Becoming a Force for Good: Daniel's Story

For programmers and engineers, 教室是学习编程语言等硬技能的好地方. 但是当涉及到软技能时，比如与非技术人员一起工作，技术专业的狭窄限制有时会阻碍学生的发展.

In early 2021, that was a concern for Daniel Monteiro, 12bet官方软件工程计划(SEP)的软件工程师, 一个为期两年的培训项目，帮助年轻的工程师顺利完成从教室到办公室的过渡. Not long after starting with the bank, he realized if he wanted to move ahead in his career, 他需要在掌握硬技能的同时，对软技能有更深的理解. At the same time, 他担心他可能没有机会继续培养他的硬技能——如果他希望保持在他的学科的前沿，这是一个至关重要的问题.

幸运的是，SEP有一个选择来帮助他建立他的技能集的两个方面:为善的力量.

Building Relationships

Force for Good是12bet官方(JPMorgan Chase)的一个项目，由6-8名技术人员组成的团队与一家非营利组织合作8个月. The team meets with the nonprofit, assesses its needs, 并建立一个可以用来推进其使命的技术解决方案.

Force for Good不仅仅是一个帮助有需要的组织的好机会，也是技术人员与最终用户直接联系的机会. With Force for Good, the technologists regularly talk to clients, learn about the client's needs, and work together to decide what to build.

“这段经历帮助我学会了如何理解用户的需求, which is an important skill required to build great products," explains Monteiro.

Getting Real Feedback...And Experience

For Monteiro's first Force for Good project, 他与农村创业与民生基金会(REAL)合作, 这是一家印度非营利组织，致力于为农村社区成员(尤其是女性)提供创业所需的培训.

REAL通过在线模块与参与者联系，这是一个很好的工具，可以接触到遥远地区的参与者. Unfortunately, 获得关于培训模块的有用反馈——如果你打算改进它们，这是必要的, as REAL did—can be difficult. For example, students might not understand a lesson, may not be able to imagine ways to improve it, 或者可能是内向的人，他们不愿意给出明确的答案. REAL needed a way to get useful feedback, 蒙泰罗的团队意识到，机器学习可能是提供这种服务的完美工具.

机器学习是计算机分析数据的一种人工智能, identifies patterns, and makes educated assumptions based on that information. 从本质上讲，它“学习”了一些东西，然后采取行动，几乎没有人类的互动. 垃圾邮件过滤器是机器学习的一个例子，每天都会影响数百万用户.

Unfortunately, designing and creating a machine learning tool takes time, money and expertise—and, like many non-profits, REAL had limited time and resources to apply to the problem. That's where Force for Good came in. By leveraging machine learning, 该团队能够创建一个情感识别系统，可以从用户那里获得有用的反馈, even when they were unable to provide it.

“它会研究学习者每一帧的面部表情，”蒙泰罗说. "While the camera is on, their emotions are tracked on a frame by frame basis, and we learn if they're happy, sad, confused, or surprised."

Obviously, the participants have to consent to being watched, and have to turn their camera on to participate. At the end of a session, 机器学习反馈被发送给培训师，以了解参与者在整个课程中情绪的变化情况, particularly if they became distracted at any point.

更好地了解学生在哪里失去重点或误解了主题, teachers can modify the way they provide the material. REAL can rewrite lessons to improve student comprehension, 这使得非营利组织能够在更短的时间内帮助更多的人, and enact more change in the world.

REAL的内部技术团队仍在努力推出整体培训平台，最终将配备面部识别组件, so Monteiro hasn't seen the results of his work in the field, but he's excited about the potential. “To help more people is just awesome," he says. “I'm excited to see how it's going to play out."

Learning and Growing

技术是不断变化的，所以技术人员的技能需要不断发展. Prior to this project, Monteiro hadn't worked with machine learning at JPMorgan Chase, although he knows it's a skill that's in high demand, and will help keep him on the cutting edge.

In terms of soft skills, 他在这个项目中结交了朋友，并与其他情况下他永远不会遇到的人进行了互动. 他说:“这很有成就感，尤其是考虑到我可以把它作为我工作的一部分。. “我一直想确保我能在世界上产生社会影响. 为善的力量让我能够尽自己的一份力量来帮助弱势群体，帮助真正伟大的非营利组织，比如REAL work.“他期待着在未来的另一个原力项目中工作.

即使在他的项目结束后，蒙泰罗对改善世界的兴趣仍在继续. "We spend loads of time at work, 通常都忽略了当今世界上存在的许多问题," he says. “行善的力量让我对世界上正在发生的事情有了更多的了解, and made me want to figure out ways I can help." After his project ended, he volunteered as an English teacher with Teach for India, where he taught 15 year-olds.

在他不断发展的技术知识和他对社区的新拓展之间, Monteiro is well on his way to becoming his own force for good.