equation that tells it

given the inputs a how do you compute

the price B in case it seems like there

are a lot of details here don't worry

about it we'll talk more about these

details later but the key takeaways are

that a neural network is a very

effective technique for learning a to be

your input-output mappings

and today determines neural network and

deep learning are used almost

interchangeably they mean essentially

the same thing many decades ago this

type of software was called a neural

network but in recent years we found

that you know deep learning was just a

much better sounding brand and so that

for better worse is the term that's been

taking off recently so what do new

networks or artificial neural networks

have to do with the brain it turns out

almost nothing new networks were

originally inspired by the brain but the

details of how they work are almost

completely unrelated to how biological

brains work so I choose very courses

today about making any analogies between

artificial neural networks and the

biological brain even though there was

some loose inspiration there so AI has

many different tools in this video you

learned about what a machine learning

and data science and also what is deep

learning and was it neural network you

might also hear in the meteor other

buzzwords like unsupervised learning

wrinkles learning graphic novels timing

knowledge graph and so on and you don't

need to know what all of these other

terms mean but these are just other

tools for getting AI systems to make

computers act intelligent you know try

to give you a sense of what some of

these terms mean in later videos as well

but the most important tools that I hope

you know about are machine learning and

data science as well as deep learning a

neural networks which are a very

powerful way to do machine learning and

sometimes data science if we were to

draw a Venn diagram showing how all

these concepts fit together and this is

what it might look like AI is this huge

set of tools for making computers behave

intelligently of AI the biggest subset

is very tools from machine learning but

AI does have other tools than machine

learning such as some of these buzz

words are listed at the bottom and of

machine learning the how the machine

learning that's most important these

days is neural networks or deep learning

which is very powerful set of tools for

carrying out supervised learning or a to

be mappings as well as some other things

but they're also other machine learning

too

that are not just deep learning tools so

how does data science fit into this

picture there is inconsistency in how

the terminologies use some people will

tell you the design is a subset of AI

some people will tell you AI is a subset

that they design so it depends a bit on

who you ask but I would say that data

science is maybe a cross-cutting subset

of all of these tools that uses many

tools from AI machine learning and deep

learning but has some other separate

tools as well that solves a very set of

important problems in driving business

insights in this video you saw what is

machine learning

once they designs and what is deep

learning and neural networks I hope this

gives you a sense of the most common and

important terminology used in AI and you

can start thinking about how these

things might apply to your company now

what does it mean for a company to be

good at AI let's talk about that in the

next video