Three Levels of Research I categorize research into three levels. However, let me be frank—many accepted papers fail to reach even the lowest of these levels, as they can scarcely be considered research. Let’s explore these levels from the lowest to the highest:
At this level, the results are easy to accept once the proposed method is understood. The novelty often comes from new tasks, experimental settings, or benchmarks. However, the solutions themselves are typically straightforward or based on common sense. Despite their limited fundamental contributions, such works deserve respect for their engineering effort and ability to produce strong results. Practical impact and reproducibility often characterize this level.
At this level, the results are surprising and counterintuitive—people often find it hard to believe the proposed method could achieve such outcomes. These works uncover insights or phenomena that no one had realized before. They are inspiring, often opening new directions for further exploration. Level 2 research demonstrates creativity, deep understanding, and a willingness to challenge existing paradigms. This is where groundbreaking methods emerge and where researchers most often push the boundaries of knowledge.
Level 1 research is rare and cannot be directly pursued—it depends on timing, opportunity, and cumulative progress in a field. Such breakthroughs emerge when the accumulation of prior work reaches a tipping point, creating the conditions for a fundamental shift. Achieving this level requires not only deep expertise but also a stroke of luck. These works redefine entire fields, inspire new disciplines, and leave a lasting impact on the scientific community.
This framework is not about labeling research as “good” or “bad” but about understanding the roles different kinds of contributions play in advancing knowledge. All levels have value—engineering efforts drive practical progress, innovative methods inspire new directions, and breakthroughs reshape entire landscapes. I hope this perspective helps researchers, especially newcomers, reflect on their work and find motivation to pursue contributions that matter to them—and to the field.
]]>In the aftermath, I left what I believed to be a measured comment pointing out that her example was, in fact, racist. If Professor Picard truly intended no bias, she could have simply acknowledged the existence of dishonest students without linking the misconduct to a particular nationality. Later, a broader and more heated discussion erupted online. Now, I would like to address the concerns of reasonable individuals who may not fully grasp why linking dishonesty to a specific ethnic group is problematic. This is not intended for those who seek to justify racist views or stoke inflammatory rhetoric, as exemplified by Professor Pedro Domingos. Instead, this essay aims to clarify the issues at hand for those genuinely open to understanding why we must confront and reject such stereotypes.
“I see many reports suggesting that Chinese students cheat more frequently. Maybe these reports simply confirm a truth you refuse to acknowledge.” Similar reasoning has appeared in equally offensive claims, such as Dr. James Watson’s remarks that African individuals are inherently less intelligent, or Dr. Pedro Domingos’s suggestion that female applicants are less qualified.
First, it is essential to understand that media coverage does not necessarily equate to fact. Media outlets often highlight stories that fit a particular narrative, and these narratives can be influenced by bias, cultural misconceptions, or even political agendas. While one can easily find reports that seem to confirm preconceived notions, another person could just as readily produce evidence to support the opposite conclusion. Thus, relying solely on media stories and anecdotes is an unreliable method of discerning any universal truth.
More importantly, no reputable scientific research supports the idea that individuals of Chinese ethnicity are inherently more likely to cheat under the same conditions as their non-Chinese peers. Academic misconduct, unfortunately, occurs in many cultures and contexts. For instance, consider the U.S. college admissions bribery scandal. This high-profile case involved affluent Americans manipulating the system to secure advantages for their children. Clearly, cheating is a global issue that transcends national or ethnic boundaries.
If the stereotype is not based on fact, then why do some believe it? One reason may be that China has the world’s second largest population and places a significant emphasis on education. Unsurprisingly, this has led to a substantial presence of Chinese students and researchers worldwide. With such a large pool, even a small fraction of misconduct cases can appear more frequent. Consequently, these numbers are often misconstrued or taken out of context, giving the false impression that Chinese students, specifically, are more prone to cheating. In reality, what we are witnessing is a distortion created by scale, not a truth based on character or culture. Therefore, concluding that “Chinese students are more likely to cheat” is a prejudiced overgeneralization rather than a reasoned judgment.
“Even if it’s not entirely true, why can’t I say it anyway? Don’t I have freedom of speech?”
Indeed, freedom of speech is a fundamental right, at least in many countries. However, it is crucial to distinguish between legal rights and moral or ethical responsibilities. While the First Amendment protects individuals in the United States from government censorship or punishment for their speech, it does not guarantee freedom from social consequences. Our communities—whether they are academic societies like NeurIPS, professional associations, or just groups of individuals who share certain values—have the right to uphold standards of fairness, respect, and inclusivity.
For instance, the NeurIPS community and various academic institutions have codes of conduct that prohibit hate speech and the spreading of harmful stereotypes. Such policies are not designed to stifle honest debate or critical inquiry. Rather, they exist to maintain an environment where everyone feels safe and valued. When people choose to perpetuate bigotry—be it towards the Chinese or any other group—they undermine the core values these communities hold dear. Thus, while one may have the legal freedom to say prejudiced things, responsible and compassionate individuals recognize that doing so erodes trust, harmony, and collaboration.
Ultimately, acknowledging that Professor Picard’s comment was racist is not about silencing discussion or policing thought. On the contrary, it is about encouraging thoughtful dialogue grounded in evidence, fairness, and empathy. Addressing stereotypes, especially those as pervasive as the “cheating Chinese student” trope, is a collective effort that requires courage, understanding, and a willingness to learn.
In the face of global challenges—ranging from academic integrity to broader societal inequities—it is far more productive to work together to find genuine solutions rather than scapegoating particular groups. By refusing to accept harmful stereotypes and advocating for a level playing field, we honor the fundamental principles of our academic and professional communities. These principles—openness, integrity, respect, and a commitment to truth—are what drive innovation, understanding, and progress.
In the end, our goal should be to foster an environment where people can thrive, contribute, and excel based on their merits, not their background. Recognizing the harm caused by linking negative behavior to nationality is a crucial first step toward creating the kind of inclusive, respectful, and enlightened community we all aspire to build.
]]>In stark contrast, the present-day research community appears to have lost its way. The focus has shifted towards heavy, cumbersome ideas that, despite their grandiose appearances, often lack substance and originality. Imagination seems to have taken a backseat, replaced by a mechanical pursuit of safe, predictable outcomes. The vibrancy that once characterized the research community has been replaced by a facade of prosperity, where genuine progress is overshadowed by the superficiality of logrolling and mutual back-scratching.
This environment, steeped in mediocrity, stifles true innovation. The community is ensnared in a cycle of producing voluminous but uninspired work, more concerned with maintaining appearances than with making meaningful contributions. The result is a stagnation of ideas, where the real progress is increasingly rare and hard to come by.
Interestingly, the machine learning community stands as a stark exception to this trend. Here, the spirit of imagination and genuine inquiry thrives. Researchers in this field are not afraid to venture into the unknown, to experiment, and to fail. It is a realm where bold, creative ideas are not only encouraged but necessary for advancement. The machine learning community, with its emphasis on innovation and its willingness to embrace the unorthodox, is driving real progress in a way that other fields seem to have forgotten.
The difference is striking and serves as a reminder of what the research community once was and what it has the potential to be. It is a call to rekindle the spirit of imagination, to shed the heavy, uninspired ideas that weigh us down, and to remember that true progress comes from daring to think differently. The king may not be wearing clothes, but it is up to us to see beyond the facade and strive for a future where imagination and genuine inquiry lead the way once more.
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