[Simulated voice] - "Hi, I'm not Tom Scott."
- But I am. Hello!
This is the real me, not simulated.
This week's guest video is from Jordan Harrod
who is probably the smartest person I know.
Jordan is working on a joint Harvard-MIT doctorate
in medical engineering and medical physics,
is researching using AI in neuroscience,
and also has a channel with deep dives into AI and algorithms.
So if there was anyone I was going to trust
with making a simulated version of me, it was Jordan.
But I want to take a moment to manage expectations,
this isn't a face replacement on a body double. That's mainstream now.
Hollywood's doing it, anyone can do it.
This is trying to create a cheaper complete replacement for me,
an automatic digital puppet on a budget of just $100.
Oh, and one more thing. Just because I trust Jordan to do this,
this is not consent for anyone else.
Please: don't try this at home!
- "Anytime you see or hear Tom Scott in this video,
"what you're actually seeing is a fake version of Tom Scott
"that was generated using machine learning."
- Why?
Well, some of you are getting better and better at creating Tom Scott-style videos,
and it seemed inevitable that someone might try to use machine learning next,
given the amount of free training data Tom has posted on the internet over the years.
Given that, I decided to get ahead of you and do it myself.
In fact, you're probably already familiar with this type of video. It's called a deepfake,
and contrary to what you might think,
it was relatively straightforward to make this video.
Which is why the whole world was pretty concerned
about how deepfakes might affect anything
from election results to general misinformation.
But we're getting a little ahead of ourselves.
- "After all, what is a deep fake anyway?"
- Well, deepfakes are a type of synthetic media
or media created using indirect acquisition methods.
For example, the video that you're watching right now
is not synthetic media because it was acquired
through direct means. By filming me making this video.
- "On the other hand, this clip of Tom is synthetic media
"because I took video of Tom and used machine learning
"to create a new one with things he's never said."
- Now, some types of synthetic media generation
are easier than others.
For example, I had originally planned to make this video
by generating a script using machine learning,
based on past scripts from Tom.
Unfortunately, or rather, fortunately,
there's a difference between generating text that sounds human and generating text
that actually makes sense, or is even correct.
- "These robots are made of a lot of different kinds of sensors,
"and they’re all built to be really precise.
"So that means that you can’t program it by hand.
"And this is a really big problem,
"because the most advanced robots are made out of metal that can’t be moved precisely.
"You can’t even drive a robot arm with a mouse and keyboard."
- The generated text was riddled with factual errors and nonsensical phrases.
So in the interest of you actually getting something
out of this video and not spreading misinformation...
- "I decided to stick with synthetically putting my own words into Tom's mouth."
- For those with a decent technical background or willingness to learn,
generating things like deepfakes are fairly straightforward.
And as to not incentivize you to do so in this video,
I won't be sharing specific resources that you can use to do this.
Although I will credit the authors of the tools that I used in the description.
In most cases, synthetic media can be generated
using models called Generative Adversarial Networks or GANs,
which pit two models against each other.
On one side, we have the generator model which wants to create synthetic data
that is essentially indistinguishable from real data.
And on the other side, we have the discriminator model
which wants to accurately predict whether any given piece
of data is synthesised from the generator model,
or whether it's a real piece of data.
As the two models go back and forth, the generator learns to create data
that is increasingly realistic and the discriminator learns
to find increasingly subtle differences between that synthetic data and the real data.
- And once you decide that your generator is good enough,
you can go on to use it to create synthetic data as needed
typically in the form of audio, video or images.
- Now, just a few years ago, generators weren't actually that good
at creating realistic data to the point where you could look
at synthetic media and pretty easily tell that something was up.
However, with advances in machine learning techniques
and increased access to computing resources,
deepfake generation systems have gotten to the point where
they can create synthetic media that you may not
realise is synthetic when you see it.
Understandably with the advent of synthetic media generation
came a lot of concern about how synthetic media
might be used to spread misinformation and literal fake news.
Interestingly, however, we haven't actually seen
that many examples of synthetic media being used
to influence things like elections, international politics or public figures.
- "At least, as far as we know."
- And this is probably because it turns out
that there are just easier ways of spreading misinformation.
For example, most of the cases of claimed interference
in the 2016 and 2020 US presidential elections came
from things like fake social media accounts, emails from fake groups
and leaked information from national political organisations.
This isn't to say that we shouldn't be worried
about how deepfakes might be used going forward,
especially considering that there is a long history
of deepfake generation being used to create sexual images
and videos without the consent of the people in them.
However, it is to say that at least right now,
it seems like the work required to generate synthetic media
is a little bit more than groups who are spreading misinformation
nationally and globally are willing to put in.
However, this doesn't mean that we shouldn't look for ways
to identify synthetic media.
In fact, recent research has shown that the methods
that we use to generate synthetic media actually
leave fingerprints on the media itself,
which can later be identified using algorithms.
Even better, it turns out that we as living, breathing human beings,
leave our own fingerprints in video data
that can't currently be replicated via synthetic media generation.
And I did a whole video on this, so if it sounds interesting and you want to learn more,
I will leave a link in the description.
It's also important to note that there are some proposed positive applications of deepfakes.
In particular, there's a lot of interest in using deepfakes to dub media more realistically,
so that people from other countries,
who speak other languages or have other cultures,
can experience things that they might not have access to otherwise.
So while we keep trying to find new ways
to identify deepfakes in the wild, we can also look
towards positive applications of them in our everyday lives.
Unfortunately, however, the nature of Generative Adversarial Networks means
that deepfake detection and deepfake generation
will always be a bit of a cat-and-mouse game.
After all, as we develop better deepfake detection systems,
deepfake generators also improve with them,
learning to create that more realistic media.
There are also other proposed methods
of identifying deepfakes, such as embedding signals
in anything that could be used as training data
that would be preserved through the synthesis process,
similar to the fingerprints that synthetic media generation techniques already leave.
And who knows, maybe we will all start having
to wear necklaces or pins that emit signals
that automatically poison any sort of recording that someone might be taking.
So while you might not be able to spot synthetic media
with your eyes and ears going forward,
websites and other platforms that host media may be able
to deploy tools that may still be able to let you know
that what you're watching isn't real.
- "After all, I've told you that I'm not Tom Scott."
- Who's to say that I'm Jordan either?
- Go check out Jordan's channel!
She doesn't do clickbait videos,
she just explains concepts about AI really well.
You know, when she's not doing potentially lifesaving medical research.
I am not doing enough with my life.
Try her video about AI detecting COVID from your cough.
Next week, on the final guest video,
a little bit of radioactivity.