Awesome AutoGTP part 1: generate post

AutoGTP is an experimental open-source application showcasing the capabilities of the GPT-4 language model.

While experimenting with the current version (v0.3.1), I accomplished already promising results. However, there are some pitfalls and lessons learned.

The goal is to write a blog post on the role ‚The analytics Tranlator’; see the result below.

ChatGPT constraints and lessons learned

• Version 0.31, no plugins
• Installation with docker is straightforward.
• Be aware that the speaking module does not work within the docker container. By the design philosophy of docker, this isn’t easy.
• Always go with the budget limit for your Open AI call; this can be enforced via a ai_settings.yaml file, see at the end
• it is beneficial to use specific instructions with a settings file, see the specification at the end of this post
• the links are all broken. I will fix this in part 2 via plugins.
• AutoGTP tends to get stuck in loops; try out your commands with the manual first

Finally, remark: unbelievable this will get of high relevance for us all

— — — — — — — — — — — — BEGIN RESULT AUTO GPT

Subtopic 1: Responsibilities

An analytics translator is a professional who bridges the gap between data scientists and business stakeholders. They are responsible for translating complex data science concepts into business language that can be easily understood by non-technical stakeholders. The role of an analytics translator is becoming increasingly important as more and more companies are investing in data science to gain a competitive advantage.

Subtopic 1: Responsibilities

An analytics translator is responsible for the following:

- Communicating with business stakeholders to understand their needs and requirements
— Translating complex data science concepts into business language
— Presenting data insights to business stakeholders in a way that is easy to understand
— Collaborating with data scientists to ensure that their work aligns with business goals

For example, an analytics translator might work with a marketing team to help them understand the results of a customer segmentation analysis. They would translate the technical jargon into language that the marketing team can understand and use to make informed decisions.

Subtopic 2: Skills

An analytics translator requires a unique set of skills that combines technical expertise with business acumen. The following skills are essential for an analytics translator:

- Strong communication skills
— Data science knowledge
— Business acumen
— Project management skills
— Critical thinking skills

For example, an analytics translator might need to communicate complex data science concepts to a non-technical stakeholder. They would need to have strong communication skills to ensure that the stakeholder understands the concept and its implications.

Subtopic 3: Education and Training

An analytics translator requires a combination of technical and business education and training. The following are some of the educational and training requirements for an analytics translator:

- Bachelor’s degree in a technical field such as computer science, statistics, or mathematics
— Master’s degree in business administration or a related field
— Training in data science and analytics
— Training in business and management

For example, an analytics translator might have a bachelor’s degree in computer science and a master’s degree in business administration. They would have received training in both data science and business to prepare them for the role of an analytics translator.

Subtopic 4: Career Path

The career path for an analytics translator can vary depending on the industry and the company. The following are some of the career paths for an analytics translator:

- Data analyst
— Business analyst
— Data scientist
— Analytics manager

For example, an analytics translator might start their career as a data analyst and then move into the role of an analytics manager as they gain more experience.

Subtopic 5: Future of the Role

The role of an analytics translator is becoming increasingly important as more and more companies are investing in data science. The following are some of the trends that are shaping the future of the role:

- Increased demand for data-driven decision making
— Need for collaboration between data scientists and business stakeholders
— Emergence of new technologies such as machine learning and artificial intelligence

For example, an analytics translator might need to work with data scientists to develop machine learning models that can be used to make predictions about customer behavior.

Subtopic 6: Tools

An analytics translator can use a variety of tools to help them perform their job more effectively. The following are some of the tools that an analytics translator might use:

- Data visualization tools such as Tableau or Power BI
— Statistical analysis tools such as R or Python
— Business intelligence tools such as SAP or Oracle
— Project management tools such as Asana or Trello

For example, an analytics translator might use Tableau to create a dashboard that visualizes the results of a customer segmentation analysis. They would use R or Python to perform statistical analysis on the data and SAP or Oracle to access business intelligence data.

Sources:

[1](https://www.datasciencecentral.com/profiles/blogs/the-role-of-the-analytics-translator-in-data-science) [2](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-analytics-translator)

— — — — — — — — — — — — END RESULT AUTO GPT

The command to run it:

docker-compose run -rm auto-gpt -gpt3only -ai-settings data/ai_settings_md.yaml

ai_settings_md.yaml file with content:

ai_name: analytics-translator-md
ai_role: an AI designed to fill results for analytics translator in a markup language autonomously
ai_goals:
— design the content to explain the role of an analytics translator
— write all the content into the file auto_gpt.md
— structure the content into 5 subtopics
— a subtopic should always have a short executive summary
— a subtopic should have an example in a second paragraph
— for each subtopic add the link at the end where the information is from
— ensure multiple sources for a subtopic
— go subtopic by subtopic and append the content to the file
api_budget: 0.2

Originally published at https://theanalyticstranslator.org on May 23, 2023.