Understanding the Differences Between the Smart Home and Helpful Home Concepts
The smart home concept is the result of a unique convergence in computer technology, a timely “coming of age” for three important technical factors which make 21st century helpful home management a reality. These are cloud-based computing, improved memory capacity, and perhaps most importantly, inexpensive chips which make a cost-effective Internet of Things (IoT) possible. Developers can now use virtual machines that are easily accessible via the cloud to come up with new software applications on IoT chips costing as little as $1.60.
The obstacles of expensive mainframe computing resources have been removed by the cloud, leveling the playing field for smart home app developers. Today, smart home product developers don’t need to be IT goliaths such as IBM, Google, Apple, or Microsoft (with R&D budgets to match) to deploy the power of the cloud and the super-connectivity of the Internet of Things. Easy-to-afford IoT chips make it tempting for manufacturers to include them in consumer products of all types.
In the age of super-connectivity, smart home gadgets with economical IoT chips can potentially do everything from remotely diagnosing engine trouble to letting us know that we’re running low on our favorite brands of cereal. Oh, and by the way, the smart refrigerator is sending an alert that the milk you’re about to put on the kid’s morning cereal is nearing the expiration date. That’s just one simple example of moving beyond the smart home concept to truly helpful home applications.
The Smart Home Works for You. The Helpful Home Works With You
With cost-effective research and development freedom and affordable hardware (cheap chips!), smart home technology is advancing rapidly beyond mere voice-activated control. The scalable capacity of the cloud makes it possible for any “smart home” to use advanced AI and machine learning to move beyond command and control. The smart home thermostat is pretty cool, and smart home locks and security systems can keep us feeling snug and safe, but many of these smart home applications are stand-alone apps operating independently, lacking critical “context-aware” capability.
Sure, it’s fun to issue voice commands to a compliant smart home device, assuming the role of master of the manor with a willing and ready domestic ambient computing staff prepared to cater to our every whim. It gives us a sense of control and mastery of the home, especially when Mom and Dad are both working and the details of managing home, hearth, and kids can easily fall by the wayside. But with stand-alone smart home devices, the human master is still the hub of smart gadget function and results. The human hub of all of these independent smart team members still bears the brunt of happy, economical home management.
As efficient as AI assistants such as SIRI, Alexa, and Google Assistant may be, can they learn from experience and adjust accordingly without a constant stream of manual programming or voice commands? Will we “the masters” always bear the burden of manually analyzing smart technology functions, continually reprogramming and tweaking to achieve a balanced “homeostasis”? Or will we reach that truly helpful home which keeps itself firmly in the Goldilocks zone where everything is just right?
That’s where machine learning comes into play, allowing us to retain control without the need to micromanage each independent smart home function. Machine learning can use the data from available smart home appliances, lighting, and smart home thermostats to boost energy efficiency and reduce carbon footprints. IoT chips on consumables can allow the helpful home to write the next shopping list to keep the cupboards fully stocked, and if we desire, our helpful AI can even place those orders for us over the internet and track it until it arrives at the doorstep. When deliveries arrive, it sends us an SMS or email alert while keeping the doors locked because the UPS man isn’t recognized as an authorized resident of the house. And the longer our deep learning AI works for us, the better it becomes at fulfilling our helpful home requirements.
Machine Learning in the Helpful Home
What does the helpful home of the future look like? Go ahead and check out our article at the preceding link and you’ll get a good overview of the current state-of-the-art and future goals of ambient computing. Artificial Intelligence (AI) and machine learning, also known as “deep learning”, has been heralded in the IT (information technology) sector as one of the most significant innovations since the microchip. With machine learning, all of those independent smart gadgets can be connected in one cooperating comprehensive team which learns from experience and adjusts accordingly without the need for human input at all. The human master of the house has now been promoted from “micromanager” to Chief Executive Officer. In theory, we can set our helpful home priorities with a deep learning AI which learns from the environment and experience.
The neural networks of AI’s capability for deep learning imitate the same processes used by human neurons in the brain. The results have been phenomenal. IBM’s Deep Blue and WATSON put machine learning in the spotlight when Deep Blue defeated chess champion Gary Kasparov in 1997. WATSON achieved supercomputer fame by whooping human opponents on the game show, Jeopardy.
Age in Place and Accessibility in Helpful Homes
Now, twenty years later, machine learning is even more formidable. It has vast data resources available with the cloud, improved memory, and much faster processing speeds. Combined with an abundance of IoT chips, IBM is now focusing the power of deep learning for “age in place” homes to care for the elderly and improve accessibility for the physically challenged. Smart virtual assistants working across a network of connected IoT devices “can turn on lights and unlock doors, adjust the temperature, turn on the oven, make phone calls, text, open window blinds, and order groceries”. Machine vision can interpret gestures and body language and machine learning can distinguish between different individuals to accommodate their special accessibility needs.
Smart Cameras and Smarter Security
Let’s look at smart home security and how it can be enhanced by harnessing the power of machine learning. Today, smart home cameras are combining facial recognition and AI to learn from what is happening around the camera. Smart home security cameras such as Ooma Homes’ AI-powered Butterfly can detect and recognize people, pets, and sounds, and advanced battery technology keeps it working in the event of a power outage. Facial recognition unlocks the door for easy access when a known house member is approaching with an armload of groceries. It keeps the door locked and rings the doorbell if it doesn’t recognize the individual.
The Ooma Butterfly can even record video when unknown parties approach and send out SMS text messages, email alerts, or phone calls when it detects “stranger danger” on the premises. Machine learning can allow smart home security systems to learn the comings and goings of the household to reduce the false alarms which are the Achilles heel of any security system. You don’t want to be receiving an alert every time the neighbor’s cat takes a shortcut through the yard, but you will want to know that the kids came home unexpectedly on a school day.
ThinQ: Helpful Home Assistant Anticipates Household Demands
The best helpful home technology communicates with the cloud and detects and learns your individual household patterns without relying on continuous voice commands or programmed routines. The best AI assistants don’t simply react- they predict, like the ThinQ AI, a kitchen helper by LG. ThinQ recognizes voices so it knows which household member is speaking. Based on Deep ThinQ technology, ThinQ can deploy robot mops and vacuums to spruce up the floors after it senses that all house members are off to work or school.
In her informative article at Techlicious, “Artificial Intelligence is Making Your Smart Home Smarter” writer Barb Gonzales reports on LG’s commitment to smart helpful home AI. The company’s complete line of Kitchen Solution appliances will all be controllable with the innovative ThinQ AI. With deep learning capabilities, smart LG washers automatically apply your preferred washing settings and relay preferences such as “wash sports clothing” to the dryer. LG air conditioners adjust room temperatures according to ThinQ data about just who is in the room- data gathered by video, sensors, and voice commands to the LG CLOi Hub Bot.
Did you think we were stretching the truth a bit when we mentioned a smart refrigerator sounding off about expired milk? The LG refrigerator is one of the main helpful hubs in the ThinQ system. This smart refrigerator can suggest recipes to use up food on hand before it expires. A touch panel display in the door gives you an inventory of what’s in the fridge, with expiration dates. When you’re running low, the refrigerator generates a shopping list and sends it to the ThinQ app on your phone so you won’t forget to stock up on the way home.
About HelpfulHome.com
At HelpfulHome.com, we’ve made it our mission to help busy homeowners stay on the cutting edge of smart helpful home technology. We provide tips, articles, and informative how-to’s to keep you up to speed on the industry-leading smart home innovations which can enhance our lives and enrich the family experience in today’s hectic world.
Discussion about this post