An AI Talk to understand AI Talks

when i was

at university my professor wanted me to

write a white paper

about the application of an algorithm

that belongs to the area of artificial

intelligence and at the beginning i was

a little bit skeptical

i mean how to find a feasible use case

can i handle the complexity will i still

be able to hang out with my friends a

lot of

important questions to clarify before i

started but then i thought

challenge accepted let’s do it i was

motivated i mean i was

really motivated i went home i opened up

my laptop

and the first question i had to look at

was

what the hell is artificial intelligence

right and now imagine you

are a student you are lazy as hell

there’s only

one way how to approach this and this is

you open up a search engine

like google and you type in dear google

please show me what you say

i and the result i was presented looked

like this

so the first sentence of my white paper

was artificial intelligence

is blue what i also learned is that ai

has something to do with

digital brains and robots that look like

humans i mean

in the end of the day i did not learn

anything at all by looking at these

results right and exactly this is the

problem

because to most people in society ai

is just blue and let me illustrate this

problem with a comparison to online

banking

i think most of you guys use online

banking

and i think most of you have a rough

idea how a bank actually works

like you’ve got a bank account you

deposit money

the bank rents the money to other people

you get paid interest rates

on the other hand millions of people are

using digital services

of google amazon facebook whatever

without being aware of their value of

the personal data

and the power of artificial intelligence

and this is a problem

and the only way i see to escape this

problem

is learning and the good news is there

are many great ways how to learn

nowadays for example

you can watch all those really really

fantastic ted talks

about ai which you can find on youtube

however

in order to understand what the speakers

are talking about

you need to have a certain understanding

about this topic and you need to be able

to ask the right

questions and exactly this is what i

want to deliver you today

so in my ted talk you will receive two

learnings number one

you will get a basic understanding of

modern

artificial intelligence and number two

you will be equipped with a simple

toolkit

consisting of three guiding questions

that you can apply

to each and every ai talk you’re going

to listen to

and now let’s get started and the first

thing we have to clarify

is the terminology because i very often

observe

that people mix up the terms and this

leads to confusion

so the main concept we talk about today

is artificial intelligence and this is a

very broad concept so

the official definition of ai is the

science and engineering

of making intelligent machines however

where does intelligence start where does

it end

all this is really up to your

interpretation and this leads to the

fact that

different people can have a different

perspective on the same topic

of ai for example if you talk to a

computer scientist

or group psychologist those guys can

have a completely different

understanding of ai and this is

absolutely okay because it is a broad

topic

however what most people mean when they

talk about ai today

is machine learning and machine learning

is about the imitation

of the cognitive ability to learn

so what you do you apply mathematics

and statistics to historic data with the

goal to identify

pattern in this data in order to

understand more recent data

or to create predictions and

in other words this means machine

learning is the perfect tool

to support rational decision making in

an uncertain

environment because it’s completely

based on math

and data and when i talk about ai today

i refer to machine learning based

artificial intelligence

and deep learning again is one subset of

machine learning and it does

pretty much the same so again we look at

historic data

apply math and statistics to find

pattern in order to create predictions

however the difference is the type of

algorithm that is applied so

whereas in classical machine learning we

apply pretty straightforward statistical

mathematical math methods whereas in

deep learning you apply something called

deep

neural networks and this is a very

sophisticated type of algorithm that is

certainly inspired by the human brain

but it does also certainly not work

the same way than a human brain works

so after being familiar with the

definition you want to know

what can i do with artificial

intelligence and again here are

different ways how to look at this topic

but i prefer those four application

clusters

let me start with computer vision so

computer vision is about recognizing

and interpreting the content of images

and an image is nothing else than a

piece of unstructured data right because

we can

break down an image into single pixels

and express each pixel with the value

and when we’ve got the numbers

we can apply the math and computer

vision for example is one of the

core technologies of autonomous driving

because together with other sensors

it helps the car to perceive its

environment

for example to identify other cars

pedestrians traffic signs whatever

in the field of vision in order to

derive the most

suitable follow-up action in the current

situation

what you can also do with computer

vision is to distinguish

a chihuahua from a muffin and if you

think this is an easy task

just have a look at this picture

so the one thing is the thing you want

to bite in

the other one is the thing that might

want to bite you

this is a small but very important

difference right

and as you can see not every problem

that sounds easy

is actually easy for ai to solve in the

end

of the day and now let me continue with

natural language processing so human

natural language is very complex for

machines

to understand it has got a broad

terminology

it has got many rules it has got

exceptions its context based and i did

not even start to talk about accents and

dialects right and

natural language processing is about the

understanding

interpretation and even generation of

human natural language for example think

about all those

automatic translation services where you

can translate the text from one language

to another language or think about those

smart voice assistants

like alexa or siri and as long as

a huge metal fan can either keep me cool

during a hot day or keep me awake

during a quiet night language will

remain a complex thing

for machines to understand

so now let’s have a look at data mining

data mining is about leveraging the

potential

of structured and semi-structured data

that you have captured just think about

a time

series analysis where you have got costs

or sales figures from the past and you

want to analyze how they will continue

or think about an automatic proper

customer analysis

where you try to provide the right

customer with the right information in

the

right point in time and this is heavily

applied in social media and is called

micro targeting

what you can also do and we are familiar

with this

are recommendation engines i mean think

about amazon or netflix right

so people who look at this product were

usually also interested in these

products

and i’ll just imagine i want to give an

example you visit an e-commerce

store and you search let’s say a

baseball

bat you know to hit some balls together

with your friends

