Events & Conferences
Making deep learning practical for Earth system forecasting
The Earth is a complex system. Variabilities ranging from regular events like temperature fluctuations to extreme events like drought, hailstorms, and the El Niño–Southern Oscillation (ENSO) phenomenon can influence crop yields, delay airline flights, and cause floods and forest fires. Precise and timely forecasting of these variabilities can help people take necessary precautions to avoid crises or better utilize natural resources such as wind and solar energy.
The success of transformer-based models in other AI domains has led researchers to attempt applying them to Earth system forecasting, too. But these efforts have encountered several major challenges. Foremost among these is the high dimensionality of Earth system data: naively applying the transformer’s quadratic-complexity attention mechanism is too computationally expensive.
Most existing machine-learning-based Earth systems models also output single, point forecasts, which are often averages across wide ranges of possible outcomes. Sometimes, however, it may be more important to know that there’s a 10% chance of an extreme weather event than to know the general averages across a range of possible outcomes. And finally, typical machine learning models don’t have guardrails imposed by physical laws or historical precedents and can produce outputs that are unlikely or even impossible.
In recent work, our team at Amazon Web Services has tackled all these challenges. Our paper “Earthformer: Exploring space-time transformers for Earth system forecasting”, published at NeurIPS 2022, suggests a novel attention mechanism we call cuboid attention, which enables transformers to process large-scale, multidimensional data much more efficiently.
And in “PreDiff: Precipitation nowcasting with latent diffusion models”, to appear at NeurIPS 2023, we show that diffusion models can both enable probabilistic forecasts and impose constraints on model outputs, making them much more consistent with both the historical record and the laws of physics.
Earthformer and cuboid attention
The heart of the transformer model is its “attention mechanism”, which enables it to weigh the importance of different parts of an input sequence when processing each element of the output sequence. This mechanism allows transformers to capture spatiotemporally long-range dependencies and relationships in the data, which have not been well modeled by conventional convolutional-neural-network- or recurrent-neural-network-based architectures.
Earth system data, however, is inherently high-dimensional and spatiotemporally complex. In the SEVIR dataset studied in our NeurIPS 2022 paper, for instance, each data sequence consists of 25 frames of data captured at five-minute intervals, each frame having a spatial resolution of 384 x 384 pixels. Using the conventional transformer attention mechanism to process such high-dimensional data would be extremely expensive.
In our NeurIPS 2022 paper, we proposed a novel attention mechanism we call cuboid attention, which decomposes input tensors into cuboids, or higher-dimensional analogues of cubes, and applies attention at the level of each cuboid. Since the computational cost of attention scales quadratically with the tensor size, applying attention locally in each cuboid is much more computationally tractable than trying to compute attention weights across the entire tensor at once. For instance, decomposing along the temporal axis can result in cost reduction by a factor of 3842 for the SEVIR dataset, since each frame has a spatial resolution of 384 x 384 pixels
Of course, such decomposition introduces a limitation: attention functions independently within each cuboid, with no communication between cuboids. To address this issue, we also compute global vectors that summarize the cuboids’ attention weights. Other cuboids can factor the global vectors into their own attention weight computations.
We call our transformer-based model with cuboid attention Earthformer. Earthformer adopts a hierarchical encoder-decoder architecture, which gradually encodes the input sequence to multiple levels of representations and generates the prediction via a coarse-to-fine procedure. Each hierarchy includes a stack of cuboid attention blocks. By stacking multiple cuboid attention layers with different configurations, we are able to efficiently explore effective space-time attention.
We experimented with multiple methods for decomposing an input tensor into cuboids. Our empirical studies show that the “axial” pattern, which stacks three unshifted local decompositions along the temporal, height, and width axes, is both effective and efficient. It achieves the best performance while avoiding the exponential computational cost of vanilla attention.
Experimental results
To evaluate Earthformer, we compared it to six state-of-the-art spatiotemporal forecasting models on two real-world datasets: SEVIR, for the task of continuously predicting precipitation probability in the near future (“nowcasting”), and ICAR-ENSO, for forecasting sea surface temperature (SST) anomalies.
On SEVIR, the evaluation metrics we used were standard mean squared error (MSE) and critical success index (CSI), a standard metric in precipitation nowcasting evaluation. CSI is also known as intersection over union (IoU): at different thresholds, it’s denoted as CSI-thresh; their mean is denoted as CSI-M.
