AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: AI Mastery: Detecting Fatigue Through Video - Scientists' Breakthrough

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So really what's happening here, I guess the headline is that researchers have developed

a neural network to detect operator fatigue and they're doing this using IVE movement.

So in a collaborative effort to enhance operational safety, researchers from St. Petersburg University

and St. Petersburg Federal Research Center at the Russian Academy of Science have developed

a comprehensive database of IVE movement strategies.

So this data actually aims to inform neural network modules designed to monitor the psychological

states of individuals working in various critical roles such as vehicle fleet drivers, air traffic

controllers, and industrial plant operators.

So the first thing I want to mention here is really when they're talking about this

research of why they're doing it, they're like, look, this is for individuals in really

critical roles where nothing can go wrong, air traffic control.

You cannot have an air traffic controller getting sleepy and missing the mark.

There's all sorts of terrible things that can happen.

Now at the beginning of this podcast, in the intro, I alluded to the fact that this could

be used for advertising in other areas.

While that may have been a little bit of a stretch, because really how this technology

works is by monitoring your iris and by monitoring your eye, I think that it's still not that

far off that that could be a place where this gets to.

A lot of times you're on your phone using your phone and the camera is activated or you give

camera access to TikTok or you give Instagram access to your camera, you give all sorts of

apps access to your camera that theoretically could be on and tracking you, or maybe when

you upload a video that's like a camera based video to a social media platform, TikTok,

Instagram, Facebook, X, whatever, let's say it knows that you actually did the recording.

So for example, on TikTok, you can be recording a video on TikTok, it sees your eyes on there.

So there is a case to be made that even a lot of different apps that you wouldn't really

think about or expect when you're uploading a TikTok video, TikTok could literally be

tracking and detecting if they think you're tired at that very moment.

So not saying that everyone's doing this or this may be like a big huge invasion of privacy

thing that's immediately happening, but the fact that it's possible, you have to discuss

the implications of something that's possible because inevitably, whether it's a bad actor

or someone claiming to do it for safety, it will get embedded into stuff.

If it's possible, people will do it.

So that's all I'm saying.

I'm not saying that TikTok and Facebook are going to be spying on you and turning on your

camera when you're not expecting it and seeing how tired you are or your laptop company,

but also think about the fact that there is a big prevalent problem, which is obviously

made known by the fact that a lot of modern laptops you buy today have a manual hard shutter

to cover your camera because people hack into cameras all the time.

You can download a piece of software that has access to your camera, theoretically something

like Zoom that needs access to your camera for video calling, could also be hacked into

and secretly access your camera when you don't know, and a lot of people feel like this is

a major invasion of privacy, so you can go on Amazon and buy these little slider window

shutters that go on top of your camera lens on your laptop to manually cover it with a

piece of plastic, and this is so popular to the point that a lot of really modern Windows

laptops I've seen, some of the newest ones coming out, literally have hard built in camera

lens covers, so obviously they're doing this for a reason.

People can hack into your camera, and so whether it's your phone or whether it's your laptop

and you're worried about exploitation, imagine if someone was hacking into your laptop, seeing

that you're running this technology through your camera on you to see that you are tired,

and then maybe that's when scammers go and try to give you a scam call because they know

you're more likely to fall for something or become a victim if you are sleep deprived

and you're not functioning at your full potential, so there's the whole spiel about how this

can be used for bad, I try to always give that for every AI advancement, but there's

definitely some ways this can be used for good, so let's jump back into the research.

The research team utilized a multifaceted approach, essentially they were capturing

a range of behavioral and neuropsychological indicators to offer a more comprehensive understanding

of operator states, so whether they are tired or alert, the research findings were published

in the Scientific Journal Sensor, so Irina Soshina, who's the director of biological

sciences and the professor at the Institute of Cognitive Research at St. Petersburg State

University, emphasized the advantages of an integrated methodology, she noted, quote,

an integrated approach provides a more complete picture and a more objective assessment of

the functional state, so in contrast to approaches involving separate registrations of certain

indicators that reflect the state of fatigue, Satoshi pointed out really kind of the limitations

of current methods like cardiac time revival measures, which are often considered unreliable

indicators of fatigue levels, that's currently what we use a lot of the time, and instead

of kind of this research focused on a unique approach involving eye movement, which are

believed to accurately portray the interplay between neural networks of static and dynamic

vision and psychological indicators, so this is really, really interesting, they're literally

looking at your eye movements, running a neural net, and they're able to predict how tired

you are just from your eye movements, which is interesting because they don't need to

see anything else about you, just your eyes, and if you think about it, your phone and

your laptop camera are almost always going to be pointed in a place where they can see

your eyes, whether they can see the rest of you or not, so anything that could get access

to that camera could pretty much tell when you're tired or not tired, and they just have

information on you based off of that, so I think the researchers have made their comprehensive

database publicly available, and they're encouraging software developers to leverage the data to

improve their own systems, they said, quote, we have developed a comprehensive database

suited for training neural networks that classify a person's state of as tired or alert, that

was Alex Kurnitskyev, and that's a senior research associate in the laboratory of integrated

automation systems at the St. Petersburg Federal Research Facility at the Russian Academy

of Science. The data collected for the study was really robust, incorporating various sensors,

including video, camera, eye trackers, heart rate monitors, and electro-cento-phenographs.

The operators were also assessed for other factors such as sleep quality, fatigue, and

complex visual motor reactions, so the data collection took place at multiple times during

the day, morning, afternoon, and evening to capture a broad spectrum of operator conditions,

and the research spanned eight days and included 10 participants engaging in a mix of passive

and active tasks such as reading and playing Tetris. The entire process was video recorded

for further analysis, I think that's a great move on their part, if people see holes in

the way that stuff was trained, you know, maybe they'll be like, well, it's just predicting

that someone's tired because the lighting's darker or something, although I'm sure it's

just in a laboratory with the exact same lighting, but you know, that kind of thing, the whole

thing has been recorded, and by adopting a multi-dimensional approach and making the

data publicly available, I think the research holds the promise of advancing the field of

neural network-based fatigue detection systems, so this in turn could significantly bolster

safety protocols across various transport industries and defense sectors, that's what they're

saying in any way. I see something slightly different, and I see the fact that if they're

open sourcing this and giving it away to everyone, great time for hackers and scammers to grab

it and use it for bad, but you know, it'll inevitably hopefully be used for good as well,

so just take it with a grain of salt and know that this exists, so keep that on your radar

as there are some very interesting implications, I believe, with this technology.

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Machine-generated transcript that may contain inaccuracies.