"artificial Intelligence Explorations: Ai And Machine Learning Degrees" - Stephen Hawking said, AI is the best we have, or the worst (Hawking, 2017). It cannot be denied that the impact of Artificial Intelligence (AI) on the future of technologies has become a highly relevant topic at the moment - it is in the air. Its major development makes AI more attractive to researchers because of its ability to provide different opportunities for the general public.
This article highlights the challenges and opportunities in exploration on Mars and highlights the potential uses of AI in the space sector. In the last 60 years, AI has become an important aspect of computer science, allowing machines to perform tasks independently with simple human intelligence input (Anastassov, 2021). Since then, the term 'Artificial Intelligence' has gone through a variety of definitions based on several activities and technologies (Boulanin et al., 2020). It is the "basic technology that does not stand alone but increases and adds functionality" (Verbruggen, 2020, p. 12) when integrated into systems.
"artificial Intelligence Explorations: Ai And Machine Learning Degrees"
Those systems are mainly taught by humans, writing a set of codes or, in the case of machine learning (ML), fed into the algorithm and correcting itself in the time (European Space Agency, 2021). In ML, the special deep learning (DL) technique uses multi-layered artificial neural networks to learn itself and is often used in aircraft engines (ibid.). Especially in the exploration of Mars, where humans will be exposed to extreme conditions, AI is considered a suitable complement to perform tasks that humans cannot do.
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With the beginning of the 21st century, AI has made progress in exploration on Mars, becoming a pioneer (Liu et al., 2018). The successful implementation of new and innovative tools has been long overdue when the public sector is facing economic limitations and increasing criticism, despite its efforts importance (Krichevsky, 2018).
Next, the author presents the challenges and opportunities of AI in the robotic and human exploration of Mars through selected examples. The first case of AI used in space exploration was the Deep Space 1 probe, a technical probe that guided the comet Borrelly and the asteroid 9969 Braille in 1998. The algorithm used during the mission it is called Remote Agent (Havelund et al. 2001) and failures are detected on board (Williams & Braddock, 2019). In a similar way, AI has been successfully used in space rover software to improve communication between the rover and Earth during extraterrestrial missions (Soroka & Kurkova, 2019).
Looking closely at the role of AI and machine learning, scientists argue that both can make quick decisions and help satellites navigate these systems without human assistance. . Through the implementation of AI systems, these cognitive technologies can enable communication companies to work more effectively and efficiently (Soroka & Kurkova, 2019). For example, the signal between the Mars rover and Earth can take up to 24 minutes to pass during a Mars visit. To shorten this time, engineers are using space robots, which make independent decisions during the collection and analysis of data and decide what information to return to the Earth.
In the case of an autonomous system called the Curiosity rover, sent by NASA to Mars in 2016 in order to explore Gail crater, the AEGIS system (Autonomous Exploration for Gathering Increased Science) was used (Good , 2017). By helping to place more laser beams on the Red Planet, it has really changed the way we study Mars. AEGIS was also used as part of 'Mars 2020' for autonomous target selection, identifying geological targets in images from the rover's navigation camera and selecting targets for itself. without the world (Francis et al, 2017; Good, 2020). This led to a good time reduction during the mission since both sides, the robot and the Earth did not have to wait for the same tasks.
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Robotic systems have been shown to be suitable for frequent tasks in difficult and dangerous environments (Campbell, 2010). Previous studies have shown that with AI, the level of automation can be increased beyond the work automation in robotics to facilitate the work of Mars exploration (Williams & Braddock, 2019). Another major challenge is the ability to effectively implement AI for complex software tasks in space exploration.
This article provides a comparative overview of selected priorities of AI and Mars exploration, looking at the challenges and opportunities of AI in this context. As Campbell (2010) argues, the level of deep intelligence required to create software is difficult. The mismatch between knowledge from a deterministic point of view, which leads to a reactive rather than an intelligent approach to robotics in aerospace, was first examined. For example, unmanned aerial vehicles (UAVs) whose main task is to find and observe objects of interest (Campell & Whitacre, 2007) lose track of the main target and then they move out of sight. during missions. To solve this problem, systems such as UAVs need improved communication between sensors, information and planning (Thrun et al. 2005) since the first step is to transfer data to the knowledge.
Solving UAV-related problems is just one of the many challenges facing space exploration. A major challenge related to the success and failure of deep space exploration and Mars missions is On-Orbit construction (Biswal & Annavarapum 2021). Difficult situations such as the effect of zero gravity on physical health or exposure to sunlight and sunburn expose the person to stressful situations and therefore become difficult. The company in orbit eats people. One solution is AI robots for building orbit to solve the above problems. All organizations such as NASA or ESA are working to prepare AI robots for constructions on orbit in order to ensure 100% stability in the assembly, thus eliminating the problems physical health or exposure to solar irradiance (Rybus, 2018).
Another major challenge for astronauts is spaceship control (Biswal & Annavarapum 2021). Due to the microgravity and the special radiation environment, health problems occur and can lead to the inability to control the aircraft. AI-based automated robots can respond to this problem given their powerful electronic systems and their ability to reduce the threat [EM1] to health in long-duration missions (Chien et al., 2006 ). AI may be necessary for long-duration missions to Mars and other places outside the Earth-Moon system that involve a large number of crew members, autonomous navigation and communication systems. (Daniela & Dario, 2007). The crew lives in space capsules for many months, which can be kept in an artificial hibernation, although not exposed to high levels of radiation. According to NASA's Glenn Research Center, a 'cognitive radio' can be used to transmit atmospheric data during flight. This technology combines cognitive processing and machine learning while handling large amounts of traffic.
Artificial Intelligence For Space Exploration
As argued above, AI is playing a major role in helping space exploration. Due to the increased duration and complexity of future missions, there is a need to improve the management and coordination of operations. Systems such as AEGIS provide tracking as a major aid in facilitating processes, such as exploring the volcanoes of Mars. With applications ranging from decision support to the management of long-duration missions, AI can greatly support human capabilities, such as constructions in orbit and maintaining the health and well-being of the person.
However, despite its ability to store large amounts of important data and provide complex tasks, AI should be considered as an aid to missions, because it has not yet desired level of technical maturity. Human-robotic maneuvers are one of the most challenging problems in Mars exploration. However, the next phase of digital transformation is just around the corner and AI is a big part of it. A critical look at the reality and the creation of interdisciplinary solutions related to the implementation of AI in a practical and solution-oriented manner should be of utmost importance in future research. on AI in Mars exploration.
Linda is a graduate student in International Affairs at the Hertie School of Governance where she specializes in security. He studied his master's degree in communication with a focus on European foreign policy at the University of Vienna and KU Leuven. He gained further experience with NGOs in Austria, the Czech Republic and Belgium and worked for the Vice President of the European Parliament. Linda works as a program assistant at the Aspen Institute.
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