[DataFramed AI Series #3] GPT and Generative AI for Data Teams

Wed May 10 2023

Generative AI

Generative AI is democratizing fields that needed specialized knowledge before.
Generative AI can generate code and manipulate it to make it more efficient.
Generative AI can analyze data sets and identify key features.
Generative AI can help with non-data use cases such as writing presentations, summarizing data, and suggesting visualizations.
Editing code written by GPT or other generative AI models is similar to editing code written by colleagues.
Data analysts and those in code-heavy roles will likely use generative AI to generate a lot of code.
Generative AI can be used for project management, creating reports, and developing lesson plans.
Half the job of being a data scientist involves working with stakeholders, getting projects into production, writing business cases, and making presentations.

AI can be used for structure presentations and paraphrasing things.
Drawing a shield with different things in the quarters to represent your life is a good exercise.
GPT language models are the runaway success story and still the favorite of many data practitioners.
There are other models like Dali that can draw images and Ritrado that generates different images of you in various art styles.
Managers have more responsibility to think about AI ethics and use it safely for appropriate purposes.
Writing a good assessment question has all the same key features as writing a good prompt.

Writing a good assessment question is similar to writing a good prompt.
Generative AI should not be used for state secrets or personally identifiable information.
Ignoring Generative AI's existence is not an option.
Banning Generative AI would be counterproductive in the long run.
Asking good questions and critically evaluating information are important skills for data scientists using generative AI.
Being specific and detailed in asking questions leads to more specific and detailed answers from generative AI.
It's important to critically evaluate responses from generative AI, test code, and look up sources on critical information.
Templates can be used for routine communication with plug-ins as needed.
There is fear that some jobs will be completely replaced by generative AI.

Not all jobs will be completely replaced by AI, some tasks will be automated away.
Jobs will shift rather than completely disappear.
AI and humans working together is a productive approach, as seen in medicine studies.
There are no cases where AI should never be used, but it shouldn't do everything.
Privacy or security problems are the biggest blocker to using AI.
Be careful about putting information about people into AI models.
If you wouldn't want something on the front page of a newspaper, don't put it in public-facing data.
Think about the impacts if something becomes public or if you get a wrong answer when using AI.
AI has implications for education and can help people learn new skills.

AI has big implications for education, acting as a private tutor and providing tailored learning.
Personalization, interactivity, and adaptability are key features of AI-powered learning.
AI can quiz learners on job interviews and evaluate their responses.
Asking the right questions and critically evaluating information remain important skills in data analysis despite the use of AI.
The use of AI reduces rote memorization but requires deeper understanding of natural language processing and deep learning for those who want to work with it at a higher level.

Generative AI requires NLP and computer vision skills.
Awareness of generative AI is important for data professionals.
NLP and deep learning will become more important in the future.
Fundamental skills like logic, creativity, and adaptability are more important than specific technologies.
Prompt engineering involves asking the right questions and giving context.
Prompt engineering is a skill that everyone needs, but may be a specific job in some organizations.
Kubrick trains data professionals in machine learning engineering and other related skills.

AI ethics is a big issue when it comes to using AI for assessment.
Using chat GPT on an exam is closer to plagiarism than automating something.
It's important to set expectations around the use of AI in assessments.
Collaboration with humans is more beneficial than copying from them.
There are opportunities for developing new computational chemistry with large language models in life sciences.
Automating document classification and supply chains can be more efficient.
The barrier to entry for adopting AI has never been lower, so data managers and teams should try it out.
Keep perspective on the potential of technology revolutions.