Podcast Summary
A few large corporations dominate AI research: The study reveals that a small number of companies control the majority of AI research, potentially impacting other organizations and the research community.
The power in AI research, specifically in the areas of deep learning and democratization, is heavily concentrated in the hands of a few large corporations. This is according to a recent paper co-authored by Nora Ahmed, a strategy PhD candidate at IV Business School at Western University Canada, and a research fellow at the Scotia Bank Digital Banking Lab. The study was motivated by concerns that while there is much discussion about the need to democratize AI and make it more accessible, there was a lack of solid evidence on the current state of affairs. To answer this question, the researchers examined major computer science conferences, consulting csranking.org, and found that a small number of companies are dominating the scene. This concentration of power in AI research could have consequences for other organizations and the broader research community. The study provides valuable insights into the current state of AI research and highlights the importance of addressing the issue of concentration of power.
Industry presence in AI research has grown significantly in the last decade: The study found that the number of papers with industry co-authors in AI conferences has significantly increased from around 0.2 to over 0.4 between 2010 and 2020, while the presence of elite universities has not seen a similar increase, raising concerns about the potential impact on academic research and the tenure decision process for assistant professors.
The presence of large companies in AI research has significantly increased in the last decade compared to non-AI conferences. This trend is evident in figures one and two of the study, which show the share of papers with at least one co-author from companies in AI conferences. The synthetic control method was used to provide more solid evidence, and the increase in industry affiliation was found to be particularly pronounced in the case of the largest conferences. For instance, at one of the biggest conferences, the ratio of papers with at least one industry co-author jumped from around 0.2 to over 0.4 between 2010 and 2020. In comparison, the presence of elite universities in AI research has not seen a similar increase. This trend is concerning for some as it raises questions about the potential impact on academic research and the tenure decision process for assistant professors in the field of AI.
Elite universities dominate AI research, leaving mid-ranked institutions behind: Elite universities collaborate with tech companies for AI research, while mid-ranked institutions lose ground due to lack of resources and expertise. Corporations have increased their presence in AI research, focusing on different types than universities.
While elite universities have increased their presence in Artificial Intelligence (AI) research through collaborations with large technology companies, universities ranked between 200 and 500 have lost ground. Large companies have the computing power and data, while elite universities have the expertise in deep learning. This trend is concerning as it may lead to less democratization and diversity in AI research. Despite the common belief that corporations have reduced R&D in recent years, the findings suggest that they have actually increased their presence in AI research. The analysis also revealed that companies focus on different types of AI research compared to universities, as shown in Figure 8 of the paper. These insights provide valuable information for policymakers, researchers, and industry professionals to promote more inclusive and diverse AI research and development.
Disparity in Deep Learning Research between Large Companies and Non-Elite Universities: Large companies dominate deep learning research due to access to resources, while non-elite universities focus on traditional machine learning. Diversity in AI research is also a concern, with underrepresentation of non-elite universities potentially leading to less inclusive AI tools.
The TFIDF analysis of papers presented at AAAI conference reveals a gap between large companies, elite universities, and non-elite universities in the field of deep learning research. Large companies are leading in deep learning methods, while non-elite universities are more active in traditional machine learning methods. This disparity may be due to the fact that deep learning research requires significant computing power and resources, which non-elite universities may not have access to. Another concern raised in the paper is the lack of diversity in AI research. The results suggest that AI research might be becoming less diverse as large technology companies are less diverse than non-elite universities. Increasing representation from non-elite universities could help ensure that AI tools developed are more inclusive and reflective of the real population. The paper also implies that governments may need to increase their efforts to provide computing power and resources to support AI research, particularly in non-elite universities, to promote diversity and ensure that the field remains inclusive and accessible to all.
Elite universities lead in AI research, large companies lag behind: Researchers are concerned about the growing gap between elite universities and large companies in AI research, and its potential impact on diversity and innovation.
The gap between elite universities and large companies in AI research is significant and growing. Researchers are calling for more studies on the consequences of this trend, as it could have implications for diversity and innovation in the field. The data suggests that this divergence is expected to continue in the next few years. The researchers were surprised by the extent of this gap, as they had expected some presence from large companies, but not to the degree observed. The reasons behind this trend, beyond the involvement of large tech companies, require further investigation.
AI research in agriculture gaining prominence at conferences: Study reveals growing focus on AI in agriculture at major computer science conferences, raising concerns for potential digital divide between elite and non-elite institutions
The study highlights the increasing presence of farms in AI research at major computer science conferences, which could lead to a gap between elite and non-elite institutions. The researchers identified this as an important area for further investigation, as it could have implications for the innovation ecosystem and potentially create a new form of digital divide. Another limitation of the study was the lack of data from other conferences or journals, which the researchers plan to address in future research. The researchers also plan to explore the consequences of this trend, using machine learning and advanced statistical methods. Additionally, they suggest that policy implications could include the impact on startups without the resources to compete in AI research and the potential consequences for universities and developing countries that may fall behind due to the requirements of large amounts of computing and well-trained computer scientists.
The de-democratization of AI: Large companies and elite universities dominate deep learning research due to computational resources, potentially limiting diversity and innovation. International organizations and other universities should help bridge the gap to the benefit of AI research.
The "de-democratization of AI" is a growing concern as large companies and elite universities dominate the field of deep learning research due to their access to vast computational resources. This divide could lead to negative consequences, including a lack of diversity and innovation in the field. International organizations and other universities may need to find ways to help bridge this gap and ensure that more institutions and actors have equal opportunities to contribute to AI research. Listeners interested in this topic can read the paper "The de-democratization of AI" for more technical details. It's available to explore online. We hope you enjoyed this episode of "Let's Talk AI," and don't forget to check out our website, SkynitToday.com, for more articles on similar topics and to subscribe to our weekly newsletter. Be sure to leave us a rating and a review if you like the show, and stay tuned for future episodes.
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