"Behind each 'dark horse,' there is a coherent internal logic and a specific implication."

If there is one word to describe the 2024 Nobel Prizes, it might be "upset."

For instance, the Nobel Prize in Physics was unexpectedly awarded to scientists researching AI, leading some to exclaim, "Physics is dead." The Nobel Prize in Chemistry winner also utilized artificial intelligence models to solve issues related to protein structures, thus claiming the laureate.

When it came to the literature award, which is difficult to cross over, the popular Chinese candidate Can Xue continued to be a runner-up, while the underdog South Korean female writer Han Kang emerged victorious. Even the Nobel Peace Prize favorite, the Secretary-General of the United Nations, was overshadowed by a Japanese organization of atomic bomb survivors.

When there are too many upsets, they become the "new normal," but in Xiaoba's view, behind the many surprises, there is actually a new trend in the development of the Nobel Prizes. It is the renewal and iteration that the Nobel Prizes should have, and it reveals to us "a glimpse of the future."

"Not sticking to one's own business"? AI becomes the biggest winner.

Past Nobel Prize winners in Physics probably never imagined that one day they might have to compete with the Turing Award, which represents the highest honor in the field of computer science.

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On October 8th in Stockholm, Sweden, the 2024 Nobel Prize in Physics was finally decided. The blue background of the large screen informed the world that this year's Physics Prize winners are John Hopfield from Princeton University in the United States and Geoffrey Hinton from the University of Toronto in Canada.

Why were they able to win the award? The Royal Swedish Academy of Sciences believes that their contributions lie in the fundamental discoveries and inventions in the use of artificial neural networks for machine learning.

However, deep learning and AI are clearly within the realm of computer science and should theoretically be more suitable for the Turing Award in the field of computer science. It was unexpected that the Nobel Prize, especially the Nobel Prize in Physics, which has never been awarded to computer science, let alone AI, would intervene.Even the award-winning Hinton said: "I never thought I would be nominated for the Nobel Prize in Physics. How do I know if you are not pranking me?"

The second "non-professional" is the Nobel Prize in Chemistry. This year's Nobel Prize in Chemistry was awarded to American scientist David Baker, who received half of the prize money, while the other half was shared by British scientist Demis Hassabis and American scientist John Jumper. Their contribution lies in how to obtain the three-dimensional structure of proteins. As we all know, proteins are the most important chemical substances in the human body. Knowing its structure can help us understand how many diseases are generated and how to develop drugs for treatment. In this field, the best previous work was done by Chinese scientist Yan Ning, whose laboratory is an international authority on protein structure research.

However, Yan Ning's research method is relatively traditional, requiring a large amount of manpower, resources, and time to obtain protein crystals, and then studying the crystals to achieve research success. This is not only true for Yan Ning, but also for scientists around the world who follow this research route.

Theoretically, the best solution is to directly infer the three-dimensional structure of proteins through calculations, obtaining the final results extremely efficiently without repeated experiments.

American scientist Baker has taken this path. As early as 1993, he developed a set of software that can directly predict the structure of proteins, and even design completely new types of proteins using the software, which is equivalent to God creating things. This is Baker's contribution to the field of computational protein.

Hassabis and Jumper have gone a step further. They once developed the famous Go program AlphaGo, which defeated the top Go player in South Korea, Lee Sedol, and became famous worldwide. After experiencing the power of AI, they shifted their focus to protein structure prediction, the pinnacle of the biological field.

In 2020, they jointly developed an AI software called AlphaFold 2. This software has successfully predicted 98.5% of human protein structures, with an accuracy that is almost identical to experimental results.

It can be said that the winners of this year's Nobel Prize in Chemistry, although they used deep learning methods, have indeed demonstrated AI's ability to predict the complex structures of proteins and completed exploration in the field of chemistry.The current state of the scientific community is such that achievements in individual fields have been largely claimed by previous generations. The problems that humanity is facing have become increasingly complex, whether in physics or chemistry. To make progress, additional support is needed, and this support is a new trend in the Nobel Prize: "interdisciplinarity."

The 2002 Nobel Prize in Economic Sciences is a famous example of interdisciplinary work. Psychologist Daniel Kahneman was awarded the Nobel Prize in Economic Sciences for his pioneering research on human thinking and decision-making processes, which even led to the creation of a renowned school of thought known as behavioral economics.

The fact that a psychologist received the Economics Prize and facilitated the cross-integration of psychology and economics demonstrates that interdisciplinary research has injected new vitality and inspiration into the development of multiple disciplines.

Furthermore, the 2024 Nobel Prizes in Physics and Chemistry awarded for achievements in physics and chemistry made using AI actually serve as a reminder to many scientists: the creation and use of tools is a significant factor that distinguishes humans from different animals.

Are young people increasingly likely to win the Nobel Prize?

To win a Nobel Prize, how old do you have to be?

