15x4 - 15 минут про Искусственный Интеллект

Summary

Александр рассказывает о различных подходах к созданию искусственного интеллекта (ИИ), разделяя их на нисходящий и восходящий подходы. Он обсуждает разницу между сильным и слабым ИИ, а также вопросы, связанные с определением интеллекта и сознания в машинах, ссылаясь на тест Тьюринга. Александр затрагивает тему обучения и способности предсказания как основных компонентов интеллекта, размышляя о будущем ИИ и его влиянии на человечество, подчеркивая важность совместных целей между ИИ и людьми для избежания потенциальных угроз.

Takeaways

• 😀 Есть два основных подхода к созданию искусственного интеллекта: снизу вверх, изучая интеллект, и сверху вниз, изучая поведение.
• 🤖 Тест Тьюринга предлагает метод верификации интеллекта машины через игру в имитацию, где машина должна запутать судью.
• 🧠 Искусственный интеллект делится на сильный и слабый, причём реально существующие системы пока не достигли уровня сильного ИИ.
• 📚 Человеческий интеллект включает способность анализировать внешний мир, обучаться и генерировать рациональное поведение.
• 🐊 Примеры поведения животных, такие как крокодилы и мыши, демонстрируют различные уровни интеллектуального поведения и памяти.
• 🔬 Нейрокортекс млекопитающих играет ключевую роль в высших интеллектуальных функциях, включая память и принятие решений.
• 💡 Глубокое обучение и нейронные сети вдохновлены структурой и функциями человеческого мозга, что привело к значительным успехам в ИИ.
• 👁 ИИ превосходит человека в некоторых задачах, включая распознавание образов, благодаря быстрому развитию и обучению на больших данных.
• 🚀 Технологический прогресс и развитие ИИ могут привести к моменту, когда искусственный интеллект превзойдёт человеческий по всем параметрам.
• 🌍 Перед обществом стоит задача адаптации к миру, где ИИ выполняет большинство работ, требующихся для поддержания современного образа жизни.

Q & A

Что такое искусственный интеллект?

Искусственный интеллект - это раздел компьютерных наук, который занимается созданием программ или машин, способных выполнять задачи, требующие человеческого интеллекта, такие как обучение, понимание языка и решение проблем.

Какие существуют подходы к созданию искусственного интеллекта?

Существуют два основных подхода: bottom-up (снизу вверх), который изучает внутреннюю структуру объекта с интеллектом (например, человека) для имитации интеллектуального поведения, и top-down (сверху вниз), который сосредотачивается на изучении самого интеллектуального поведения и разработке систем, способных его воспроизводить.

Чем отличается сильный искусственный интеллект от слабого?

Слабый искусственный интеллект способен выполнять конкретные задачи в рамках определенной области, в то время как сильный ИИ обладает общим интеллектом, сравнимым с человеческим, и может решать задачи в различных областях.

Какова основная идея теста Тьюринга?

Основная идея теста Тьюринга заключается в том, что если машина способна убедить человека в том, что она не отличается от другого человека, отвечая на вопросы через текстовый чат, то машина может считаться обладающей интеллектом.

Почему поведение не всегда является показателем интеллекта?

Поведение может быть сложным и разнообразным даже у существ без развитого интеллекта, как, например, у крокодилов. Наличие сложного поведения не обязательно указывает на способность к планированию или размышлению, которые являются ключевыми аспектами интеллекта.

Что такое обучение в контексте искусственного интеллекта?

Обучение в контексте искусственного интеллекта - это процесс, в ходе которого машина или программа улучшает свои способности к решению задач или адаптации к новым условиям на основе предыдущего опыта или данных.

Какие методы используются для оценки детского интеллекта?

Для оценки детского интеллекта используются различные задачи, например, тесты на сообразительность, в которых детям предлагается дополнить последовательность слов или изображений, либо решить логические головоломки.

Что такое глубокое обучение?

