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Will robots ever turn against us and take over
the world?
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I'm going to tell you all about it.
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Will robots outsmart us?
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This the university of the
Netherlands
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New technologies emerge quickly and movies show somehow...
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frightening scenarios in which
robots are smarter than humans...
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and are taking over control.
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Some movies even go a step further and predict...
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that robots can replicate themselves.
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However will this ever become reality? Will robots outsmart us?
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As an applied mathematician and mechanical engineer...
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I'm trying to answer this question.
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Together with the team we work on recreating intelligence in robots.
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We strive to build robots that can learn, make decisions, recognize environments...
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and possibly create without
human intervention.
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This is known as artificial intelligence or AI.
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If Ai can think as a human or
basically if AI is conscious...
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then we call such AI general AI.
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On the other hand, if AI can
replicate itself, then we call it super AI.
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However our society is far
from achieving these two.
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Currently we mostly work on the so-called weak artificial intelligence.
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In weak AI we strive
to use human reasoning...
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as the model for the robot's training,
but not for its end goal.
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Basically we do not try to replicate full human mental capabilities in a robot...
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but instead we train robots
to perform simple operations.
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For example that a robot is saying hi when somebody is passing by
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or that is laughing at on one of your jokes.
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At this moment probably most of you would think that AI and robotics...
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have the same goal.
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However this is not true. In robotics we strive to mimic physical human actions.
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We build robots that can pick up objects for example a ball...
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and place it on a specific position
that is predefined by a human.
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In artificial intelligence
on the other hand
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we are trying to recreate
intelligence in robots.
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Basically we train robots to recognize
objects, to understand that the object...
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is a ball and also to place them accordingly to a specific position...
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without human interaction.
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Therefore if we have AI and
robotics together...
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then we get an artificially intelligent robot.
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In which AI acts as a brain and
robotics acts as a body.
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So now you know what is artificial intelligence.
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However how can we train robots to be artificially intelligent?
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How can we train robots to make decisions, to recognize environments...
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or to act? Well for this we need to understand human behaviour...
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and therefore we need to understand
how the human brain is functioning.
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by zooming in on the human brain, we see that it consists...
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of billions of brain cells.
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They are also called neurons.
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Each of these cells is an
electrochemical structure.
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Basically each neuron starts
with a set of input antennas...
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which are called dendrites. And in these antennas we receive signals...
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from the outside world or from the
other cells.
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Once when the input signal is high enough...
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then the cell starts being activated and it's releasing a set of neurotransmitters...
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that travel right to the back of the cell to the synaps...
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which is a terminal for communication with another cell.
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On that moment we say that our cell is firing or being activated.
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Connecting cells into large networks, we can fire specific patterns...
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of brain cells and those are then understood as specific situations.
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For example if we have a spider in front of us,
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we will suddenly get afraid and some of us will run, the others will start screaming.
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Simply explained, this visual stimuli or spider is activating a set of cells...
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in our brain that are recognized as a pattern that means dangerous situation.
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In order to mimic this in robots, we have to understand this pattern...
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and we have to program it.
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To do so we actually mimic brain cells
by artificial cells
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And then we connect them into large
networks...
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which are similar to natural ones.
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However note that new artificial networks are not electrochemical devices.
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They are mathematical devices. Each cell is a mathematical element...
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with a set of inputs and a set
of output signals
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If the input signal is strong enough
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then our mathematical element
will get activated.
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It will release an output signal which then can activate some other cell.
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In this way we build patterns in large neural networks.
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However note that we need to train these patterns...
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because we need to train the neural network...
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that specific situations mean something.
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For example that a spider means dangerous situation.
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To do so we have to provide neural networks with huge amounts of data.
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For example in order that a robot learns that a spider means dangerous situation...
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we have to provide the robot with a lot of images of different spiders.
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In this way the robot can recognize features of an animal called spider.
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However in this way we are only training robots...
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to recognize objects, situations or environments.
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But not to act to it. For example if we see a spider we immediately run.
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So we would like that our robot does the same.
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To do so we have to actually
understand human actions...
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in order to train robots to act and for this we are searching solutions in psychology.
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In 19th century Ivan Pavlov has studied
something called classical conditioning.
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In his famous experiment he let the dog hear a bell sound...
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whenever he got some food.
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In this way the dog started drooling.
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Over time the association between food and bell sound became so strong...
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that actually the dog started drooling by only hearing the bell sound.
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And this is exactly conditioned learning.
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In conditioned learning we are learning an association between two stimuli...
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natural and neutral one.
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natural is food, neutral one is bell sound.
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Over time they are actually having a strong association among each other...
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and they will give the same response.
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Classical conditioning can also be translated to the robot's world.
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For example if we have a robot and two objects, car and a ball...
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and we would like that the robot is tracing a ball...
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then whenever the robot sees a ball, we actually can play a specific melody...
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that is implemented in the computer program as a reward.
