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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 computersorthe capability

  • of a machine to imitate intelligent human behaviorBoth 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 timethe Greek myths contain

  • stories of mechanical men designed to mimic our own behavior.

  • Very early European computers were conceived aslogical machinesand 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

  • ways.

  • 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 moviesaid 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 Hawkingwho 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 asthe science of getting computers to

  • act without being explicitly programmedThey 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 WiredBig 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 ArticleMachine 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

  • mistake.

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

  • That's machine learning in its simplest form.

  • 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

  • brain.

  • In ANNs, there areneuronswhich have discrete layers and connections to otherneurons”.

  • 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 istrainedusing 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

  • brainsall 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 inputhow 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.

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

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