Artificial intelligence (AI) is a powerful tool that has evolved significantly, moving from science fiction fears to practical daily applications, and understanding its current capabilities, limitations, and potential future developments is crucial for navigating its impact.
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Should we be afraid of artificial
intelligence? It is a question that
keeps many of us up at night. Most
people first heard about AI through
1990s movies like Terminator or The
Matrix, where it was portrayed as a
villain turning against humanity. And it
was not just the movies. Renowned
figures like Steven Hawking have also
warned about the possible dangers of AI
rising against human beings. So back
then it was completely natural to feel
uneasy whenever we heard the term
artificial intelligence. But for nearly
20 years after those movies, AI made
little real world progress that could
cause serious concern. Things stayed
relatively quiet. But today the
situation has completely changed. AI is
now part of our daily lives through AI
cameras, Alexa, Siri, ChatGpt, Google
Gemini, and many more tools. Even the
YouTube algorithm that suggested this
video to you works using AI. And it is
not just YouTube. On platforms like
Facebook and Instagram, AI decides what
you see on your feed. Another unsettling
use of AI is in deep fake videos, which
many of you may have already come
across. In several countries, banks now
use AI to approve loans. Insurance
companies use it to calculate your
premium, and AI even plays a major role
in stock market trading. In the medical
field, AI is now being used for
diagnosing diseases. In short, AI is
being used in areas where serious
decisions are being made. Yet despite
all this, we have not seen AI behaving
like the evil villain shown in those
science fiction movies. And that
naturally leads to an important
question. Is there a difference between
the AI we saw in movies and the AI we
are seeing today? What exactly is
artificial intelligence? How does it
work? How many types of AI are there?
And could AI ever become a villain as
shown in the films? Let us explore the
Hi friends, welcome to a new video from
science simplified for all. Most of us
have been familiar with AI based
personal assistants like Alexa, Siri and
Google Assistant for some time now. But
it was only after the arrival of Chat
GPT and Google Gemini that many people
truly realized just how far artificial
intelligence has evolved. Chad GPT seems
to understand what we say in natural
language and respond in a way that feels
remarkably human. Because of this, many
people unknowingly assume that chat GPT
is a conscious being, something with
self-awareness. But we must always
remember Chat GPT is just a software
program specifically designed to mimic
the way a human respond in conversation.
It does not truly understand things the
way a human does. To clearly grasp this
idea, we first need a basic
understanding of what artificial
intelligence actually means and how it
works. This video will give a very
simple explanation aimed at everyday
viewers with no technical background or
prior knowledge about AI. So if you are
someone already familiar with the core
concepts of AI, feel free to skip this
part as it may seem like an oversimplification.
oversimplification.
Let us begin with the term intelligence.
The ability to learn new things, make
logical decisions and solve problems.
That is what we call intelligence in a
human being. When a machine or computer
begins to exhibit this kind of ability,
we call it artificial intelligence. That
is the most basic definition of AI. But
this definition alone does not give us
the full picture. We already know that
computers have been capable of doing
mathematical calculations much faster
than humans for decades. For example, if
you ask a computer to multiply 1,230
by 2480,
it will do it instantly, far faster than
any human ever could. In fact, a
computer can perform millions of such
calculations every second. In that
sense, computers have always been ahead
of us in solving arithmetic problems.
But here is the key point. These
calculations are all based on
instructions that we the humans have
already given to the computer in
advance. These instructions are what we
call programs. A standard computer can
only follow those predefined
instructions exactly as given. If it
encounters a situation slightly
different from what we programmed it
for, it will usually fail. Now, here is
the difference. When a computer is able
to do something new, something we did
not specifically teach it by learning
from the data and recognizing patterns
on its own, that is when we say the
computer has artificial intelligence.
Let us take an example. Suppose we
create a program to identify birds and
upload it into a computer. Then we show
the computer pictures of 10 different
types of birds. A crow, an eagle, a
sparrow, a parrot, and so on. We also
tell the computer these are all birds.
Now imagine we show the computer a new
picture of a bird it has never seen
before. If the computer is able to
recognize that the new image is still a
bird even though we never showed it that
specific example, that is a simple form
of artificial intelligence. It has
learned from the previous examples and
applied that knowledge to something new.
That is one of the key traits of AI. Of
course, what today's artificial
intelligence is capable of goes far
beyond this simple example. AI systems
now handle much more complex and
powerful tasks. But we are using this
bird example here to make the core
concept easier to understand. Now, let
us take a closer look at how the
artificial intelligence program in our
bird example actually works. We begin by
defining the unique features that
distinguish a bird from other objects.
