Why AI and Machine Learning are Important

We’ve been so busy trying to explain blockchain and highlight the importance of this technology that all other industries contributing to our hi-tech future were left behind. Besides, tech is a lot like art — you’ve got to be creative and think big; experimenting and combining different fields might make an important breakthrough one day.

So what is one of the most interesting and discussed trends in modern-day technology?

Artificial Intelligence

What is the first thing that comes to your mind when you hear the words ‘artificial intelligence’? The rise of the machines? Or Tesla and Alexa?

Although modern machines can easily best human competitors while playing logic games such as chess or checkers, we are still very far from the AI takeover. It’s not that surprising anymore that modern machines use algorithms that they’ve developed on their own, analyzing game strategies, learningб and even creating their own techniques.

Using neural networks, AI enables deep learning models. Basically, the more data you provide and the more you use the AI-driven system for different, everyday life purposes, the smarter AI becomes. Have you noticed that your Google services become even more accurate each day?

But why is AI important and why should we continue researching?

The entertainment and commercial industries have successfully been implementing AI for ages, all for the sake of keeping us amused and paying. Netflix will tell you what you should watch next and Amazon will tell you what you need to buy, all depending on your “unique” personal preferences. Although there is a powerful prediction technology behind this, there has to be a higher purpose. Just like that robot dog that learned how to run faster using machine-learning — it’s very entertaining but currently won’t help with making the world a better place.

Truth be told, there’s hardly any field that wouldn’t benefit from implementing AI technologies. Medicine, manufacturing, finance, security, smart homes, and navigation are already using AI to minimize “human factor” mistakes and get more accurate results. AI can bring analytics to the industries that have been left behind. AI unites people by breaking down not only economic, but also language barriers.

AI in Medical Care

One of the greatest possibilities for AI and human alliance lies in the medical and healthcare industry. It may help us become stronger, healthier, and minimize the need for doctors. The use of AI and the Internet of Medical Things in consumer health applications is not in the future, but has been in practice for a while now.

For more than thirty years we have been using the help of robots to improve the state of medical care, from laboratory robots to highly complex surgical robots who are capable of assisting human doctors or even perform the surgeries themselves. They also do a significant amount of work in the processes of rehabilitation, physical therapy, and in support of those with long-term conditions.

But that’s not it. AI is able to detect diseases, such as cancer. According to the American Cancer Society, too many mammograms show incorrect results, so that one in two women are told they have cancer when they actually don’t. AI is enabling review and translation of mammograms 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies.

AI and Art

Recently, there have been many talks about whether or not the art made by AI is real. While some question if these ‘alternative’ works of art make any sense, others are buying AI’s creations for thousands of dollars. AI has been involved in the art industry even longer than in medicine, around 50 years. Modern day artists no longer argue about whether or not AI can produce a genuine masterpiece; they prefer to work together. They write programs, build machines, and then use the machine’s help to go beyond their own capacity. These artists create algorithms to “learn” a specific aesthetic by analyzing sets of images. After that, the algorithm can produce something completely new. The results are controversial but so is the art itself.

AI vs Machine Learning

We’ve already mentioned the ability of robots to study on their own using machine-learning. Quite often AI and machine learning are used as synonyms but that’s not really accurate.

Machine learning is a subcategory of AI that teaches machines how to learn on their own. The greatest thing about machine learning that it allows us to avoid difficult coding work and instead, use a set of examples which can be analyzed and later on, with the help of a certain algorithm, solve the problem.

To make it more simple, here how it goes:

  1. Establish the problem
  2. Collect as many examples as possible
  3. Start training the algorithm
  4. Try it out
  5. Analyze the results
  6. Improve the algorithm

How exactly the algorithms work?

Actually, there are two main types of these algorithms: supervised learning and unsupervised learning (there are more, though). In one of the most detailed and in-depth series of articles about machine learning, the author, Adam Geitgey, explains the difference between these two types of learning using real estate as an example. If you need to set the price on a house, years of experience in real estate would come in handy. But in case you’re not well-acquainted with the industry — machine learning might help. All you have to do is collect all the data on the other houses that are usually sold in the area you’re interested in. This data should include the exact location, size, and price that the house has been sold for. Obviously, the more details you provide, the more accurate the final result will be. The algorithm will do all the math for you and eventually provide you with an answer.

And then there is unsupervised learning when you have all the inputs but no outputs. In our case, the output would be the price of the houses. Even if you only know the size, the neighborhood, and the age of the buyers, the algorithm can still make certain conclusions based on this knowledge. For example, it can tell you what type of houses the younger generation usually buys and much more as it, again, depends on the number of parameters you provide.

One of the most popular examples of machine learning is your spam filter. Just sorting out the emails with trigger words is not enough anymore, you have to consider other things like who sent it, where from, and more.

Have you seen much spam in your inbox lately? Probably not, which means the technology is doing its job after all.

Another example also related to our emails is sorting out the emails by categories like promotions, social media, and important emails. There is a big chance that in the near future, we will see less useless information and most letters will be answered automatically, without our help.

If that’s not enough, then imagine how machine learning can help in the areas of translation, plagiarism detection, credit decisions, social media, personal recommendations, and assistance.

And that’s not the entire story.

Conclusion

Sometimes, we all get blown away by the exciting perspectives that these fascinating new technologies offer. It’s hard to argue that AI and machine learning, together with blockchain, are the disciplines of the future, and sometimes it may seem like each of them on their own can do wonders and solve every problem of humanity. Technologies like AI are extremely important and it is essential to use them for good and not just think of the profits they promise. Our main task as humans is to set the right direction for further development, and find the best ways we can implement these technologies.

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