GPT for hardware Engineering

A workflow study.

Wednesday, Mar 8th, 2023

Generative Artificial intelligence (GenAI) has kick-started 2023 with a bang. But it has yet to prove its value for hardware engineers.

The limitation? Math and logic.

Although LLM based tools like OpenAI’s ChatGPT, Microsoft Bing, and Google’s Bard are proving the first mass use of AI’s generative powers, they have proven already that what they say is not to be trusted.

Screenshot from Google's Bard announcement embarrassingly claiming that JWST took the first pictures of an Exoplanet. There are two mistakes in this sentence.

From what we can see, these tools still don’t work well with Math, logic, and facts. However, I have isolated three main great abilities for Engineering work:

Generating good enough quality text, tables, lists and very basic code (reliably).
Analyzing bodies of text. This is great for: summarizing text, tagging, semantic analysis, interconnecting information, and much more.
Transforming text. That being style transfer (change the writing style, personality, cadence, language, etc. of a text), turning text into bullet points, tables, etc. Merging multiple sources of text.

Ok, I’m being exhaustive here, but this will be useful for the account below.

Cheaper APIs

Recently, OpenAI announced their GPT 3.5 turbo API. They called it ChatGPT API and can be found here: https://openai.com/blog/introducing-chatgpt-and-whisper-apis

Why is this important? Well: price.

Not only is this API accessing the same model used in the ChatGPT product, it is 10x cheaper than the other existing GPT-3.5 models.

GPT + Google Sheets

Turns out, the new API is already available in the GPTforWork Google Docs Add-On that you can find here: https://gptforwork.com/.

To get started, you need to:

create an account at OpenAI
Click View API key
Create a new secret key
install GPTforwork add-on on your Google Sheets + Docs
Add the key

There are a few GPT Google Sheets functions. But we will mainly be using two: GPT() and GPT_TABLE().

All the available functions from GPTforWork Google Sheets add-on.

Desigining a space mission with GPT and Google Sheets

Be advised that this mission is a simplified version and would require many more iterations and detail. However, it can give you a glimpse of how fast one can build this type of documents.

So let’s begin. First let me be clear on the goals of this article, in fact there are two goals:

1. Design a space mission – an Orbiter Spacecraft that will search for landing and drilling sites on Jupiter’s Moon Europa.

So basically, we will be designing something analogous to the Europa Clipper mission. More on this real life mission here: https://europa.nasa.gov/.

Artist's illustration of NASA’s Europa Clipper spacecraft, which will perform multiple flybys of the ocean-harboring Jupiter moon. (Image credit: NASA/JPL-Caltech)

Heat and materials from Europa’s interior might be released through hydrothermal vents on the moon’s ocean floors. Warm water rising toward the base of the icy shell could cause cracks and other features, such as diapirs, while large chunks of the surface — ice rafts — may detach and float to new locations. Plumes could spout the ocean’s contents high above the moon, while radiation, impacts, and sunlight can all cause changes on the ice from above. Astronomy: Roen Kelly

A concept for a Europa lander samples the moon’s surface in this artist’s rendering, which also shows a plume erupting in the background and the gas giant Jupiter hanging in the sky. NASA/JPL-Caltech

This way, the Clipper Mission will serve as a reference for what this new mission could be. This will serve as the “source for fact control” for this AI experiment.

2. Create a reusable workflow – This one is important as ideally this workflow should be useful not only in other space mission designs but in other hardware industry projects such as:
- 🚀 space engineering
- 🎮 consumer electronics
- 🚗 automotive
- 🦾 robotics
- 🏗️ construction.

The template

The Project Overview
This page will work as a way to write in the context of the project. It will have a summary cell that will be inserted in every query we do to GPT. Yes, you read that well, since we are using GPT’s API, it doesn’t save context, so you need to explain everything in each query.

The cool part about using GPT is that we don’t need to preserve any type of data structure, as long as we provide enough information on the project. This is helpful if we want to use this page to describe a consumer electronics product, a robotic submarine, the design plan for a house with 3 bedrooms, etc.

The Project Overview page with the Summary cell below.

Architecture
Let’s use all that context from the project to ask GPT’s API to create an architecture layout for us.
We will use the GPT_TABLE() function. Here’s how it will work:

The Architecture page with the AI generated table. We will reuse this information later.

Here's the GPT's API query breakdown. You need to “talk” to GPT in an organized manner to get the best generation possible.

Requirements
One of the first activities that is needed for a Preliminary mission design report is a “System concept and requirements document”. We will go through this in a simplified way for illustrative purposes.

For a more detailed workflow, you can check out Valispace’s features here: https://www.valispace.com/ai/

The Requirements page. Each “sub-requirement” is generated based on the main table and the Project Overview.

Yep, that's a long query. To keep things tidy, try writing the query before in a structured way. In this case, we are referencing three data sources + binder text to ask GPT a very composed question.

Semantic matching
In my experience, this is a major GPT use-case: matching data with semantics.

The end result: a table of 125 subsystem requirements with the matching subsystem assigned.

Here's how to build a page like this. See that In this case we are referencing two lists generated from tables.

Concept of operations
Now, as I mentioned before, GPT is not a source of science. And asking it for a CONOPs took some time, a many iterations. It would sometimes hallucinate strange actions like “Aero-break capture in Europa’s Atmosphere”. In the end, this type of data should not be generated by an AI (yet).

Here's the final CONOPs using a VEEGA plan. Yep, GPT is good at presentation, but it is no scientist.

Sidenote
However, I have some ideas on how eventually training a GPT analog with libraries such as Poliastro can turn astronavigation into an automated task (Space Autopilot?).

Here’s a plan I made in Poliastro using ChatGPT as a reviewer for a flight plan to Jupiter using an Earth flyby for gravity assist (I used the dates from the Juno mission).

You can check out the Colab document here: https://colab.research.google.com/drive/1fU0VNmFFJk5wxjwE2L0teErH3ur7r--I?usp=sharing

Check out Poliastro here: https://docs.poliastro.space/en/stable/

The Google Colab notebook I used to create this flight plan.

Project plan
Alright, let's wrap this in a nice plan. This was the most expensive single query. Also, I did misspell “apparatus” and GPT just trusted me and replicated that word everywhere.

The final table for the Project plan. Notice how it takes into account the mission duration and breaks it down in a semi-realistic manner.

Here's the decent prompt to generate the Project Plan table.

Cost analysis

Although OpenAI is doing an amazing job in scaling the GPT API and therefore lowering the price, it is still somewhat expensive.

In the end, working on this file cost me not much less than $1. Mainly because I used a few dozen of prompts, and because the prompts refresh when the document is reloaded.

As you can see below, the number of requests grew exponentially as the document grew in number of cells and iterations.

Things to avoid reducing cost:
- “Bake” your generations and add your prompt as a comment. This avoids the queries firing every time the document is reloaded or opened.
- Avoid “drag and drop”, especially when using linked queries (like the ones I did above), as your token usage will exponentiate.

Here's the cost breakdown for this document. See the amount of requests per hour increasing exponentially.

Conclusion

GPT can definitely be used for engineering projects as a “delta” increase in productivity. It is great at extrapolating explanations from data and organizing explanations into data. However, we still need a mix between GPT and a rigid scientific, logical, and mathematical engine.

In the meantime, be my guest to copy this template, I’m looking forward to seeing if you find it useful and what you will use it for.

Get the template here:

Or go to this link:
https://docs.google.com/spreadsheets/d/1ukF0TIF17nZ7e3yN3yZCqw8Y7xu3-DT-k-tPcdmZjzk/edit?usp=sharing

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