Глубокое обучение - это подмножество методов машинного обучения, использующее многослойные нейронные сети для анализа больших объемов данных, что позволяет системам обучаться, распознавать образы и делать выводы на уровне, сравнимом с человеческим.

Почему искусственный интеллект считается менее развитым, чем интеллект мыши?

На момент речи искусственный интеллект считается менее развитым, чем интеллект мыши, потому что ИИ еще не способен к такому же уровню принятия решений, обучения и адаптации к изменяющимся условиям, как живые существа с развитым мозгом.

Как изменится общество с развитием искусственного интеллекта?

С развитием искусственного интеллекта многие задачи, традиционно выполняемые людьми, будут автоматизированы, что может привести к изменениям в структуре трудового рынка, требованиях к образованию и социальной организации общества.

Outlines

00:00

🧠 Введение в искусственный интеллект

Александр рассказывает об основах искусственного интеллекта (ИИ), различая подходы к его созданию: снизу вверх, изучая внутреннее устройство интеллекта, и сверху вниз, фокусируясь на внешнем поведении. Обсуждается разделение ИИ на слабый и сильный, а также введение в тест Тьюринга как метода определения наличия интеллекта у машин.

05:01

🧩 Понимание интеллекта и обучения

Обсуждается различие между поведением и интеллектом, подчеркивается, что наличие сложного поведения не обязательно указывает на интеллект. Александр разъясняет, что истинная суть интеллекта заключается не в вычислениях в ответ на вопросы, а в способности к обучению и запоминанию информации для будущего использования.

10:04

🔬 Структура мозга и нейрокортекс

Разбирается структура мозга, включая нейрокортекс, который отвечает за высшие когнитивные функции. Александр объясняет, как работают нейроны и нейронные сети в мозгу, создавая иерархические связи для обработки и анализа информации.

15:04

📚 Обработка информации и глубокое обучение

Обсуждается, как человек обрабатывает информацию, начиная с восприятия простых форм и заканчивая сложными абстракциями. Александр вводит концепцию глубокого обучения в нейронных сетях, сходство их структуры с нейрокортексом человека и их способность к обучению на примере контента из интернета.

20:06

💡 Искусственный интеллект и общество

Александр рассматривает социальные и экономические последствия развития искусственного интеллекта, включая потенциальное превосходство ИИ над человеческим интеллектом и вызовы, связанные с автоматизацией труда. Предлагаются способы адаптации общества к новым реалиям, в которых ИИ играет ключевую роль.

Keywords

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Highlights

Two different approaches to creating artificial intelligence: bottom-up and top-down.

In 2015, creating programs that act as if they were intellectual is called programming.

Artificial intelligence can be divided into strong and weak.

Alan Turing's test for machine intelligence involves a machine persuading us it is human.

Turing's test involves a quizmaster, two players, and a machine, with the goal of indistinguishing the machine from a human.

Intelligence is often associated with human abilities such as analyzing the world, learning, and rational behavior.

Behavior alone may not be an index of intelligence, as demonstrated by the complex behaviors of crocodiles versus humans.

The human brain's cerebral cortex is responsible for high-level memory and intellectual functions.

The neocortex is a crucial part of the human brain, involved in complex behaviors and decision-making.

Intelligence is the ability to predict the future and create programs or algorithms based on those predictions.

Deep learning, a significant advancement in neural networks, is inspired by the structure of the human neocortex.

Artificial intelligence has surpassed human accuracy in picture recognition tasks, including identifying cats in images.

The evolution of AI is expected to surpass human intelligence at an exponential rate once it reaches a certain threshold.

The development of AI may lead to machines outperforming humans in most tasks, necessitating a reevaluation of human labor and purpose.

The speaker suggests that AI could potentially be the last invention of humanity, as it may evolve beyond our control.

The future of AI development requires careful consideration to ensure it aligns with human goals and survival.