For example, we might say that birds
usually have two legs, wings, feathers,
and a beak. These are the features that
we define in the program. Next, we
assign a weightage to each of these
features. That means we decide how
important each feature is in identifying
a bird. For instance, two legs 20%,
wings 30%, feathers 30%, beak 20%.
Together that adds up to 100%. Now if we
show the computer a picture of a crow
which has all four features it would
score 100% and the program would
confidently say yes this is a bird. But
suppose we show it a picture of a
penguin. A penguin does not have visible
wings or feathers like other birds. Its
wings look more like arms. So the
program might only find 40% of the
features it is looking for. Based on
this low score, it may wrongly decide
this is not a bird. But we know that a
penguin is a bird. So we correct the
program and tell it that it made a
mistake. When the program receives this
correction, it begins to adjust the
weights it had assigned earlier. It
might reduce the importance of features
like feathers and wings and increase the
weightage for features like a beacon two
legs. It also learns that a perfect 100%
match is not always necessary. Even if a
creature matches only 80% of the key
features, it could still be a bird. This
program is written in such a way that it
can adjust these weightages by itself
based on feedback. As we continue to
show the program more and more images of
different birds, it keeps refining its
internal settings. Each time it makes a
mistake, we correct it and the program
learns from that error. In other words,
it adjusts its internal parameters.
Eventually, after seeing enough examples
and making enough adjustments, the
program becomes capable of correctly
identifying any bird, even one it has
never seen before. This process is what
we mean when we say an AI program is
being trained. In reality, the working
of an AI system is far more complex than
what we just explained. Modern AI
programs use many different parameters
and weight values and these are
processed through multiple layers and
stages before reaching a final decision.
Still, we chose to present the concept
in such a simple way for one important
reason to help you understand three key
points clearly. First, in the example we
discussed, the AI does not actually
understand what a bird is. In fact, the
concept of understanding or mind itself
is quite vague. But even if we leave
that aside, what a human understands
when they see a bird is fundamentally
different from what an AI sees. For the
AI, a bird is just a group of numbers,
weight values associated with different
features like a beak, feathers, and
other such traits. That is all. Second,
to train an AI, we need a massive amount
of data. Even in the bird example, the
AI needs to be shown thousands of bird
images to learn what a bird looks like.
And not just that, every time the AI
makes a mistake, a human has to step in
and correct it, telling the AI that it
was wrong. Without that feedback,
learning will not happen. Third, even
after all this effort, the AI only
learns to answer one specific question.
Is this a bird or not? If you want the
AI to answer a different question like
which bird it is, you need to write a
different program and train it
separately. And if the image is not a
bird at all and you want the AI to say
whether it is a mammal or something
else, that too requires a separate
program and new training. In other
words, each AI can only perform the
specific task it was trained for. If you
want the AI to do a new job, it must be
trained again, often from scratch. To
put it simply, every specific task
requires a specially trained AI system.
Just because something is called
artificial intelligence does not mean it
can do everything. All these limitations
we discussed so far explain why even
though science fiction movies talked
about AI decades ago, the real
development of artificial intelligence
took much longer to happen. The rapid
growth of AI that we see today became
possible mainly because of two key
factors. The first reason is the
dramatic increase in computer processing
power. Back in the 1990s, computers
simply did not have the capability to
run AI programs. But over the last 20
years, the computational speed of
computers has increased by several
thousand times. This incredible
improvement is what finally made it
possible to run complex programs like
artificial intelligence on regular
machines. The second major factor was
the rise of social media. Let me give
you an example. When you upload a photo
of your pet dog to Facebook and tag it
as dog, you're actually helping
Facebook's artificial intelligence learn
what a dog looks like. Every time
someone does this, the AI gets better at
recognizing dogs, even when it sees a
totally new photo of a dog later. With
the explosion of social media, AI
systems suddenly had access to huge
amounts of data for training. Today,
millions of people across the world
upload photos every single day. Many of
those photos are used behind the scenes
to train AI programs. The same is true
for public messages, comments, and
captions that we post. All of this
content is used to help train artificial
intelligence in understanding natural
language. the way we humans actually
speak. And this is important because the
way we speak is very different from what
you find in books or dictionaries.
Spoken language is filled with informal
phrases, slang, and regional styles. Yet
today, we have reached a point where AI
can understand meaning even in informal
speech. And that is largely thanks to
the vast amount of training data AI has
received from language used on social
media. By now you should have a general
idea of how an artificial intelligence
system is trained. But there are a few
important things you need to know. When
an AI is trained using millions of
images and messages, the process is not
done manually by humans. That would be
practically impossible. Instead, the
training is handled automatically by
powerful software systems designed for
that purpose. And this leads to a major
problem. By the time training is
complete and the AI is released for
public use, even the developers who
created the system often have very
little idea of what exactly is happening
inside the AI's decision-making process.
