Shumai (Meta)

最简单的方式来构建先进的人工智能聊天机器人。我们的平台使开发人员容易和非开发人员构建聊天机器人,可以在自然语言与用户沟通。(The simplest way to build advanced AI chat bots. Our platform makes it easy for developers and non-developers alike to build chatbots that can communicate with users in natural language.)

加速你的视力AI模型创建。合成数据是一种廉价的替代真实世界数据用于训练和提高人工智能模型。为了培养准确的人工智能模型,大量的数据是必要的。使用真实的3 d模型可以很容易地创建合成人工智能分类和数据对象检测。(Accelerate Your Vision AI Model Creation. Synthetic data is an inexpensive alternative to real world data that is used to train and improve AI models. In order to train accurate AI models, a large amount of data is needed. With use of realistic 3D models you can easily create synthetic data for AI classification and object detection.)

RTutor是一个基于ai的应用程序,可以快速生成R和测试代码。由API调用OpenAI达芬奇(ChatGPT的兄弟姐妹),RTutor将自然语言转换为R脚本,然后在闪亮的执行平台。R减价可以生成源文件和HTML报告。看看这里的github回购:https://github.com/gexijin/RTutor(RTutor is an AI-based app that can quickly generate and test R code. Powered by API calls to OpenAI's Davinci (ChatGPT's sibling), RTutor translates natural languages into R scripts, which are then executed within the Shiny platform. An R Markdown source file and HTML report can be generated. Check out the github repo here: https://github.com/gexijin/RTutor)

问答对任何Github库。输入一个Github库,我们将下载到服务器并创建嵌入的代码。回购需要公开,< 200文件,< 100 mb。(Question answering against any Github repository. Input a Github repository, we will download it to the server and create embeddings against the code. Repos need to be public, <200 files, and <100MB.)

Nuclia自动索引你的任何内部和外部来源的非结构化数据,提供优化的搜索结果。它可以处理视频和音频转录、图像内容提取,和文档解析。(Nuclia automatically indexes your unstructured data from any internal and external source, providing optimized search results. It can handle video and audio transcription, image content extraction, and document parsing.)

常识的机器提供了api、接口和开放源码软件综合输入和经验转化为数字内容创建和AI培训模拟器。我们相信,学习世界模型生成系统的路径实现AGI通讯社,类似于一个孩子对其世界从经验中学习。(Common Sense Machines provides APIs, interfaces, and open source software to translate multi-modal inputs and experiences into a digital simulator for AI training and content creation. We believe that learning generative world models is a systematic path towards achieving AGI, similar to how a child learns about its world from experience.)