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In this way whenever a robot
is tracking a ball...
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he will know that he's doing good job.
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Over time the association between ball
and melody will become so strong
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that actually the robot will start tracking any ball...
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without hearing any melody.
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In classical conditioning therefore we are learning association...
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between two stimuli: ball and melody,
food and a bell sound.
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In machine learning this is known
as prediction algorithm...
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in which we predict
the outcome of each situation.
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However there is also a main issue in this learning technique
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If we repeat Pavlov's experiment and then give to a dog food...
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without any bell sound and if we do this repeatedly, the dog will forget...
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to drool whenever hearing a bell sound.
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Therefore this learning process
can be diminished.
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And this is something we do not
want to use for robots...
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because they will forget what
they were trained for.
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Hence we need a more complex learning technique.
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And this is reinforcement learning.
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In reinforcement learning an animal or human will exhibit more frequently...
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an action that has led to a reward in the past and less frequently...
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actions that have led to punishments.
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For example, if you let a cat into a cage with four different buttons...
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and let's say that one of these buttons is providing a reward or food to a cat...
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and the three other buttons are representing punishments.
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For example by electrical current. Then
after some time the cat will realize...
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That actually the fourth button is the one that provides food...
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and it will start using this button more often.
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This can be translated to
the robot's world as well.
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In such a case we have to build an algorithm that is based on law of effect.
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That means that the algorithm has to
compare different actions...
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by their outcomes and also
has to build associations.
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It has to associate each situation
with one pre-learned action.
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And this is allowing us then to train
robots for unknown environments.
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For example if you want
to send the robot to mars...
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then you would like that this robot can move without hitting any obstacles.
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And for this we use
reinforcement learning.
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However the biggest issue in reinforcement learning
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is that it has to interact
with an environment.
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We need to get feedback
from the environment.
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And this is not always possible.
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For example we have environments that give us very sparse rewards...
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or no reward at all.
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For example if you want to train a robot to automatically play a computer game,
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then you can't use reinforcement learning if you are winning or losing...
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only in the end of the game.
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In such situations we need
a new type of learning.
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This is called imitation learning
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As its name says in imitation learning we bring an imitator or teacher...
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that is actually demonstrating
an action to a robot.
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In this way the robots
gets more data...
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and the learning process
becomes shorter.
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Imitation learning can be used for
example if we send our robots to Mars.
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And we would like them to build settlements.
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By imitating human behaviour...
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robots will start building these
structures in acollaborative manner.
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So now we are learning that actually it's not enough to train one robot individually...
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but we have to train them in a group.
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This is similar to human society.
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Humans are grouping together in order to collaborate for better survival.
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Therefore if we want to build
human free factories...
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we have to use collaborative robotics.
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Imagine two robots that are welding together or two robots...
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that are printing material together into some very precise structure.
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In such case we can get a lot of issues.
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For example one robot could recognize an object and the other not.
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One is processing information
much faster than the other.
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Then they are not aware of
its own physical constraints...
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or physical constraints of other robots.
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And also they need to be
aware of all actions....
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that all other robots
in the group are taking.
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Therefore we need to enhance AI
to be collaborative
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It doesn't matter if we train an
individual robot or a group of them.
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We still need to use big data sets to train robots.
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And therefore we need
huge computing power.
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For this purpose we use supercomputers.
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However even a bee's brain is more powerful...
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than the largest supercomputer on earth.
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Of course supercomputers are
faster in processing information.
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In this case this would be around
200 000 times faster than a bee's brain.
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However they require more energy.
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For processing the same information the bee's brain needs 10 microwatts of energy
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while the supercomputer
needs 13 megawatts.
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Therefore AI algorithms require
a lot of energy...
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and we need to build new ones that would be based only on small data sets...
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and hence use less energy.
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On the other hand when we train robots...
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we train them based on
our previous knowledge.
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That means that a robot can make
a new piece of music or a new drawing.
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However this is all based
on our previous knowledge.
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And therefore they can't
create anything new.
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Some researchers argue that then artificial intelligence is not intelligent at all.
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However I do believe that we will make robots more intelligent than they are today.
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For this we need to pass from building associative relationships...
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between stimuli or stimulus and action...
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and start building
cause-effect relationships.
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For example, our robots today
can very precisely move...
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but they don't know enough to
predict possible accidents.
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Their knowledge is lower than
knowledge of a 16 month old baby.
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Therefore we need to build AI that can build such cause-effect relationships.
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Today we have learned that
the machine and robot intelligence...
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depends on big data sets, complex
algorithms and huge computing power.
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However is this enough to
achieve general or super AI?
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Well, only time will tell.
However one thing is for sure.
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Robot intelligence will be
different from human intelligence...
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in the same way as animal intelligence is different from human one.
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Robots will just excel in different
types of skills than humans.
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And I do believe that one day
they will make more...
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than what human species have
done so far. Thank you.
