You know, standard benchmarks are a big deal, man. They’re like the agreed upon ways of measuring important product qualities, and they exist in so many fields. For example, when a car manufacturer says they got a “five-star overall safety rating,” they’re talking about a benchmark. Now, in the world of machine learning and AI, we got the MLPerf benchmarks that measure the speed of cutting-edge AI hardware, you know, like Google’s TPUs. But here’s the thing, man, when it comes to AI safety, we still don’t have those standard benchmarks.
But hey, we got some good news. There’s this non-profit called MLCommons Association, and they’re setting out to change that. They’re on a mission to develop standard AI safety benchmarks, and we’re stoked to support their efforts. This is gonna require bringing together experts from academia and industry, man, to develop these benchmarks and make sure they’re effective and trusted. We’re talking about measuring the safety of AI systems in a way that everyone can understand, you know? And we’re calling on the whole community to get involved, from AI researchers to policy experts. Let’s all contribute to this important effort, man.
Why do we need AI safety benchmarks, man?
Now, AI is a powerful technology with some awesome benefits, but listen, it can also have some negative outcomes if we’re not careful. We’re talking about things like harmful use, biased responses, and all that jazz. So, these standard AI safety benchmarks, man, they can help us harness the benefits of AI while making sure we take enough precautions to avoid those risks. At first, these benchmarks will drive AI safety research and responsible development, you know? But as they mature, they can also inform users, purchasers, and even policy makers. It’s like benchmarks in computer hardware, man, they align the whole industry and drive progress. We can do the same with AI safety, bro.
What makes a good AI safety benchmark, man?
Alright, so there’s been some cool experiments with AI safety tests, you know? We got tests that measure fairness, bias, toxicity, and all that stuff. But here’s the deal, most of these tests are limited to the prompts they provide and the datasets they use. We need to go beyond that, man. That’s where MLCommons comes in. They’re proposing a process to select tests and group them into subsets that measure safety for specific AI use-cases. And they wanna translate those technical results into scores that everyone can understand. It’s like a one-stop-shop for AI safety testing, bro. You can do it online or offline, whatever floats your boat.
AI safety is a collective effort, man
Now, when it comes to AI safety, responsible developers use all kinds of safety measures, you know? We’re talking about automatic testing, manual testing, red teaming, restrictions, best practices, auditing, and more. But determining if we’re taking enough precautions can be a challenge, especially as the AI community keeps growing. That’s where these AI safety benchmarks come in, man. They can help us measure and improve AI safety in a collective way. We need researchers, engineers, companies, and even public advocates and policy makers to come together, bro. We gotta incorporate multiple perspectives and build trusted benchmarks that everyone can rely on.
Google’s got your back, man
You know, at Google, we’re all about responsible AI development. It’s part of our AI Principles, man. We wanna make sure AI is safe, secure, and trustworthy, and we’re taking specific practices to make that happen. So, we’re supporting the MLCommons Association in their efforts to develop AI safety benchmarks. We’re not just talking, man. We’re providing funding for a testing platform, technical expertise, and resources. We’re even sharing our datasets and contributing to the research. We’re all in, bro.
The future is looking bright, man
I gotta tell you, these AI safety benchmarks are gonna be a game-changer, man. They’re gonna advance research, ensure responsible development, and help us create new generative AI tools in a responsible way. And it’s not just MLCommons, you know? We got other groups like the Frontier Model Forum and Partnership on AI leading the charge too. It’s gonna take a collective effort, bro. Let’s promote responsible AI development and make the world a better place.
Shoutout to the Google team, man
Big thanks to our awesome Google team for contributing to this work. You guys rock, man! We’re talking about Peter Mattson, Lora Aroyo, Chris Welty, Kathy Meier-Hellstern, Parker Barnes, Tulsee Doshi, Manvinder Singh, Brian Goldman, Nitesh Goyal, Alice Friend, Nicole Delange, Kerry Barker, Madeleine Elish, Shruti Sheth, Dawn Bloxwich, William Isaac, and Christina Butterfield. Keep doing what you’re doing, guys!