The speaker proposes that work may need to become independent of salary, with people working because they enjoy it, not for financial necessity.

AI development must focus on creating AI that will not destroy humanity, which is an even more challenging task than creating AI itself.

Transcripts

Hello,

I’m Alexander and I read

popular-science lectures. And now I’m going to tell you something

about artificial intelligence.

We’ll talk little about

computers,

much about intellect and a bit about

artificial intelligence -

what it is, how to create it and so on.

There are two essentially different approaches to

the creation of artificial intelligence:

a bottom-up

approach,

when we

study something with

an intellect, for example,

us,

and try to represent

the inner stucture with a view

to get some kind of intellectual behaviour.

A top-down approach is

when we study

the intellectual behaviour itself,

for example an ability to

distinguish a text by hearing it or to distinguish

a text from images

and then design some

computer systems that

implement this intellectual

function.

In 2015, creation of programs that act as

if they were intellectual

is called just programming.

Nobody needs silly programs

so we’ll talk about

bottom-up coding, about

creation of programs that are

or may some day be intellectual.

Except dividing into bottom-up and

top-down, we can also divide artificial intelligence into

strong and weak ones.

All the things you see

can discern the object,

pursue an aim and wash your sweater gently,

they all exist,

but none of them has

AI.

On the other hand,

all these guys

(laughing)

all these guys

have AI, in my opinion,

but still do not

exist. We will talk mostly about them.

How to distinguish intelligence or consciousness

in a robot or a computer

program?

Alan Turing, one of the computer science pioneers,

offered an elegant answer:

let the machine persuade us itself.

His method of verification is, basically, as follows:

there is a quizmaster and two players,

one of which is a machine.

The quizmaster does not know who is who.

The quizmaster asks players some questions

using a chat, the players answer them.

The quizmaster’s task is to define which of players

is a machine.

The machine’s task

is to confuse the quizmaster in order

he couldn`t define who is a machine

and who is a person.

The game is not cut down to

the ordinary phrases, such as “what’s your name?”

One can ask, for instance: “Could you please compose a poem about poetry

and autumn?”

And a normal adequate

computer’s answer would be “For goodness sake, I can’t write a poem!”

Or, you can ask a computer:

What, in your opinion, equals 314 plus 567?

And a computer, having self-respect,

should think for three seconds at least

before he answers 881.

But by highest standards he should make a mistake.

That is, this game is more difficult

than just correctly answering questions -

you need to answer as a person.

If you ask which question I would ask as quizmaster in a Turing test

I would answer:

"Which question would you ask

as a quizmaster in a Turing test?”

Turing’s idea is as follows:

if a machine can convince us

that

we can’t distinguish between a machine and a person,

then it is probably intelligent,

has consciousness or at least

is more intelligent then people, then how can people define

what is intelligent?

On the other hand, Turing’s test

is just an imitation game.

That is, does a computer which

says that he loves autumn

or that he loves autumn and poetry really loves autumn and poetry?

Maybe he lies?

And if he lies, does he feel

remorse?

And, eventually, why do we believe that

an ability to make a fool of a person

proves intelligence?

The “I” in letter combination “AI”

means “intelligence”

Usually, one has in mind human

intelligence.

And usually one considers intelligence,

at least the “Wikipedia” tells us that -

as humans’ ability to

analyze the outer world,

to learn and to generate some

rational behaviour.

A long phrase, meaning nothing. If we throw out

humans, hesitate about

“rational”, we’ll have only “behaviour” left. May behaviour be an index of

intelligence?

I am a human. It is a crocodile.

Both of us have very complex behaviour.

Both of us can move,

attack a prey, run away if the battle is

unequal,

but crocodiles can not plan

future. A crocodile does not think:

“Now I’ll kill this antelope,

I’ll leave half of it for tomorrow, I’ll call my friends

and we’ll discuss the problems of

artificial intelligence."

A crocodile does not make plans,

he just implements his complex low-level

predator behaviour.

