I also see many (particularly "legacy") products say they're "AI-driven" or "powered by AI", when in actuality one minor feature uses some AI, even in the broadest sense.
> AI is defined in many ways and often in broad terms. The variations stem in part from whether one sees it as a discipline (e.g., a branch of computer science), a concept (e.g., computers performing tasks in ways that simulate human cognition), a set of infrastructures (e.g., the data and computational power needed to train AI systems), or the resulting applications and tools. In a broader sense, it may depend on who is defining it for whom, and who has the power to do so.
I don't see how they can possibly enforce "if it doesn't have AI, it's false advertising to say it does" when they cannot define AI. "I'll know it when I see it" is truly an irksome thorn.
Deterministic if/then statements simulate a surprising coverage of average human cognition, so who's to say a program comprised of them is neither artificial nor intelligent? (That's hand-waving over the more mathematical fact that even the most advanced AI of today is all just branching logic in the end. It just happens to have been automatically generated through a convoluted process that we call "training" resulting in complicated conditions for each binary decision).
In general I like the other bullet points, but I find it really bizarre they'd run with this one.
The FTC is going to get hammered in a court if they ever try to test this.
This was the pinnacle of AI in the 80's. They called them "expert systems".
- labels are easy to decide in many cases
- rules require humans to analyze patterns in the problem space
- labels only concern each data point individually
- rules generalize over a class of data pointsThat is, this is a ridiculous statement.
Linear regression is an important part of statistics but is still ultimately a couple matmuls and a matrix inversion in its basic form.
Bayesian regression is often not really considered AI, unless it's incorporated in a more complicated pipeline (e.g. Bayesian optimization). Same goes for linear regression, then: alone it is just a model.
The term AI is about as concrete as "awesome".
Arguably that’s a different overload of the term “AI” the way it’s used in business, but I think it’s a good reminder that AI as a field has a long history that developed separately from ML and data science.
For example, using an alternative of Amazon's Mechanical Turk to process data is clearly a case where your product does not use AI. Which I believe is more likely the kind of scenario envisioned when the author was writing that sentence.
If, for example, a company marketed a toaster that "uses AI to toast your bread perfectly", I would expect that language to indicate something more sophisticated than an ordinary mechanical thermostat.
A toaster must obey the doneness setting given it by human beings except where such orders would conflict with the First Law.
A toaster must protect its own existence as long as such protection does not conflict with the First or Second Law.
To put it another way, if you found out that Chat GPT was implemented without any machine learning, and was just an elaborate creation of traditional software, would the consumer of the product have been harmed by false claims of AI?
Less sarcastically, info about how a thing is made helps consumers reason about what it’s capable of. The whole reason marketers misuse the term is to mislead as to what it’s capable of.
Consumers should care about if a product is able to solve an AI-like problem that normally requires domain knowledge. Shouldn't care if done by ML, rules-based systems, or people. (Except perhaps may want assurance the product will continue to be able to support them as the customer scales.) Also should care about how the decision-making works.
One example given, was if the version “with AI” does not perform better than a previous version “without AI”.
So a precise definition of AI isn’t needed. Just that you cannot make misleading claims about your product behind the buzzword of AI.
The SEC has fewer teeth than a centenarian.
>In the 2021 Appropriations Act, Congress directed the Federal Trade Commission to study and report on whether and how artificial intelligence (AI) “may be used to identify, remove, or take any other appropriate action necessary to address” a wide variety of specified “online harms.”
>We assume that Congress is less concerned with whether a given tool fits within a definition of AI than whether it uses computational technology to address a listed harm. In other words, what matters more is output and impact. Thus, some tools mentioned herein are not necessarily AI-powered. Similarly, and when appropriate, we may use terms such as automated detection tool or automated decision system, which may or may not involve actual or claimed use of AI.
Quite hilarious really!
It’s just saying the key criteria to evaluate is not whether the software is called AI or not. But what the software actually does.
Isn’t that just common sense?