According to research by the Japanese Ministry of Education, Culture, Sports, Science, and Technology, the average age of scientific giants when they receive the Nobel Prize is 69 years old, a figure that has increased by 16 years in the last 80 years.

Despite these giants having achieved significant research results and gained worldwide attention, the time it takes for them to receive the Nobel Prize after their important research findings are published can be 20 or even 30 years.

Historical data shows that research active in the United States during the 1920s and 1930s often did not receive recognition and awards until the 1950s and beyond.Why do Nobel Prize winners tend to be older? One theory is that the field of scientific research is advancing rapidly, and those who come later must immerse themselves in learning the scientific and technological achievements of the past before they can "go further" on the shoulders of giants.

Another theory is that contributions worthy of a Nobel Prize must withstand the test of time. However, in recent years, this traditional practice has been gradually broken. This year, the trend towards younger winners has become more apparent.

For example, John Jiangpo, who won the Nobel Prize in Chemistry, was born in 1985 and is a solid member of the post-80s generation. At the age of 39 this year, he can still be called a young talent in the scientific community. His partner, Demis Hassabis, born in 1976, is also very young.

And Han Jiang, the winner of the Nobel Prize in Literature, was born in 1970, and compared to other Nobel Prize winners, his age is also relatively young.

Why is there a trend towards younger Nobel Prize winners? An important reason is AI. The explosive growth in the field of deep learning has been very short, especially with the popularization of generative AI only a few years.

The newly lit branches of technology often contain more fruits, which also gives researchers in the field of artificial intelligence more opportunities to produce results.

In addition, "empirical research" based on evidence, data, and factual observations that can be verified and replicated is becoming a trend in the scientific community. The rise of this trend allows more research results that were once buried deep to have more opportunities to be discovered and verified.

This is equivalent to the saying "good wine fears deep alleys" in the past, but now as long as your research results have potential, they will be quickly unearthed by peers for study. "Good wine is not afraid of deep alleys anymore."

The "counter-attack" of Nobel Prize winners is a refreshing narrative.

"After ten years of hard study unnoticed, one becomes famous overnight," this phrase can be very aptly used to describe the Nobel Prize winner Ambrose, who struggled at Harvard.During his youth, Ambrose had a relatively smooth journey. He obtained a Bachelor's degree in Biology from MIT in 1975 and a Ph.D. in 1979, studying under Nobel laureate David Baltimore. In 1985, he became a faculty member at Harvard University, and his research career seemed to be going well.

During this period, he discovered the first known microRNA in humans, a discovery that ultimately led to him winning this year's Nobel Prize in Physiology or Medicine. However, this groundbreaking discovery of Nobel caliber was completely overlooked. Harvard University even believed that Ambrose had not made any significant contributions, leading to his failure to pass the tenure review and the loss of his job.

Ambrose had no choice but to pack his bags and leave Harvard University in disgrace, moving to Dartmouth College (an Ivy League member) in 1992, a place most people had never heard of, to continue his research quietly.

After eight years of perseverance, his doctoral colleague Gary Ruvkun discovered the second microRNA, and humanity finally realized that microRNAs exist in all living organisms, including humans. Ambrose's research finally gained the attention it deserved.

In 2008, the two jointly received the Lasker Basic Medical Research Award, which is known as the "Nobel Prize weathervane." It was then that Harvard University realized its mistake and invited Ambrose back to continue his research. Ambrose was deeply moved, but he declined and chose to conduct research at the University of Massachusetts Medical School.

From struggling at Harvard to winning the Nobel Prize 32 years later, Ambrose used his experience to tell Harvard: "I waited for 32 years for an opportunity. I wanted to fight for a breath, not to prove how great I am, but to show others that I will definitely reclaim what I have lost."

Another individual with a similar experience is Nobel Prize winner in Physics, Hinton.

At least Ambrose had a colleague to help him win the award, while Hinton深耕ed his field for 30 years, persisting to the end and laughing last when even his colleagues and teachers had given up.

Geoffrey Hinton was born into a legendary scientific family. His great-great-grandfather George Boole proposed "Boolean logic," which is the mathematical foundation of modern computers. His father was an entomologist at the University of Cambridge. His family can be considered a scientific dynasty. As for him, he was a "punk youth."At the age of 18, Hinton entered King's College, Cambridge, where he successively studied physics, chemistry, mathematics, architecture, and even philosophy, but soon gave them all up. Fortunately, Hinton eventually became fascinated with the question of "how the brain works," which prevented him from completely abandoning his studies.

In 1972, he found his true interest—artificial intelligence. He entered the University of Edinburgh to pursue his Ph.D. under the guidance of Professor Hinton, an expert in the field of neural networks.