Mammals' brain structure has a

crucial difference from the

other animals’ brain structure. We have cerebral cortex.

And this cortex perform many functions that

we call intellectual

and which include high-level memory.

Lizards

can not move through the labirynth in which

the reward is hidden

once more,

they just seek everywhere again.

In the same time mice

having done the labirynth

will at once go to the place where they found the

reward last time, because they can memorize

the way to this place.

It is what the cortex is responsible for.

We are all present here,

we all seem to be in cosciousness

and seem to be intelligent.

I can’t be sure about you, but we are alike, and I believe that I’m in consciousness

But still we implement actually no

behaviour.

You may do nothing, just lie

and consider yourself to be intelligent.

Thus,

behaviour

is not intelligence.

And the most wonderful consequence of our

intelligence is that we do not calculate an answer

when we are asked a question.

We know it, we remember it, well, you know this effect.

And the calculation is made

when we come to know

the information necessary to

give an answer in future.

And this process is called learning.

And

I want to hold an experiment.

I need your help.

At first sight it may seem

that I got crazy,

but wait a little bit.

So, I need your help. Now I will

give you rather difficult

tasks,

you need to concentrate

and answer in chorus

in a very concentrated way.

You’ll manage, I believe in you.

You need silence and concentration.

Are you ready?

I feel that you are ready.

It’s so fluffy and so nice

il licks milk and catches mice

it’s a cozy lovely pet

who is it?

It is a cat!

Squirrel’s hollow’ s safe and good On an oak in the

wood. Well done!

If you want a chocolate cake, mix ingredients and

bake!

I can jump and I like carrot. Who am I? I am a… rabbit!

With the help of such question we test children’s intelligence.

Let us find out what is going on.

First we need to understand the speech

then to divide the sentences into words

then to understand the meaning of the words

and so on.

And after that

we need to make a prediction.

We need, using some parametres,

to complete a sentence,

to predict the following word.

According to this

prediction algorithm

our perception and our intellect

generally

function.

A simple example is face recognition.

Your eyes are moving in short

hops, called saccades.

When you suddenly find an eye,

your brain says like: “wow, an eye!”

And he adds:

“Dear visual cortex, responding for the eye-movement,

look around, there should be one more

eye, nose and mouth,

look closely."

And now your eye which has found

the first eye, has a particular task

to find the second eye, and to find nose and mouth.

And we quickly - blink-blink-blink - realize that

it is a face.

If you

suddenly teleport to

some other place, you’ll be ultimately

surprised.

If you close your eyes and then open them,

you won’t be surprised.

Your brain can predict that barely

something could change during this blink.

Another example of how

prediction works

is reading.

Remember how you read

in childhood.

You read the first letter,

the second one,

you can see: m, o

and you think: “phew, maybe, I’m lucky”, - mother!

And they answer you:

“no, motorbike!”

Children so to say try to guess

what is next, and parents critisize them for that

but that’s how we actually learn to read.

When an adult person reads

a sentence, he moves over it

in such hops,

containing several words.

What is our brain doing in this moment?

Well you are actually relaxed,

you are inside the story,

your brain tries to predict

what will happen next, what are the following

words.

Each such hop checks if everything is ok, is the prediction correct and if

not

he compensates everything,

it corrects something

and you read the text. It seems to you

that everything comes into your head very smoothly

but in fact you do make these hops.

Let us get back

to the question of the machines’ intelligence defining

and recall how we define

adults’ intelligence, because we have just

proven that on children's level we are surely intelligent.

IQ

IQ... Test

Ah, by the way here’s one more good example

considerig how our brain works in prediction sphere. You look at the picture

and you can see spots, spots, spots. Your brain tries to set up a hypothesis.

It sets up a hypothesis and tries to find something

similar to understand what is depicted there.

As soon as the hypothesis is set up,

you can not cease to see

this Dalmatian dog.

