As AI continues to evolve rapidly, it becomes increasingly important to document and analyze the most impactful models that have shaped the landscape. By providing a comprehensive overview of these notable models—detailing their benchmark performance, citation impact, historical significance, and real-world applications—this resource offers valuable insights into the factors driving AI innovation.

This dataset highlights prominent machine learning models, where a model qualifies as notable if it fulfills any of the following conditions: (i) achieves state-of-the-art performance on a widely recognized benchmark; (ii) garners over 1000 citations; (iii) holds historical importance; or (iv) sees substantial real-world application.

Source:kaggle 

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Q1.What are the most common domains for AI models, and how do they vary by organization type (e.g., academia vs. industry)?

Q2.Which countries are most frequently associated with the development of notable AI models, and how does this distribution change over time?

Q3.Are there any patterns in the types of hardware used for training AI models, and how does hardware utilization vary across different models?

Q4.What is the relationship between the number of citations and the notability criteria of AI models?

Q5.How does the model accessibility (e.g., open access, API access) relate to the notability criteria and training compute cost?

Q6.What are the trends in the publication of AI models over time, and how do these trends relate to the development of frontier models?

Q7.Which companies are the top 10 most used AI models from? 

Here are the results!

Q1.What are the most common domains for AI models, and how do they vary by organization type?

Domain Frequency

• Language: The most common domain with a frequency of 373.

• Vision: Second most common, with a frequency of 282.

• Image Generation: Has a frequency of 54.

• Speech and Multimodal: Both have similar frequencies of 48 and 46, respectively.

Domain by Organization Type

• Academia: Focuses heavily on domains like Biology and Language.

• Industry: Predominantly involved in Language and Vision domains.

• Government and Research Collectives: Have varied interests but less frequency compared to academia and industry.

Visualization Insights

• Language Domain: Dominates across all organization types, especially in academia and industry.

• Vision Domain: Significant in industry, with notable contributions from academia.

• Other Domains: Such as Image Generation and Speech, show more balanced distribution across different organization types.

Conclusion and Insights

• Language and Vision: These are the most prevalent domains for AI models, with strong representation in both academia and industry.

• Diverse Interests: While academia and industry focus on Language and Vision, other domains like Biology and Multimodal are also significant, reflecting diverse research and application interests across different organization types.

Q2.Which countries are most frequently associated with the development of notable AI models, and how does this distribution change over time?

Overall Frequency

• United States of America: The most frequently associated country with AI model development, with a count of 751.

• China: Second in frequency, with 136 occurrences.

• United Kingdom: Third, with 89 occurrences.

• Canada and Multinational: Also notable, with 78 and 71 occurrences respectively.

Yearly Distribution Changes

• 1950s to 1980s: The United States was the primary contributor to AI model development.

• 1990s to 2000s: Gradual increase in contributions from other countries like China, the United Kingdom, and Canada.

• 2010s to 2020s: Significant increase in contributions from multiple countries, with the United States maintaining a leading position.

Visualization Insights

• Trend Over Time: The line chart shows a sharp increase in AI model development contributions from the United States in recent years, with other countries also showing upward trends.

• Diverse Contributions: The chart highlights the growing diversity of countries involved in AI development, especially post-2000.

Conclusion and Insights 

• Dominance of the United States: The United States remains the dominant country in AI model development.

• Increasing Global Participation: There is a clear trend of increasing global participation, with more countries contributing to AI advancements over time.

Q3.Are there any patterns in the types of hardware used for training AI models, and how does hardware utilization vary across different models?

Hardware Types

• Most Common Hardware: The most frequently used hardware for training AI models includes Google TPU v3, NVIDIA V100, and NVIDIA A100, with Google TPU v3 being the most prevalent at a frequency of 47.

• Diverse Options: There is a wide variety of hardware types used, indicating a diverse ecosystem of hardware options for AI training.

Hardware Utilization 

• Mean Utilization: The average hardware utilization across models is approximately 0.37.

• Median Utilization: The median utilization is slightly lower at 0.359, suggesting a skew towards higher utilization in some cases.

• Standard Deviation: The standard deviation is 0.108, indicating moderate variability in utilization rates.

Visualization Insights

• Frequency Distribution: The bar chart shows a clear preference for certain hardware types, with a steep drop-off in frequency after the top few options. 

• Utilization Distribution: The box plot reveals that most utilization values are clustered around the higher end, with a few outliers at the lower end.

Conclusion and Insights

• Hardware Preferences: There is a clear preference for specific high-performance hardware types, likely due to their efficiency and capability in handling complex AI models.

• Utilization Variability: While utilization is generally high, there is some variability, which could be influenced by the specific requirements of different AI models or the efficiency of the hardware used.

Q4.What is the relationship between the number of citations and the notability criteria of AI models?