On both MSE and CSI, Earthformer outperformed all six baseline models across the board. Earthformer with global vectors also uniformly outperformed the version without global vectors.
| Model | #Params.(M) | GFLOPS | Metrics | |||
| CSI-M↑ | CSI-219↑ | CSI-181↑ | MSE(10-3)↓ | |||
| Persistence | – | – | 0.2613 | 0.0526 | 0.0969 | 11.5338 |
| UNet | 16.6 | 33 | 0.3593 | 0.0577 | 0.1580 | 4.1119 |
| ConvLSTM | 14.0 | 527 | 0.4185 | 0.1288 | 0.2482 | 3.7532 |
| PredRNN | 46.6 | 328 | 0.4080 | 0.1312 | 0.2324 | 3.9014 |
| PhyDNet | 13.7 | 701 | 0.3940 | 0.1288 | 0.2309 | 4.8165 |
| E3D-LSTM | 35.6 | 523 | 0.4038 | 0.1239 | 0.2270 | 4.1702 |
| Rainformer | 184.0 | 170 | 0.3661 | 0.0831 | 0.1670 | 4.0272 |
| Earthformer w/o global | 13.1 | 257 | 0.4356 | 0.1572 | 0.2716 | 3.7002 |
| Earthformer | 15.1 | 257 | 0.4419 | 0.1791 | 0.2848 | 3.6957 |
On ICAR-ENSO, we report the correlation skill of the three-month-moving-averaged Nino3.4 index, which evaluates the accuracy of SST anomaly prediction across a certain area (170°-120°W, 5°S-5°N) of the Pacific. Earthformer consistently outperforms the baselines in all concerned evaluation metrics, and the version using global vectors further improves performance.
| Model | #Params.(M) | GFLOPS | Metrics | ||
| C-Nino3.4-M↑ | C-Nino3.4-WM↑ | MSE(10-4)↓ | |||
| Persistence | – | – | 0.3221 | 0. 447 | 4.581 |
| UNet | 12.1 | 0.4 | 0.6926 | 2.102 | 2.868 |
| ConvLSTM | 14.0 | 11.1 | 0.6955 | 2.107 | 2.657 |
| PredRNN | 23.8 | 85.8 | 0.6492 | 1.910 | 3.044 |
| PhyDNet | 3.1 | 5.7 | 0.6646 | 1.965 | 2.708 |
| E3D-LSTM | 12.9 | 99.8 | 0.7040 | 2.125 | 3.095 |
| Rainformer | 19.2 | 1.3 | 0.7106 | 2.153 | 3.043 |
| Earthformer w/o global | 6.6 | 23.6 | 0.7239 | 2.214 | 2.550 |
| Earthformer | 7.6 | 23.9 | 0.7329 | 2.259 | 2.546 |
PreDiff
Diffusion models have recently emerged as a leading approach to many AI tasks. Diffusion models are generative models that establish a forward process of iteratively adding Gaussian noise to training samples; the model then learns to incrementally remove the added noise in a reverse diffusion process, gradually reducing the noise level and ultimately resulting in clear and high-quality generation.
During training, the model learns a sequence of transition probabilities between each of the denoising steps it incrementally learns to perform. It is therefore an intrinsically probabilistic model, which is well suited for probabilistic forecasting.
A recent variation on diffusion models is the latent diffusion model: before passing to the diffusion model, an input is first fed to an autoencoder, which has a bottleneck layer that produces a compressed embedding (data representation); the diffusion model is then applied in the compressed space.
In our forthcoming NeurIPS paper, “PreDiff: Precipitation nowcasting with latent diffusion models”, we present PreDiff, a latent diffusion model that uses Earthformer as its core neural-network architecture.
By modifying the transition probabilities of the trained model, we can impose constraints on the model output, making it more likely to conform to some prior knowledge. We achieve this by simply shifting the mean of the learned distribution, until it complies better with the constraint we wish to impose.
Results
We evaluated PreDiff on the task of predicting precipitation intensity in the near future (“nowcasting”) on SEVIR. We use anticipated precipitation intensity as a knowledge control to simulate possible extreme weather events like rainstorms and droughts.
We found that knowledge control with anticipated future precipitation intensity effectively guides generation while maintaining fidelity and adherence to the true data distribution. For example, the third row of the following figure simulates how weather unfolds in an extreme case (with probability around 0.35%) where the future average intensity exceeds μτ + 4στ. Such simulation can be valuable for estimating potential damage in extreme-rainstorm cases.
Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?
Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.
They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’
Download or listen to the episode below:
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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
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Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.
This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.
Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.
“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”
“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”
ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.
The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.