This is because the training process
changes the AI program so extensively
that it becomes difficult to track how
it arrives at its conclusions. Here lies
the real issue. If such an AI makes a
mistake, it is incredibly difficult to
figure out where it went wrong or why it
made that mistake. This lack of clarity,
often called the blackbox problem, is
one of the biggest drawbacks of many
modern AI systems. Let me give you a
real world example. An AI system was
trained to identify animals in pictures.
But during testing, it started
mclassifying certain dog photos as
wolves. At first, the reason for this
behavior was unclear. Only after
detailed investigation did researchers
discover the actual cause. In the
training data set, almost all wolf
images had snow in the background. So,
whenever the AI saw snow behind a dog,
it assumed the image must be of a wolf.
In other words, it was giving more
importance or weight to the snowy
background than to the animal itself.
This kind of issue happens because after
training we no longer fully understand
what the AI is focusing on internally.
That is why it becomes so difficult to
trace and correct these kinds of errors.
To address this problem, a new approach
called transparent AI has been proposed.
The idea is to make AI systems more
understandable where we can see and
interpret what is happening inside.
However, transparent AI is still a
developing concept and has not yet been
widely adopted in real world
applications. There is one more crucial
point to remember. The data used to
train an AI must be completely accurate
and unbiased. If the training data
contains flaws or biases, those issues
will reflect directly in the AI's
behavior. Let me give you a real world
example. In one case, a company used an
AI system to shortlist candidates for
job interviews by analyzing job
applications. But when the results came
out, it was found that the AI had
selected only male candidates. On
investigation, the reason became clear.
The data used to train the AI mostly
came from past hiring decisions. And in
the past, the company had preferred
hiring men for that specific job role.
This bias in the historical data, even
if unintentional, got passed on to the
AI system. As a result, the AI learned
to favor male candidates simply because
that is what the past data showed. This
is a classic example of how bias in
training data can lead to discrimination
in AI decisions. And this can apply to
other human biases as well, such as bias
based on skin color, religion, or any
number of other prejudices. That is why
it is absolutely essential that the data
used to train AI is carefully checked
for fairness and neutrality. Otherwise,
our own biases will get transferred into
the AI we build. And here is something
we must never forget while using AI.
When an AI makes a mistake, it has no
idea that it made a mistake, nor does it
feel any regret. That is because AI has
no concept of understanding. It does not
know what is right or wrong. It simply
follows the patterns it was trained on.
So the responsibility to monitor AI
behavior will always rest on us the
humans. We must be the ones to watch to
correct and to decide what is
acceptable. Now let us look at the
different types of artificial
intelligence used today and the kinds of
jobs they are designed to do. The first
major type is called natural language
processing or NLP. This refers to AI
systems that can understand and respond
in human language, the way we actually
speak. Examples you're already familiar
with include Alexa, Siri, and Google
Assistant. But NLP is used in many other
areas as well beyond just virtual
assistants. The second type is
generative AI. This is a kind of AI that
can create new content, things that
never existed before. For example,
imagine an AI that has read thousands of
novels. Now, if you ask it to write a
completely new novel, it can do that. Or
if it has seen thousands of human faces,
you can ask it to generate a picture of
a face that does not belong to any real
person and it will do that too. That is
what generative AI does. Chat GPT and
Google Gemini are examples of generative
AI. To be more specific, they are
generative text AI, which means they
create new text based on what they have
learned from existing text data.
Similarly, there are generative image
AI, which can create new images from
learned visual data. One such tool is
Deli. For instance, you could ask it if
a famous painter from the past were
alive today, what would their painting
of a modern city look like? and the AI
would generate an image based on that
prompt. The third type is called
computer vision AI. This kind of AI is
used for image and face recognition. AI
cameras which can identify people,
objects or license plates fall under
this category. Beyond these, there are
many other specialized types of AI
available today. Robotic AI helps robots
navigate and interact with the world.
Speech recognizing AI converts spoken
words into text. Explainable AI is
designed to make AI decisions more
transparent. Planning and scheduling AI
are used in logistics, project
management, and more. In some cases, two
or more types of AI work together to
perform a more complex task. You might
have seen deep fake videos where a
famous actor's face is seamlessly
swapped onto another person's body or a
politician appears to say something they
never actually said. This is possible
because two AI systems are working
together in tandem. The first AI system
is trained specifically to swap faces in
a video. But if you look closely at
those videos, you might notice something
odd, some slight unnaturalenness that
makes you realize it is fake. Here comes
the role of the second AI program. Its
job is to detect flaws in the video.
