科林·安德森

2023年4月25日

图片来源:科林·安德森作品企业有限公司加入高管在旧金山7月11 - 12,听到领导人是如何整合和优化AI投资成功。California-headquartered公司了解更多Arize AI提供机器学习可观测性能力,今天宣布凤凰城,一个开源库监控大型语言模型(llm)幻觉。该行业软件解决方案之际,改良llm周围和数据科学家大型基础模型适用于新的用例,包括那些涉及医学和法律数据——即使是最轻微的幻觉或偏见可以创建在现实世界中一个主要的问题。它被设计成一个独立的提供交付毫升可观察性数据科学笔记本环境中的数据科学家构建模型,该公司表示。凤凰帮llm到底如何?大型语言模型像OpenAI GPT-4和谷歌的吟游诗人风靡的今天,数据科学家和ML工程师赛车上构建应用程序。这可以是任何东西,从一个虚拟产品律师提供法律建议医疗chatbot旨在总结医患会议或提供现有的保险信息。事件转换2023加入我们在旧金山7月11 - 12,高管将分享他们如何集成和优化AI投资成功,避免了常见的陷阱。现在,这些应用程序可能非常有效,模型运行仍然容易受到幻觉——他们可能产生虚假或误导的结果。凤凰城,今天宣布在Arize艾未未的观察2023年峰会上,通过可视化复杂LLM决策和目标这个问题何时何地模型失败,出错,给可怜的反应,或错误地推广。 “Phoenix runs locally, in an environment that interfaces with Notebook cells on the Notebook server. Its library uses embeddings (vectors representing meaning and context of data points that the model processes and generates) and clustering of those embeddings as a method for data visualization and debugging,” Jason Lopatecki, CEO and co-founder of Arize, tells VentureBeat. In the real world, this means a user just has to upload the chatbot conversation – complete with prompts and responses – and start the software. The library will automatically leverage the foundational embeddings (mapping out how they connect, how they are related to and how they progress as sentences are generated) and LLM-assisted evaluation to generate scores for responses and visualize them to show where the bot gave a good response and where it failed. Phoenix visualizing LLM responses As the visualization is produced, the user can investigate, grab groups of responses representing a problem (like questions from Spanish-speaking end-users where the LLM responded incorrectly) and troubleshoot for fine-tuning the model and improving its outcomes. “Once in a notebook environment, the downloaded data can power observability workflows that are highly interactive. Phoenix can be used to find clusters of data problems and export those clusters back to the observability platform for use in monitoring and active learning workflows,” Lopatecki added. It can also help surface issues like data drift for generative AI , LLMs, computer vision and tabular models, the company noted. Rapidly evolving space While Arize AI claims that Phoenix, which is available starting today, is the first software library designed to help with LLM evaluation and risk management, enterprises should keep in mind that this is a rapidly evolving space with new players cropping up almost every day. “The current generation of AI models is a black box to almost everyone. Almost no one understands how they do what they do. Phoenix is the first step to building software that helps map out the internals of how these models think and what decisions they are making, designed for the users of LLMs,” the CEO said. He added that over 100 users and researchers at different companies and organizations advised on the development of Phoenix with initial feedback being quite positive. Christopher Brown , CEO and co-founder of Decision Patterns and a former UC Berkeley lecturer, called the solution a “much-appreciated” advancement in model observability and production. He said the integration of observability utilities directly into the development process not only saves time but encourages model development and production teams to actively think about model use and ongoing improvements before releasing to production. VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings. Most Read