问答：发展中国家的AI必须是适应性和低成本的

即使是技术局外人也听到了有关人工智能的炒作 - 无论是促进新药发现还是早期疾病检测，这似乎每周都有一个有希望的人工智能（AI）申请。

作为依靠强大的计算能力和受过良好教育的软件工程师的干预措施，AI可以轻松地加深世界上富裕和低收入地区之间的分裂。

但是，印度非营利组织Wadhwani AI等组织寻求避免这种结果。该小组正在开展三个项目：帮助棉花农民通过更明智的害虫管理减少农作物的损失；为农村医疗保健工人提供配备计算机视觉的智能手机，以筛选新生儿的危险低出生体重（无需数据或网络连接）；并通过更准确地估计危险因素和区域案件量来解决结核病。

“政府，实施机构和计划需要了解AI可以做什么以及通过AI无法解决问题的问题。”

Wadhwani AI高级计划总监Neeraj Agrawal

Wadhwani AI高级计划主任Neeraj Agrawal与scidev.net关于他的组织如何对低收入社区的AI解决方案以及如何在其他地方扩展这些解决方案。

新兴技术通常被视为高增加解决方案。Wadhwani的工作如何成为具有成本效益的AI解决方案的模型？

We pay a lot of attention to making our AI-based solutions potentially adaptable to a variety of contexts and believe that AI-based solutions must work within existing broader systems and programmes, with minimal additional input.

We continuously interact and engage with all stakeholders — beneficiaries, users and decision makers — to identify big problems worth solving. Most importantly, our solutions are available free of cost for implementation and scaling-up in contexts where these are needed.

AI成本要缩减需要做些什么，以使其更像是一个平易近人，可用的选择？

Several things. Governments, implementation agencies and programmes need an understanding of what AI can do and what problems cannot be solved through AI. Systems and programmes need to be able to prioritise their problems. Stakeholders including donors, development partners, research organisations and governments need to come together not only to develop partnerships, but also to identify and develop sustainable business models around AI solutions.

Wadhwani如何选择孕产妇/新生儿健康，棉花种植和结核病作为重点领域？

We interacted with a variety of stakeholders in the areas of health and agriculture to gather insights around current status, data, issues around service delivery, client perspectives and programmes. For example, along with looking at a lot of data, we engaged and gathered insights from 30 global tuberculosis experts for deciding to take on TB as a thrust area.

We typically ask seven questions that serve as a set of anchoring principles to help us decide if a project can benefit from the use of modern AI. Is this a big problem? Does it have an AI solution? Will solving the AI part make enough of a difference? Will the solution be accepted by stakeholders? Does the data exist, or can it be created easily enough? Are there partner organisations that can co-create and pilot the solution? Are there existing programmes and pathways to scale?

您是否看到了每个重点领域的结果或可测量的差异？

我们在棉花养殖和基于智能手机的人体测量法（对人体进行测量）的最初结果非常有前途，我们正在进行现场实验。最近，我们被任命为印度中央结核部（CTD）的官方AI合作伙伴[卫生和家庭福利部的一部分]。我们很高兴使用人工智能和机器学习来帮助CTD到2025年结束印度结核病的威胁。

超越成本，让当地用户在现场采用或测试AI解决方案面临哪些挑战？Wadhwani打算如何克服这些挑战？

With our field experiments, the initial challenges we are facing include availability of mobile and data networks, hesitancy in using new technology, and tech illiteracy. Several community-based healthcare providers do not own a smartphone, and are unable to use app features due to poor literacy. “Trainability” of farmers and frontline health workers was also quite challenging initially.

To overcome these challenges, we are working on making the solutions as intuitive as possible, so that a minimum amount of orientation can help end-users start using them. We are also working to make these solutions work without network connectivity.