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WHAT PEOPLE ASK ME MOST. ALSO, SOME ANSWERS.


A FAQ OF SORTS

Ethan Mollick
Oct 12, 2023
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I have been talking to a lot of people about Generative AI, from teachers to
business executives to artists to people actually building LLMs. In these
conversations, a few key questions and themes keep coming up over and over
again. Many of those questions are more informed by viral news articles about AI
than about the real thing, so I thought I would try to answer a few of the most
common, to the best of my ability.

I can’t blame people for asking because, for whatever reason, the companies
actually building and releasing Large Language Models often seem allergic to
providing any sort of documentation or tutorial besides technical notes. I was
given much better documentation for the generic garden hose I bought on Amazon
than for the immensely powerful AI tools being released by the world’s largest
companies. So, it is no surprise that rumor has been the way that people learn
about AI capabilities.

In an attempt to address rumors, consider this a micro-FAQ on some of the
questions I get asked most. Yet take my answers with a grain of salt: I make
mistakes, the ground is shifting fast, and I may either be wrong already, or
will soon be wrong, about some of these points. But that disclaimer doesn’t hold
true for the first point, on AI detectors, where I feel very strongly about the
answer:


DETECTING AI



CAN YOU DETECT AI WRITING?


No.


BUT WHAT ABOUT AI WRITING DETECTORS THAT CLAIM TO DO THAT?


AI detectors don’t work. To the extent that they work at all, they can be
defeated by making slight changes to text. And, what might be worse, they have
high false positive rates and they tend to accuse people of using AI when they
don’t use AI, especially students to whom English is a second language. The
falsely accused have no recourse because they can’t prove they didn’t use AI.

You can’t detect AI writing automatically. Even OpenAI says you can’t.


BUT I AM SURE I AM REALLY GOOD AT DETECTING AI WRITING MYSEL-


Look, I am going to cut you off here. You might think you are good at detecting
AI writing, but you are just okay at detecting bad AI writing, and you combine
that with your own biases and heuristics about who might be using AI. After a
couple of prompts, AI writing doesn’t sound like generic AI writing.



I am sure that teachers who know their students well can guess at who might be
cheating, as they always could, but you are going to miss a lot of cheaters who
are doing it more subtly, which is a problem of fairness in and of itself.

I hate to say it, but homework as we know it is over, we educators are going to
have to adjust. There are plenty of paths forward, but it is not going to
include cheat-proof homework.


WHAT ABOUT AI-GENERATED IMAGES?


While there are more techniques to detect AI images, they are already very hard
to identify just by looking, and in the long-term likely impossible. All the
hints you think you know (bad fingers on hands, etc) are no longer true. Here’s
an illustration: one of my innovation classes had students build a full board
game with AI help (my syllabus now requires students to do at least one
impossible thing for their project - if they can’t code, for example, I want
working software). I took a picture of one of the teams showing off their game,
and then generated three other images myself using Midjourney.



At a glance, without zooming in and examining every detail, which do you think
is the real image? The answer will be in the comments…

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AT A GLANCE, WHICH IMAGE DO YOU THINK IS THE REAL ONE?

1st image (the one on the left)
2nd image
3rd image
4th image (the one on the right)
3660 VOTES · 2 DAYS REMAINING


USING AI



WHO KNOWS HOW TO BEST USE AI TO HELP ME WITH MY WORK?


I have good news and bad news: the answer is probably nobody. That is bad news
because there is no instruction manual out there that will tell you how to best
apply AI to your job or school, so there is really no one to help you get the
most out of this tool, or to teach you to avoid its specific pitfalls in your
area of expertise. This can be challenging because AI has a Jagged Frontier - it
is good at some tasks and bad at others in ways that are difficult to predict if
you haven’t used AI a lot.

The good news is that, by using it a lot, you can figure out the best way to use
AI. Then you have a valuable secret. You can decide whether you are going to
share it with the world (my preference, hence this newsletter!) or keep it to
yourself unless your organization incentivizes you to do otherwise.


SO, WHAT IS THE BEST WAY TO GET GOOD AT USING AI?