With a face, paws

and body.

As soon as your brain has set up a correct hypothesis,

it can predict what is

on the picture, it knows what to look at.

(Laugh)

Not everyone has such a talent.

Well, adults’ intelligence

defining.

IQ test consists of the questions

where you should continue a sequence.

Sometimes this sequence consists of several ciphres, sometimes of several words or

pictures.

Sometimes it is some logical puzzles.

But in any case it is an IQ test, where

you should

recognize a pattern

and make a prediction.

I offer to use an IQ test

or some similar things instead of the

Turing test, which is

wonderful and nice but shows absolutely other things,

to measure one’s/ something’s

intelligence.

And I want to finally represent

the definition of the intelligence,

which will be appropriate for us today.

Intelligence is an ability

to predict future

and to create programs.

Programs are not obligatory

computer progrms, they may be

just some easy programs, for instance,

‘if there are bananas, buy, please,

some bananas, and if there are no, then buy some apples’.

It is a short computer

program in which we make

two predictions.

There will be apples, almost for sure;

if we are lucky there will be bananas.

Bananas will be there less likely,

we are not sure that they will be there. So, we make

some predictions about future and according to them

we predict some actions, we predict that

we will have to act in this or that

way.

And

the ability to create such algorythms

and the ability to solve such tasks

make up, in my opinion, intelligence.

Let us find out how it works

with humans

and other living beings before we

move on to computers.

Here is an image of a brain.

It consists,

roughly speaking, of three sections.

This section, this section and this

section.

This first section we share with

fish, snakes and others, and

it is responsible

for the physiological

behaviour of an organism,

for us having pulse, breathing and not forgetting

to do that.

The next section

we share with all the mammals and with

some other animals.

It is responsible for our complex hormone

reactions - all this complex social

behaviour, love, affection, friendship, everything around,

for our non-adequate actions, for our adequate ac..

And it is our adequate actions

that the third section is responsible for.

Some mammals

have some traces of it, but

it takes the most considerable part in humans’ brain.

And this section is called ‘neocortex’.

It is 2-millimetre’s thick,

2-4 millimeters.

And it is nearly of this size.

Well, I won’t tell you that it is like a napkin or

something. It is of this size.

My brain is of that size.

And that is how big neocortex is.

And it is squeezed like that

and is put into the cranium, that is why there are tubers.

There is no other reason for that,

it is not an architectural project,

there’s no magic in those tubers.

The neocortex is flat,

it is just put into a brainpan,

that is why there are tubers.

It consists of four sections.

Here we’ve got the left cerebral hemisphere, you see, the eye is here.

It consists of the left hemisphere and the right hemisphere

and besides,

it is virtually divided

into the front and the back part of neocortex.

The back part responds mostly

for the sensorial information processing, and the front part

of neocortex responds mostly

for various motor activity and

for processing the behaviour itself.

It is very homogeneous,

a piece from here and a piece from here

and a piece from here will be very

similar.

There will be the same neurons,

the same composed layers,

the same complicated connections.

So, what is its structure

if we look at it closer?

If we make a cut - we have two millimetres here -

you imagine, don’t you? We’ve taken a piece of neocortex

and we’ve made a cut.

We can see different neurons - such cells -

Merkulov has given

a separate talk about neurons,

so I won’t explain in detail.

Roughly speaking, they have one output,

many inputs,

and, you see, they are all turned there

and in that direction their amount grows.

If we look closer, we will see some

virtual layers.

Look, there’s some differentiation.

Let us look at these layers even

closer.

Usually they single out six layers,

and besides,

four first layers

process information

and the others are more likely to summarize it. And the last layer

consists of, if you remember

the last slide,

greater amount of axons

which lead to other neurons to

the other parts of neocortex. That is, we have

such a column.

Why do we need to know it and why do I show you

neocortex and neurons?

This thing

is a piece of our neocortex,

having six layers,

it is of two millimetres.