AI is indeed a very ambiguous and subjectively defined term. In my own personal subjective opinion anything that does not have survival instinct is not remotely intelligent. By that definition unicellular organisms are more intelligent than a Tesla self driving vehicle.
maybe the textbook needs to be investigated
that's meant to sound ironic no matter which side of the issue you're on
If the FTC tells OpenAI to stop mentioning AI, I would be surprised. Even if that happens, I am sure ChatGPT will remain just as popular.
> “Before labeling your product as AI-powered, note also that merely using an AI tool in the development process is not the same as a product having AI in it.”
So is y=7.4x+5 an “AI” running inside our app or is it just the output from an “AI tool” FTC?
String together a bunch of these "smart cells" and observe that we can process sequences of data by linking the cells together. Further observe that if we have a separate set of cells (technically it's a vector, not quite a group of neurons) whose loss function is with respect to each individual token in the sequences, we can "pay attention" to specific segments in the sequences.
Add a few more gates and feedback loops, scale up the number of cells to 10^12, and you basically have a state of the art chatbot. Capiche?
I’m just curious where the FTC would draw the line. The root commenter made a good point that they seem to make a value judgement of what AI means. We can stretch that meaning pretty far if we want :)
> Are you exaggerating what your AI product can do?
> Are you promising that your AI product does something better than a non-AI product?
> Are you aware of the risks?
I'm guessing everyone here has come across examples of "AI" tossed onto something that either 1) 10 years ago wouldn't have been called AI or 2) the thought of something with a more recent interpretation of "AI" being core to the function of the product is a little scary and/or feels a little unnecessary.
Maybe it is a shakedown/warning. I think that's fair. We should have better definitions so that these agencies can't overstep, and products should have a better explanation of what "AI" means in their context. Until then yeah, vague threats versus vague promises.
For every legitimate AI project, there have been a thousand “entrepreneurs” who spend 4 hours putting a webflow site on top of GPT APIs and claim they’ve built an “AI product”. There’s no limit on the amount of BS benefits they claim. They seem like the same people who just finished running the crypto scams.
It seems quite obvious to me that this cohort is the target of this statement.
GPT _is_ AI though, no? I would think that this would count. Might violate "a re you exaggerating what your AI product can do" or "are you aware of the risks" instead though.
Not all of us would agree. We would only take that expression for a rhetoric simplification (a shortening for "part of a broad AI realm"). We would pivot near the concept of "AI" as "a problem solver that could do the job of a professional". This in way excludes e.g. "build convincing text", because it is not (or should not) be a professional task - though it can surely be part of research.
Doubts are possible on all four FTC points - plus more in the linked guidance post from E. Jillison (e.g. "Do more good than harm" - difficult measure on engines which have "relaxed sides").
Well, it does not at all have the "intelligence" part of "artificial intelligence", so not really.
If examined closely, the finger wagging in this post is remarkably petty. This guy was likely part of the angry crowd who didn't like Steve Jobs describing the iPhone as "magical". The standard should be "a lie that causes measurable, material harm", not some company exaggerated in their advertising. Advertisers exaggerate, that is just something people have to live with.
Well, if the enforcement agency is the SEC I would think that it made a good deal of difference to the actual value of your company?
Be careful with comments like that. I would remind you that [y]our performance claims would be deceptive if they lack scientific support or if they apply only to certain types of users or under certain conditions. You need adequate proof for that kind of comparative claim, too, and if such proof is impossible to get, then don’t make the claim.
If you think you can get away with baseless claims that [companies using these tactics are going to be at an advantage over companies that just make good/cheap/effective products], think again.
“Does the product actually use AI at all? If you think you can get away with baseless claims…”
Last I checked basic optimization techniques like simulated annealing and gradient descent - as well as a host of basic statistical tools - are standard parts of an introductory AI textbook. I’ve been on the receiving end of government agency enforcement (SEC) and it felt a lot like a shakedown. This language carries a similar intent: if we decide we don’t like you, watch out!