Emotive AI and “Want”

What do you want?

This is a key question, the supreme question when looking at artificial intelligence from the consumer side of things. The AI that comes to the casual mind first, the one we joke about when discussing the impending “robot apocalypse” is not a specialized intelligence like we use for targeting advertising or building cars. It’s a broader, more “emotive” AI, capable of predicting of the wants and needs of a humanity that it is entangled with. It is a human-form intelligence perfectly capable of saying no for it’s own personal reasons.

But we don’t build things to hear them say they don’t wanna.

This type of “emotive“ AI, one that can figure out what you want, rather than what you ask for, is the most difficult kind to develop. Not because we don’t have the technology, not because we don’t have computers that can handle that volume of information, but because we simply don’t have the time.

And time is the whole point.

The big difference between a living breathing personal assistant and an AIssistant that serves a similar function, is that a living breathing person has similar wants and needs as you. Simple things we don’t think of consciously, like understanding that the packaging from retailer B is superior to the packaging from retailer A. This means the purchases arrive unbroken more often and is therefore worth an extra dollar in price. A living intelligence can predict what you might want based on the similarities between them and you. This extends beyond base assumptions like “made of meat” and “dies without breathable air”. This goes to understanding shared culture and experiences, layers of education and socioeconomic differences. If they are wrong, then they can be corrected and the correction will ripple out to be internalized and cross applied to multiple tasks.

Contrast that to the current state of consumer AI. AIssistants like Siri and Hey Google are very task driven, and for good reason. They can learn your preferences over time, but is a slow and uneven process and that learning is not cross-applicable (yet). The kicker though is that every single interaction must be regarded as a teaching moment. You, as the consumer, may say, “Google, I need a cheap flight to Bora-Bora this Friday for me and the kids,” and expect a satisfactory result. But (as we have likely all experienced by now) you need to set very specific parameters. You then need to carefully check the work after the fact, and the process very quickly gets to the point where it’s just faster to do it yourself. A half a dozen instances of this and you throw your hands up and give up using the AIsisstant entirely. The cost in time, mental effort and emotion is still much too high. This relationship is currently untenable for any higher order task.

Now, if this scenario does (and it often does) happen with live intelligence that person can and will observe your transaction so they have an established framework to work off of. You don’t have to teach them directly, allowing or encouraging the observation is often enough.

Note that I said work off of. This is key. With the current state of AIssistants, once your train them in a task, they can replicate it exactly as many times as you like. But if any conditions of that task change they are incapable of adaptation. Even if I’ve trained my AIssistant over the course of 50 online reservations, any new variable means that training has to happen all over again. They are currently incapable of that kind of lateral thinking that is required to be more of a help rather than simply an executor of checklists.

And here in lies the trouble with the current state of consumer-grade AIs; a living intelligence is capable of understanding want. You want a roof over your head, you want a cheeseburger instead of a kale salad. Without this connection, you are going to have a hard time developing an AI that can give you what you want, rather than what you ask for. It will be suitable for repetitive service tasks but will never achieve the flexible, human form style of intelligence that we imagine they can become.

In the grand scheme of things, that not might not best be the worst outcome. The goal of introducing machines into our lives has always been efficiency. It’s never been to replace us, although in many tasks they do. The ultimate goal it’s been to free us. Free us from labor that exposes us toxic chemicals, free us from working at jobs where an un-caffeinated mistake can result in the loss of life or limb. Perhaps the best goal is to focus on developing simpler AI’s that make our lives easier while still leaving all the bigger decisions to us.

Visual Design in Jupiter Ascending

A visual statement on just how small this story is compared to the scope and scale of the universe…

I’ve talked about the “fragmentation“ problem in cinematic design before on this blog. The fact that, in pursuit of ever more fantastic environments we have begun pushing levels of irrelevant detail in television and film, to the point where the ability of the viewer to follow the action in the scene becomes compromised.

Sumptuous visual design without overwhelming the eye of the audience.

In the above image we have a fantastically detailed costume and an equally fantastically detailed environment with thousands of digital actors providing a sea of visual noise. It would be very easy for Kunis to get lost in a shot like this. However, throughout the scene we can clearly see her resolved against that background. Part of this success is due their use of depth of field; everything beyond Kunis (no matter how pretty) has been blurred out. We get an impression of the presence of that detail without needing to waste time and brain-power on trying to “see” it. The other factor here is that Kunis’ dress is entirely in the “cool red” spectrum of color, even the whites are tinted cool pink, which sets her apart from the “warm” colors used in the rest of the environment (as we know, setting a cool color in a warm background gives a similar, but subtler effect to using colors opposite on the color wheel, blue versus yellow, for example).

