> For the complete documentation index, see [llms.txt](https://synesis.gitbook.io/synesis-academy/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://synesis.gitbook.io/synesis-academy/train2earn/builder-role.md). # Builder Role **Human Language** Human language is complex and nuanced. Words can have ambiguous meanings. There's sarcasm, double-entendres, and innuendo. Sometimes we speak volumes without saying a word, communicating instead through a wry grin or a raised eyebrow. Humans often miss these subtle cues. Imagine then how challenging it must be for a computer to understand humans, especially when you throw in misspellings, emojis, and slang? **Building a Better Bot** Big Tech’s approach to this problem is based on the premise that the more processing power and the larger the data set, the more proficient and ‘intelligent’ our AI systems will become. Though skilled at detecting patterns in human language, algorithms based on a machine learning approach fall short in natural language *understanding* (the ability to determine intent). That’s because they ignore the logic embedded in human language and rely instead on statistical computations to generate predictive approximations of human intention. In contrast, the Mind AI reasoning engine learns through contextualization and abstract reasoning, just like humans do. This ‘Canonical’ approach gives the Mind AI engine an edge in working out human intention, which is why Mind AI decided to focus on conversational AI for its first commercial application. The Mind AI reasoning engine can reason and draw logical conclusions based on inputs. For example, if I teach the AI that “my phone battery is dead” is a cause for “my phone won’t turn on”, then the AI will understand that a phone needs power from its battery to function. It will know this when it encounters the same issue in the future. To understand human requests across multiple domains, the Mind AI reasoning engine needs a robust database built on natural language inputs. To build this database, Mind AI teamed up with Synesis One to crowdsource the knowledge (through our train2earn App) the AI needs to create a ‘mental map’ of the world. That's where you come in! **How train2earn works** Our architects create campaigns (based on client needs) to crowdsource the domain-specific knowledge needed to answer customer queries. Next, Builders choose which campaigns to work on and use their creativity to come up with ‘utterances’ (from linguistics, the smallest unit of speech) to express every possible way that someone might make a particular query in a given domain. Finally, the Validators review and either reject or approve each submission. Builders whose utterances are approved are paid in SNS tokens. In this way, over time, the Mind AI reasoning engine expands its ‘mental map’ of the world, which will help it contextualize human queries and make logical inferences about human intentions. **Building a Mental Map** The Mind AI engine classifies sentence utterances into three types: specific, general, and entailment. These categories are rooted in linguistic theory but also map on to Mind AI’s unique three-node data structure (called canonicals) that the engine uses to establish logical relations between ontologies (see technical [white paper](https://s3-us-west-2.amazonaws.com/mindwebresources/MindAI_Tech_Whitepaper.pdf)). Mind AI’s linguists have determined that we need approximately 300 good quality, validated utterances with pattern diversity per topic for the AI to grasp the various ways humans might express a given query. Crafting utterances that pass validation and improve the AI can be challenging, especially when you’re just getting started. To help you increase your success rate in train2earn, we’ve put together the following **Builder** **Guidelines**. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://synesis.gitbook.io/synesis-academy/train2earn/builder-role.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.