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Recently I have been hearing a lot about Artificial
Intelligence and Machine Learning and I became

interested in looking into AI and Machine
Learning a bit more.

I sought to get my questions answered.
What is Artificial Intelligence (AI)?
What is Machine Learning?
And How Exactly do they differ?
This is gonna be an interesting video.
Artificial Intelligence or AI can be simply
defined as “a branch of computer science

dealing with the simulation of intelligent
behavior in computers” or “the capability

of a machine to imitate intelligent human
behavior” Both definitions from the Merriam

Webster Online Dictionary.
Both these definitions deal with the concept
of computers being able to go about doing

complex tasks by themselves, and in this case,
we aren't speaking of simple processes,

we're speaking of the ability of a machine
to act completely on its own without the need

of human intervention.
Well, after being created of course.
We can see examples of this is many places,
but the most common is in movies like WALL-E.

You can consider WALL-E an AI Robot.
He thinks for himself, does what he needs
to do for himself, and has something that

resembles a conscience.
We can actually see this in a majority of
Disney and Pixar movies where inanimate objects

come to life and make decisions of their own.
Its pretty interesting.
Upon doing my research, I came across a very
interesting article which proceeds to give

a bit more information on the history of AI.
Here's a short extract from Bernard Marr,
A Writer at Forbes.

“Artificial Intelligence has been around
for a long time – the Greek myths contain

stories of mechanical men designed to mimic
our own behavior.

Very early European computers were conceived
as “logical machines” and by reproducing

capabilities such as basic arithmetic and
memory, engineers saw their job, fundamentally,

as attempting to create mechanical brains.
As technology, and, importantly, our understanding
of how our minds work, has progressed, our

concept of what constitutes AI has changed.
Rather than increasingly complex calculations,
work in the field of AI concentrated on mimicking

human decision making processes and carrying
out tasks in ever more human ways.”

Now personally, I don't like the idea of
US humans attempting to create machines which

can think for themselves, mainly because I
don't see it going anywhere positively.

I'm not gonna go in depth, but its not a
good idea, things can go wrong in so many

And someone else who has a big impact in the
field of technology and automation feels the

same way.
Elon Musk.
The Giant who has led Tesla to where it is
today, in not only Vehicle Automation but

also in the creation of many clean energy
stations around the world.

But something interesting that he said got
my attention.

This was back in 2017 and not only did it
get my attention, but it got the attention

of many Tech Enthusiasts out there.
Elon Musk Referred to the dangers associated
with the development of AI as possibly a bigger

threat than North Korea.
And we're talking about a Nation Which has
at their hands, the power to launch a barrage

of Nuclear Missiles at America and Possibly
Start another world war.

In this article from CNBC, “Tesla CEO Elon
Musk fired off a new and ominous warning on

Friday about artificial intelligence, suggesting
the emerging technology poses an even greater

risk to the world than a nuclear conflagration
with North Korea.

Musk—a fierce and long time critic of A.I.
who once likened it to "summoning the demon"

in a horror movie—said in a Twitter post
that people should be concerned about the

rise of the machines than they are.
Reacting to the news that autonomous tech
had bested competitive players in an electronic

sports competition, Musk posted what appeared
to be a photo of a poster bearing the chilling

words "In the end, the machines will win.
Musk, who is spearheading commercial space
travel with his venture SpaceX, is also the

founder of OpenAI, a nonprofit that promotes
the "safe" development of AI.

His stance puts him at odds with much of the
tech industry, but echoes remarks of prominent

voices like Stephen Hawking—who has also
issued dire warnings about machine learning.”

End Of Quote.
Now this is indeed interesting because it
shows that not everyone is on the side of

developing AI, because in the end, we never
know what's gonna happen.

And from how its being described to us, its
not really looking that safe.