At that time, artificial intelligence was still a very niche field, and the neural network field he studied was even more neglected, to the point of being considered a fantasy by the academic community. During his long-term exploration, even Hinton's teacher, Hinton, shifted to the symbolicism camp and left the path of neural networks. As his school began to decline, Hinton struggled to hold on. This persistence lasted for 30 years.

The 30 years of persistence eventually paid off. In 2006, Hinton officially proposed the concept of deep learning, and from that year on, deep learning迎来了 a wave of explosive development. Subsequently, his research results in neural networks were applied to speech recognition, image recognition, and even generative artificial intelligence like ChatGPT.

Today, almost all the AI technologies we use are inseparable from neural networks. It can be said that Hinton almost single-handedly turned the development direction of an industry with decades of persistence, making deep learning one of the hottest research fields in the world from one of the coldest, directly driving the explosive development of artificial intelligence.

His achievements are not only a milestone in the development of AI but also the best interpretation of the scientific spirit of perseverance. From the perspective of contribution alone, Hinton's Nobel Prize is well deserved.

Whether it's Hinton or Ambrose, their counterattacks can be described as "invigorating" plotlines. However, such novel-like scenarios are, after all, the minority. True science requires time to verify, and seemingly dramatic breakthroughs actually come from repeated experiments and bursts of inspiration.

All accidents and counterattacks are ultimately rewards for the diligence and persistence possessed by Nobel Prize winners.

In the past four years, the number of female Nobel Prize winners has been comparable to that of the past few decades.

How difficult is it for women to win the Nobel Prize?Looking at the data, in the 120-year history of the Nobel Prize, there have been a total of 66 female winners and 912 male winners, with women accounting for only 6.7%. Especially in the 40 years from 1936 to 1976, there were only 10 female Nobel Prize winners.

However, since the beginning of this century, the number of women winning the Nobel Prize has increased significantly: from 2001 to 2023, there were 35 female Nobel Prize winners; and in just four years from 2020 to now, there have been 12 female Nobel Prize winners, surpassing the total of the previous decades.

Why has the number of women increased significantly? Interestingly, this question has been studied by Nobel Prize winners themselves.

Last year's Nobel Prize winner in Economics, Claudia Goldin, used data to prove that work is "greedy", and employees who are willing to work "996" and be on call will receive higher compensation and more promotion opportunities than average employees.

Men are obviously more likely to become "workaholics". Relatively speaking, women will pay more attention to family and children's education. Therefore, childbirth is an important reason for women to face discrimination in the labor market.

She further found that over the past half century, women's competitiveness in the workplace and public affairs has been continuously increasing, which is partly due to the invention of the contraceptive pill. Once women have the right to choose childbirth, they can better plan their careers and do more "greedy work".

With the awakening and improvement of the status of modern women, they have shown stronger and stronger competitiveness in scientific research and other fields. Their achievements show that women can also achieve results comparable to or better than men through their own talents and efforts, which is a side proof of the progress of gender equality in various fields.

This is a new trend of the Nobel Prize in recent years: the increase in the number of female winners.

The 2024 Nobel Prize in Literature was awarded to South Korean female writer Han Jiang. She is the first female writer in Asia to win this award, and her representative work is the novel "The Vegetarian". The jury said that she "confronts historical trauma with strong poetic prose, revealing the fragility of human life".

After winning the Nobel Prize in Literature, the whole country in South Korea celebrated, and Han Jiang's father also hoped that Han Jiang could choose a publishing house to hold a press conference. However, Han Jiang said: "The war is escalating, and people are being taken away every day. How can we hold a celebration or hold a press conference?" This has to be said to be another small "accident".The Underlying Trends Behind the "Dark Horses"

Han Jiang won the award but refused to celebrate, and some netizens jokingly said, "She deserves a Nobel Peace Prize." In fact, this year's Nobel Peace Prize was awarded to the Japan Confederation of A- and H-Bomb Sufferers Organizations. The Nobel Committee commended their efforts to achieve a nuclear-free world.

If awarding the Nobel Prize to AI experts is to "ride" the wave of AI's popularity, and awarding it to Han Jiang in a certain sense reflects gender equality, then awarding it to this anti-nuclear organization serves as a warning to people about the escalation of geopolitical risks and the existence of nuclear risks.

Although it has been more than half a century since the end of World War II, the gloom brought by nuclear risks has never truly dissipated. This year's Nobel Peace Prize winner may be somewhat unexpected, but considering the current situations in the Middle East and Russia-Ukraine, as well as the tense status between China and the United States, the implications behind it are not difficult to understand.

Reviewing this year's Nobel Prizes, we will find that there are many "dark horses" in this edition, but behind each "dark horse," there is its own coherent internal logic and its own directional implications.

They show us the potential trends of the Nobel Prizes, including AI as a powerful new tool, rising in various research fields; the cross-disciplinary crossover, and the Nobel Prizes increasingly focusing on practicality; the Nobel seems to be getting younger and younger; the number of female Nobel Prize winners is accelerating.