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From aerospace to Amazon, Nia Jetter is blazing new paths
“I work in robotics and artificial intelligence. We’re building robots that are going to help the world.” As introductions — or elevator pitches — go, that’s an especially strong one.
That’s how Nia Jetter, senior principal technologist for Amazon Fulfillment Technology Robotics, answers the question: What do you do?
Jetter is an engineer who has been recognized throughout her career for her accomplishments in autonomous systems, so her confidence is earned. Her goals extend beyond developing new algorithms, and include lowering barriers to understanding technology and cultivating a more diverse workforce.
“At Amazon, I am working on laying a foundation for how we build collaborative autonomous systems safely across our robotics platforms,” Jetter notes. “I’m also working on forward looking research on ways to architect and develop safety critical autonomous systems in a way that is verifiable while leveraging techniques like machine learning.”
Jetter’s work is centered on improving components of Amazon’s delivery operations by focusing on embedding best practices into the design process. She believes automation, achieved with artificial intelligence and next-generation robots, can deliver improvements for both Amazon employees and customers.
“People want their packages quickly, including me,” she says with a laugh. “So, when you look at our fulfillment centers, I’m hugely passionate about: What are the ways that we can help my colleagues working there? How can we help our customers?”
To that end, Jetter, along with other scientists and engineers within her organization, is analyzing activities that could be more easily and safely accomplished with robots. In order to support this work, her team and others across Amazon collaborate with a variety of universities, including the University of Washington. Jetter sits on the advisory board for the UW-Amazon Science Hub and also serves as an Amazon research liaison.
“We are working on developing solutions to challenges faced across multiple industries and are working to do so in a scalable fashion by developing in a way that supports modularity. There is a lot of space for innovation in safe autonomy, AI, and robotics,” she said. “I am passionate about pursuing research that can be inserted into products in that space.”
An early love for learning
Jetter displayed engineering talent from a young age.
As a second grader she would find scrap insulated wire at the base of utility poles, and would save quarters given to her by her grandfather for small chores to buy LEDs, light bulbs, and batteries from RadioShack. Her father, a mail carrier, would help her find books that explained electrical circuits. While in elementary school, she used a piece of foam core and her RadioShack purchases to create an illuminated Valentine’s Day card for her science teacher.
Her path shifted toward computer programming while she was still in elementary school. She took a computer class and said her interest was immediately piqued. She began spending her spare moments in the computer room writing programs in HyperCard, soon followed by Fortran, Pascal, and C.
“I loved programming at school,” she says. “I would go on my lunch hour and stay after school. Looking back, while at the time I did not think of it as something I would do for a career, I realize I was good at it.”
In high school, she received a letter from MIT encouraging her to apply to the MIT Introduction to Technology, Engineering, and Science (MITES) program. At the time, the program took 50 high school students and brought them to campus to take intense science and engineering classes and to familiarize them with the institute.
He didn’t see me as a black girl who was good at math. He saw me as a mathematician. That meant the world to me.
Jetter said the magnitude of the potentially life-changing opportunity was not immediately evident to her, namely because she had never heard of MIT. “Little did I realize that that letter, and attending the MITES program, would become a significant part of my origin story as an engineer,” she noted.
Her experience with MITES led directly to enrolling at MIT. She intended to study biochemical engineering, but while there she was exposed to more advanced math and computer science classes and found that she loved them. Her career path was set when, in her sophomore year, she took an artificial intelligence class with the late Patrick Henry Winston, her future mentor and then director of the MIT Artificial Intelligence Laboratory.
“There are several points in my journey where I met people who saw more in me than I saw in myself, people who filled a gap for me through exposure to what was possible. Professor Winston saw me as a scientist and a mathematician first, and encouraged me to push the envelope and be all that I could be.
“He didn’t see me as a black girl who was good at math. He saw me as a mathematician. That meant the world to me,” Jetter says.
A lifelong science fiction fan, Jetter also set her sights on working for NASA. She interned there for three summers.
“When I was on the atmospheric experiments team, I recognized that their algorithms could be improved. I’m not sure they took the suggestion from an intern seriously, but I wrote a paper explaining what I saw, and I gave it to the department head,” she recalled. “The next Monday, he came into the office and told me to get started.
