How can the development of artificial intelligence technologies and applications help the development of science, the conduct of scientific research, the processing of results obtained from scientific research?
In recent discussions on the ongoing rapid development of artificial intelligence technologies, including generative artificial intelligence and general artificial intelligence, and their rapidly growing applications, a number of both positive determinants of this development are emerging but also a number of potential risks and threats are being identified. Recently, the key risks associated with the development of artificial intelligence technologies include not only the possibility of using AI technologies by cyber criminals and in hacking activities; the use of open-access tools based on generative artificial intelligence on the Internet to create crafted texts, photos, graphics and videos and their posting on social media sites to create fake news and generate disinformation; the use of "creations" created with applications based on intelligent chatbots in the field of marketing communications; the potential threat to many jobs being replaced by AI technology but also in the development of increasingly superior generative artificial intelligence technology, which may soon be creating new, even more superior AI technologies that could escape human control. Currently, all leading technology and Internet companies are developing their intelligent chatbots and AI-based tools, including generative AI and/or general AI, which they are already making available on the Internet or will soon do so. In this way, a kind of technological arms race is currently being realized between major technology companies at the forefront of ICT, Internet and Industry 4.0/5.0 information technologies. The technological progress that is currently taking place is accelerating as part of the transition from Industry 4.0 to Industry 5.0 technologies. In the context of the emerging threats mentioned above, many companies, enterprises, banks are already implementing and developing certain tools, applications based on AI in order to increase the efficiency of certain processes carried out within the framework of their business, logistics, financial activities, etc. In addition, in the ongoing discussions on the possibility of applying AI technologies in aspects interpreted positively, in solving various problems of the current development of civilization, including to support ongoing scientific research, to support the development of science in various disciplines of science. Accordingly, an important area of positive applications of AI technology is the use of this technology to improve the efficiency of reliably and ethically conducted scientific research. Thus, the development of science could be supported by the implementation of AI technology into the realm of science.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How can the development of artificial intelligence technologies and applications help the development of science, the conduct of scientific research, the processing of results obtained from scientific research?
How can the development of artificial intelligence help the development of science and scientific research?
And what is your opinion on this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research. In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
The development of artificial intelligence (AI) technologies offers several ways to enhance science and scientific research. AI can analyze vast datasets at unprecedented speeds, aiding researchers in identifying patterns and drawing insights. It can streamline repetitive tasks, allowing scientists to focus on more complex aspects of their work.
Additionally, AI-powered tools can contribute to experimental design, suggesting optimal parameters and predicting potential outcomes. This accelerates the research process and promotes innovation. Moreover, in fields like medicine, AI can assist in drug discovery and personalized treatment plans by analyzing genetic and clinical data.
However, ethical considerations and the potential risks associated with AI, as you mentioned, should be carefully addressed. Balancing technological advancement with responsible use is crucial to ensure the positive impact of AI on scientific development.
AI has the potential to revolutionize scientific research by expediting processes, offering data-driven insights, and contributing to breakthrough discoveries. As with any powerful tool, responsible development and application are essential to harness its benefits effectively.
The development of artificial intelligence (AI) technologies has been transformative across various domains, including science and space exploration. In scientific research, AI is instrumental in data analysis, creating simulations, automating repetitive tasks, enhancing accuracy, aiding predictive analytics, and facilitating global collaboration.
A prime example of AI's impact on science and exploration is its role in the Mars 2020 Perseverance rover mission, particularly in the operation of the MOXIE (Mars Oxygen In-Situ Resource Utilization Experiment) instrument. Here's how AI contributes to this groundbreaking endeavor:
1. Optimizing Oxygen Production: The MOXIE experiment aims to produce oxygen from Mars' carbon dioxide-rich atmosphere, a key challenge for future human missions. AI is crucial in monitoring MOXIE's performance, adjusting its operation based on the Martian environment, and optimizing the oxygen production process.
2. Autonomous Navigation and Data Analysis: The Perseverance rover, equipped with advanced AI, autonomously navigates the Martian terrain and analyzes environmental data. This capability is vital for mission success, ensuring the rover and its instruments operate efficiently in the harsh Martian environment.
3. Enhancing Space Exploration: The successful demonstration of in-situ oxygen production on Mars, aided by AI, marks a significant milestone in space exploration. It lays the groundwork for future manned missions to Mars, highlighting how AI can solve complex problems in challenging settings.
4. Broader Implications for Science: Beyond space exploration, AI's role in the rapid development of COVID-19 vaccines exemplifies its capability to accelerate scientific research. AI algorithms were used to swiftly analyze vast data about the coronavirus, aiding in the development of effective vaccines in a remarkably short time.
In summary, AI's role in scientific advancement is multifaceted, enhancing data processing, automating tasks, and solving complex problems, as exemplified in the Perseverance rover's mission and the rapid development of COVID-19 vaccines. These achievements underscore AI's potential in pushing the boundaries of scientific exploration and research.
The current AI helps to retrieve the best results from the currently available human knowledge.
in the future, AI will create knowledge from data collected using instruments like LC-MSMS and images of ultrasound and CT.
If you do not limit the discussion to the neural network related subset of AI, then one would have to say that AI has already been helpful to Science. In the early 70's a set of programs, known as the DENDRAL programs was created to help chemists and other biomedical researchers. Chief among these programs was CONGEN (for CONstrained GENeration). This program helped chemists understand the range of molecules that might be possible from the bits of information they had about an unknown compound. The chemist entered information they had collected on bits of structure that HAD to be contained in the molecule, as well as bits that could NOT possibly be there (deduced from spectral and other methods). CONGEN took this info and generated the possible structures that met the stated criteria. The results were often quite enlightening to the chemist, who might not have been able to visualize all possible combinations of possible data. Much has been written about these programs, and CONGEN was demonstrated at an ACS meeting in the early 70's. Use your favorite browser to search on DENDRAL CONGEN to learn more.
There are two practices that I would like to see more fully implemented. The first is to design projects that focus on the combination of traditional software, with its "deterministic" code, and artificial neural nets, with their ability to capture and exploit complex, multidimensional patterns with their trained "weight spaces." Secondly, I would also like to see us explore methods for peering inside of pre-trained neural nets for the purposes of exhuming their black box secrets. In the end, we need our AGI to be both explainable and controllable.
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