Artificial Intelligence and the Wave of Automation


With the birth of the famous software CHATGPT, the powerful Artificial Intelligence has shown us a technology like no other. In the past, our perception of ordinary ai assistants such as Siri or Alexa was that they more or less played the role of a secondary voice assistant in our lives, helping us to set alarms, play songs, or make phone calls, but it was obvious – they were not smart enough. But in 2023, GPT4 comes out, directly jumping the development of ai from the 2g era to 5g. it is no longer a simple life assistant, but really become a big language model that understands almost all the knowledge in the world, and has a powerful logic. Coupled with the open source nature of this technology, developers are applying this language model in a variety of industries, all reflecting its power. The reliability and capabilities of the compiled AI can far exceed the highest levels achieved by human experts without help, and even humans may never be able to reach them(Crawford, 2021). Not just chatgpt, AI has slowly made its presence felt in a variety of industries and personal lives in recent years, and while they bring efficient productivity to humans, they also pose many ethical and moral challenges. How to properly use this powerful tool of AI has become an inevitable challenge in modern society.


In medicine, the Google deepmind team used AI to solve the long-standing problem of protein folding. Using AlphaFold’s deep learning system, the scientists first submitted a database of more than 100,000 protein sequences and their known structures to AlphaFold for repeated training. During the training process, AlphaFold learns and looks for patterns, and it predicts the 3-D structure and spatial relationships of proteins by evaluating the distances and angles between amino acid molecules and the chemical bonds that link them, in order to construct new models of the protein structure that make sense and are accurate (Ewan Birney, 2022).

The accuracy of this technique was later validated by the CASP competition and this algorithm is far superior to others (Moult, 2021). This breakthrough has provided researchers with detailed spatial models of protein targets, accelerated the study of novel drugs, and helped immensely in understanding cases and treatments. Typical Parkinson’s-type disorders and Alzheimer’s, for example, are caused by misfolded proteins. With the scientists’ research, there is hope that these diseases can be completely overcome.

This algorithm is far superior to others (Moult, 2021)

This project is not only in the medical method to show human beings the powerful ability of AI. It gives human beings a short time and significant breakthrough in protein research. It also demonstrates the ability of AI in cross-disciplinary fields, combining the expertise of biology, physics, chemistry, and computers at the same time. This is not only a milestone in structural biology, but also an important example of how science is changing.

There are many more cases like these and apart from these medical studies, healthcare, which is closely related to life, is also being impacted by AI. According to the report, the healthcare market for AI is expected to grow from $32.3 billion in 2024 to $208.2 billion in 2030, a growth rate of more than 36% (GRAND VIEW RESEARCH, 2021). Automation in healthcare and AI recognition systems can make the diagnosis and treatment of early-stage diseases more sophisticated and reliable.


The second season of the famous Marvel Studios’ TV series production, LOki, was critically acclaimed upon its release, but at the same time it was revealed that the promotional posters for this TV series were surprisingly generated by artificial intelligence, which made mistakes in the Roman alphabet and strange character poses hard to spot (Redmond, 2023). And, before that, it was revealed that Marvel Studios used AI to create the credits for Secret Invasion. These were met with strong opposition from fans.

Loki poster (Redmond, 2023)

The principle of AI-generated images is that it carries out continuous machine learning on thousands of images, analyzes the images and finds out the pattern, and then fuses multiple materials to generate relatively non-contradictory images, such as midjournal, and Openai’s DALL.E, both of which can generate corresponding images through the user’s description. This approach allows you to generate the content you need in a short period of time, and more and more industries are experimenting with making their own ai images than looking for designers to conceptualize and design them from scratch. This not only saves on budget, but also massively reduces the time it takes to get a design. But at the same time, it may seem unfair to the hardworking artists who make a living by selling their own carefully crafted illustrations. Also, since the generation of images is a fusion of different designers’ ideas, the issue of copyright becomes murky again and there are no perfect laws to protect these people.

Al generates images as a shortcut, and his nature is to integrate previous works. As a result, it is unable to generate new ideas, and a heavy reliance on AI for art creation could lead to a gradual loss of art-making skills as traditional artists are unable to focus on their art. This could have a wider impact on the development of the world of art, which is gradually disappearing.


Tesla’s Super factory in Berlin shows major advances in automation. The factory uses more than 600 robots to perform welding and assembly tasks, allowing about 3,000 new Tesla cars to be built here each week (Lambert, 2023).

Tesla’s Super factory (Lambert, 2023)

The factory uses more than 600 robots to perform welding and assembly tasks, resulting in about 3,000 new Tesla cars being produced every week. The highly automated processes demonstrated by the “Godzilla” robots improve productivity and quality in high-precision and rapid production, meeting the demand for increased capacity. In agriculture, Tesla’s automated intelligent robot Harvest Automation, which performs planting and harvesting tasks based on automated programming and an artificial intelligence recognition system, not only reduces the need for manual labor, but also significantly increases the efficiency of planting.

