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With the almost startling increase in both AI’s evolution and its current capabilities, it’s not a surprise that the technology would find a use in nearly everything we do. From the obvious tasks like internet searches and knowledge gathering, to navigational tools we now completely depend on, to the doctor using AI to see if that weird spot on your back is something you should worry about.
This major improvement in technology, especially one that has appeared so suddenly, has a mixed bag of benefits and risks. We as people are not great at handling such rapid changes in our lives. If you think about the overall evolution of society, the hunting/gathering period lasted a very long time before we thought of planting our own food and keeping animals. It took a long time to develop tools, proper clothing, and housing. It took a long time to develop anything resembling mass production, and the Industrial Revolution, though it seemed sudden and shocking at the time, took nearly 150 years to become mainstream. Even then, the Luddites freaked out about the rapid pace of technology.
Compare that to the rapid developments of AI in the past three years alone. AI has moved from a highly bespoke, custom job for every model, easily broken tool, and is now at a point where even though we aren’t there yet, we can see a path toward Artificial General Intelligence, the holy grail of AI.
This major trend of AI improvements is amazing, true, and we should explore what good can be done with this newfound superpower of sorts. At the same time, changes this rapid tend to create new problems as well. These problems and risks shouldn’t prevent progress, but we should watch out for them and work to prevent them ideally, or solve them as quickly as possible if they emerge. One of the biggest concerns around AI today is the role of data: your data, my data, everyone’s data. What was once useless details and breadcrumbs about our lives can now be used by AI to find patterns and use that data against someone. This can be as easy as targeting an ad to that person, or as nefarious as using that data to cause direct harm. The lesson here is that we now have to look at the data we create in a whole new light. It is both more valuable than ever, but also more vulnerable than ever. In order to have the good without the bad, we need to know what we are doing: working with platforms that protect our data, and working with platforms that value our data.
An interesting intersection with AI and overall technology is in the field of wearables, the range of smart devices that can track out health-related metrics while also tying into our digital footprint, including messaging, calendars, location movement, and social media presence. Let’s dive in to see what can be accomplished for good in this reinvigorated area, and what we need to watch out for to keep our data (and ourselves) safe.
First, let’s explore what might be accomplished in the near future when we pair wearables with well-designed AI. To really understand this, we need to first explore what data we can collect with wearables, what data we can combine that with, and then we can determine some of the problems that AI might be able to solve for us.
In terms of the data wearables can collect, it is a decent-sized list. It is time-based, so everything can be tracked on a specific schedule and time patterns, giving it even more insight. A typical wearable can track your pulse, body movement and location movement (eg., are you jumping up and down or did you just walk down the street), and some can track blood pressure, sleeping patterns, and more. Advanced wearables could capture other key data like air quality, blood pressure, ECG, fibrillation patterns, skin temperature, and potentially even some stress indicators.
Now, what if we combine that data with other data already being tracked? What if we could see your daily calendar, your emails, your social posts, and where you go each day? Without jumping the gun and being nervous about this, let’s look at what problems AI could solve.
A key problem that AI can solve is predicting things before they happen. Your daily life determines your longer term chance of preventable diseases, and what happens in your daily life can give clues about early signs of many different diseases (preventable or not). For many health issues, early warning signs to change behaviors can prevent or reverse issues, while early warning to seek care can save your life. A properly designed AI can help with all of these problems, not only predicting potential issues but also alerting your doctor, reminding you to make appointments, or giving you updates about potential issues (eg., the AI notices that you get stressed out and sleep less when you see your ex on social media, and gently reminds you that this isn’t healthy).
There is a second wave of wearables hitting the market now that AI has caught up. As a key example, Vyvo is a tech company focused on each of these issues, creating an AI called Life CoPilot (as part of their larger VAI OS), tying it to their own and third party wearables, and giving a comprehensive view of your health and the actions needed to maximize it. The challenge for Vyvo, along with every other platform working in this area, is to develop excellent hardware and software to explore what is possible. However, an even bigger challenge is to control, protect, and use your data in a trustworthy manner, with the key phrase being “Your Data.”
As big a risk as your data is, understanding the risk is easy. Simply put, companies use end user license agreements (EULAs) to quietly get you to give up your data. They take your data and either use it themselves, sell it to a data broker, or both. Data brokers can then sell the data to anyone who wants it, including many companies using AI to better understand and manipulate the average person. And the more sensitive the data, the greater the risk. This means that wearables could take on a much more dangerous role in our lives.
The good news is, the solution to this is also easy. The answer is for companies to ensure that their customers’ data remains under the control of the customer. The problem is that companies often build in the data sales as part of the business model, which is why there are so many “free” services available today. However, this needs to change, especially with private and sensitive data. A key way to make this happen is through Web3, where data can be tokenized and kept in the possession of the customer. However, the customer can then decide to sell their data if they choose, giving economic benefit to themselves and valuable data for AI companies. There are pros and cons to this, but the major pro is that the customer is both the decision maker and benefits directly from the data they created. The platform mentioned above, Vyvo, was given as a specific example because they are one of the few companies actively building with this foundation in mind. Customers own their data using Web3, and Vyvo provably does not have access to it without the customer’s permission. This creates an honest ecosystem with willing partners, not EULA-deceived partners. Because platforms like Vyvo operate with Web3, there are other opportunities to protect data while serving a global market, eliminating the issues created with lax privacy regulations in some countries while supporting those that have guidelines protecting data. With Vyvo’s upcoming launch of their $VAI token, the ability to subscribe to these services (and get rewards for sharing data) becomes globally feasible as well.
We will watch to see how this evolution continues to unfold, and to see if AI and data companies will finally give the customer a voice, or continue the self-destructive cycle we are currently in. However, if companies like Vyvo and others like it can show a better way that has room for both profit and customer respect, AI might hold a very positive place in our future.
