The term artificial intelligence refers to the ability of machines to act as a human – to learn, to respond appropriately to stimuli, to solve problems, and to engage imagination. In the past, AI was used to refer to virtually all machines that have precisely defined responses to different stimuli in their source code.
However, these machines simulated human behaviour only slightly. For example, you had to ask a question in a specific form, and you always received a corresponding answer. Otherwise, the machine asked you to phrase your question differently.
Presently, the situation is evolving. Computers with huge computational power allow the creation of so-called neural networks and machine learning that have propelled artificial intelligence to a higher level. These are complex areas of informatics that try to emulate the way the human brain works and the process of learning in humans. Only, it’s way faster with computers.
How does an artificial brain learn?
What we now call artificial intelligence no longer has a specific way of solving a problem. It only has a set of rules that can be used to solve the given problem.
It learns from input data and feedback received during the problem-solving process. It can continually correct its calculations. It repeats the solutions several thousand times, gradually improving upon them.
In some cases, it may even create its own rules, which help it achieve the most accurate result.
As such, artificial intelligence can perform these jaw-dropping tasks:
The formation of metastases in the human body is a major problem in cancer treatment. Small cancerous cells travel through the lymphatic system to temporarily nest elsewhere and create a new tumour.
It is very difficult to detect them and check if they are in the body or not. Therefore, a relapse is a constant threat to all those who have encountered this disease.
In the detection of metastases in breast cancer called LYNA, artificial intelligence has achieved an incredible 99% success rate in identifying tumours in lymph node images. In addition, it was not affected by imperfections in the images or the presence of air bubbles or blood clots in the samples.
Although not perfect and occasionally misidentifying, e.g., white blood cells from bone marrow as cancer cells, the results are still very promising for the future of cancer treatment.
Autopilots in aircraft are now quite commonplace. Even the development of autonomous cars is also progressing at a decent pace. However, it is still a challenge for AI to keep the car on a dedicated lane.
The system must learn to monitor and evaluate roadside situations. In many cases, it has to anticipate the behaviour of other road users. The reaction time is the big advantage.
Where one needs seconds to evaluate the situation and the response of the unit, the computer will only use hundredths of that time. It is already possible to find interesting videos on the Internet where autopilot has avoided a rear-end collision with rapid acceleration or a frontal collision with perfectly timed braking.
Only we, humans, can create works of art or design something. It is based on experience, preferences, moods, and tastes. Simply put, things that are strange to machines.
Despite this, a recently auctioned picture was painted by artificial intelligence, and NVIDIA came up with the use of AI to design car models, people and interiors.
In both cases, machine learning was applied, where the system processed thousands of already existing images or car models. Based on that, it tried to create its own completely new design. While the picture yielded an interesting result, funny and bizarre proposals have been attributed to cars.
When using translators, you might have certainly noticed that their accuracy has significantly improved in recent years. It is no longer just translations of individual words or phrases tied together, but in many cases a full translation. We owe artificial intelligence for that too.
In the beginning, the translators depended on tables with translated words or the most common sentences, but they were lost in more complex sentences. Neural networks, along with machine learning, can do much more.
They can learn not only to translate common sentences, but also to understand the context or to use learned translation processes of other languages. Since there is a huge amount of materials in the world and data they can draw from, it is only a matter of time until translators become perfect.
Facial biometrics also owes its growing popularity to neural networks. Because telling people apart is difficult for us, we have left it to artificial intelligence. The task was simply to determine which points on the face are suitable for unambiguous identification of a person.
In the first phase of learning, three photos were given to the neural network: two belonged to the same person and one to a completely different person. The algorithm generated numerical values based on different points on the face. These were then corrected so that they differ in the case of photos of different people more than in the case of the same person’s photos.
This was repeated countless times until the neural network learned to generate points on the face, making the person reliably recognizable.
Simply put, numerical values generated from 10 different photos of one and the same person will be much more similar than those generated from two almost identical photos, but different people. And since it is just a comparison of numbers, this method is not only reliable but also incredibly fast.
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