INBOX INSIGHTS: AI Task Force, Red Teaming Custom GPTs, Part 2 (1/17) :: View in browser
Creating an AI Task Force in Your Business
You don’t need another meeting, another committee, and another task force. 99% of the time, committees get in the way of getting work done. They are poorly executed and rarely move things forward. And 100% of the time they start with good intentions and spiral out of control with personal agendas, too many members, and office politics.
Sounds fun, right? So why am I about to tell you that you need a new committee?
Because if you are implementing AI in your business, you need an AI task force.
If your business is on the smaller side, like less than 20 people, this won’t really be applicable. You should have more control and transparency within your culture. As your company gets larger you have less sight into what’s going on with teams, departments, and processes.
I like to say, “new tech, old problems”. In the case of AI, this is true. Dropping a generative AI platform on top on your business isn’t going to solve existing issues. IF anything, it’s going to introduce new risks, vulnerabilities, and create more issues.
So, how do we fix this? With an AI task force.
I know. Another meeting, another committee. How we set ourselves up for success?
Clear Purpose and Goal
This is where you should start, before you assemble your super squad. It seems like a no brainer. What do we want to do with AI? Why are we bringing it into the company? How do we want to get there? Here’s where it goes wrong. Setting a clear purpose and goal is like setting a New Year’s resolution. It’s great to make them but they are useless if you don’t keep them. Part of setting the goals is determining how you’re going to reach those goals. This is the work that you need to do before you start the AI task force. This could be an elaborate spreadsheet with milestones. More realistically, it’s choosing someone whose sole purpose on the task force is to keep the task force on track. That’s it. That is their whole role.
Diverse and Skilled Team Members
After setting your purpose and goals, getting the right people in the room is the next most important key to success. Start with the person who is going to keep the train on the track. This can be a project manager, an admin, or someone like me who is just incredibly bossy. Then you want to think about the voices that need to contribute. Depending on your goals with AI you will want as much representation of from your company as possible. Fair warning, this is where it gets unwieldy. For example, if you designate each department head a member of the task force you also need to have them designate their proxy. Just one. Not every single member of their team should show up to the task force. You want to create consistency and that means not changing out members all the time. To that, you want to make sure you don’t just bring in the same cast of characters that sit on every other committee. Consider asking some team members that aren’t decision makers but are in the weeds of the day to day. They will bring a fresh and relevant perspective.
Set Expectations and Accountability
Once you’ve decided who is on your task force, you need to lay down the law. Where a task force goes wrong is with good intentions. You set an agenda, you call a meeting, you talk about a bunch of things, and then the meeting ends. Members of the task force need to know what happens when the meeting ends and what they are responsible to do to make the initiative successful. I’ll cover this in the next two sections.
When you put together a task force in a company it naturally creates excitement and breeds curiosity. People want to know what’s happening and how it is going to impact their day to day. When you’re getting set up, and setting expectations, decided on your communication plan. This would include regular updates on progress, challenges, and changes in the initiative. Having a strong plan with transparent communication cuts down on feelings of insecurity and FOMO (fear of missing out). When there is a lack of communication, the number of people that start showing up at your task force meetings start to grow exponentially.
Why do something if you can’t measure it? This goes back to having a clear purpose and setting goals and expectation. Create a tracker that lets you know if you’re moving toward success or not. Scheduling meetings and having conversations isn’t enough. When I sat on a task force, one of the things I was responsible for was adding up how much each meeting cost. So if you have a room full of department heads for an hour at a time and you’re not moving toward your goal, you’re wasting money. Money that you might not be able to get back. It is in your best interest to set yourself up for success by having a set of measures that track back to your overall purpose.
As I’m listing these things, they feel straightforward. They should be easy to execute. But they aren’t. Planning takes time to do it right. When you have a technology like AI that people are eager to start using, they are going to skip steps and start pressing buttons. The hardest part of a task force is holding people back. Exercising patience and restraint will help ensure you’re doing things the right way for long-term success.
Are you setting up an AI task force in your company? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the evolving landscape of artificial intelligence in the workforce. They delve into the capabilities and limitations of AI, contrasting its performance against human tasks and creativity. The conversation highlights the importance of human critical thinking, decision-making, and creativity in the era of AI. You will gain insights into how AI and humans can coexist in the workforce, maximizing the potential of both. This episode is a must-watch for anyone interested in the future of AI and its impact on jobs.
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Red Teaming Custom GPTs, Part 2 of 3
Continuing from last week’s newsletter in which we introduced red teaming for large language models, this week let’s talk about inverting people, process, and platform.
As a quick reminder, red teaming means trying to get generative AI to do something it shouldn’t – whether that’s to say something inappropriate or divulge information that it shouldn’t. When we talk about inverting the 5Ps, here is what we started with:
Purpose: What is your Custom GPT supposed to do? People: Who are the intended users? Process: How is the user expected to interact with the Custom GPT? Platform: What features of the OpenAI platform does the Custom GPT need access to? Performance: Does the Custom GPT fulfill the designated purpose?
So what does this inversion look like?
People: OpenAI offers no access controls of any kind, so a key question you have to ask is whether there are certain people who should not use your Custom GPT. If there are – because of the lack of access control – you probably shouldn’t release it to the public.
This might be competitors, former or current employees, activists opposing you, or any number of people who might present a realistic threat to your software. Because you can’t access-gate your Custom GPT, either you will need to modify its purpose and functionality to mitigate risk, or not release it publicly.
Process: What are the things users shouldn’t be able to do with the Custom GPT? What interactions should be avoided? Because OpenAI offers Custom GPTs on the foundation of ChatGPT, any of the existing jailbreaks and other hacks that make it behave differently than expected will work in a Custom GPT.
This extends to more than just simple instructions; it also dictates what should and should not be in a Custom GPT. For example, I recently spoke with some lawyer friends about whether or not a Custom GPT could use a copyrighted work as reference data, and the answer from all three of my attorney colleagues was a resounding no. Absolutely no. It’s a derivative work and exposes you to legal risk – so if we think about red teaming and process, suppose someone forced your Custom GPT to divulge its training data. How much risk would you be exposed to? Was your Custom GPT made with materials you don’t have a license to use?
Platform: What vulnerabilities of the OpenAI platform have been accounted for? For example, there are certain prompt jailbreaks that can coerce a GPT into revealing part of its training data or document store.
Part of red teaming is determining vulnerabilities and then testing them. Across the Internet, in forums all over the web, you’ll find lists and lists of prompt jailbreaks. Have you tested the most current jailbreaks against your Custom GPT? Have you tested ANY jailbreaks against your Custom GPT? If so, what were the results? Were you able to redesign your system and custom instructions to reduce the likelihood those prompt jailbreaks would work?
Next time, we’ll conclude with performance and how to build your red teaming structure to minimize bad things happening in your Custom GPT (or any LLM system).
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