# [MLSN #5]: Prize Compilation

*Link to original post: https://newsletter.mlsafety.org/p/ml-safety-newsletter-5*

Welcome to the 5th issue of the ML Safety Newsletter. In this special edition, we focus on prizes and competitions:

*   ML Safety Workshop Prize
*   Trojan Detection Prize
*   Forecasting Prize
*   Uncertainty Estimation Prize
*   Inverse Scaling Prize
*   AI Worldview Writing Prize

Awards for Best Papers and AI Risk Analyses
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[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F0dd716ae-06b3-455e-84e7-0445054fddd7_1600x643.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F0dd716ae-06b3-455e-84e7-0445054fddd7_1600x643.png)

This workshop will bring together researchers from machine learning communities to focus on Robustness, Monitoring, Alignment, and Systemic Safety. Robustness is designing systems to be resilient to adversaries and unusual situations. Monitoring is detecting malicious use and discovering unexpected model functionality. Alignment is building models that represent and safely optimize hard-to-specify human values. Systemic Safety is using ML to address broader risks related to how ML systems are handled, such as cyberattacks. To learn more about these topics, consider taking [the ML Safety course](https://course.mlsafety.org/). $100,000 in prizes will be awarded at this workshop. In total, **$50,000** will go to the best papers. A separate **$50,000** will be awarded to papers that discuss how their paper relates to the reduction of catastrophic or existential risks from AI. **The deadline to submit is September 30th.**

Website: [neurips2022.mlsafety.org](https://neurips2022.mlsafety.org/)

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NeurIPS Trojan Detection Competition
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[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F195b8a7e-6609-411f-8906-666d1231aa06_2488x982.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F195b8a7e-6609-411f-8906-666d1231aa06_2488x982.png)

The Trojan Detection Challenge (hosted at NeurIPS 2022) challenges researchers to detect and analyze Trojan attacks on deep neural networks that are designed to be difficult to detect. The challenge is to identify whether a deep neural network will suddenly change behavior if certain unknown conditions are met. This **$50,000** competition invites researchers to help answer an important research question for deep neural networks: How hard is it to detect hidden functionality that is trying to stay hidden? Learn more about our competition with this [Towards Data Science article](https://towardsdatascience.com/neural-trojan-attacks-and-how-you-can-help-df56c8a3fcdc).

Website: [trojandetection.ai](https://trojandetection.ai)

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Improve Institutional Decision-Making with ML
---------------------------------------------

[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F188d1f86-630a-4035-875e-134914c30831_1052x279.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F188d1f86-630a-4035-875e-134914c30831_1052x279.png)

Forecasting future world events is a challenging but valuable task. Forecasts of geopolitical conflict, pandemics, and economic indicators help shape policy and institutional decision-making. An example question is “Before 1 January 2023, will North Korea launch an ICBM with an estimated range of at least 10,000 km?” Recently, the [Autocast benchmark](https://openreview.net/pdf?id=LbOdQrnOb2q) (NeurIPS 2022) was created to measure how well models can predict future events. Since this is a challenging problem for current ML models, a competition with **$625,000** in prizes has been created. The warm-up round (**$100,000**) ends on February 10th, 2023.

Website: [forecasting.mlsafety.org](https://forecasting.mlsafety.org/)

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Uncertainty Estimation Research Prize
-------------------------------------

[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd27cca03-607e-4cc1-8ce4-ce1f94821a0b_1052x279.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd27cca03-607e-4cc1-8ce4-ce1f94821a0b_1052x279.png)

ML Systems often make real-world decisions that involve ethical considerations. If these systems can reliably identify moral ambiguity, they are more likely to proceed cautiously or indicate an operator should intervene. This **$100,000** competition aims to incentivize research on improved uncertainty estimation by measuring calibrated ethical awareness. Submissions are open for the first iteration until May 31st, 2023.

Website: [moraluncertainty.mlsafety.org](https://moraluncertainty.mlsafety.org/)

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Inverse Scaling Prize
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[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F47d6b5e4-08b0-48d4-a262-6e7e54f32d10_1200x628.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F47d6b5e4-08b0-48d4-a262-6e7e54f32d10_1200x628.png)

Scaling laws show that language models get predictably better as the number of parameters, amount of compute used, and dataset size increase. This competition searches for tasks with trends in the opposite direction: task performance gets monotonically, predictably worse as the overall test loss improves. This phenomenon, dubbed “inverse scaling” by the organizers, highlights potential issues with the current paradigm of pretraining and scaling. As language models continue to get bigger and used in more real-world applications, it is important that they are not increasingly getting worse or harming users in yet-undetected ways. There is a total of **$250,000** in prizes. The final round of submissions closes before October 27, 2022.

Website: [github.com/inverse-scaling/prize](https://github.com/inverse-scaling/prize)

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AI Worldview Prize
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[![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcfbab58e-62e5-461a-b6c4-048403c8dd53_1172x604.png)](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcfbab58e-62e5-461a-b6c4-048403c8dd53_1172x604.png)

The FTX Future Fund is hosting a competition with prizes ranging from **$15,000** to **$1,500,000** for work that informs the Future Fund’s fundamental assumptions about the future of AI, or is informative to a panel of independent judges. In hosting this competition, the Future Fund hopes to expose their assumptions about the future of AI to intense external scrutiny and improve them. They write that increased clarity about the future of AI “would change how we allocate hundreds of millions of dollars (or more) and help us better serve our mission of improving humanity’s longterm prospects.”

Website: [ftxfuturefund.org/announcing-the-future-funds-ai-worldview-prize](https://ftxfuturefund.org/announcing-the-future-funds-ai-worldview-prize/)

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In the next newsletter, we will cover empirical research papers and announce a new $500,000 benchmarking competition.

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