Personal AI Is Here — But Who's Really in Charge?

We have a very complicated relationship with AI.

Even decades ago, well before AI existed beyond the hypothetical, we had thoughts and opinions about it. Movies, books, and every type of media had visions of what AI might look like, how we might interact with it, and what risks it might hold. Depending on the story, AI could solve all of humanity's problems. Or, and more likely, it could enslave humanity, destroy it, or just make us all miserable.

Thankfully, so far, AI has been perfect! Well, maybe not perfect, but we as a species are still here and other than a crippling addiction to our phones, AI has yet to enslave the planet. As with all science fiction themes, the reality is both far less dramatic, but at the same time has bits of truth that we can't afford to ignore. Let's look at the realities of AI in our current world and discuss how personal AI in particular holds incredible promises to improve our lives, but only if we manage the risks as well. We can also see how Web3 can help to manage several of AI's biggest risks. Vyvo's VAI OS, in particular, is demonstrating a balance of personal AI's benefits, with the ability to manage data risk successfully.

Risky AI

One thing to keep in mind when looking at AI is that it rarely acts like it does in science fiction. It's just another technological tool that we keep improving, and each improvement seems initially unbelievable. AI, at its core, is just a mathematical model that can learn the patterns that you want it to learn and make decisions that will be helpful. It is trained using data, and then it really only has a few key tricks: it can classify things (like identifying the animal in a photo), it can predict things (like seeing if a machine might break soon), it can optimize things (think of your GPS navigation), and it can generate things (like your school essay or a very realistic but odd image). We come up with very creative ways to use these four tricks, and we have changed our lives with AI as a result. As good as it can be, however, there are some known risks to using AI. The dangers of AI include things like not being transparent in how it makes decisions, creating biased decisions that can be subtle and not well understood by the people who trust them, and it can remove major elements of privacy in our lives through things like visual and data surveillance, and more. The larger effects of these risks include the theft of your private data (although the companies taking it might technically be allowed to through EULAs and other shady practices). Having this data can allow bad actors to build AI that can manipulate what you see online, what you are marketed, and can prey on those people who are most vulnerable to this kind of attack. There are larger spheres of risk as well, such as the manipulation of financial markets and negatively affecting entire socioeconomic groups, but these are risks that we are still trying to grapple with from a higher, longer-term viewpoint. The risk we face today is a combination of AI models that are not trained in a way that is bias-free, and AI models that either use or create private data from us, leading to a loss of control and dangerous repercussions down the road.

Using AI for Good While Managing Risks

So, given the risks, how can we get the most benefit out of AI while understanding and minimizing potential issues? The challenge is that the more personal we get with AI, the more vulnerable we become. There has to be a transparent and secure system for handling our data, our privacy, and we need to know how the AI uses the data to make decisions.

Take the VAI OS as an example of this balance at work. The platform was designed as a "Life CoPilot" in order to learn about the person it supports, understand habits and patterns, alert when issues are detected, and promote a healthier lifestyle. What does this mean in practice? At its core, the platform is designed to integrate with multiple wearables and third-party health platforms, as well as connect to the person's communication and scheduling tools such as WhatsApp, email, and even voice-based communication. This allows for intensive and personalized health monitoring using many different sensors and data streams, depending on the user. What does it do with this data? Unlike a basic wearable, this system can develop a much more complex AI model because it has much more data, meaning it can train itself over time to dial in on the subtle and unique elements of its owner. With this information, it can recognize immediate issues that need the user's attention, but it can also track longer-term issues and predict before the issues become emergencies. For example, a key use case is tracking data related to cardiovascular disease (CVD), which includes blood pressure, cholesterol, obesity, physical inactivity, stress (measured through spikes in blood pressure and other metrics), and even sleep disturbances. Together, and over time, these can create an excellent predictive tool to minimize its risk. This is what AI does best, and we are at a point where wearables and other tools can actually collect all of this data continuously, and a tool like VAI OS can learn and predict true life-threatening issues. Tied into communication tools, you can proactively schedule events like doctor's appointments, set reminders for activity and healthy eating, and provide other support to ensure that the key metrics that measure CVD are at comfortable levels. This type of system is the pinnacle of what AI can do today, combined in different ways, to solve a problem we don't currently have great solutions for.

You can see how this example could be taken in other contexts and inspire many other use cases, whether they are related to health, personal finance, scheduling, communication, or even challenges like learning a new skill. The process is the same: collect enough of the right data, build a model that will predict things and recommend best actions, connect it to the systems necessary to do much of that automatically, and then continue to learn about the person's unique needs, differences, and habits as time goes on. Commitment to the process means a product that literally gets better every day.

Final Piece of the Puzzle

For those more pessimistic readers, you can see how this "best case" scenario can become a nightmare if all that very sensitive data gets into the wrong hands. This is where the Web3 element comes in. Platforms such as Vyvo use encryption to secure the data from outright attack, but just as importantly, are very clear about who owns the data and what can be done with it. Using Web3 allows this data to be packaged up and secured, owned as an NFT or some other format by the user and the user alone. Given that our current world's currency is data, there is a way for companies to get the data: by purchasing it from the user, if they are willing. This is the missing piece to today's data broker world, and it is absolutely critical if we want to make the best of what AI can offer, without giving over our data (and much of our freedom) to the system we have built to help us.