What is Generative AI?

Current Generative Artificial Intelligence (AI) tools work similar to text messaging on your phone. You can send messages to the AI and it can text you back. When the AI sends a message back to you, it is “generating” a response. The interaction is very similar to texting a human.

The messages that you send to the Generative AI are called “prompts” and the messages that the Generative AI sends back are called “outputs”. Many of the Generative AI tools respond to your prompts with text, but some can also send back images or video, just like a person can respond to your request for a “picture of a flower” by texting you back a photo that they take. Prompts can also tell the AI to do things. Just like you could give a human step-by-step instructions to perform a task via text message, you can do the same thing with Generative AI and it can follow your instructions.

What is new about Generative AI is the sophistication shown in the reasoning and computation behind the replies to your messages. Generative AI can perform surprising tasks, like reasoning about what would need to change in a world without odd numbers or analyzing and critiquing the assumptions in a document, which would be difficult to replicate with any other computing technology. It isn’t writing simple poems and answering quiz questions that is creating the real buzz behind these tools, it is the deeper computing capabilities and accessibility of these capabilities that are the sources of the excitement. The step-by-step instructions that you send to the Generative AI can be things like asking it to turn enrollment data from Vanderbilt’s instructional report into a set of visualizations and then insert each visualization into a slide in a PowerPoint presentation. It can then reply to your message with a link to the PowerPoint presentation it created based on your instructions.

Just like most tools, Generative AI tools require training to use them effectively. Although it appears that you can write a prompt about anything and get a result, what you write and how you write it directly impacts the quality of the output. Writing clearly is important for communicating with humans. Writing clearly is also important for conversing with Generative AI. Knowing how to break a complex task down into a set of simple step-by-step instructions is important for telling another human being how to perform a task for you. Similarly, learning how to explain step-by-step what you want the Generative AI to do for you is important.

Learning the techniques for writing prompts, which is part of the discipline of “prompt engineering”, is very important. Generative AI was taught to identify and respond to patterns in human language. By understanding patterns that it responds to and how it responds, you can more effectively structure the writing in your prompts and solve more complex problems with these tools. Often, you can get significant improvements in the quality of the output by changing how you word and structure your requests. The skill of the user in writing prompts and knowing what can be done with the tool is incredibly important.

Certain uses of the tool simply don’t make sense. You probably wouldn’t scramble eggs with a hammer. Similarly, there are uses of Generative AI that just don’t make sense. If you are using the tool like an Internet search engine or expecting it to be a perfect source of truth, you shouldn’t be.

Generative AI tools are meant to generate content, not facts. There is no distinction between the two inside of most current tools. Although its output is usually right and the tool can perform extremely complex tasks, such as creating a meal plan that combines flavors from Uzbekistan and Ethiopia and is Keto-friendly, the output may not be error-free. A fundamental tenant of these tools is that you need to check the output for errors.

Knowing that the tools make errors is important in selecting what types of problems you attempt to solve with them. You want to use Generative AI to tackle problems where either: 1) a partially correct solution is valuable or 2) checking if the solution is correct isn’t time consuming or expensive. For example, having Generative AI help you brainstorm ideas is a reasonable use case, particularly for domains that combine multiple diverse topics and don’t have a single right answer, such as combining cuisines from Ethiopia and Uzbekistan that are Keto-friendly. Another example would be generating solutions to a crossword puzzle. It is easy to check if the solutions are correct by simply seeing if the generated words fit into the constraints of the puzzle. In contrast, having these tools generate output that you can’t easily check for correctness isn’t usually a good idea, such as having them produce a translation of “Computer Science” into Babylonian Cuneiform.

It is clear that the deep capabilities of these tools will cause dramatic changes in the world over the next few years. The tools will require changes in education. There will be many opportunities for innovation and many flawed approaches to how people and organizations attempt to use the tools. It is important that you think about how you employ these tools.

Generative AI tools can augment and amplify human creativity and reasoning. They can form an “exoskeleton for the mind” that helps you find new ways of solving problems, solve larger problems than before, or provide a new medium for artistic expression. We should seek to use these tools for “Augmented Intelligence” and not as an artificial substitute for human reasoning. When you use the tools, make sure that you use them in a way that augments and amplifies your own unique human spark and doesn’t diminish it.