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Course with employment: "Data Scientist Profession"
Find out moreWhat types of artificial intelligence are there?
Researchers classify artificial intelligence into three Main categories. The first group is narrow AI, which specializes in specific tasks, such as natural language processing or image recognition. The second group is general AI, which can perform any intellectual task at a human level. The third group is superintelligence, which exceeds human capabilities in most areas. Understanding these categories helps better understand the current state and future development of artificial intelligence technologies. Weak intelligence, or narrow artificial intelligence, is a system capable of solving specific problems with high efficiency, often surpassing humans. A prime example is the Deep Blue program, which defeated chess genius Garry Kasparov in 1996. However, Deep Blue lacks general skills and cannot perform tasks outside its narrow specialization. Weak AI is widely used in fields such as medicine, logistics, banking, and business, where it helps optimize processes, increase efficiency, and make more informed decisions.
- Google's artificial intelligence outperformed experienced doctors in accurately diagnosing breast cancer. Hundreds of thousands of screening results were used to achieve this. According to the American Cancer Society, doctors misdiagnose cancer approximately 20% of the time and often misdiagnose. Not only did the AI provide a more accurate diagnosis than doctors—9.4%—but it also more often identified the disease in cases where oncologists failed to recognize it.
- Amazon, one of the world's leading AI companies, has developed the Fraud Detector system. It helps combat online fraud, which costs people and companies millions of dollars. The algorithm monitors user actions in real time, detects them, and reports anomalies—for example, flagging suspicious orders that need to be checked before payment. This can be used in banks, online stores, and large companies.
- Thanks to machine learning, Waymo's self-driving cars are able to navigate real roads without harming passengers and passersby. Incidentally, such cars—albeit from Toyota—will be used at the next Olympic Games in Japan to transport guests.
There are many examples of the application of this technology, and in reality, there are many more.
Strong artificial intelligence can be clearly seen in the game Detroit: Become Human. This game demonstrates how AI can develop, make decisions, and interact with people. It presents various scenarios in which artificial intelligence not only learns but also understands its actions, which opens new horizons for understanding the future of technology.
In the Detroit universe, robots possess the ability to learn, think, feel, self-aware, and make decisions, making them similar to humans. In real life, the closest thing to the concept of artificial general intelligence are chatbots and virtual assistants that imitate human interaction. However, it's important to understand that they are only imitating. Siri and Alice lack the ability to think independently and cannot make decisions in situations for which they have not been trained. Strong artificial intelligence remains an elusive goal.
Today, not only have we not created superintelligence, but we also lack a clear understanding of how to develop it or whether it is even possible. We are not simply talking about highly intelligent machines, but about computers that are capable of surpassing human capabilities in every aspect. This truly resembles a science fiction scenario that remains beyond the reach of science and technology.
Machine Learning: How AI Learns
Machine learning is an important area of artificial intelligence (AI). This discipline uses algorithms to analyze data, allowing for inferences and predictions. Instead of the traditional approach in which a programmer manually enters all commands, machine learning involves the system learning on its own, adapting to new data and improving its results. This process involves the use of various models and methods, making machine learning an indispensable tool in areas such as data analytics, forecasting, and process automation.
For a car to be able to make decisions, three key elements are required: high-quality sensors, powerful data processing algorithms, and an effective control system. Sensors collect information about the environment, such as the distance to objects, driving speed, and road surface conditions. Data analysis algorithms process this information, allowing the machine to assess the situation and make informed decisions. Finally, the control system is responsible for implementing the decisions, ensuring the car moves precisely and safely. All of these components work synergistically, making it possible to create autonomous vehicles capable of navigating complex environments.
- An algorithm is a special program that tells the computer what to do and where to get data. For example, we can write a program that sorts pizzas: Margherita, with mushrooms, with sausage.
- A dataset is examples on which the machine is trained. These can be images, videos, text—anything. In our case, we will need thousands of photographs of different pizzas. The more examples, the richer the experience, just like with people.
- Features—what should the computer look for when making a decision? If we are engaged in supervised machine learning, then we manually highlight mushrooms and pieces of sausage. In unsupervised learning, we feed all the data into the program and let the computer figure out what's what, making adjustments if necessary.
There are many algorithms in machine learning, and one of the simplest is linear regression. This method is used when there is a linear relationship between variables. For example, an increase in the order amount usually leads to an increase in the tip amount. Based on the available data, we can effectively predict how much a tip will be left in the future. This illustrates the simplicity and effectiveness of linear regression in solving prediction problems.
