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What is Machine Learning and Why is it Important

Machine Learning

Machine learning is a powerful new tool for solving problems, from filtering batches of images to helping humans tackle some of the world's most pressing challenges in health, the environment, and more.

In this content, we will learn about machine learning techniques and their practical applications that help companies grow.

How do you understand the world despite the chaos?

Over the last few decades we have seen some shifts in the way we think about computing. The term artificial intelligence has had a huge impact on the scientific community.

It is called “machine learning” sometimes, but these days we tend to call it “machine learning”, whereas I prefer to just use the term “intelligence”. Sometimes it signifies the effort to build better machines.

In the beginning, everything was based on logic, like solving math integration problems or playing chess, but we realized that the real challenges are related to those tasks, which people can perform on a daily basis.

The real world is very messy, and strict logical rules are no way to solve important real-world problems.

You have to have a system that learns how to acquire knowledge on its own, because you simply cannot program the entire knowledge.

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What is artificial intelligence?

Artificial intelligence is an attempt to build machines that can learn from their environment, mistakes and even humans.

We are still at a stage where we don't know the right path and the right development, what I'm trying to say is that there are a whole bunch of different approaches, including:

  • Recognize patterns.
  • Artificial neural network.
  • Reinforcement learning.
  • Statistical inference.
  • Probabilistic machine learning.
  • Observed learning.
  • Unattended learning.

We're not entirely sure which technology will help develop better systems.

In fact, there will not be just one technology; It may even be a combination of different technologies and a combination of them.

Any progress we make in building intelligent systems will depend on how advanced the technology is in general, and until recently, we didn't have enough fast computers or big enough data sets to make it happen.

Undoubtedly, being able to distribute a given problem to many, many machines is an important approach because it helps speed up our research.

There are AI applications all around us all the time, but when these applications start working, we are surprised that their name changes. All of us already use it and feel comfortable using it.

Items that we find routine today were considered amazing models of AI 30 years ago, such as:

  • Anti-lock brake system.
  • Aircraft autopilot systems.
  • Search.
  • Recommendations.
  • Maps.
  • Determine if the email is malicious or not.
  • The ability to translate from one language to another using your phone.

10 years ago, it was for nothing if you tried to talk to a computer or phone, today we're seeing a wave of these scams, one after the other, unfolding to us now.

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It is an incremental process of incremental improvement for the better.

In fact, intelligence is not going to be an achievement that can be succinctly and in one way defined. It is a world of different abilities that fit beautifully and work together.

Predicting the long-term future is very difficult, and no one can actually achieve it, and the worst thing to do is to assume that the future will be limited to what works best for us at the moment.

Solve problems small, big and complex

During the last five years, we have witnessed a quantum leap in the capabilities of the machine compared to the past decade or two.

With more data and more computing power, we can think of big projects and change the rules of the game when envisioning the kinds of models we can create.

The real world is chaotic, and its problems cannot be solved by adopting strict logical rules, and therefore, machine learning is based on learning from examples.

Instead of writing more than 500,000 lines of electronic code, we enable a machine to learn by observing the world. 

We look at examples in a machine learning algorithm, i.e. check millions or even billions of examples to see what pattern prevails among them and then generalize it.

In an image recognition project we were able to train models to detect image pixels and learn from those high-level features.

When working with machines, you understand the beauty of humans and the importance of your four-year-old's ability to recognize faces.

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Machine learning marked the beginning of a big revolution in the field of speech recognition, to teach speech recognition we try to work in a noisy room, we use sounds from the real world and mix them with the examples we want to work on.

Now, speech recognition systems can understand what you are saying no matter how loud the noise is around you, and can distinguish between one speaker and another.

With the development of machine learning, there is an algorithm that learns how to simulate human language, most of the structures in language today are informal, where they talk and talk and then say “Okay,” and their conversation is interspersed with emojis and stickers.

Many are the users who have been able to take advantage of the system in an innovative way without knowing anything about machine learning, meaning that they have a specific idea and do not have to make the efforts that we have already made.

