What’s Old Is New Again: GPT-3 Prompt Injection Attack Affects AI

What do SQL injection attacks have in common with the nuances of GPT-3 prompting? More than one might think, it turns out.

Many security exploits hinge on getting user-supplied data incorrectly treated as instruction. With that in mind, read on to see [Simon Willison] explain how GPT-3 — a natural-language AI —  can be made to act incorrectly via what he’s calling prompt injection attacks.

This all started with a fascinating tweet from [Riley Goodside] demonstrating the ability to exploit GPT-3 prompts with malicious instructions that order the model to behave differently than one would expect.

Continue reading “What’s Old Is New Again: GPT-3 Prompt Injection Attack Affects AI”

Blog Title Optimizer Uses AI, But How Well Does It Work?

[Max Woolf] sometimes struggles to create ideal headlines for his blog posts, and decided to apply his experience with machine learning to the problem. He asked: could an AI be trained to optimize his blog titles? It is a fascinating application of natural language processing, and [Max] explains all about what it does and how it works.

The machine learning framework [Max] uses is GPT-3, a language model that works with natural-seeming human language that is capable of being tweaked in different ways. [Max] uses OpenAI’s GPT-3 API (which, by the way, is much easier to experiment with than one might think) and here is the basic workflow for his title optimizer:

  1. The optimizer takes as input a blog post title to optimize.
  2. OpenAI’s pre-trained GPT-3 engine is used to generate six alternate titles.
  3. For each of those alternate titles, a fine-tuned version of GPT-3 is consulted to judge how “good” they are based on custom training data. (“Good” in this context means “similar to titles of successful submissions on Hacker News“, but more on that in a moment.)
  4. Print the results.

Continue reading “Blog Title Optimizer Uses AI, But How Well Does It Work?”

AI Creates Your Spreadsheets, Sometimes

We’ve been interested in looking at how AI can process things other than silly images. That’s why the “Free AI Bot that Generates the Excel Formula for Any Problem” caught our eye. Based on GPT-3, it supposedly transforms your problem description into a formula suitable for Excel or Google Sheets.

Our first prompt didn’t work out very well. But that was sort of our fault. When they say “Excel formula” they mean that quite literally. So trying to describe the actual result you want in terms of columns or rows seems to be beyond it. Not realizing that, we asked:

If the sum of column H is greater than 50, multiply column A by 0.33

And got:

=IF(SUM(H:H)>50,A*0.33,0)

A Better Try

Which is close, but not really how anyone even mildly proficient with Excel would interpret that request. But that’s not fair. It really needs to be a y=f(x) sort of problem, we suppose.

Continue reading “AI Creates Your Spreadsheets, Sometimes”

Natural Language AI In Your Next Project? It’s Easier Than You Think

Want your next project to trash talk? Dynamically rewrite boring log messages as sci-fi technobabble? Happily (or grudgingly) answer questions? Doing that sort of thing and more can be done with OpenAI’s GPT-3, a natural language prediction model with an API that is probably a lot easier to use than you might think.

In fact, if you have basic Python coding skills, or even just the ability to craft a curl statement, you have just about everything you need to add this ability to your next project. It’s not free in the long run, although initial use is free on signup, but for personal projects the costs will be very small.

Basic Concepts

OpenAI has an API that provides access to GPT-3, a machine learning model with the ability to perform just about any task that involves understanding or generating natural-sounding language.

OpenAI provides some excellent documentation as well as a web tool through which one can experiment interactively. First, however, one must create an account and receive an API key. After that is done, the doors are open.

Creating an account also gives one a number of free credits that can be used to experiment with ideas. Once the free trial is used up or expires, using the API will cost money. How much? Not a lot, frankly. Everything sent to (and received from) the API is broken into tokens, and pricing is from $0.0008 to $0.06 per thousand tokens. A thousand tokens is roughly 750 words, so small projects are really not a big financial commitment. My free trial came with 18 USD of credits, of which I have so far barely managed to spend 5%.

Let’s take a closer look at how it works, and what can be done with it!

Continue reading “Natural Language AI In Your Next Project? It’s Easier Than You Think”

GitHub Copilot And The Unfulfilled Promises Of An Artificial Intelligence Future

In late June of 2021, GitHub launched a ‘technical preview’ of what they termed GitHub Copilot, described as an ‘AI pair programmer which helps you write better code’. Quite predictably, responses to this announcement varied from glee at the glorious arrival of our code-generating AI overlords, to dismay and predictions of doom and gloom as before long companies would be firing software developers en-masse.

As is usually the case with such controversial topics, neither of these extremes are even remotely close to the truth. In fact, the OpenAI Codex machine learning model which underlies GitHub’s Copilot is derived from OpenAI’s GPT-3 natural language model,  and features many of the same stumbles and gaffes which GTP-3 has. So if Codex and with it Copilot isn’t everything it’s cracked up to be, what is the big deal, and why show it at all?

Continue reading “GitHub Copilot And The Unfulfilled Promises Of An Artificial Intelligence Future”