At the same time, the revolution in automation is transforming industries at an unprecedented rate, heralding a new era of productivity. However, this all-too-rapid change is not without its challenges, with economic redistribution and job losses now inevitable. In a study by the World Economic Forum, automation is expected to displace 85 million jobs by 2025, while creating 97 million new positions in occupations that often require more advanced skills With robots being able to perform human tasks better (Liu, 2021), such as assembly work on assembly lines, cognitive tasks such as data analytics, and other cognitive tasks, there is a direct threat to employment in these industries. More and more workers are being laid off because robots can do a better job, and in the short term the labor force is lost and unable to find a job, and how to rationally allocate the belonging of such people has become a serious social problem. Although many new occupations will be created at this stage including maintenance personnel for automated systems, writers of artificial intelligence programs, and equipment supervisors, these skills cannot be mastered in a short period of time, so skill re-engineering and upgrading are of particular importance. Therefore, the challenges posed by automation will require a multifaceted response from government, industry, and educational institutions in order to solve the current short-term problems.


The “Trolley Problem” has become the biggest challenge for AI when it is applied to life, especially in the self-driving car scenario. This thought experiment explores the ethical dilemma of smart cars and revisits the decision-making power of AI. The dilemma presented by the Tram Problem is that a fast form of automobile is heading towards five workers, and one can intervene by shifting the tram to another track where there is only one worker, perhaps saving the lives of the other five by sacrificing only one person. In all of this, it highlights the difficulty of the ethical decisions made by the self-driving programming, because these machines, faced with the reality of the situation, will choose between saving or harming, and may only judge the pros and cons, and in the ai’s perspective, the lives of 5 people are far greater than one, so it is inevitable that the ai will control the car to go to the railroad tracks of one person. So this has led to a positive take on self-driving cars, with some arguing that AI oversimplifies the process of determining danger in such extremely uncertain situations. And that these syncopations may carry an unequal impact, that programming AI to calculate the merits of these people, to make one life superior to others, may perpetuate social inequality and make one group more vulnerable to victimization.

Researchers are currently exploring new ways to address this moral dilemma, blending utilitarian and deontological perspectives. In the UK, the main thrust of utilitarianism is to maximize overall well-being, so sacrificing one to save others is considered a reasonable situation. In contrast, German deontology emphasized autonomy and individual dignity. This is because German law focuses on defending the rights of the individual, rather than gaining benefits by harming any other person. Ultimately, Japanese law takes a unique approach that combines elements of utilitarianism and deontology, weighing the conflict and distinguishing between the duty to act out of necessity and the duty to intentionally cause harm. This approach is the additive theory, which means that people need to fulfill as many obligations as possible in situations of conflicting interests. For example, if a lifeguard has an obligation to save A, B, and C, they can fulfill those obligations simultaneously if possible, prioritizing happiness and in a way that does not harm others (Toolify, 2024).

Overall, Trolley Theory exemplifies modern scientists’ discussion of ethics in AI by emphasizing the complexity of decision-making and the importance of how to program to resolve ethical dilemmas, and to continue to play a role in AI by adopting a balance between utilitarianism and obligation to maintain fairness for individuals and society. As technology advances, society must deal with the implications and responsibilities that come with AI.


In the future, AI will be seen everywhere in life, not just in manufacturing and healthcare. For individuals, AI will become a tool that we can all use, reducing monotonous and repetitive tasks and elevating our knowledge to a higher realm. Making innovation and creativity the focus of future technological developments, creating a wonderful environment for the pursuit of new ideas and inner strengths.

However, as AI continues to evolve, privacy protection and ethical considerations need to be kept in the forefront of society’s mind. Artificial intelligence is already hidden in every aspect of our lives, but the systems that govern it are still very much in place, and policy makers, programmers, as well as governments and society need to be responsible for the privacy of each user. While AI has already brought us unimaginable new horizons in other ways. But it still brings some drawbacks that we cannot ignore.

Overall, advances in AI have revolutionized the way we work and live and learn. We have created unlimited value in working together with AI. In the future, if AI’s capabilities are utilized correctly and society guides it accordingly, AI will become an important tool in our pursuit of creativity. As technology evolves, it is critical to find a balance between harnessing the disruptive power of AI and protecting core human values.


Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.

Ewan Birney. (2022). Alphafold: Protein folding explained. Google DeepMind.

GRAND VIEW RESEARCH. (2021). Ai in healthcare market size, share & growth report, 2030. AI In Healthcare Market Size, Share & Growth Report, 2030.

Lambert, F. (2023). Tesla shows new “godzilla” robot and in-depth look at latest electric car production line. Electrek.

Liu, X. (2021). World Economic Forum report: 85 million jobs may be filled by machines within 5 years. China Youth Network.

Moult, J. (2021). Alphafold: A solution to a 50-year-old Grand Challenge in Biology. Google DeepMind.

Redmond, M. (2023). The massively popular “Loki” Season 2 is under fire for using AI Art. UPROXX. (2024). Navigating Ethical Dilemmas: Trolley Problem in the Age of AI. Navigating ethical dilemmas: Trolley problem in the age of ai.


Moult, J. (2021). Alphafold: A solution to a 50-year-old Grand Challenge in Biology. Google DeepMind.

Redmond, M. (2023). The massively popular “Loki” Season 2 is under fire for using AI Art. UPROXX.

Lambert, F. (2023). Tesla shows new “godzilla” robot and in-depth look at latest electric car production line. Electrek.

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