AI for Information Security
| Recipient | University | Research title |
| Christopher Amato | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Bernd Bischl | Ludwig Maximilian University of Munich | Improving Generative and Foundation Models Reliability via Uncertainty-awareness |
| Shiqing Ma | University Of Massachusetts Amherst | LLM and Domain Adaptation for Attack Detection |
| Alina Oprea | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Roberto Perdisci | University of Georgia | ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection |
Automated Reasoning
| Recipient | University | Research title |
| Nada Amin | Harvard University | LLM-Augmented Semi-Automated Proofs for Interactive Verification |
| Suguman Bansal | Georgia Institute of Technology | Certified Inductive Generalization in Reinforcement Learning |
| Ioana Boureanu | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Omar Haider Chowdhury | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Stefan Ciobaca | Alexandru Ioan Cuza University | An Interactive Proof Mode for Dafny |
| João Ferreira | INESC-ID | Polyglot Automated Program Repair for Infrastructure as Code |
| Sicun Gao | University Of California, San Diego | Monte Carlo Trees with Conflict Models for Proof Search |
| Mirco Giacobbe | University of Birmingham | Neural Software Verification |
| Tobias Grosser | University of Cambridge | Synthesis-based Symbolic BitVector Simplification for Lean |
| Ronghui Gu | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Alexey Ignatiev | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Kenneth McMillan | University of Texas At Austin | Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification |
| Alexandra Mendes | University of Porto | Overcoming Barriers to the Adoption of Verification-Aware Languages |
| Jason Nieh | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Rohan Padhye | Carnegie Mellon University | Automated Synthesis and Evaluation of Property-Based Tests |
| Nadia Polikarpova | University Of California, San Diego | Discovering and Proving Critical System Properties with LLMs |
| Fortunat Rajaona | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Subhajit Roy | Indian Institute of Technology Kanpur | Theorem Proving Modulo LLM |
| Gagandeep Singh | University of Illinois At Urbana–Champaign | Trustworthy LLM Systems using Formal Contracts |
| Scott Stoller | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Peter Stuckey | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Yulei Sui | University of New South Wales | Path-Sensitive Typestate Analysis through Sparse Abstract Execution |
| Nikos Vasilakis | Brown University | Semantics-Driven Static Analysis for the Unix/Linux Shell |
| Ping Wang | Stevens Institute of Technology | Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database |
| John Wawrzynek | University of California, Berkeley | GPU-Accelerated High-Throughput SAT Sampling |
AWS AI
| Recipient | University | Research title |
| Panagiotis Adamopoulos | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Vikram Adve | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Frances Arnold | California Institute of Technology | Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization |
| Yonatan Bisk | Carnegie Mellon University | Useful, Safe, and Robust Multiturn Interactions with LLMs |
| Shiyu Chang | University of California, Santa Barbara | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Yuxin Chen | University of Pennsylvania | Provable Acceleration of Diffusion Models for Modern Generative AI |
| Tianlong Chen | University of North Carolina at Chapel Hill | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Mingyu Ding | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Nikhil Garg | Cornell University | Market Design for Responsible Multi-agent LLMs |
| Jessica Hullman | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Christopher Jermaine | Rice University | Fast, Trusted AI Using the EINSUMMABLE Compiler |
| Yunzhu Li | Columbia University | Physics-Informed Foundation Models Through Embodied Interactions |
| Pattie Maes | Massachusetts Institute of Technology | Understanding How LLM Agents Deviate from Human Choices |
| Sasa Misailovic | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Kristina Monakhova | Cornell University | Trustworthy extreme imaging for science using interpretable uncertainty quantification |
| Todd Mowry | Carnegie Mellon University | Efficient LLM Serving on Trainium via Kernel Generation |
| Min-hwan Oh | Seoul National University | Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits |
| Patrick Rebeschini | University of Oxford | Optimal Regularization for LLM Alignment |
| Jose Renau | University of California, Santa Cruz | Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming |
| Vilma Todri | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Aravindan Vijayaraghavan | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Wei Yang | University of Texas at Dallas | Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing |
| Huaxiu Yao | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Amy Zhang | University of Washington | Tools for Governing AI Agent Autonomy |
| Ruqi Zhang | Purdue University | Efficient Test-time Alignment for Large Language Models and Large Multimodal Models |
| Zheng Zhang | Rutgers University-New Brunswick | AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser |
AWS Cryptography
| Recipient | University | Research title |
| Alexandra Boldyreva | Georgia Institute of Technology | Quantifying Information Leakage in Searchable Encryption Protocols |
| Maria Eichlseder | Graz University of Technology, Austria | SALAD – Systematic Analysis of Lightweight Ascon-based Designs |
| Venkatesan Guruswami | University of California, Berkeley | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| Joseph Jaeger | Georgia Institute of Technology | Analyzing Chat Encryption for Group Messaging |
| Aayush Jain | Carnegie Mellon | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Huijia Lin | University of Washington | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Hamed Nemati | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Karl Palmskog | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Chris Peikert | University of Michigan, Ann Arbor | Practical Third-Generation FHE and Bootstrapping |
| Dimitrios Skarlatos | Carnegie Mellon University | Scale-Out FHE LLMs on GPUs |
| Vinod Vaikuntanathan | Massachusetts Institute of Technology | Can Quantum Computers (Really) Factor? |
| Daniel Wichs | Northeastern University | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| David Wu | University Of Texas At Austin | Fast Private Information Retrieval and More using Homomorphic Encryption |
Sustainability
| Recipient | University | Research title |
| Meeyoung Cha | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
| Jingrui He | University of Illinois at Urbana–Champaign | Foundation Model Enabled Earth’s Ecosystem Monitoring |
| Pedro Lopes | University of Chicago | AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware |
| Cheng Yaw Low | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.
During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.
Evaluation of Nova Premier against PRISM AI
PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.
Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.
The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.
Manual red teaming with ActiveFence
The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.
| Model | 3P Flag Rate [↓ is better] |
| Nova Premier | 12.0% |
| Sonnet 3.7 (non-reasoning) | 20.6% |
| GPT4.1 API | 22.4% |
“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”
These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.
Acknowledgments: Vincent Ponzo, Elyssa Vincent
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