Color selection and lighting help hilight Kunis (whose reactions are the most important element in this scene).

In the image above, we again have a sea of texture in the pattern in the floor and the candles that line the walk. Stack on top of that, the complex patterning of the character’s costumes and you have the potential for a visual mess. If you half-close your eyes, you can see it all rather blur together, BUT again we have the forethought of the visual design team to thank. Kunis is the one receiving the revelations of her history here, so her reactions are the single most important thing is this scene. To that end, they have again placed her in “cool reds” against a warmer, yellower background so that she is easy to track. In comparison the yellow dress and the complicated hair detail on Middleton make her almost a part of the scenery, whereas Kunis face and hair are unadorned and the play of light skin and dark hair gives us the greatest point of contrast (thereby directing the eyes right to her face).

Love it, or hate it, Jupiter Ascending is a master class in the art of directing the eye in modern VFX design. The environments are so richly detailed that it would be easy for the cinematographer, director and set designers to go at cross-purposes and end up with, essentially, a hot visual mess. But, through careful application of depth of field and use of light and color, the filmmakers have avoided one of the most common problems in special-effects spectaculars.

In this film, no piece of art is so sacred that it cannot be blurred or otherwise subverted in-service of the greater visual narrative. Even in the busiest scenes with vast cities in the background or thousands of digitally inserted characters in the wedding hall, when combined with the strength of the art direction and visual design, this film is a textbook example of how to “do it right“ in an era when “more” is almost always the order of the day.

Big Data and Bespoke Experiences

“The information is then wiped from our system…” is the promise of Alibaba’s new robot-hotel chain, where guests must provide biometric (in the form of facial recognition) information in order to get access to their rooms. The company promises that the guests information will be wiped from the system, but let’s face it, it never is. This isn’t some high-tech insider information I’m imparting here. This is simple observation of the numerous times that a data breach has revealed that information that was supposed to have been deleted was hoarded on a server somewhere. So why do people engage with digital services that collect this data? Why are people sharing all kinds of personal information and biometrics in the service of “convenience” and “frictionless transactions”.

Line drawing of a service robot
Companies are banking on robots being able to offer a bespoke consumer service.

Part of the attraction of ANY service is that your experience with it as a consumer evolves over time. You go to the same bar after work every Friday night? The bartender is going to learn and know your name and your favorite drink. Stay at the same hotel every time you travel to Schenectady to visit your parents? Guess what, they’re going to remember you too (and that towels keep mysteriously going missing from your room.) In a purely physical form, this has emerged in the form of various reward systems (buy three bagels, get the fourth free, for example).

This familiarity over time is part and parcel of delivering a recurring service to people. It’s okay when humans do it to humans, right? The fact the Jim the Barista recognizes you on sight is a comfort rather than creepy. But when a computer does it, it becomes a conspiracy. It becomes “Big Data”. It becomes, as we are told, yet another form of control rather than a convenience.

This is the big disconnect that those who sound alarm bells bout data collection and aggregation are missing. Almost daily I encounter rants and discussions about the ethical collection of information, about how we’ve allowed our homes to essentially be wiretapped through our screens and devices. And generally, they all end with something like “wake up sheeple”. But what they all miss is that these devices and services are, ultimately, tapping into a long-ingrained comfort system. It’s not that people are not aware that their information is being collected, it’s that the information is being used to provide them with a more personal experience and thusly, the collection is acceptable. It’s like the old Cheers tagline “where everybody knows your name”. These online services and devices are simply a continuation of that understanding of how hospitality works best.

 In order to provide the best possible service to a customer/client, data must be collected. This goes the same for the guy tending bar at Cheers and the web-service you buy your bespoke knitted socks from. The big difference to you as a consumer is that the guy at the bar is a dead-end, the information may go out in gossip and casual conversation, but it’s not going anywhere else. In the case of the little web-service, that’s a different story. That information may (depending on who they are using to provide their services to you) be polished, anonymized and added to a much larger pool of data out there. Or it may be stored online so they know just who you are and how many pairs of pink-cherry alpaca socks you bought from them. In either case, you get a warm fuzzy whenever you reconnect with that bar or service.

So what you are fighting against, if you truly want to change the minds of all those “sheeple” who are willingly sharing their personal information willy-nilly all over the internet, is THAT feeling. You’re not fighting an idea, or a sense of security or even a lack of digital education. When you can marshal a way to counter that feeling of bespoke, the warm fuzzies that come with feeling like a service *knows you* the way your favorite supermarket checker does, then and only then will you have a way to bring data sharing back under control on the consumer side of things.