以下是一些主要从事数据处理，将文档转化为 AI 可理解内容的公司：
1.19.AI is already delivering major advances and efficiencies in many areas.AI quietly automates aspects of our everyday activities,from systems that monitor traffic to make our commutes smoother,17 to those that detect fraud in our bank accounts.18 AI has revolutionised large-scale safety-critical practices in industry,like controlling the process of nuclear fusion.19 And it has also been used to accelerate scientific advancements,such as the discovery of new medicine20 or the technologies we need to tackle climate change.212.20.But this is just the beginning.AI can be used in a huge variety of settings and has the extraordinary potential to transform our society and economy.22 It could have as much impact as electricity or the internet,and has been identified as one of five critical technologies in the UK Science and Technology Framework.23 As AI becomes more powerful,and as innovators explore new ways to use it,we will see more applications of AI emerge.As a result,AI has a huge potential to drive growth24 and create jobs.25 It will support people to carry out their existing jobs,by helping to improve workforce efficiency and workplace safety.26 To remain world leaders in AI,attract global talent and create high-skilled jobs in the UK,we must create a regulatory environment where such innovation can thrive.3.21.Technological advances like large language models(LLMs)are an indication of the transformative developments yet to come.27 LLMs provide substantial opportunities to transform the economy and society.For example,LLMs can automate the process of writing code and17 Transport apps like Google Maps,and CityMapper,use AI.18 Artificial Intelligence in Banking Industry:A Review on Fraud Detection,Credit Management,and Document Processing,ResearchBerg Review of Science and Technology,2018.19 Accelerating fusion science through learned plasma control,Deepmind,2022;Magnetic control of tokamak plasmas through deep reinforcement learning,Degrave et al.,2022.
随着模型规模和自然语言理解能力的进一步增强（扩大训练规模和参数就行），我们可以预期非常多的专业创作和企业应用会得到改变甚至是颠覆。企业的大部分业务实际上是在“销售语言”——营销文案、邮件沟通、客户服务，包括更专业的法律顾问，这些都是语言的表达，而且这些表达可以二维化成声音、图像、视频，也能三维化成更真实的模型用于元宇宙之中。机器能理解文档或者直接生成文档，将是自2010年前后的移动互联网革命和云计算以来，最具颠覆性的转变之一。参考移动时代的格局，我们最终也会有三种类型的公司：1、平台和基础设施移动平台的终点是iPhone和Android，这之后都没有任何机会了。但在基础模型领域OpenAI、Google、Cohere、AI21、Stability.ai还有那些构建LLMs的公司的竞争才刚刚开始。这里还有许多许新兴的开源选项例如Eleuther。云计算时代，代码共享社区Github几乎托管了软件1.0的半壁江山，所以像Hugging Face这种共享神经网络模型的社群，应该也会成为软件2.0时代智慧的枢纽和人才中心。2、平台上的独立应用因为有了移动设备的定位、感知、相机等硬件特性，才有了像Instagram，Uber，Doordash这种离开手机就不会存在的服务。现在基于LLMs服务或者训练Transformer模型，也会诞生一批新的应用，例如Jasper（创意文案）、Synthesia（合成语音与视频），它们会涉及Creator&Visual Tools、Sales&Marketing、Customer Support、Doctor&Lawyers、Assistants、Code、Testing、Security等等各种行业，如果没有先进的Machine Learning突破，这些就不可能存在。