Earlier this year, Microsoft added a new key to Windows keyboards for the first time since 1994. Before the news dropped, your mind might’ve raced with the possibilities and potential usefulness of a new addition. However, the button ended up being a Copilot launcher button that doesn’t even work in an innovative way.
Logitech announced a new mouse last week. I was disappointed to learn that the most distinct feature of the Logitech Signature AI Edition M750 is a button located south of the scroll wheel. This button is preprogrammed to launch the ChatGPT prompt builder, which Logitech recently added to its peripherals configuration app Options+.
Similarly to Logitech, Nothing is trying to give its customers access to ChatGPT quickly. In this case, access occurs by pinching the device. This month, Nothing announced that it "integrated Nothing earbuds and Nothing OS with ChatGPT to offer users instant access to knowledge directly from the devices they use most, earbuds and smartphones."
In the gaming world, for example, MSI announced this year a monitor with a built-in NPU and the ability to quickly show League of Legends players when an enemy from outside of their field of view is arriving.
Another example is AI Shark's vague claims. This year, it announced technology that brands could license in order to make an "AI keyboard," "AI mouse," "AI game controller" or "AI headphones." The products claim to use some unspecified AI tech to learn gaming patterns and adjust accordingly.
Despite my pessimism about the droves of AI marketing hype, if not AI washing, likely to barrage the next couple of years of tech announcements, I have hope that consumer interest and common sense will yield skepticism that stops some of the worst so-called AI gadgets from getting popular or misleading people.
That'd actually probably be a not-unreasonable application for machine learning, if you could figure out some kind of way to measure short-term biological arousal to use as an input. I don't know if blood pressure or pulse is fast enough. Breathing? Pupil dilation?
Like, you've got inputs and outputs that you don't know the relationship between. You have a limited number of them, so the scale of learning is doable. Weighting of any input in determining an output probably varies somewhat from person to person. It's probably hard to get weighting data in person. Those are in line with what one would want to try doing machine learning on.
IIRC, vibrators tend to have peak effect somewhere around 200 Hz, but I'd very much be willing to believe that that varies from person to person and situation to situation. If one has an electric motor driving an eccentric cam to produce vibration, as game controllers do for rumble effects, then as long as the motor's controller supports it, you could probably train that pretty precisely, maybe use some other inputs like length of time running.
I don't know if it's possible to have a cam with variable eccentricity -- sort of a sliding weight that moves towards or away the outer edge of the cam -- but if so, one could decouple vibration frequency and magnitude.
So that's an output that'd work with a variety of sex toys.
There's an open-source layer at buttplug.io -- not, despite the name, focusing specifically on butt plugs -- that abstracts device control across a collection of sex toys, so learning software doesn't need to be specific to a given toy, can just treat the specific toy involved as another input.
I'm sure that there's a variety of auditory and visual stimuli that has different effect from person to person and isn't generally-optimal today.
And, well, sex sells. So if one can produce something effective, monetizing it probably isn't incredibly hard, if that's what one would want to do.
EDIT: Actually, that variable-eccentricity cam is designed to be human- rather than machine-adjusted. That might not be the best design if the aim is to have machine control.
Looking through the hardware compatibility list on buttplug.io, one such device is the "Edge-o-Matic 3000". This claims to keep a user near orgasm without actually having an orgasm. For that to work, there have to be sensors, and fairly reactive to arousal in the short term. It looks like they're using a pneumatic pressure sensor driven off a bulb in a user's butt to measure muscle contractions, and are trying to link that to arousal.
The Edge-o-Matic is a smarter orgasm denial device (for all humans, which also includes men and women) that uses a hollow inflatable butt plug to detect orgasm via muscle contractions in the area. As the user approaches orgasm, these involuntary contractions are recorded and measured to estimate arousal levels and control external stimuli accordingly.
If they're trying to have software learn to recognize a relationship between muscle contractions and arousal sufficient to produce orgasm, if it's automatic rather than having someone tweaking variables, that's machine learning. Maybe "AI" is a bit pretentious, but it'd be a sex toy doing machine learning today.
That's an interesting idea, but:
I'm dubious that it actually works well. It's described as being a work in progress.
Even if it works and solves the problem they're trying to solve (being able to reliably predict orgasm), I'm not sure that muscle contractions can be used to predict arousal more-broadly.
My guess is that as sensors go, mandating that someone have an inflatable bulb up their butt to let the sensor get readings is kind of constraining; not everyone is going to want that at all, much less when they're, well, playing with sex toys. Their butt might be otherwise-occupied.
