As we reach the end of 2017, our CEO & Founder Peter Davies has written down some thoughts on the ‘smart’ home and the important addition of AI and Machine Learning for delivering a personalised solution, tapping into untapped data and delivering a future-proof roadmap…enjoy!
We all remember the scene in Back to the future where Marty’s girlfriend’s thumbprint is used to open the door to her house in 2015. With us now being 2 years ahead of what was then considered to be a futuristic world, it’s interesting to look at how technology has evolved and in particular what the smart home actually looks like now as we head into 2018.
In reality, the smart home revolution started over a decade ago with the advent of fast, reliable internet and a boom in software advancement. And the number of connected Internet of Things (IoT) devices being developed for the home has increased exponentially since then – from connected doorbells to, yes, even fingerprint activated locks.
But while there are some really cool and innovative technologies being developed for the smart home, products that make a real difference in the way people live and manage their homes are still relatively few and far between. Artificial intelligence and machine learning open up new opportunities to create truly smart homes that unlock big data, such as utility usage, and then deliver it to the consumer in a way that’s understandable and can create positive behavioural change. In addition, machine learning creates homes that can learn as they go, offering a more personalised solution.
Connecting the dots just won’t cut it
Gartner predicts that by 2020, there will be around 20.8 billion IoT enabled devices in the world. Out of this staggering total, 12.9 million of those connected devices are expected to be used by consumers, including within the home.
Currently, there are a number of benefits that connected devices can bring consumers and the wider community, from connected fridge cameras that can help to minimise food waste to connected doorbells with integrated cameras which provide increased levels of security.
However, while these devices provide more insight into the home than before, in reality they fail to address real consumer pain-points. Most of these ‘smart’ devices require even more steps than the traditional method. Take connected lights for example – homeowners are required to unlock their phone, find and open the right app, select the room they want and then press a series of combinations to turn the lights on or off. More often than not, this takes more time to do than simply standing up and turning the light on! These devices may create ‘connected homes’ but they certainly aren’t smart.
Artificial Intelligence is the ‘smart’ in smart home
What is needed to drive mass adoption of smart homes is integrated technology which addresses both time-saving and money-saving, with added support from machine learning to tailor experiences based on individual consumer behaviour. One of the biggest barriers to smart home adoption is the unnatural environment connected devices create. AI removes this barrier by adding an extra layer of intelligence to the home.
This extra layer of intelligence is what allows big data to be turned into tangible insights into how the home is being run, helping out the consumer and manufacturers (with permission to use said data) alike. Without these machine learning algorithms analysing the home data at great speeds, really valuable information is sitting there, untapped.
Take electricity consumption – one of the biggest frustrations is that you can never tell how much each appliance is costing you or which ones are draining energy. With AI-enabled smart devices, such as Verv, energy consumption can be analysed per appliance, and relayed back to homeowners in real-time via a mobile app. And as Verv learns your home, consumers can be alerted if appliances are deteriorating before they actually break down, whether they’ve left something on by accident and whether they should replace an appliance with a more eco-friendly one. For the first time, this hands real intelligence about energy usage and the health of their home back to the consumer. Not only can this help to bring down utility bills and improve existing home efficiency, it creates a truly smart home. One that delivers a personalised solution using information based on behavioural trends. Critically, AI-enabled smart devices learn over time, analysing behavioural trends and constantly improving its algorithm based on user choice and feedback.
Looking to the future, AI-enabled smart products like ours can also be combined with other technologies, such as blockchain, to drive even more innovation in the home. And that’s exactly what we’ve done in order to create a peer-to-peer energy trading solution which brings rise to the prosumer and ultimately will bring new meaning to smart cities, as well improve access to low carbon electricity which is at the core of our mission. And being able to deliver a solution like this to our existing smart home product via a simple software update is what makes it so game-changing.
AI is the missing link in today’s smart home puzzle and I look forward to seeing it infiltrate more and more aspects of the market in order to create a home that is truly personalised
By Peter Davies, CEO & Founder, Verv