These pieces of neocortex which are very

homogeneous and very similar

are connected hierarchically.

That is, one piece is connected with its output with

some other piece,

those pieces are connected with

some other pieces and they make up a kind of hierarchy,

so, all these rectangles

are the pieces of neocortex of some

size,

which have a great amount of inputs and

less outputs, less outputs, less outputs.

This structure

transfers some abstract things into some

more specific things. Let us

examine this process through

reading.

We read the word “mother”.

We have the lower layer

which accepts the information

from our visual

receptors.

And this lower layer can do

some easy things. There is a big

group or neurons in it,

a huge family (this small piece)

which can distinguish

this kind of lines.

A piece next to it

can distinguish this kind of lines,

another one - this kind of lines.

Now if they are in the correct order,

these pieces get activated,

and on the second or the third level

we already can distinguish letter ‘A’.

If we have found a letter, having simultaneously

this line, this line and

this line,

then on the next level we recognize letter ‘A’.

Here we can

already recognize

syllables, there we recognize words,

and so on, and so on and so on. Why do we need to know it?

We are now focused on

recogniton, what does this have to do with the intelligence?

It appears a bit later,

because

you remember, we have just seen

all that

things, responsible for

recognition, which are interconnected

into more high-level things

and even more high-level things

and even more high-level things.

When I say the word “mother” or when I read the word “mother”

or when I think about my mother

or when something reminds me my mother,

let it be Mom’s shoes,

quite different information comes

through the inputs:

it may be either visual information or audio

but in all cases you will eventually recall

the same image of your mother from your brain.

When you hear me talking about your Mom

or read me talking about you Mom,

you think about the same Mom,

you see?

Thus, all these sections

are connected in the end with high-level

abstractions

and the arrows go into both directions.

What are the characteristics of all this structure?

First of all,

this structure keeps the

sequences of patterns.

That is, the letters in the word “mother” are arranged in some

sequence.

If I start singing some melody

which you know,

you will be able to continue from the place I stopped,

but you don’t the member the whole song, you can

take up from this place.

You can not

start singing it from a random sound,

can not sing it from the end to the beginning,

you see. There is a sequence

and there is direction in which we

read this sequence.

Our memory works in associated way:

in order to recall something

we need to give to an input something

similar to what we want to recall.

You do not keep your Mom in your head all the time.

But as soon as I say “mother”,

something connected with the word “mother”

gets activated in your brain.

When you try to recall some

fact,

it is very helpful to enumerate synonyms or associations.

In such a way you activate some

neurons close to this fact and

you recall this fact.

Thus, our memory works with associations.

We

need some association to match it

with some association.

Our memory, due to the great amount of layers,

there may be much more of them,

keeps the patterns

in an unchanged form.

Look, here is letter ‘A’.

And it is obvious for all of us, that it is letter ‘A’

but we have just put a sheet on its side,

you see.

But in fact this letter ‘A’ and this letter ‘A’

consist of compeletely different components.

Our memory, using many layers,

eventually turns all these letters into

some abstraction of letter “A”,

and it is of no importance after that, how it is written.

It is easy to understand on the example of letters,

but it is the same with all the other things. When you look at

me like that,

when you look at me like that,

you do not have any difficulties to recognize

the same person, though

in such a way I don’t look like myself at all.

Ok, what does it result in?

Since 2007 approximately we

witness a huge fresh leap

in neuron networks development (which have been

developing for the last 50 years),

called Deep learning.

What is it about?

Its essence, in a couple of words, is in multilayer

hierarchic neuron networks, similar to our

neocortex.

Multilayer neuron networks

are known since long ago,

approximately since the middle-the end

of the twentieth,

but there was a range of difficulties and problems, connected

with their learning.

Usually,

simple neuron networks are educated in a

comparatively easy way. Imagine, here is a simple neuron network:

we have an input,

we have some outputs

and now we need to tune

these inputs and these outputs in such a way,

that with some coefficients some signal on an input

lead to some signal on the outputs.

