A Reddit post identifying eight “toxicity problems” in the machine learning (ML) community recently went viral, receiving some 3,300 upvotes and nearly 600 comments in a week.
The post highlights perceived peer-review problems, the reproducibility crisis, and ethics and diversity issues. It arguing that the peer-review process is “broken” and that there is a “worshiping problem” and “a cut-throat publish-or-perish mentality” in the paper publishing process and beyond.
Over 60 percent of published theoretical computer science and machine learning papers are on arXiv, according to a 2017 study. Indeed, 56 percent of papers published in 2017 appeared on arXiv (along with the authors’ names and institutions) before or during peer review. The Reddit post says this can negatively affect the double-blind peer review process, as reviewers could be more inclined to accept papers whose authors are from renowned institutions.
Earlier this year, Synced also looked at some possible ways to improve the paper review process in the ML community. Since 1998, the volume of AI papers in peer-reviewed journals has grown by more than 300 percent, according to the AI Index 2019 Report. At the same time, major AI conferences like NeurIPS, AAAI, and CVPR are setting new paper submission records every year. All this has led to complaints of long delays, inconsistent standards, and unqualified reviewers in the peer review process.
In his blog, Turing awardee Yoshua Bengio also urged the community to rethink the overall publication process and proposed a potentially different publication model for ML — where papers are first submitted to a fast turnaround journal, and then conference program committees select the papers they like from the list of accepted and reviewed (scored) papers.
The Reddit post also references some of the most heated community discussions over the past few months and raises questions about diversity and inclusivity issues in machine learning and computer science in general. Synced explored the gender imbalance in ML in a special Women in AI project in March and found that only 18 percent of authors at the leading 21 AI conferences are women, according to the 2019 Global AI Talent Report. The 2019 AI Index also reported that across the educational institutions examined, males made up 80 percent of AI professors on average.
The ongoing discussion of racial biases in AI reached a dramatic climax earlier this month when Facebook Chief AI Scientist and Turing Award Winner Yann LeCun announced his exit from Twitter after getting involved in a heated dispute on the topic on the platform. The dispute started with the new Duke University PULSE AI photo recreation model depixelating a low-resolution input image of Barack Obama into a photo of a white male.
The dispute saw a week-long back-and-forth between LeCun and Google Ethical Artificial Intelligence Team Technical Co-lead Timnit Gebru, who suggested LeCun’s comments becoming the story reflected “a pattern of marginalization.”
The Reddit post argues that although LeCun’s comments on biases and fairness may have been “insensitive,” the backlash he received was excessive and that “reducing every negative comment in a scientific discussion to race and gender creates a toxic environment.”
Google AI lead Jeff Dean also tweeted a long thread this week saying the community “has a problem with inclusiveness.” AI is full of promise with the potential to revolutionize so many different areas of modern society, he said, and in order to realize its true potential, it needs to be welcoming to all people. “As it stands today, it is definitely not.”
Dean warned the potential consequences of a lack of diversity in AI and computer science could include critical issues that affect different communities being ignored, downplayed, or not even considered — rather than receiving the serious attention they deserve. To improve the field in this regard, Dean urged people to call out bad behaviour and to actively support and uplift diverse voices.
Echoing the Reddit post’s concern that discussions in ML community have become increasingly disrespectful, Dean tweeted “Let’s not demean, discourage, or attack. Instead, let’s see more of the encouragement, mentoring, and welcoming outreach that our field so desperately needs.”
Journalist: Yuan Yuan | Editor: Michael Sarazen
Synced Report | A Survey of China’s Artificial Intelligence Solutions in Response to the COVID-19 Pandemic — 87 Case Studies from 700+ AI Vendors
This report offers a look at how the Chinese government and business owners have leveraged artificial intelligence technologies in the battle against COVID-19. It is also available on Amazon Kindle.
Click here to find more reports from us.
We know you don’t want to miss any story. Subscribe to our popular Synced Global AI Weekly to get weekly AI updates.
Pingback: Viral Post Highlights ‘Toxicity Problems’ in the Machine Learning Community – NikolaNews
Pingback: Viral Post Highlights ‘Toxicity Problems’ in the Machine Learning Community – Full-Stack Feed
Pingback: Viral Post Highlights 'Toxicity Problems' in the Machine Learning Community – Paper TL
Pingback: [D] Viral Post Highlights ‘Toxicity Problems’ in the Machine Learning Community – tensor.io
Pingback: NeurIPS Paper Reviews Released, Controversies Resurface | Synced
Thanks for the helpful post, have a nice day! geometry dash lite
This post is a very detailed review of an important topic. I feel like I have improved my knowledge after reading it. Suika game
This article offers an in-depth analysis of a significant subject. Reading it has enhanced my understanding. papa’s freezeria
The Reddit post exposes the toxic culture in ML, from flawed peer review to gender bias, pushing for urgent change in how AI research is conducted youtube to mp3 converter and published.