There is a great example of that; A person would see a cat wandering around his house all the time; Therefore, the model was trained to recognize the presence of a cat and to operate the water sprinklers when spotted, to make the cat afraid and leave.

There is an elderly couple in Japan who ran a cucumber farm, and one of the major tasks on the farm was to sort the cucumber between thorny, less thorny, straight and crooked.

It is a complex task, requiring the wife to spend several hours a day sorting cucumbers; So, the son learned a computer vision model and was able to create a system for automatically classifying and sorting cucumbers.

Thus, his parents could use the time wasted in sorting the choice in other tasks.

387 million people have diabetes and are at risk of developing blinding diabetic retinopathy.

Signs of diabetic retinopathy can be detected by taking pictures of the back of the eye, but there are not enough doctors and the diagnosis takes many hours; So there is an algorithm to read the images in real time.

Thus, the algorithm can help doctors diagnose this disease in more people.

The more you know about machine learning and the tasks that a machine can perform, the more chance you have of improving people's lives.

You can use machine learning to save energy on a large scale and track the spread of diseases and epidemics.

We can use a computer vision model for every visually impaired person.

We can make a machine that recognizes speech in different languages for every person on the planet and fundamentally improves the experience of millions and billions of people.

A learned system can play a fundamental role in any scientific field or even a human endeavor. If you had asked me a few years ago if a computer would be able to do that any time soon, I would have said, “I don't think so.”

It is great to be able to imagine what science has in store for us in the future, we have ideas and actions that no one has thought or done before, and we are taking our first steps into a new intellectual world. 

What AI and machine learning promise us is to enable us to find solutions to humanity's previously unaddressed problems.

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Applying machine learning to business problems

The basic idea behind the concept of machine learning is to take a piece of a software system that has been used for a programming process according to specific rules and ask the machine to learn how to do that programming.

Machine learning is important on two levels:

  • First, process automation
  • Second, to improve the efficiency of operations

Businesses that benefit from machine learning today are those that provide a certain type of simple information.

For example, at Google we use machine learning in our products and services and we have implemented it at many levels such as machine translation of web pages and helping users search for their images and write responses to Emails instantly.

Your business may be similar in many respects to the examples we mentioned especially in terms of sharing information.

For example, if a bank wants to detect fraud in its transactions or an online store owner wants to suggest a piece of art to its customers. 

The goal is basically the same not to It is transforming a task from a process that is somewhat repetitive and boring for humans, automating it and making it work on a different level.

Machine learning can achieve a lot, but that doesn't mean it's the right solution for every problem.

If you're the owner of an accounting firm, you don't need machine learning to do a calculation, you have a dedicated software for such calculations.

If you are trying to figure out how you can implement machine learning in your business and you have a team that specializes in data science, first start by discussing the topic with them but if you are a small business it is better for you to focus on one idea that is important to your business, any opportunity that can be learned The machine makes a difference in what it offers or how it is presented.

One of the most important things is to have examples of the behavior that you want the machine to learn, because the machine learns the machine better from the right examples you show it and then learns how to do the same.

So you are looking for a situation where some tasks are repeated hundreds or thousands of times , and you have records of how the tasks are being carried out correctly.

So you can use machines to do it automatically and in the same way millions or billions of times, and since machines learn from examples, you learn from data, you have to have enough data to get started.

Machines learn much slower than humans, so while a task might need only ten times to be reinterpreted for a human to learn, it might take a machine hundreds or thousands of times to learn how to do the same task.

Not so long ago, one of the main barriers to companies entering the world of machine learning was the availability of good software. TensorFlow is an open source software package that Google developed internally for its machine learning systems and then released externally for other companies and academic institutions to use.

So instead of having to build machine learning from scratch, this package offers a set of essential machine learning elements that you can use to build your own products and services.

Machine learning can help automate existing processes or make those processes more efficient, but machine learning cannot detect or decide your next move, and that kind of creativity and leadership is what you, as entrepreneurs, must do.

Every new tool you own, you have to give yourself time to discover it and try it. The first time you try it, it will not work perfectly and magically solve all your problems.

The opportunities offered by machine learning are unique to every business, and there are so many possibilities that we haven't even imagined yet.

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