When we have a greater amount of the layers,

the quantity of the elements taking part in the process becomes so high,

that the classical

methodics of learning do not work.

In 1995 a new mathematical machine was invented.

At the beginning of 2000-s

was developed a speсific soft

which could do that, and in the same time

appeared the Internet,

developed the Internet.

The Internet has lead us, sapienses to

furious generation of content:

various images,

videos and so forth. This content appeared to be

this very thing, necessary for

neuron networks learning.

It needs to learn on some material.

And Google, having bought Youtube in 2012

first of all -

what would you do? -

certainly, it took 16 000 computers,

installed a neuron network , set it at Youtube.

They were doing something for a week (there was no

specific task, just Youtube and neuron network)

so, a week later

they said “We found object “A”,

it is noticed in all those videos.

It happened to be homo sapiens.

“We found object “B” - it occurs in all those videos. This happened to be a cat.

In a week’s term a neuron network came to know

how to distinguish people from cats

and how to find cats in a video,

though there was no task to find cats.

Cats just seem to be popular enough

for it to decide that cats are important

for sapienses.

Simultaneously, the same neuron network,

absolutely the same, the same abstract of code,

learned on different test-kits

may perform different

tasks. Assume,

often we can teach

the same piece of code to recognize

pictures

and, with minimal changes,

recognize sound.

These are quite different tasks, but remember our neocortex.

It is absolutely homogeneous,

its sections complete different tasks

and it is almost identic everywhere.

Google’s cars

have already run more then a million kilometres -

when I gave a talk this summer I said:

“and not single a car crash has taken place”,

but after that there was a crash,

they were stricken, they are not guilty.

So,

in 2015, this year

many interesting things have happened. Since 2013,

nearly since 2013 the amount of mistakes

made by the AI

in the process of different items recognition

dramatically diminished.

And,

it moved, moved, moved and here

in 2013 it considerably decreased

and this is 100% of

accuracy.

So,

we are in this point.

Machines now recognize pictures

in average better than people. Computers

defeated people in several competitions

concerning picture recognition,

including finding a cat in a picture,

including road signs recognition,

and so on, and so on,

and so forth.

Why am I telling al these things?

The important question is as follows.

When we compare and speculate about

the intelligence, the foolness and such things,

we usually

compare a rural fool

and a Nobel prize

winner.

Here we need to understand

that even a rural fool is dramatically, considerably much more clever than any other non-homo sapiens.

If we take a mouse,

mouse is a mammal, mice have a thin

layer of neocortex - they can do

a lot of cool things, they are really clever. If we take

a mouse,

its intelligence leaves much to be desired, its

difference from a rural’s fool intelligence is enormously huge, still the difference between a fool and a

genius is tiny.

If we would like to compare some other animals,

they will arrange like that

and a lot of them will be sillier than a mouse.

And when we look at this picture,

we need to understan, that the artificial intelligence

existing now

is, in average, more primitiv, than the mouse’s one.

Mice are cool.

Contemporary artificial intelligence is a bit less

cool than a mouse’s one,

but it improves itself.

One day it will overcome mouse,

then we will make more efforts for a long time

and artificial will overcome

primates

and then some moment artificial intelligence

will reach a rural fool’s level.

And in the next moment after that moment

just in a couple of days

artificial intelligence will overcome a genius.

This difference is not so huge as

that one.

And very soon, in a couple of days after

the day when artificial intelligence reaches

the fool’s level, the situation will be like that.

And this difference is really

huge.

Why will it happen like that?

Now I will show you an example of a

normal process.

Here is a diagram of GDP for the last 10 thousand years.

Moreover, it is a normal process, many

things in the Universe move like that.

If we take the figures away

we will get an amount of iron mined on our planet for any

amount of years, and so on.

Many thing related

to technological progress look

quite the same. At first there is nothing and then, well

you know, don’t you?

