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他妈的
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吸鸡巴
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比克迪克
粉红色的山雀
粉红色的猫
他妈的
吸大鸡巴
角质母狗
小猫
吸 黑色 公鸡
轮奸小猫
钢棒 黑发 女孩
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粉红色的山雀
粉红色的猫
他妈的
吸大鸡巴
角质母狗
小猫
吸 黑色 公鸡
轮奸小猫
钢棒 黑发 女孩
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他妈的 粉红色 猫
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粉红色的山雀
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他妈的
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小猫
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吸鸡巴
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比克迪克
粉红色的山雀
粉红色的猫
他妈的
吸大鸡巴
角质母狗
小猫
吸 黑色 公鸡
轮奸小猫
钢棒 黑发 女孩
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吸鸡巴
大黑公鸡
比克迪克
粉红色的山雀
粉红色的猫
他妈的
吸大鸡巴
角质母狗
小猫
吸 黑色 公鸡
轮奸小猫
钢棒 黑发 女孩
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他妈的
吸大鸡巴
角质母狗
小猫
吸 黑色 公鸡
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Evaluate Red crew tests and worry-screening can surface area situations of unique dangers, but in output customers may have numerous queries in Bing with diverse levels of lookup intent. To better recognize and deal with the likely for pitfalls in Bing’s generative AI experiences, we created metrics to measure opportunity danger of dangerous information becoming revealed to people. The metrics are employed for new aspect evaluation being a Section of Responsible AI testimonials together with ongoing monitoring for options submit start. We also enabled measurement at scale as a result of partly automatic measurement pipelines. Every time the merchandise alterations, existing mitigations are current, or new mitigations are proposed, we update our measurement pipelines to assess both equally product overall performance as well as dependable AI metrics. Our measurement pipelines allow us to quickly carry out measurement for potential threats at scale. As we recognize new troubles with the preview time period and ongoing crimson team testing, we keep on to develop the measurement sets to assess further risks. Mitigate As we discovered potential pitfalls and misuse via processes like purple staff tests and tension-testing and measured them While using the modern techniques explained higher than, we developed supplemental mitigations to These useful for conventional research. Underneath, we describe some of All those mitigations. We will continue monitoring the generative AI ordeals in Bing to boost product effectiveness and mitigations. Phased launch, continual evaluation. We've been devoted to learning and enhancing our responsible AI solution continually as our systems and consumer habits evolve. Our incremental launch method has become a core Portion of how we move our engineering safely in the labs into the whole world, and we’re devoted to a deliberate, considerate method to safe the key benefits of generative AI features in Bing. We've been routinely earning alterations to generative AI capabilities in Bing to enhance item efficiency, increase present mitigations, and put into practice new mitigations in reaction to our learnings during the preview time period.
A technique we do This really is by getting rid of webpages which were reviewed by credible agencies (or determined through Microsoft PhotoDNA material detection applications) and found to have or relate towards the sexual exploitation or abuse of children.
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Like other transformational technologies, harnessing the many benefits of AI just isn't risk-no cost, plus a Main Portion of Microsoft’s Liable AI program is designed to detect likely hazards, measure their propensity to occur, and Develop mitigations to handle them. Guided by our AI Rules and our Responsible AI Common, we sought to establish, evaluate, and mitigate likely dangers whilst securing the transformative and useful takes advantage of which the new practical experience offers. During the sections beneath we explain our iterative method of identify, measure, and mitigate possible risks. Determine With the design stage, our get the job get more info done began with exploratory analyses of GPT-four inside the late summer of 2022. This involved conducting substantial pink crew tests in collaboration with OpenAI. This tests was designed to assess how the most recent technological innovation would operate with none extra safeguards applied to it. Our certain intention right now was to make dangerous responses, area likely avenues for misuse, and determine abilities and constraints. Our put together learnings across OpenAI and Microsoft contributed to developments in product growth and, for us at Microsoft, educated our understanding of risks and contributed to early mitigation strategies for generative AI characteristics in Bing. Together with product-amount red workforce tests, a multidisciplinary crew of gurus performed numerous rounds of application-stage purple staff testing over the earlier Copilot in Bing AI ordeals before making them publicly offered inside our limited release preview. This method helped us superior know how the process may very well be exploited by adversarial actors and increase our mitigations. Non-adversarial anxiety-testers also extensively evaluated new Bing attributes for shortcomings and vulnerabilities. Submit-release, the new AI ordeals in Bing are built-in into your Bing engineering Business’s current generation measurement and screening infrastructure. One example is, red group testers from distinct regions and backgrounds consistently and systematically try to compromise the system, and their conclusions are accustomed to increase the datasets that Bing employs for enhancing the method.
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