Veronica Mas Investigates the Use of Apps and AI

More and more often I am thrilled with amazement at novel technological advancements related to artificial intelligence (AI). The latest I have heard of is a “Cervical Cancer App”, an AI algorithm which will enable the diagnosis of the disease from images. The app is currently under development and aims to identify precancerous or cancerous cells with just a photograph. The technology, if proved efficient, could in theory render current pap smear procedures obsolete.
From the user’s point of view, apps with AI algorithms are powerful tools that can help their users stay healthy, by suggesting how much exercise do or what best to eat, make it easier to find a partner or even help couples conceive. They can teach us how to best learn a language based on our past learning experiences or even predict a heart attack.
The future of AI apps with personalised outcomes is endless, especially within the learning environment. As algorithms are designed to teach themselves, a computer cannot only be trained itself to beat the best chess player in the world, but can also instruct itself to become only good enough to provide the perfect challenge to someone who is training to be a professional chess player.
We all know motivation is key to personal development and learning. People tend to get discouraged when trying to learn something that proves too difficult, or lose interest when there is not enough challenge. The use of AI apps to develop personalised learning environments can provide a great platform for the development of a customised learning pace that help users identify and fill in the gaps in their knowledge. Personalise learning apps have the potential to enhance human’s abilities to acquire new skills to previous unimaginable levels.
But let’s apply that same logic to the other end of the stick. Apps can be perceived as machines which collect data from users and generate new knowledge. Can apps mature and be designed so that the AI systems can learn to collect the necessary information to find answers to bigger questions? How does the unconscious mind work? What controls the way information is remembered? What rules our state of mind? How much of what we are is set up at birth and how much is down to our experiences? What experiences? And many more.
Quite often I ruminate on why some women suffer from strong PMT and others barely have any symptoms. How that shapes our characters or the choice of a partner? I recently downloaded an app that enable female users to tick from more than thirty possible premenstrual symptoms. My first thought was, how will all information from users be handled? How can it be used to generate new knowledge? Will the app mature to collect the right information from the right people to be used to draw “the right” conclusions?
As I see it, apps can be described as systems that operate like a two-way road. The user gives information to the system in exchange of some customised experience, perceived as a theoretical improvement in the user’s quality of life. On the other hand, the AI system can potentially train itself to extract the necessary information from users in order to satiate its thirst of data for some other ulterior motive.
We can think of data, apps and AI as a tool for research and development, as apps will not only generate data but also generate new knowledge. They will feed themselves with the data they need to find patterns, confirm tendencies, identify outliers, explain exceptions.
And as I watched part two of Blade Runner, a bigger question raises. Could AI inherit our own confirmation bias?
If you think your business can benefit from this data revolution, please contact Veronica Mas at: v.mas@aston.ac.uk