If you are new to AI, you may find our free YouTube 5-part video series useful
(it is built around an education context, but people have told me it was broadly
helpful)

But, generally, my recommendation is to follow a simple two-step plan. First,
get access to the most advanced and largest Large Language Model you can get
your hands on. There are lots of AI models and apps out there, but, to get
started, you don’t need to worry about them. Currently, there are only really
three AIs to consider: (1) OpenAI’s GPT-4 (which you can get access to with a
Plus subscription or via Microsoft Bing in creative mode, for free), (2)
Google’s Bard (free), or (3) Anthropic’s Claude 2 (free, but paid mode gets you
faster access). As of today, GPT-4 is the clear leader, Claude 2 is second best
(but can handle longer documents), and Google trails, but that will likely
change very soon when Google updates its model, which is rumored to be happening
in the near future.

Then use it to do everything that you are legally and ethically allowed to use
it for. Generating ideas? Ask the AI for suggestions. In a meeting? Record the
transcript and ask the AI to summarize action items. Writing an email? Work on
drafting it with AI help. My rule of thumb is you need about 10 hours of AI use
time to understand whether and how it might help you. You need to learn the
shape of the Jagged Frontier in your industry or job, and there is no
instruction manual, so just use it and learn.

I do this all the time when new tools come out. For example, I just got access
to DALL-E3, the latest image creation tool for OpenAI. It works very differently
than other previous AI image tools because you tell ChatGPT-4 what you want, and
the AI decides what to create. I fed it this entire article and asked it to
create illustrations that would be good cover art. And here is what it came up
with:


The prompt that GPT-4 generated for itself: Photo-realistic image of a
diorama-style theater stage. On the left, a miniature puppet show scene nestled
in a vintage wooden box for 'AI Myths'. On the right, a sleek modern
presentation setup with tiny 3D holographic infographics labeled 'AI Reality',
both placed on a grand theater stage under spotlights.




I FOUND SOMETHING AI CAN’T DO, DOES THAT MEAN THAT IT IS OUTSIDE THE JAGGED
FRONTIER?


Maybe? But often better prompting, or enabling Advanced Data Analytics, or a
different model, or a different approach can get the AI to solve a problem. I
found that AI struggled filling in crossword puzzles, but a little while later
Princeton Professor Arvind Narayanan figured out a way to get GPT-4 to do it. I
wouldn’t feel too certain that a capability is outside the realm of AI until you
have spent some time with different approaches.

And, if it is truly impossible for AI to do, wait a few months and try it again
when a new model comes out.


POLICY STUFF


Before you read this, please note I am not a lawyer, so I asked the AI to read
the material you are about to read and give me a disclaimer. Here it is:

Disclaimer (Generated by AI): The opinions and information expressed in this
article are those of the author and do not necessarily reflect the views of any
organizations or companies mentioned. This disclaimer itself was generated by an
AI after reviewing the material. The information is presented for informational
purposes only and should not be interpreted as legal, business, or any other
form of professional advice. Readers are encouraged to conduct their own
research and consult with professionals regarding any concerns or questions they
may have.


OUR COMPANY WON’T LET US USE AI BECAUSE WE DON’T WANT OUR DATA STOLEN, IS THAT
RIGHT?


There are lots of reasons to be concerned about the data sources for Large
Language Models. No company is forthcoming about the training material that was
used to build their AIs. It is likely that some, or maybe all, of the major LLMs
have incorporated copyright material into their models. The data itself contains
biases that make their way into the model in ways that can be difficult to
detect. And human labor plays a role in part of the training process, which
means both that more human biases can creep in, and also that low-wage workers
in developing countries are exposed to toxic content in order to train the AI to
filter it out.

All of these things are true… but the privacy issue that many people talk to me
about is likely less of a barrier than you think. As a default, AI companies say
they may use your interactions with their chatbots to refine their model (though
it is extremely hard to extract any one piece of data from the AI, making direct
data leaks unlikely), but it is relatively easy to get more privacy. Individual
users of ChatGPT can turn on a privacy mode where the company says they will not
retain or train AI your data. But large organizations have even more options,
including HIPAA compliant versions of the major AIs. All the big AI companies
want organizations to work with them, so it is not surprising that all of them
are eager to offer data guarantees. The short answer is that data privacy is
probably not as big a concern as it might seem at first glance.


WHAT’S THE DEAL WITH COPYRIGHT AND AI?