and now imagine you are recommended to

buy

these items based on popular customer

combinations again

i have got two learnings for you number

one some people might have

interesting hobbies number two ai does

not always generate the results

that you have expected

and now let me finish with behavioral ai

and behavioral ai

is about the ability of autonomous

systems

to learn a successful strategy by trial

and error

just think about a smart agent in a

computer game this agent has to learn

how to win the game

by exploring different strategies and

one very impressive example

was presented by google by a google

company a couple of years ago

so alphago zero is a computer program

that learned how to play the board game

go and go is a very famous sport game

in asia it’s played with a 19 by 19 grid

board and what’s so special about this

game is the complexity

because the amount of possible

constellations on this board

surpasses the amount of atoms in the

universe

this is a really huge number

and this algorithm learned how to play

the game

basically by getting provided with the

rules

and by playing against itself so

exploring the right

strategy in the end of the day it was

even able to beat the south korean grand

master

in an official match but behavioral ai

is also

applied in robotics for example to teach

robots

the desired behavior i mean just think

about

assembly lines where you want robots to

assemble certain production items

in the right way

so now you know the definition of ai and

you know what you can do with ai

the last missing piece is how to create

ai

and let me start with the training

because this is the biggest difference

to classical computing because

ai is not programmed but trained

and for ai training we need three

ingredients

data the algorithm and computing power

and let me first quickly start with the

algorithm because

most people think the algorithm is the

most important

asset in the game you know this musical

facebook

algorithm that controls our lives

however an algorithm is nothing else

than a simple

cooking recipe that tells us how to

process data

to solve a certain mathematical problem

so it’s the math it’s there since the

1950s i can

read it in papers and books i can even

download it from the internet so yes

the algorithm is important but it’s not

the most important asset

what we also need is computing power and

looking back we observe

a strong increase of inexpensive

computing resources and this was

certainly

an accelerator however the most

important

part in the game is the data because

what you have learned already is

machine learning is based on historic

data

if we do not have this data we cannot do

machine learning right

but in times of internet of things

social media online shopping and so on

we produce

tons and tons of data every day and the

more data you have

the more fun you will have with machine

learning and when all three elements

come together the data the algorithm

plus the computing

an iterative training process starts

for example i provide the algorithm with

thousands of examples how to hours look

like on images

and after the training time what we get

is a

trained machine learning model and this

model is basically a

mathematical representation of the real

world

so it contains for example all the rules

how to identify a chihuahua

on an image the good thing is this model

can be so complex especially in deep

learning

i would never ever be able to come up

with a model of this complexity

in a reasonable amount of time when i do

it manually

however the downside because of this

complexity

i might not be able to understand how

the model works and how it will behave

in different situations so after the

training

we come to the inference phase and in

the inference phase

we need new data points so new images

of chihuahuas the algorithm has never

seen before again we apply

computing power but here not as much as

during

training i mean your iphone is doing

machine learning every day

so that’s very often sufficient and we

apply now

the trained model from the training

phase to the new data points and what we

get is a prediction

and this prediction could be i identify

a chihuahua on this image and i’m

confident to

97 so this tells us that machine

learning is a

probabilistic approach rather than a

deterministic approach

and this implies we always have a

certain amount of uncertainty

and depending on the use case we have to

think about

how much uncertainty we can accept

so congratulations now you are familiar

with the basics of modern

artificial intelligence and now i think

you guys are excited about

the toolkit the three questions so let’s

have a look at the first one

and the first question you should ask

yourself when you watch an ai talk is

is the speaker talking about artificial

narrow intelligence

or something superior like artificial

general intelligence and let me quickly

tell you uh what’s the difference so

narrow ai is all the stuff we have

talked about today

it’s a very specialized type of ai that

performs well

in a certain niche whereas the other

types like

general ai or super ai those types of

i come close to human level of

intelligence or even surpass

human level of intelligence for example

just think about this

smart small robot in the star wars movie

called

r2d2 and just think about this guy

had to save a jedi’s life but the only

thing it can do

is to distinguish a chihuahua and a

muffin i mean

this does not really work out well right

so

those types of ai what they can do they

can learn

new stuff independently and they can

apply

knowledge from one domain to another

domain

based on the world knowledge they have

and this is really human-like

and the reason why you should ask this

question so

if a speaker talks about narrow ai the

talk might be much

more based on concrete scientific

statistical mathematical

solutions and methods whereas if she

talks about something superior like

generally i or super ai

the talk might be much more on a

visionary or philosophical

level the speaker

actually talks about narrow ai the next

question is is she talking about machine

learning or other ai methods

so if it’s machine learning the good

thing is you can apply

all the fundamentals you have learned

already you know the pros

and you know the cons whereas if it’s

another method you first have to find

out about this method

you have to understand how it works in

order to be able to understand the talk

so this is also very important

because machine learning is one subset

of ai a very famous one at the beginning

at the moment

but it’s not the only one and the last

question is

if she talks about machine learning is

the funnel from data

to decision observable so in a high

quality talk

the speaker will always make transparent

on which data the algorithm was trained

on

and which method was applied right and

only with this information you can

really scope the solution the speaker

talks about

and you can really think about how well

it can perform in which kind of

situation

whereas if ai is represented as black

box

you might always have problems to fully

understand the solution

so artificial intelligence

is important because it will impact

our private lives and it will change the

way

how we work so learning about ai

is key for everybody and if you decide

to do so

by watching all those great tedx talks

and if you apply

the fundamental knowledge of my talk

plus the three guiding questions

you will definitely understand much more

about ai

than this thank you