What am I getting at?

I’m getting to the fact, that now we are

approximately here.

People’s productivity

grows step by step,

we learn, a 100 years ago

education became whole-planet

then higher education became available for everyone,

we have better and better and better results and people become

more and more intelligent.

At the time we didn’t have computers,

we were already cool,

we were given odds.

First computers didn’t exist,

then they became cooler and cooler

then they are on this level.

And the moment they reach this point

we will be hardly able to bother them anyhow.

Maybe, AI invention

will be last humanity’s

invention.

Why?

Chimpanzee is strong.

Chimpanzee is really strong,

it is like two times stronger

than an adult man. If a dozen of chimpanzees

will come in now

they will for sure defeat us.

I am just ready to bet.

There are two doors here, behind me and right there,

we will run to these doors

from everywhere.

We will be helpless against

chimpanzees.

But chimpanzees can not turn off

people.

They do not have such a button.

They can do nothing. And all chimpanzees’ lifes

on our planet

are connected first of all with

humans’ intention.

We can not turn off the internet,

the internet has been invented recently

it does not have its own intelligence

but there’s no button “Turn the Internet off”.

In the same way there won’t be a button

“Turn the AI off”.

And we well be like those chimpanzees

which are much stronger then us -

we will also be stronger than that iron box - we can surely beat it,

but the button “Turn AI

off” won’t exist and even hackers

constantly find weak points

in computer systems.

AI and hackers will find them much

faster. Even if

we take this iron box, disconnect it

from the internet, put it into other box,

hackers

with the help of social engineerins, bribes and other people

will be able to open those

iron boxes, disconnected from the

internet. Why should AI fail to do that?

It may set contact with the maintenance stuff,

hire people.

I think that if someone will try

to hurt really smart AI

with people’s hands,

it will hire other people

to protect it from those dangerous for it.

Well, you’ve got the idea.

They say that something like that

took place during the industrial

revolution.

During the industrial revolution, remember,

luddites and all those things.

And people decided: “Let us ruin factories

because if we install machines everywhere,

there won’t be any work for us."

Machines were being installed and installed

and finally they were installed everywhere.

And it occurred that people still do have what to do.

What is the difference between AI evolution

and the evolution of machines?

The most part of people in different countries

(80 per cent)

are now performing the tasks

which computers will soon do better

than people.

Computers will answer people’s questions

more accurately,

will drive better,

will better answer questions in call-centres,

will better identify criminals

on video surveillance cameras,

will sell goods in supermarkets

better.

Well, they will do better

almost everything that most people do now.

If people will be substituted by the computers

so what should all these people do?

Taking into the account the fact that

AI and computers’ evolution

doesn’t seem to be a process

which may

finalize.

In contrast to machine evolution

when we have equipped every

factory with a mechanized lathe

and that was actually

all

we could do.

And we.. (this “thank you” is not yet for you)

It is important to understand, that

the problem of “odd” people

should be solved now, immediately.

And being more clever or working harder

won’t change anything

because we should remember that:

here is our productivity’s

growth,

and here is the computers’ one

what means that we won’t

be able to become more intelligent and efficient than them again.

One of the possible ways of this problem solving,

the first one that comes to my mind

is to make job independent from salary

so people should work because they like it

and get money because all the

homo sapienses get money via delivery.

Well, either it may be manual labour higher appreciation,

with the growing need to buy the products

made by homo sapienses and not by

soulless machines and so on.

But all these ways are rather controversial

and some new methods should be

invented.

I want to summarize:

we won’t be able to control and restrict AI

if it appears.

And it will appear for sure

in the future 50 years or sooner.

The only thing that can help us somehow

is having common goals

with the AI and

being allies with it.

AI development is a very

difficult task, but

AI which won’t destroy the humanity development is

an even more difficult one.

And it is very important

that these tasks were solved in a

correct order.

Thanks a lot!