Again, not a lawyer, but, as I understand it, current US copyright rules around
AI material are sort of unclear and in flux. However, large AI companies seem
eager to ensure their customers that using their AI output commercially is safe.
For example, Adobe and Microsoft offer legal guarantees that if you are sued
over the output of their AIs, they will protect you (at least under some
circumstances). But also remember that legal use isn’t always going to be
ethical use, especially as we consider cases where AI work displaces human labor
or produces art “in the style” of a living artist.


THE FUTURE



AREN’T AIS LIKE GPT-4 GETTING WORSE WITH TIME?


No, this turned out to be an incorrect conclusion from a working paper examining
the performance of AI on certain math problems. Crossword solver Prof. Arvind
Narayanan and Prof. Sayash Kapoor (who have an excellent Substack that I
occasionally disagree with but always find valuable), found that AI models are
not getting worse at these sorts of problem, but they are changing, which alters
the way you need to prompt the AI. You see, what you call GPT-4 or Bard or Bing
today is not the same thing as what Bard or Bing or GPT-4 was a few months ago.
Models are continually getting additional training and tuning that improves
performance in some ways, while also changing behavior in others. It is part of
why it is so hard to treat AIs like normal software, and sometimes easier to
treat it like a person, even though it isn’t.


WON’T AI DEVELOPMENT GROUND TO A HALT AS THE INTERNET FILLS WITH AI DATA? OR AS
IT RUNS OUT OF DATA TO TRAIN ON?


I hear this a lot. It may be true, this paper argues that we will be out of
training data in the next decade or two (or even by 2026 if we restrict
ourselves to high quality data). And this paper suggests that AI models will
indeed start to struggle as the web fills up with AI content. But many computer
scientists argue that neither of these are actually long-term problems, and
offer various solutions, including ways of training AIs on data that the AI
makes up.

Ultimately, these issues are unlikely to stop LLMs from improving over the next
couple years, which I think is what people are really asking when they ask me
this question.


HOW GOOD DOES AI GET?


Honestly, I have no idea. And I suspect no one else does either, given the
debates among prominent AI experts. Right now, models get better as they get
larger, which requires more data and more computers and more money. At some
point, technical, economic, or regulatory limits are likely to kick in and slow
the advance of AI. But, at the same time, there is a lot of experimentation on
how to make smaller models perform like bigger ones, and similar experiments on
how to make larger models perform even better. I suspect there is a lot of room
left for rapid improvement.

What all of this means is absolutely unclear. Do we reach the feared/longed-for
level of Artificial General Intelligence, where AIs are smarter than humans
(thus, depending on who you ask, creating a machine that will start saving,
killing, or ignoring humanity)? Do we “just” get order of magnitude improvements
in AIs that are already performing at high human levels on many tasks? Do AIs
stop improving quickly? There is no clear consensus, which, uncomfortably, means
that we should be thinking about all three scenarios. The only thing I know for
sure is that the AI you are using today is the worst AI you are ever going to
use, since we are in for at least one major round of AI advances, and likely
many more.

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Ethan Mollick
Oct 12Author

I realized revealing the answer now will ruin the survey, so I'll add it here in
a few hours.

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11 replies by Ethan Mollick and others

Red
Oct 12·edited Oct 12

I love that closing sentence: "The only thing I know for sure is that the AI you
are using today is the worst AI you are ever going to use."

Also, I wanted to address an earlier point about the internet being a finite
source of content for AI training, and using AI-generated content to bypass
that. There's a potential phenomenon called model collapse that might occur if
LLM output becomes too strongly the primary source of information that
subsequent generations are trained on. Paper here:
https://arxiv.org/abs/2305.17493

but TLDR version: the probable gets overrepresented, and the improbable (but
real) slowly gets erased. Based on the probabilistic way that these large models
work, this makes a lot of sense-- but a probable reality and an actual reality
are two extremely different things.

LLMs and LMMs (large multimodal models) are likely to improve for quite a while
yet, but it's quite possible that it will not be a linear or even exponential
direction upwards. There will probably be some hidden valleys of performance
loss that we might not notice until we solve them with novel architectures (if
we ever even notice them at all!)

So I'll close with a sentiment that echoes yours: "The only thing I know for
sure is that the AI you are using today is the worst AI you are ever going to
use - but the same thing might not be true in the future."

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