2019, domestic investment and financing in the field of artificial intelligence, enthusiasm greatly reduced, a considerable number of AI companies completely disappeared in the course of history, "AI has been to the cold wave," even as the industry annual hot words.
Compared with previous years, entrepreneurship and investment go hand in hand grand passion, Recently the AI industry is clearly a lot of depression.
The reason, "AI hard landing" should bear the main responsibility.
From the era to the era of intelligent automation, artificial intelligence to create value is growing. At the same time, finesse and complexity of the business scene is also rising, brought a series of challenges for landing artificial intelligence technology.
Domestic enterprises as an example of artificial intelligence. At present, several large artificial intelligence unicorn enterprise, commercial landing mainly in the financial, security monitoring, mobile phone Internet three areas, while other areas are mediocre.
Down to specific business scenarios, automobile autopilot AI is the most important commercial landing field, artificial intelligence-related businesses in unmanned / automated driving a huge investment, but from large-scale commercial application is still very far away.
Currently autopilot main scenario is nothing more than road measuring about exhibition show how, unmanned Park test drive, but these obviously can not bring any substantial revenue for a for-profit enterprise.
Car autopilot is still some distance from the large-scale commercial
Long-term health of firms' survival earnings, AI companies were no exception. AI put in front of a public company is the most urgent practical needs, the breakdown of how "hard landing AI" dilemma.
As the old saying goes, "started the trouble should end it," the key to breaking AI ground hard, to find what factors led to this result.
In the field of artificial intelligence, algorithms, and data are considered force three major elements constitute an important foundation for the industry. For a long time, the focus of AI business concern focused on the areas of algorithms and calculate power, the data field of the attention is generally low.
In fact, as the basis for the industry of artificial intelligence, AI data in the process of landing in the role apparently been ignored. AI should be applied to specific business scenarios, you first need to address issues related to data acquisition and data governance, data that is specific to the industry in the labeling industry needs to take the lead change.
After a picture after the data label (Source: Man Fu Technology data tagging platform)
There is a simple but important consensus within the industry of artificial intelligence:
Directly determine the quality of the final data set is good or bad quality of the model.
在人工智能行业兴起初期，行业关注的重点主要集中于理论与技术本身，此时一种前沿的技术概念都有可能为企业带来规模庞大的外部投资。
但是，到了技术相对成熟期，投资人与AI企业关注的重点就转向了技术的商业化落地，毕竟企业与投资人最为看重的还是盈利。
然而，理论与实践的结合总是不那么一帆风顺。AI企业在商业化落地的过程中，发现了一个很棘手的问题：标注数据集的质量可以满足实验室的基本需求，但却无法支撑起AI落地的发展洪流。
我们以实例为证：
在人脸识别等单点场景，涉及到的数据类型一般比较简单。但在更完整的业务场景中，数据就会变得更加复杂起来;
工业场景中，会涉及到工业现场图像数据、工艺流程文本数据和设备运行的时序数据等更加精细化数据的标注;
医疗场景中，对医疗影像和文本的标注，需要具备医学专业知识的人员进行……
以往在实验室里仅需少量且质量尚可的数据集即可满足基本实验的需求，但是到了具体化的商业落地场景中，现实给标注数据集提出了诸多新的要求：
海量、高质量、场景化、定制化、智能化……
高质量标注数据集才能撑起人工智能行业的未来(图片来源：曼孚科技数据标注平台)
在这样的新形势下，破局AI落地难的关键，就在于数据标注行业的率先变革。
作为人工智能行业的基础，数据标注行业长期处于刀耕火种的粗放状态中，披着人工智能的外衣，但是本质上仍然属于劳动密集型产业。
在AI商业化落地的大潮下，数据标注行业不应拖了行业发展的后腿，而应该主动为人工智能行业的发展保驾护航。
以曼孚科技数据标注服务为例，一方面通过培训专业标注团队与提供定制化服务，来解决数据采集、数据标注的质量问题;另一方面，通过自研SaaS数据标注服务平台与自动化的辅助工具，来解决数据标注的效率问题，具体的努力如下：
1. 专业团队打造优质数据服务平台，服务成本降低30%以上;
2. 独立自研SaaS数据标注平台，预标注技术加持下标注效率可提升4倍以上;
3. 实时精确估算与AI辅助筛查，数据精确至99%以上;
4. 支持私有云部署，实时监测加强安全保护;
5. 定制化场景搭建，7X24小时快速技术响应。
通过以上努力，曼孚科技希望重新构建起人工智能行业发展的基石，用高质量的标注数据集破局“AI落地难”的困境，为相关人工智能企业的商业化落地之路扫清障碍。
目前，曼孚科技的标注数据集正大规模应用于自动驾驶、安防、VR/AR、无人机、新零售、AI教育、工业机器人等相关领域，曼孚科技期望用高质量的数据撑起人工智能行业新的未来!