What is artificial intelligence (AI)
What is artificial intelligence
What is artificial intelligence (AI) can be defined as an industry of computer science related to the automation of intelligent behaviour
AI is a part of computer science, and therefore it must be based on solid theoretical principles, applicable to this field.
At present, the term is often used to refer to computers with a specific purpose and science that studies the theories and applications of artificial intelligence.
That is, each type of artificial intelligence is currently stopped at the level of computers or supercomputers used to handle a certain kind of work such as controlling a house,
studying image recognition, processing data of patients to provide treatment regimen, data processing to self-study,
the ability to answer questions about diagnosis, respond customers about a company’s products,
It is easy to understand: it is the intelligence of machines created by humans. This intelligence can think, think, learn, … like human intellect. Data processing at a broader, more scalable, systematic, scientific and faster level than humans.
Many famous technology companies have ambitions to create AI (artificial intelligence) because their value is tremendous, solving many human problems that humanity is not yet resolved.
Artificial intelligence brings a lot of value to human life, but there are also potential risks.
Many experts worry that when artificial intelligence reaches a certain level of evolution, it is also a time when humanity is eradicated.
Many films have exploited this topic with many perspectives, but they all want to warn humans about this particular threat.
Artificial intelligence It’s something used to answer emails automatically on Gmail, learn how to drive us to play, rearrange photos of outings into individual albums, and even help manage the house well.
Shopping anymore. But artificial intelligence is not merely an entity, but is it divided into smaller categories?
What are the current limitations of synthetic intelligence products? And why don’t we need (or don’t need) to worry about artificial intelligence flaring over the world?
What is a neural network?
These are phrases that you often see in information related to artificial intelligence. You can think of these things like layers of artificial intelligence.
A neural network, temporarily called an artificial neural network, will lie at the bottom.
This is a system of computers and computer system devices that are generally connected in some way to simulate a part of how neurons work in the human brain.
The computers in the neural network can be located close to each other in the same room or thousands of miles away from each other, each of which can be viewed as a nerve unit, called a node.
Neural networks are not necessarily hardware based; they can still be software and algorithms.
The concept of neural network dates back to the 1950s with the advent of artificial intelligence research.
What is a neural network
It is said that when located separately, these computer nodes only run what is pre-programmed and can only answer simple questions, or in other words, it is “not smart.”
Just like in the human body, a neuron has not made a difference, but when connecting them into a full network, things will be much different.
when computer systems are connected, they can solve more difficult problems. And most importantly, when applying the right algorithms, people can “teach” computers. The next class is machine learning.
This is a program that runs on the neural network; it will train the computer to “learn” something, such as learning from the line of users’ handwriting to guess if it is a What character,
or learn from thousands of photos of the beach to find common ground and then look at the broadcast is to know this picture of the sea, not the mountain.
Deep learning is located at the top. This is a particular branch of machine learning science. In-depth knowledge has become popular in the
last decade thanks to the rapid increase in the amount of digital data that humanity has created, in addition to the increased processing power of computers while costs have fallen. Will talk more about deep learning below.
How artificial intelligence work?
Suppose you want a computer that knows how to cross the street. Traditionally, we will program it to look left, right, how to wait for the car to run through,
how to follow the right track according to the law and many other things,
then let the machine go by itself.
As for artificial intelligence, specifically a machine learning program, you will let your computer see 10,000 videos of how to safely tape.
Next, you show him another 10,000 more videos, but this time showing people getting hit by a car when they hit the road. Now you let it freeze itself.
The hardest part is to you have to make the computer understand and acquire information from these videos, just as the hardest part of teaching is to prepare students to know what you say and remember it.
For decades, many different methods have been used to teach computer learning. One of the ways that are used is “reinforcement learning,” which means you will “reward”
the computer when it does exactly what you want and slowly optimizes for the best results. People often train animals in this way. Another way is “natural selection,”
which means that many ways to solve the same problem will be applied in parallel, this solution is the fastest and most accurate will win the remaining ones.
In this day and age, people use a method called deep learning.
Deep learning uses multiple layers in a neural network to analyze data in many different aspects. For example, if you give a computer a picture of a deep learning technique, each layer in this artificial neural network will uniquely see the problem.
The bottom layer will draw a 5×5 grid into eight images and mark “yes” or “no” when an object appears in the cell. If “yes,” the upper layer will start looking at each of these cells more carefully,
it analyzes whether this is the beginning of a straight line, or is this an angle?
How artificial intelligence work??
So many layers will help the software understand complex issues, all based on breaking it down and “investigating” slowly. It is also for this reason that people call this “deep,” i.e., deep and multi-layered.
We have only talked about it until now; now it’s time to teach the computer what it has just realized.
A neural network is used again, but this time it will consider many characteristics of a cat.
Many pictures of cats will also be shown to the system along with the following statement: this is a picture of that cat. Then they showed another series of images of dogs, pigs, bears, and ducks and told them: this is not a cat. Through such a set of data, the software will know what cats usually have in common, how their nails, feathers, limbs, heads, and tails are called cats …
Over time, the device will remember these data and arrange them in order of importance.
For example, claws are not only available for cats but if the nails come with big feet and moustache, this is precisely the cat.
Relationships like these will also be provided from time to time during the process of learning machine software learning images.
