Surfing the AI wave, a hackathon recap

Welcome to another edition of what we’re determined to make the best damn newsletter in AI. Here we’ll break down AI topics that matter, open your mind to use cases, and keep you ahead of the curve.

Our #1 goal is to be useful. So please shoot us an email 📩 if you have questions or feedback, and especially if you implement something we share!

Here's what we're covering today:

• AI hackathon debrief & what you can learn from it

• A step-by-step video for categorizing support emails using GPT in Google Sheets

• Reading list focused on law and AI: are they partners or at odds?

Let’s get to it! 👇

TODAY'S PERSPECTIVE

How to surf the AI wave

Watching AI adoption the past 6 months has been fascinating.

On one hand - we are incredibly early. The products themselves are actively being built (which we’ll cover that more below). On the other hand - people are adopting AI at a neck-breaking pace.

The combination of those forces has made us all ask - How do I best ride this wave? How do I stay ahead, not get left behind?

This weekend, we went to an AI Hackathon. If you’re not familiar with hackathons, think of it as a brainstorming and problem-solving party where people come together to create new products, usually software.

What's happening at hackathons can help us have the right mindset for adopting AI:

1. Balance between fun and business applications.

Inspiration can take many forms. We saw projects from AI generated rap battles to a bot that will scour GitHub, find engineering tasks and solve them, and lots in between.

2. Rethink everything.

Don't accept how we've been operating. We saw teams ask - what does social media look like with generative AI, or a software backend that’s just GPT 😳.

3. Everyone is researching.

Yesterday's shortcomings are today's research questions. Many smart teams are working on improving ChatGPT’s memory length, to what really constitutes the best prompts for models like ChatGPT and other big questions

4. A push to solve real problems.

Connecting startup energy and real world problems is more difficult than you’d think.

While there are talented teams with niche backgrounds building things like Copilot for Chemists, there are many talented teams eager to find meaningful problems to work on.

We spent a lot of our time chatting with these teams, and sharing what you - the AI Exchange community - have shared as your messiest problems, most labor-expensive time sucks, etc. that you face in your industry.

So keep sending ‘em. 👍 And we’ll keep trying to make connections where we can. 

USE CASE DEEP DIVE

Categorize support questions in seconds, no big data needed

You guys have been loving our Google Sheets for GPT videos and tutorials. So, we wanted to share another way we are using this in our business:

Categorizing (aka classifying) support questions for further analysis.

Problem we wanted to solve: We needed a way to take emails, categorize them, and understand what kind of questions we are getting to inspire future content.

Solution: Export emails into a Google Sheet and use GPT to categorize for us! 👇

We've created an 8 minute step by step video tutorial showing exactly how we do this with easy to follow steps and even some prompt research techniques 😉.

Let us know if you found it helpful and would like us to continue creating videos like this!

LINKS

For your reading list 📚

AI and the law today...

• DoNotPay is launching a ChatGPT extension that reads the Terms and Conditions and Lease Agreements and will flag anything that is non-standard to their customers

• Technical walk through of the potential implications of the Stable Diffusion lawsuit - includes a compelling argument that if the plaintiffs win then AI art generation models will likely only be affordable and feasible for large creative organizations like Pixar that own enough media to train their own models on

OpenAI is continuing to make headlines...

• ChatGPT users report slowly gaining access to ChatGPT Pro for $42/month

• OpenAI and Microsoft have extended their partnership into a multi-year, multi-billion dollar investment in pursuit of their mission to ensure advanced AI benefits all of humanity

And if you're really nerdy ...

• For those interested in legal applications of AI - this paper focuses on Catala, a programming language used to describe legal constructs, which could enable generation of this language from legal text via generative AI

• This paper proposes GLIGEN (grounded-language-to-image generation), a novel approach that seeks to extend the functionality of existing diffusion models by enabling conditioning of grounding inputs

That's all!

We'll see you again on Thursday. Thoughts, feedback and questions are much appreciated - respond here or shoot us a note at [email protected]. 

Cheers,

🪄 The AI Exchange Team