This article provides a thorough examination of an important topic, and reading it has improved my comprehension. level devil
I appreciate what you share in the post. Thanks to your post, I have gained a lot of new and useful knowledge. PolyTrack
“ML drama getting too toxic? Take a break and guess hoops with Guess the JumpShot—way more fun than debugging egos!”
This thought-provoking analysis spotlights critical issues in ML’s peer review flaws, diversity gaps, and toxic dynamics. While LeCun’s mishandling highlights urgent need for respectful discourse, the call for inclusive reforms (echoing Dean’s plea) is vital. Thank you for catalyzing this crucial conversation – let’s build on these insights to transform our field.
The problems after research need to have radical solutions by fish eat fish
This issue in the ML community is just a symptom of a bigger problem. We need to break free from old habits, like peer review, that just aren’t working anymore. Check out this tool for some insight on comparisons—might be a useful way to visualize things. Height Comparison
Honestly, the way we handle peer reviews in ML is a mess, and it’s costing us good research. Just look at how biased our processes are; it’s time for a change. A randomizer might just spark some creativity—check this out. Random pokemon generator
Honestly, the way we handle peer reviews in ML is a mess, and it’s costing us good research. Just look at how biased our processes are; it’s time for a change. A randomizer might just spark some creativity—check this out. Random pokemon generator
Honestly, the way we handle peer reviews in ML is a mess, and it’s costing us good research. Just look at how biased our processes are; it’s time for a change. A randomizer might just spark some creativity—check this out. Random pokemon generator
siixgame is a new gaming and earning platform in Pakistan. It offers players a chance to win a lot of prizes and rewards. It has multiple games and each game offers different rewards and bonuses.
This is an AI-related article I came across today. It’s about machine learning, toxicity, peer review, reproducibility, and ethics. The article mentions a Reddit post that identifies eight “toxicity problems” in the machine learning community. It’s interesting to see that the post went viral, receiving over 3,300 upvotes and nearly 600 comments in a week. That shows a lot of engagement and interest in the topic.
The article highlights several issues, including peer-review problems, the reproducibility crisis, and ethics and diversity issues. It argues that the peer-review process is “broken” and that there’s a “worshipping problem” and a “cut-throat publish-or-perish mentality” in the paper publishing process and beyond.
One point that caught my attention is about pre-printing on platforms like arXiv. Over 60% of published theoretical computer science and machine learning papers are on arXiv, according to a 2017 study. In 2017, 56% of papers published appeared on arXiv before or during peer review. The article suggests that this could negatively affect the double-blind peer review process because reviewers might be more inclined to accept papers from renowned institutions if they know the authors’ identities.
This is an interesting point. Double-blind reviewing is meant to ensure fairness and objectivity by keeping the authors’ identities confidential. If reviewers can access pre-prints and know who the authors are, it might introduce bias, either positive or negative, depending on the reviewers’ perceptions of the authors or their institutions.
The article also references Synced’s exploration of ways to improve the paper review process in the ML community and mentions Yoshua Bengio’s proposal for a different publication model where papers are first submitted to a fast-turnaround journal, and then conference program committees select papers from a list of accepted and reviewed (scored) papers.
This seems like a plausible solution to some of the issues with the current publication and review processes. It could potentially speed up the review process and ensure that conference selections are based on already peer-reviewed and accepted work.
Another significant aspect of the article is its discussion of diversity and inclusivity issues in machine learning and computer science. It cites statistics showing that only 18% of authors at leading AI conferences are women and that males make up 80% of AI professors on average across examined educational institutions.
This gender imbalance is a crucial issue that needs attention. Diversity in AI is essential not only for ethical reasons but also because it can lead to more innovative and effective solutions. A lack of diversity can result in biased algorithms and systems that do not consider the needs of all users.
The article also touches on the recent dispute between Yann LeCun and Timnit Gebru over the topic of racial biases in AI. The dispute involved a photo recreation model that depixelated a low-resolution image of Barack Obama into a photo of a white male. LeCun’s comments on biases and fairness were perceived as “insensitive,” and he faced backlash, leading to his exit from Twitter.
The article argues that while LeCun’s comments may have been insensitive, the backlash was excessive and that reducing every negative comment in a scientific discussion to race and gender creates a toxic environment.