Obscure tests seem to be OK but when Scientists
attempt to get this out and connect AI beings

to the internet and actually get them out
there, I think it's a good time to watch

your back.
Now let's speak a bit about Machine Learning.
According To Wired, Machine Learning is defined
as “the science of getting computers to

act without being explicitly programmed”
They got this definition from The University

Of Stanford and while I get the idea of the
concept, there's sort of a crossover with

the definitions and understanding of Artificial
Intelligence and Machine Learning.

Again, Quoted from Wired “Big technology
players such as Google and Nvidia are currently

working on developing this machine learning;
desperately pushing computers to learn the

way a human would in order to progress what
many are calling the next revolution in technology

– machines that 'think' like humans.
Over the past decade, machine learning has
given us self-driving cars, practical speech

recognition, effective web search, and a vastly
improved understanding of the human genome.”

End of Quote
Now I should state that machine learning is

sort of a sub Category Underneath AI.
Back to the Forbes Article “Machine Learning
is a current application of AI based around

the idea that we should really just be able
to give machines access to data and let them

learn for themselves.”
A typical example is Google's autofill.
When you make a typo, for instance, while
searching in Google, it gives you the message:

"Did you mean..."?
This is the result of one of Google's machine
learning algorithms; a system that detects

what searches you make a couple seconds after
making a certain search.

So if you search for one thing, misspell it,
realize you made the error, and correct that.

Google learns from that mistake.
Google's algorithm recognises that you searched
for something a couple of seconds after searching

something else, and it keeps this in mind
for future users who make a similar typing

As a result, Google 'learns' to correct it
for you.

That's machine learning in its simplest

We've just spoken about AI and Machine learning.
But another interesting thing which I came
across in my research is the term Deep Learning.

Something Very interesting.
According to Callum Mclelland of Medium.com,
“Deep learning is one of many approaches

to machine learning.
Deep learning was inspired by the structure
and function of the brain, namely the interconnecting

of many neurons.
Artificial Neural Networks (ANNs) are algorithms
that mimic the biological structure of the

In ANNs, there are “neurons” which have
discrete layers and connections to other “neurons”.

Each layer picks out a specific feature to
learn, such as curves/edges in image recognition.

It's this layering that gives deep learning
its name, depth is created by using multiple

layers as opposed to a single layer.”
End Of Quote.
So as the name implies, Deep Learning, can
be understood to be something that allows

a system to analyse and go in depth with the
data that its receiving.

Adding more and more layers when more information
is gained.

I think Nvidia Puts these terms correctly
and gives a better Understanding of what Machine

Learning and Deep Learning mean.
Machine Learning at its most basic is the
practice of using algorithms to parse data,

learn from it, and then make a determination
or prediction about something in the world.

So rather than hand-coding software routines
with a specific set of instructions to accomplish

a particular task, the machine is “trained”
using large amounts of data and algorithms

that give it the ability to learn how to perform
the task.

For Deep Learning, Neural Networks are inspired
by our understanding of the biology of our

brains – all those interconnections between
the neurons.

But, unlike a biological brain where any neuron
can connect to any other neuron within a certain

physical distance, these artificial neural
networks have discrete layers, connections,

and directions of data propagation.
You might, for example, take an image, chop
it up into a bunch of tiles that are inputted

into the first layer of the neural network.
In the first layer individual neurons, then
passes the data to a second layer.

The second layer of neurons does its task,
and so on, until the final layer and the final

output is produced.
Each neuron assigns a weighting to its input
— how correct or incorrect it is relative

to the task being performed.
The final output is then determined by the
total of those weightings.

We can see AI encompasses many things and
there are so many other things that I could

talk about, but I've mostly achieved my
purpose with this video.

So thanks for watching this video.
If you enjoyed it, learnt anything new or
want more.

Feel free to subscribe, comment your thoughts
below and leave a like.

I also recommend that you check out previous
videos using the links provided in the description

below or on screen.
I hope you learnt something new from this
video and with that, I'll see you in the

next one.


細說AI (What Is Artificial Intelligence, Machine Learning And Deep Learning?)

445 分類 收藏
高島屋 發佈於 2019 年 3 月 4 日
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