Neural networks are computer systems based on the principles of the human brain, which are used to process large amounts of data and solve complex problems. They consist of many neurons connected to each other, and are trained on the basis of examples, just as a person learns from experience. Neural networks are used in various fields, such as medicine, the automotive industry, and finance. In everyday life, they can write text and even music for you, draw a picture, process a photo or video, improve moon shots like on new Samsung smartphones. But, in addition to all this, neural networks are actively used in space exploration.
Neural networks in current space exploration
Artificial intelligence is used to process large amounts of data received from space telescopes and probes. Neural networks help to automatically identify and classify objects such as stars, planets and galaxies, as well as determine their characteristics and track changes over time. An example is the Galaxy Zoo project, in which neural networks analyze images of galaxies and classify them by shape.
They are also successfully used to analyze data collected by transit and radial velocity methods, to detect and confirm the existence of exoplanets. They are trained to recognize subtle signals and noises, which allows them to significantly increase sensitivity when exploring space and searching for planets. For example, the Google DeepMind neural network helped discover two new exoplanets by analyzing data from the Kepler telescope.
Programs with artificial intelligence are able to analyze radio signals coming from space objects and distinguish natural sources from artificial ones. This can be useful for projects to search for extraterrestrial intelligence, such as SETI, as well as for studying phenomena such as fast radio bursts. An example is the Breakthrough Listen project, where neural networks analyze millions of radio signals in search of potential evidence of an extraterrestrial civilization.
They are also used to automate navigation, maneuvering and performing scientific experiments on various devices exploring the expanses of space. For example, on the Curiosity rover, the AEGIS neural network helps to automatically identify the most interesting geological objects to study, which reduces decision-making time and increases the efficiency of the mission.
Using neural networks, scientists can create detailed models of various cosmic processes, such as the collision of galaxies, the formation of stars and the formation of planets. An example is the DeepMoD neural network, which is trained in hydrodynamic equations and can simulate the behavior of gases and plasma in space objects.
AI can also process telescope data to determine the trajectory of asteroids and assess their potential danger to the Earth. For example, NASA's Asteroid Grand Challenge project uses neural networks to detect and classify asteroids, which can be useful for preventing possible collisions with Earth.
Neural networks in further space exploration
Artificial intelligence is playing an increasingly important role in space exploration, providing automation, efficiency improvements and breakthroughs in discoveries. It helps scientists in analyzing huge amounts of data, discovering new objects, predicting and modeling cosmic phenomena. But in the future, the use of neural networks may become even more diverse, since new algorithms and architectures of neural networks will allow solving even more complex tasks and conducting a deeper analysis of the information received.
Possible future applications of neural networks in space research may include:
Image synthesis and restoration. AI can be used to improve the quality of images received from spacecraft by eliminating noise, filling in gaps and increasing resolution. This can significantly increase the scientific value of the data obtained.
Optimization of mission planning. Neural networks can be used to optimize trajectories and schedules of space missions, taking into account many limitations and risk factors. This can reduce the time and cost of planning and executing missions.
Analysis of biological and chemical data. In the future, neural networks can be used to process and analyze data on the chemical composition and biological processes on other planets, which can help in finding signs of life and understanding the conditions of habitability.
Integration with artificial intelligence. Neural networks can become the basis for creating a full-fledged artificial intelligence that will be able to independently conduct research, adapt to new conditions and learn from experience. This can radically change approaches to space exploration and open up new opportunities for humanity.
In conclusion, it is worth noting that neural networks already have a significant impact on the development of space research and open up new horizons for scientific discoveries. In the future, their potential will only increase, making space exploration even more exciting and promising.
Bonus for those who have read to the end
Reading this article, you may have felt some discomfort: repetitive words, a dry narrative style, a simple structure that looks more like a set of theses than a full-fledged discussion on the topic. I specifically did not correct these errors, making a minimum of changes to preserve the style of the author.
The most inquisitive of you could already guess what's going on, and I want to confirm your guesses:
Yes, this article was written by a neural network. This text is not perfect, and the robot still cannot replace me or you in our places. But using the example of this article, you can still understand how high the capabilities of artificial intelligence are already at the current stage of its development. And believe me, writing texts is only a small part of what neural networks are capable of today. And it is difficult to imagine what will happen in the future.
To create this article, the GPT-4 neural network was used, or rather a bot created on its basis. However, he will tell you everything himself.
You must be logged in to post a comment.