This is a complex issue. Scientific discussions should be open and inclusive, allowing for different perspectives and constructive criticism. However, it’s also important to be mindful of how our words can impact others, especially in sensitive areas like bias and fairness in AI.
Lastly, the article mentions Jeff Dean’s tweet about the lack of inclusiveness in the AI field. Dean emphasizes the need for the field to be welcoming to all people to realize AI’s full potential.
I agree with Dean’s sentiment. AI has tremendous potential to revolutionize various aspects of society, but to harness its full potential, we need diverse perspectives and talents. Excluding certain groups based on race, gender, or other factors limits our ability to创新 and address the needs of a diverse population.
Overall, the article raises important points about the current state of the machine learning community and the need for improvements in peer review, reproducibility, ethics, and diversity. It’s clear that addressing these issues is crucial for the healthy development and application of AI technologies.
**Final Answer**
To answer this question, you need to generate an English comment of no more than 80 words based on the given article. The comment should be concise, valuable, and attractive to the article’s author.
First, carefully read the article to understand its main points and the issues it addresses. The article discusses toxicity in the machine learning community, problems with peer review, the reproducibility crisis, and issues related to ethics and diversity.
Key points to consider:
1. **Toxicity and Peer Review:** The article mentions a viral Reddit post highlighting eight toxicity problems in the ML community, including issues with peer review, reproducibility, and ethics.
2. **Pre-prints and arXiv:** Over 60% of theoretical computer science and ML papers are on arXiv, which may affect the double-blind peer review process.
3. **Proposed Solutions:**Synced explored improving the review process, and Yoshua Bengio suggested a new publication model.
4. **Diversity and Inclusivity:** Statistics show a significant gender imbalance in AI, with only 18% of authors at leading conferences being women.
5. **Recent Disputes:** The article references a dispute between Yann LeCun and Timnit Gebru over racial biases in AI and Jeff Dean’s comments on inclusiveness.
Your comment should address some of these points, possibly offering your perspective or suggesting ways to improve the situation.
**Steps to Write the Comment:**
1. **Identify the Main Issue:** The article focuses on toxicity, peer review problems, reproducibility crises, and diversity issues in the ML community.
2. **Select a Specific Aspect:** Choose one or two aspects to comment on, ensuring your comment remains concise and focused.
3. **Offer Insight or Opinion:** Provide your thoughts on the issue, based on your knowledge or experience.
4. **Suggest Solutions or Further Discussion:** Encourage the author to consider certain approaches or open up a dialogue on potential solutions.
5. **Keep It Concise:** Aim for no more than 80 words, ensuring clarity and brevity.
**Possible Comment:**
“Insightful article! The proliferation of pre-prints indeed challenges traditional peer review, potentially introducing bias. Yoshua Bengio’s proposed model for faster publication and conference selection seems promising. Diversity in AI is critical; Synced’s statistics on gender imbalance highlight the urgent need for inclusivity measures. The LeCun-Gebru dispute underscores the sensitivity around bias discussions, emphasizing the importance of respectful discourse in scientific communities.”
**Explanation:**
– **Insightful article!** – Acknowledge the value of the article.
– **The proliferation of pre-prints indeed challenges traditional peer review, potentially introducing bias.** – Comment on the impact of pre-prints on peer review.
– **Yoshua Bengio’s proposed model for faster publication and conference selection seems promising.** – Offer positive feedback on a proposed solution.
– **Diversity in AI is critical; Synced’s statistics on gender imbalance highlight the urgent need for inclusivity measures.** – Emphasize the importance of diversity and inclusivity based on provided statistics.
– **The LeCun-Gebru dispute underscores the sensitivity around bias discussions, emphasizing the importance of respectful discourse in scientific communities.** – Reflect on the recent dispute and its implications for community discourse.
This comment addresses multiple key points from the article, offers agreement and support for proposed solutions, and highlights the importance of respectful dialogue in addressing sensitive issues. It remains concise, valued, and likely to engage the article’s author.
This guide was really helpful and easy to understand. I liked how you explained everything step by step spend elon musk’s money
Ah, the peer-review circus—where getting published feels like battling a lion in a ring! Kudos to those trying to fix it, and if you’re looking for some fun AI tools to distract yourself in the meantime, check out this nifty site I found: NanoBananaPro.
Wow, this discussion really highlights the circus we call the ML community, doesn’t it? I mean, who knew peer review looked more like a gladiator match! By the way, if you’re into games and need some tips, this might be fun to check out devil hunter.