“What I learned from my NASA internships was the value of being a mathematician or a computer scientist. I learned that every team needs a computer scientist.”
Before her graduation from MIT in 2000, a chance encounter with a recruiter from Hughes Space and Communications (acquired in October 2000 by Boeing) convinced her to work there on a project involving automated controls. Although she had some early challenges, she quickly realized she could solve those by drawing on her own experiences.
“I derived mathematical models and eventually I was asked to ‘Derive the gains for the controller.’ At the time I had no idea what that meant. I was fortunate to be taught by leaders in the field and quickly learned that a controller is very analogous to an intelligent agent in how it needs to perceive, make decisions and act on its environment. That work led me to enroll at Stanford in 2005 to get a master’s degree in aeronautical and astronautical engineering while I worked at Boeing.”
A milestone moment
In 2013, her work at Boeing led to her being honored as a Boeing associate technical fellow – the first tier of the Technical Fellowship. At the Boeing facility in El Segundo, California, in what is called the “hall of flags,” there is a wall with photos of the Boeing technical fellows.
“From when I first saw the wall, I knew that one day my face would be on it. I’ll never forget the day I walked down the hall and my photo was up! I was the first black woman with my face on the wall at my site. I didn’t realize the photo would mean so much to me, but when I first saw it on the wall, it really stood out.”
In 2020, Jetter made what she admitted was a hard decision. “I decided to leave aerospace in order to be able to innovate faster and to see the fruits of innovation sooner.” She knew that kind of opportunity existed at Amazon, and joined the company in January 2021 to work with the robotics team.
“While I thought that I was making a decision to leave aerospace, I was actually making a decision to expand my expertise in autonomy and AI. So much of the work that I do now is enabled by my aerospace foundation. What excites me about robotics and artificial intelligence at Amazon is the opportunity to truly change the game, change how we do things for an additional set of customers,” Jetter said.
Blazing a trail
As a leader in AI and robotics, Jetter says many people approach her with interest in pursuing a similar path, asking whether they can emulate her. Many of those who approach her have what is for her a familiar experience: a lack of exposure.
“This has inspired me because I am often approached by people who clearly have the aptitude but have not been exposed to a mechanism — including tools they need to progress down the path. Sometimes they just need exposure to people who look like them going down the path. As a result, in addition to building a solid tech foundation, when mentoring I focus on exposure, encouragement, and helping people see things that they might not see in themselves.”
That’s also why diversity matters for human beings solving complex science and engineering problems. If you have diverse perspectives in the room, you can arrive at the optimal solution for the target customer faster.
To lower the barriers to entry, Jetter makes time to provide guidance to others. She does this in a number of ways, including small group mentoring sessions that she calls “Shades of Tech”. In addition, earlier this year Jetter spearheaded the Amazon in the City Responsible AI Panel with support from Amazon’s Inclusive Experiences and Technology team. The event brought together “leaders from within and outside Amazon to share perspectives on the importance of fairness in tech as AI-based technology is developed and deployed.”
Along with Jetter, attendees heard from Nashlie Sephus, principal AI/ML evangelist with Amazon Web Services; Chad Jenkins, associate chair of undergraduate studies and professor of robotics at the University of Michigan; and Nii Simmonds, non-resident fellow at the Center For Global Development. The panelists spoke about responsible AI and the impact of diversity in the workforce.
Jetter drew on her own past experiences when pondering the initiative.
“There are certain types of optimization algorithms where, when you’re optimizing, you get to a point at which you’re actually converging on a local solution, as opposed to the global solution. And in order to get to the global solution, you actually need to inject variety – you have to inject diversity in your dataset.
“That’s also why diversity matters for human beings solving complex science and engineering problems. If you have diverse perspectives in the room, you can arrive at the optimal solution for the target customer faster.”
What is artificial intelligence?
In another effort to expand access, Jetter created a series of YouTube videos explaining automation and artificial intelligence called “Thinque Bytes.”
“I feel very fortunate to be where I am today. I want to provide exposure to enable as many people as possible who might not have easy access to the knowledge and the technology to learn and eventually have impact in these fields.”
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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