Analysis of Notability Criteria and Citations

• Highly Cited Models: Models categorized under 'Highly cited' have an average of 10,715 citations. This indicates that models recognized for their high citation count tend to have a substantial number of citations.

• Historical Significance: When combined with 'Highly cited', models with 'Historical significance' have the highest average citations at 73,839. This suggests that historical significance, when paired with high citation counts, greatly enhances the model's notability.

• SOTA Improvement: Models noted for 'SOTA improvement' have an average of 5,054 citations. This shows that state-of-the-art improvements are recognized but may not reach the citation levels of historically significant models.

• Training Cost: Models with 'Training cost' as a criterion have an average of 30,858 citations, indicating that the cost of training is a significant factor in the model's recognition and citation count.

 Conclusion and Insights 

• Historical Significance and Citations: Models that are both highly cited and historically significant receive the highest number of citations, suggesting a strong correlation between historical impact and citation count.

• Training Cost Impact: The training cost is a notable factor in the citation count, indicating that models with significant training costs are often recognized and cited more frequently.

Q5.How does the model accessibility (e.g., open access, API access) relate to the notability criteria and training compute cost?

Relationship Between Model Accessibility, Notability Criteria, and Training Compute Cost

Model Accessibility and Notability Criteria

• API Access: Models with API access show a high frequency of notability criteria, particularly in areas like SOTA improvement and significant use.

• Hosted Access (No API): This type has fewer notability criteria compared to API access, with notable criteria being less frequent.

• Open Access: Different types of open access (non-commercial, restricted, unrestricted) show varied frequencies of notability criteria, generally lower than API access.

• Unreleased Models: These have minimal notability criteria, indicating limited recognition or impact.

Model Accessibility and Training Compute Cost

• API Access: Models with API access have the highest average training compute cost, indicating significant resources are invested in these models.

• Hosted Access (No API): These models have a moderate compute cost, less than API access but higher than most open access types.

• Open Access: The compute cost varies, with non-commercial and restricted use having moderate costs, while unrestricted access has the lowest.

• Unreleased Models: These have a relatively low compute cost, reflecting their limited development or deployment.

Visual Insights

The training compute cost chart highlights the substantial investment in API access models, with a significant drop for other accessibility types.

Conclusion and Insights

• Investment and Impact: Models with API access are both highly notable and resource-intensive, suggesting a strong correlation between investment in compute resources and model impact.

• Diverse Accessibility: Open access models, while less costly, show varied notability, indicating potential for impact without high resource investment. Unreleased models remain low in both cost and notability, reflecting limited exposure or development.

Q6.What are the trends in the publication of AI models over time, and how do these trends relate to the development of frontier models? 

Publication Trends

• Steady Growth: From 1950 to around 2000, the number of AI model publications remained relatively low and stable, with an average of about 13.76 publications per year.

• Significant Increase: Post-2000, there is a noticeable increase in publications, peaking at 104 in recent years. This suggests a growing interest and advancements in AI technologies.

Frontier Models

• Lack of Data: There is no available data on the development of frontier models, making it difficult to directly analyze trends in this area.

Time Series Plot

• Exponential Growth: The plot shows a sharp rise in AI model publications starting around 2010, indicating rapid advancements and increased research activity.

• Recent Decline: A drop is observed in the most recent year, which could be due to incomplete data or other external factors.

Conclusion and Insights

• AI Model Growth: The publication of AI models has grown significantly over the past two decades, reflecting technological advancements and increased research focus.

Q7.Which companies are the top 10 most used AI models from?

Top AI Models and Associated Companies

AI Models and Their Companies

• ResNet-110 (CIFAR-10): Developed by Microsoft with 172,714 citations.

• ResNet-152 (ImageNet): Also from Microsoft, sharing the same citation count of 172,714.

• ADAM (CIFAR-10): Associated with University of Amsterdam, OpenAI, and University of Toronto, having 139,989 citations.

• AlexNet: Created by University of Toronto, with 112,228 citations.

• Transformer: Developed by Google Research and Google Brain, with 104,993 citations.

• VGG19: From University of Oxford, with 93,036 citations.

• VGG16: Also from University of Oxford, sharing the same citation count of 93,036.

• BERT-Large: Developed by Google, with 81,681 citations.

• LSTM: Associated with Technical University of Munich, having 80,987 citations.

• Faster R-CNN: From Microsoft Research, with 55,711 citations.

Conclusion and Insights

• Microsoft is prominently associated with multiple top AI models, indicating its significant influence in AI development.

• Google and its research branches are also key players, particularly with models like Transformer and BERT-Large.

• University of Oxford and University of Toronto have contributed significantly to the development of notable AI models, showcasing the importance of academic institutions in AI research. 

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