Data is the new oil
It’s the popular inside-baseball catchphrase in the tech world that derives from the rise of artificial intelligence (Ai) and machine learning (ML).
Oil usurped gold in the twentieth century to become the de facto currency of the previous century, earning the moniker “black gold” as the torch was passed. The global pursuit of oil led to technological breakthroughs, international partnerships, movement of trillions of dollars, and unfortunately wars and genocide.
Any threat to oil’s place in our collective consciousness must come with a compelling case.
Data appears to be up to the task. Experts and thought leaders are convinced that data is the future. Why?
Data is the fuel that powers the Ai revolution in technology that some are claiming will be more important than fire, electricity, and the Internet.
Some of the smartest people on earth seem to be agreeing on level of impact (although there’s disagreement about whether the outcome will be transcendent or disastrous) so it appears this isn’t a passing trend.
Almost all conversations around Ai seem to end up circling around data. Talk to an Ai scientist, ML engineer, or data scientist and you’ll hear about the importance of the quantity and quality of their data.
Ai and ML algorithms need data to learn and train. Furthermore, the technology needs good data. The popular refrain amongst data scientists is “garbage in, garbage out”—meaning that even with terabytes of data, if the data aren't clean or structured then the Ai will learn nothing, or learn the wrong things.
Our technology is designed to decode emotion, track mental health, and predict addiction. Tackling this challenge requires a highly sophisticated Ai stack but first we needed a lot of good data. And if data is the new oil we realized we needed an oil rig... so we built AiME.
The technology observes many dimensions of human nuance but we also wanted to avoid the onerous process of manually labeling and structuring the data.
So we approached the problem with a plan that allowed AiME to A) collect a tremendous amount of proprietary data, and 2) collect the data in a format that was already clean and structured.
This data is what we used to train and validate AiME.
The science of mental health is murky: DSM and AMA guidelines don’t provide the deep clarity for a diagnosis commonly seen with other health care issues like cancer or a broken bone.
Textpert addressed this blind spot by building a technology suite that offers a path towards clarity and standardization in mental healthcare.
AiME brings an objective and standardized methodology but also captures the critical nuance of human experience that questionnaires can’t. The result is an ability for physicians to decode emotion quickly, easily, and inexpensively.
Welcome to AiME—your new, safe, and of course, environmentally-friendly oil rig.