This process takes a long time and is repeated many times. Every next time it will be better than last time because of personal suggestions or even other artificial intelligence systems.
You may find that to know a cat is so complicated, while Facebook, Google or Microsoft machine learning systems must recognize many other things in life.
So, Microsoft’s pride in releasing an application that can identify difficult fast breeds sounds simple, but behind it is a complex artificial neural network and has begun to learn in a very long time.
Do Google, Facebook, and many other companies are using
Yes, Deep learning is currently being used for many different tasks. Large technology companies often set up a division of artificial intelligence. Google and Facebook also open their software learning systems for all users. Google last month also began a 3-month course on machine learning and deep learning.
Some examples of machine learning are the Google Photos tool. It can recognize and categorize images that you take on different topics, even with different faces automatically. Or Facebook M, a personal virtual assistant half-a-half can help you put some items you want. Microsoft with Cortana, Google with Google Now and Apple with Siri are all authentic examples of artificial intelligence.
This is also the reason why in recent times we have started to hear more about machine learning, deep learning, and artificial intelligence.
That’s because big consumer companies have begun to jump into the game and offer real products that people can grasp and experience.
Earlier, deep learning and machine learning were only in laboratories at research institutes and universities.
Large platforms such as Facebook, Amazon, Netflix, … all have a very strong (recommend) suggestion system that significantly increases user interaction.
Specifically, they are based on user data generated when using to suggest additional products they will like (on shopping platforms),
the movies they will want to watch (e.g., on Netflix), recommend advertising / sponsored articles (on Facebook) or interested learner courses (on online learning platforms).
Limitations of artificial intelligence
Deep learning is being used for things like speech recognition and image recognition, things that are potentially commercially viable. But in parallel, it also has many limitations.
First, deep learning needs a tremendous amount of input data so that computers can learn.
This process takes a lot of time, a lot of processing power that only large servers can do.
If there is not enough input data, or there is enough data but not enough power to handle,
everything cannot happen as intended, the result of the machine learning will be inaccurate.
Second, deep learning is still not able to recognize complicated things, such as common relationships.
They will also have trouble identifying similar items. The reason is that there is currently no technique good enough for artificial intelligence to draw those conclusions logically.
Besides, there are still many challenges in integrating abstract knowledge into machine learning systems,
such as information about what that object is, what it is used for, how people use it. In other words, machine learning does not have the same knowledge as humans.
A particular example for you to understand: in a Google project, a neural network is used to create a picture of a dumbbell that people often held in the gym.
The results are quite impressive: two grey circles are connected by a horizontal tube. But there is a human arm in the middle of this tube,
and this is not something in the “problem.” The reason is quite easy to guess: the system
is taught about dumbbells with photos of people being weightlifted, so it is evident that sticking one’s hands.
The system can know how dumb, but it does not know that the fruit will never have an arm.
With some more straightforward pictures, the machine is still confused.
The experiment of a group of researchers showed that when they showed the computer a
series of images that had only random pixels, they were undoubtedly 95% a … truck, or starfish. ..
That’s not all. According to computer scientist Hector Levesque,
the current artificial intelligence tools use many “tricks” to erase the right gaps in their knowledge.
Virtual assistants like Siri or Cortana often make you feel like you are talking to real people
because they use jokes, quotes, emotional expressions, and many other things, just for you to divide centre.
Try asking for things that need to be thoughtful, such as “can a bad fish drive?” Or “a soccer player is allowed to mount wings to fly or not.”
Questions of this type are too complicated for today’s artificial intelligence systems, so often
no results will be returned to you if any are also irrelevant or search the sentence of you on the internet.
Winter artificial intelligence
The artificial intelligence industry is a natural industry but also easy to get down to.
In 1958, the New York Times talked about a machine that distinguishes left and right as a kind of intelligent robot.
And so far we had not been able to create a robot with such intelligence. And when those promises are not made, people use the word “winter AI.”
That is the period when the amount of money invested in AI has plummeted, few have mentioned it, and people are also sceptical about the possible results.
So far there have been about six small “AI winters” and two significant seasons that appeared, in the late 70s and early 90s.
Perceptive of artificial intelligence?
Many people working in artificial intelligence think that it will be tough for us to create a sentient artificial intelligence.
“There is very little evidence at present that shows the hope of creating a highly flexible
artificial intelligence and doing things that they were not created to do,” said Andrei. Barbu from MIT.
He emphasized that the study of current artificial intelligence only creates optimized systems to solve a specific problem.
There has also been some research work on computer learning but without supervision,
that is to give data to machine learning without labelling right or wrong or explaining anything.
However, Andrei Barbu commented that projects like this have not progressed and are still far away to reach the results date.
An example that once appeared was a Google neural network system that had randomly
taken a thumbnail of 10 million videos on YouTube to teach itself what the cat looked like.
However, Google said this was just an experiment and said nothing about its accuracy.
In other words, we still do not know how to make computers self-learn without supervision. That is the most significant barrier. That is, it is still far away from the day when robots can sense and fight people.
As Elon Musk said, his company created an artificial intelligence for self-driving on Tesla cars.
But he never said that he would know everything.
This is merely a network that helps vehicles learn from each other.
When one learns something, others know the same thing.
The result is not cars that can do everything in the world; it’s just to solve a particular problem.
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