Anything that seems artificial or off.
Once the flaws are identified, the first
AI makes corrections and produces a new,
improved version of the video. Then the
second AI checks it again to see if any
new mistakes are still visible. This
process continues in multiple rounds
with both AIs competing. One trying to
make the video more realistic, the other
trying to detect any imperfections.
Eventually, the outcome is a deep fake
video so convincing that we can no
longer tell it is fake. The deep fake
videos we have seen so far are not
perfect. But today there are AI tools
capable of generating even more
realistic and convincing videos. This
raises a disturbing truth. Such highly
accurate deep fakes can potentially be
used for fraud or other malicious
purposes. So far we have been
classifying AI based on the kind of work
it does. Image recognition, text
generation, speech processing and so on.
But there is another way to classify
artificial intelligence based not on the
task it performs but on its level of
capability. This is where we come across
the terms weak AI, strong AI and super
AI. And it is in this classification
that the possibility of AI turning
against humanity like in the movies
enters the picture. The AI systems we
have discussed so far are all designed
to do one specific task. This kind of AI
is known as weak AI or narrow AI. Almost
all the AI that exists today falls into
this category. We humans on the other
hand possess what is called general
intelligence. We can learn a wide
variety of things, adapt to different
situations, make logical decisions, and
solve many types of problems. If a
computer ever develops that kind of
broad capability, it would be called
general AI or artificial general
intelligence. Sometimes simply called
strong AI. But let us be clear,
artificial general intelligence does not
exist yet. It is still a theoretical
concept. There are ongoing efforts to
build such an AI, but no one knows how
long it will actually take. Some experts
in the field who are very optimistic
believe it might take around 20 years.
Others say it could take at least 50
years or even more. As of today, general
AI remains a future possibility, not a
present reality. There is one more
category of AI that is even more
powerful than general AI. It is called
artificial super intelligence. This
refers to a future AI that will be more
intelligent than humans in every
possible way. Right now, this kind of AI
is purely speculative, but that does not
mean it can never happen. Some believe
that such an artificial intelligence
might emerge by the end of this century,
but no one really knows how long it will
take. The main concern is this. Once
artificial super intelligence is
created, it could potentially become
smart enough to design even better
versions of itself. And those versions
could go on to create even more advanced
versions and so on. If that happens, the
growth of AI would become exponential
like a chain reaction. This kind of
situation is what experts refer to as
the technological singularity. It is
this level of super intelligent AI that
is often portrayed in movies as turning
against humanity. And it is not just in
fiction. Many respected individuals have
expressed concerns about this
possibility. The late physicist Steven
Hawking and Elon Musk, CEO of Tesla and
Space X, have both openly warned about
the potential dangers of uncontrolled
AI. But there are also many experts who
believe such fears are unfounded, at
least for now. Their main argument is
this. Today's AI systems do not have
consciousness or self-awareness. As we
discussed earlier, when an AI identifies
a bird, it does not truly understand
what a bird is. It is simply processing
numbers and patterns. It has no inner
awareness or sense of meaning. In fact,
even human consciousness is still a
mystery. We do not fully understand what
consciousness is or how it arises in the
brain. So, creating a conscious
self-aware AI is far beyond our current
capabilities. And without
self-awareness, the idea of an AI
deciding to take control or destroy
humanity is not realistic. After all,
such desires for power and domination
are very much human traits. Even if an
AI becomes smarter than humans, it does
not necessarily mean it will want to
harm us. What we should actually be
concerned about is not the AI itself,
but the humans who might misuse it. AI
is a very powerful tool and like any
powerful tool it can be used for good or
for harm depending on who is using it.
That is the real risk we must watch out
for. In fact, some of this may already
be happening. AI can be used to subtly
influence the general public toward a
particular political orientation or
ideology. And with many of the AI
algorithms we interact with in daily
life, achieving this kind of influence
is relatively easy. Another common fear
people have about AI is that it will
take away jobs. But we have faced this
situation many times in history. When
electricity and machines were
introduced, people feared job losses.
When computers came, there were similar
fears. Yet each time humanity adapted.
We found new ways to work and new types
of jobs were created. Many experts
believe the same will happen with AI.
Artificial intelligence will not
directly take away your job. But someone
who learns to use artificial
intelligence effectively might out
compete you in the same field. That is
where we need to adapt to the changing
world. We must learn how to work with
AI, not against it. I hope this video
helped you gain a clear and simple
understanding of what artificial
intelligence really is and what it is
not. If you found this video useful,
give it a like and share it with someone
who might find it interesting, too. And
if you enjoy content that explains
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fascinating topics coming soon. Thank you.
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