That being said, it's gotta at least be viable enough for someone to have been willing to put work into and commercialize a device based on that input. I'd believe that muscle contractions are an input that one could reasonably derive data from that one could train a machine on.
Maybe one could use brainwaves as an input. That'd avoid physical delay. I've got no idea how or if that links to arousal, but I've seen inexpensive, noninvasive sensors before that log it. Using biofeedback off those was trendy in the 1970s or something, had people putting out products.
Neuroelectric Correlates of Human Sexuality: A Review and Meta-Analysis
Taken together, our review shows how neuroelectric methods can consistently differentiate sexual arousal from other emotional states.
If it's primitive enough, probably similar across people, easier to train a meter to measure arousal from EEG data on one set of people that can be used on others.
Hmm. Though psychologists have to have wanted to measure sexual arousal for research. You'd think that if EEGs were the best route, they'd have done that, else physical changes.
Over the past four decades, there has been a growing interest in the psychophysiological measurement of female sexual arousal. A variety of devices and methodologies have been used to quantify and evaluate sexual response with the ultimate goal of increasing our understanding of the process involved with women's sexual response, including physiological mechanisms, as well as psychological, social, and biological correlates. The physiological component of sexual response in women is typically quantified by measuring genital change. Increased blood flow to the genital and pelvic region is a marker of sexual arousal, and a number of instruments have been developed to directly or indirectly capture this change [1]. Although the most popular instrument for assessing female sexual response, the vaginal photoplethysmograph (VPP), measures genital response intravaginally, the majority of other instruments focus on capturing sexual response externally, for example, on the labia or clitoris.
Hmm. That's measuring physical changes, not the brain.
Vaginal photoplethysmography (VPG, VPP) is a technique using light to measure the amount of blood in the walls of the vagina.
The device that is used is called a vaginal photometer.
The device is used to try to obtain an objective measure of a woman's sexual arousal.There is an overall poor correlation (r = 0.26) between women's self-reported levels of desire and their VPG readings.[1]
That doesn't sound like, even concerns about responsiveness in time aside, existing methods for measuring arousal from physical changes in the body are all that great.
As in, maybe measuring the brain is gonna be a better route, if it's practical.
What are they using as input? Like, you can have software that can control a set of outputs learn what output combinations are good at producing an input.
But you gotta have an input, and looking at their products, I don't see sensors.
I guess they have smartphone integration, and that's got sensors, so if they can figure out a way to get useful data on what's arousing somehow from that, that'd work.
Launched in beta in the company’s remote control app, the “Advanced Lovense ChatGPT Pleasure Companion” invites you to indulge in juicy and erotic stories that the Companion creates based on your selected topic. Lovers of spicy fan fiction never had it this good, is all I’m saying. Once you’ve picked your topics, the Companion will even voice the story and control your Lovense toy while reading it to you. Probably not entirely what those 1990s marketers had in mind when they coined the word “multimedia,” but we’ll roll with it.
Riding off into the sunset in a galaxy far, far away? It’s got you (un)covered. A sultry Wild West drama featuring six muppets and a tap-dancing octopus? No problem, partner. Finally want to dip into that all-out orgy fantasy you have where you turn into a gingerbread man, and you’re leaning into the crisply baked gingerbread village? Probably . . . we didn’t try. But that’s part of the fun with generative AI: If you can think it, you can experience it.
Of course, all of this is a way for Lovense to sell more of its remote controllable toys. “The higher the intensity of the story, the stronger and faster the toy’s reaction will be,” the company promises.
Hmm.
Okay, so the erotica text generation stuff is legitimately machine learning, but that's not directly linked to their stuff.
Ditto for LLM-based speech synth, if that's what they're doing to generate the voice.
It looks like they've got some sort of text classifier to estimate the intensity, how erotic a given passage in the text is, then they just scale up the intensity of the device their software is controlling based on it.
The bit about trying to quantify emotional content of text isn't new -- sentiment analysis is a thing -- but I assume that they're using some existing system to do that, that they aren't able themselves to train the system further based on how people react to their specific system.
I'm guessing that this is gluing together existing systems that have used machine learning, rather than themselves doing learning. Like, they aren't learning what the relationship is between the settings on their device in a given situation and human arousal. They're assuming a simple "people want higher device intensity at more intense portions of the text" relationship, and then using existing systems that were trained as an input.
Lovense is basically just making a line go up and down to raise and lower vibration intensities with AI. They have tons of user generated patterns and probably have some tracking of what people are using through other parts of their app. It's really not that complicated of an application.
Filtering out bad data from their training corpus is kinda part of the work that any company doing machine learning is gonna have to deal with, sex toy or no.