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The Difference Between AI and Machine Learning
Artificial Intelligence Luminary: Reza Zadah, CEO and Founder of MatroidIn this video, Reza Zadah clarifies the difference between artificial intelligence and machine learning, and what role algorithms play in these fields.Read the transcript:The working definition of artificial intelligence now has become maybe 50 different definitions….
It is many different tasks that humans ...are good at but computers are not.For a long time we were trying to replicate our thought process by putting in a lot of different rules into the computer, by programming them… a lot of logical rules that went one by one — and the computer could follow them, and eventually we thought if we had enough of these rules, we could come up with AI.That turned out to be a terrible way forward, in that many of the tasks that we can now solve cannot be solved using that approach of write down a bunch of rules.
So if you have a eight hour video and — and you're interested in finding particular events that happen in this video without having to watch the whole thing, you come to Matroid and you build a detector for it, and then you look for those things that you're interested in, in a few minutes as opposed to eight hours.For these very complicated models, we can't — we can't just have a simple rule that tells us how to take the derivative of these models.
What's the Difference Between Artificial Intelligence and Machine Learning
Both artificial intelligence and machine learning are hot buzzwords right now.
It's not surprising at all, since that’s exactly what modern technologies, such as: virtual agents, decision management, and even content creation, are starting to rely on.
But before this happens, let’s try to explain these two complex terms in simple words and determine the differences between them.
In the past, even early European computers were considered by engineers as “logical machines” and “mechanical brains” because they were able to reproduce arithmetic and memory.
Simply put, AI is wrapped around mimicking human decision making processes and performing complex tasks in a more human-like way than ever.
In this case “learning” means feeding the algorithm with a massive amount of data so that it can adjust itself and continually improve.
The term “machine learning” was first coined by Artur Samuel, an American pioneer in computer gaming and artificial intelligence, back in 1959.
Let’s say you collect and input a massive number of different pictures into a system → and we’re talking about hundreds of thousands or even millions of pictures.
machine learning algorithm studies those images along with their tags, and finally it can build a model of a hot dog, which can later be used to classify pictures without any further description or tags.
Remember, artificial intelligence is a broad term that represents the general concept of machines being able to carry out smart tasks, and machine learning is a specific subset of algorithms for AI.
Difference between Machine learning and Artificial Intelligence
There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is a intelligence where we want to add all the capabilities to machine that human contain.
One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
What's the Difference Between AI, Machine Learning, and Deep Learning?
AI, machine learning, and deep learning - these terms overlap and are easily confused, so let’s start with some short definitions.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
Early successes caused the first researchers to exhibit almost boundless enthusiasm for the possibilities of AI, matched only by the extent to which they misjudged just how hard some problems were.
The reason that those early researchers found some problems to be much harder is that those problems simply weren't amenable to the early techniques used for AI.
Hard-coded algorithms or fixed, rule-based systems just didn’t work very well for things like image recognition or extracting meaning from text.
Feed an algorithm a lot of data on financial transactions, tell it which ones are fraudulent, and let it work out what indicates fraud so it can predict fraud in the future.
But machine learning still got stuck on many things that elementary school children tackled with ease: how many dogs are in this picture or are they really wolves?
It was just that simple neural networks with 100s or even 1000s of neurons, connected in a relatively simple manner, just couldn’t duplicate what the human brain could do.
And when you read about advances in computing from autonomous cars to Go-playing supercomputers to speech recognition, that’s deep learning under the covers.
Let’s look at a couple of problems to see how deep learning is different from simpler neural networks or other forms of machine learning.
You can recognize a horse because you know about the various elements that define a horse: shape of its muzzle, number and placement of legs, and so on.
implies learning Italian as I grew up (with 93% probability according to Wikipedia), assuming that you understand the implications of born, which go far beyond the day you were delivered.
Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.
If you're ready to get started with machine learning, try Oracle Cloud for free and build your own data lake to test out some of these techniques.
- On Wednesday, October 23, 2019
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