AI seems to be everywhere lately – you can’t escape it! So what is it and how do we make it work for Ambi Climate?
Nowadays, Machine Learning is considered to be the most effective approach to achieving Artificial Intelligence; Machine Learning involves training a machine to learn from a large amount of data, leading it to adapt its program pattern based on what it learns. Essentially, it’s the key to make technology gradually adapt to the needs of humans. Machine Learning is just one of several forms of AI, but definitely the most prevalent of the bunch.
Ambi Climate uses Machine Learning to be the framework and basis of our most popular Smart Mode: Comfort Mode.
According to ASHRAE, there are 6 factors which affect your thermal comfort – while your air conditioner only takes temperature into account!
With Ambi Climate, we took the existing problems your traditional AC presents and incorporated Machine Learning to solve them in the best possible way. Our smart sensors measure the “other” factors of thermal comfort: humidity and sunlight; we also collect online weather data to account for clothing, plus track the time of day to account for your metabolic cycle.
When you begin your journey with Ambi Climate, it’s a clean slate – you must now teach the AI engine about your preferences and needs; this is done via our simple feedback system!
Ambi will start learning from the very first feedback, and we suggest you give feedback more often and in different times of day on your first week of usage. This way, the AI will learn and optimize to fit your needs and preferences – based on the current environment but also your personalized experience of your surroundings.
Over time, the more feedback you give the system, the better it will adjust your air conditioner for the ultimate personalized comfort, considering your needs, changing weather, time of day and all other factors! This is optimized by slowly and surely creating your personal comfort profile, where you can see which unique factors make up how you experience thermal comfort. For instance, this user’s comfort relies mainly on his metabolism (time of day), indoor temperature and external weather.
It’s clear that Machine Learning has the amazing capability to take a simple aspect of life and enhance it by automating and learning to adjust to what the user wants. This is our aim and why we think Comfort Mode and constantly perfecting our AI engine is what leads to the ultimate results for our users – both in personal comfort and also in energy savings.