Bayesian algorithms are based on Bayes' theorem and the principles of probability theory. They are widely used to analyze text documents, particularly in spam filtering systems. To be effective, the algorithm must be provided with datasets classified as "spam" and "not spam." After this, the algorithm can independently determine the probability that phrases such as "Free tours for pensioners" or "Book a tour for mom, please" belong to one of these categories. Using Bayesian algorithms significantly improves filtering accuracy, making them indispensable in text processing and task automation.
Neural networks are an important component of deep machine learning methods, which are widely used in modern technologies. These systems mimic the human brain and are capable of analyzing large volumes of data, extracting meaningful information from it. Neural networks are used in various fields, including image recognition, natural language processing, and forecasting. Their ability to learn from examples makes them a powerful tool for solving complex problems in various industries.
Deep learning: deep learning for different purposes
Deep learning is an important area within machine learning. Deep learning algorithms are able to autonomously extract knowledge from pre-prepared and labeled data, which allows them to effectively process and analyze complex information structures. This technology is widely used in various fields, including computer vision, natural language processing, and speech recognition. Deep learning significantly expands the possibilities of automation and improves the accuracy of predictions, making it an indispensable tool in modern artificial intelligence systems.
Artificial neural networks (ANNs) are one of the most popular deep learning methods, but not the only one. These networks function similarly to the structure and operation of the human brain, which allows them to effectively process and analyze large volumes of data. Due to their ability to detect complex patterns and relationships in information, ANNs are widely used in a variety of fields, including computer vision, natural language processing, and more. Neural networks are systems of interconnected elements called neurons and their connections, known as synapses. Each synapse transmits a signal from one neuron to another, similar to how the human brain functions. Neurons and synapses are typically organized into multiple layers to efficiently process information. The first layer of a neural network receives input data, while the final layer represents the output, reflecting the results of the network's operation. For example, the output might contain several categories, one of which we want to classify the input data. Between these two layers are hidden layers, which transform and analyze the information, enabling deep processing and learning based on the provided data. Neural networks are widely used in various fields, including pattern recognition, natural language processing, and forecasting, making them an important tool in the world of artificial intelligence.
The hidden layers of neural networks perform important mathematical functions that do not require pre-configuration. Algorithms learn to generate results based on the data provided. A neural network can be trained to classify images or detect specific objects in them. For example, in the reCAPTCHA system, users are asked to find images of trucks or traffic lights to confirm they are not bots. Similarly, a neural network functions like the human brain, recognizing familiar elements and drawing conclusions such as, "This is a truck." Thus, machine learning technologies are becoming increasingly integrated into everyday life, improving object recognition and classification processes.
Neural networks have the ability to generate a variety of objects, including music, text, and images. An example of this is the company Botnik, which trained a neural network using all of the Harry Potter books and asked it to create new text. The resulting work was titled "Harry Potter and the Portrait of What Looks Like a Giant Pile of Ash." Despite the unusual title, the text is grammatically coherent and logical. This demonstration shows how modern technologies can create unique content, opening up new horizons in literature and art.
Today, neural networks are widely used in various fields. They are used in cancer diagnostics, allowing for increased accuracy and speed of disease detection. Furthermore, neural networks are actively used in sales forecasting, helping companies optimize their strategies. In security systems, neural networks are used for facial recognition, providing a high level of protection. They also play an important role in machine translation, improving the quality of text translation. Neural networks are used to process photographs and music, providing new opportunities for creativity and analysis. Such technologies are becoming an integral part of the modern world, opening new horizons for various industries.
Training a neural network requires extensive sets of well-selected data. For example, recognizing different varieties of cucumbers requires processing approximately 1.5 million unique photographs. You can't simply use random images or text from the internet; the data requires preliminary preparation. This includes standardizing and removing irrelevant elements, such as photos of trucks in a dataset intended for pizza classification. The process of data labeling, which includes preparing and systematizing the data, takes thousands of man-hours. This approach ensures the high accuracy and efficiency of neural network models, which is critical for their successful operation.
Creating a new neural network involves several stages: defining an algorithm, processing the data, testing the model, and performing optimization. This process can be complex and time-consuming. Therefore, in some cases, it is more appropriate to use simpler algorithms, such as regression, which can provide fast and effective solutions for data analysis and forecasting. Regression allows you to quickly identify relationships and trends, making it a practical choice for many tasks in machine learning and data analysis.
