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Digitizing Homes: Making Everyday Appliances Smarter with IoT and AI

While the earlier decade was all about data communication and internet proliferation, the economy of the next few years will thrive upon digitization of systems, where we will encounter implementation of smartness into everything i.e.

With the addition of smart devices and appliances, and with capabilities in sensing, connectivity, and data transmission, people can interact, collect, and analyze highly valuable data to automate various operations at home, which were previously performed manually.

Let us take a look at how smart appliances enable the digital home: Today, technology has evolved to such an extent that there’s a possibility to design meaningful collaboration between humans and machines, primarily due to advancements in AI.

Moreover, digital assistants, also called virtual assistants, work on voice-controlled AI, which can do functions like searching the internet, making calls, and connecting to other devices.

AI techniques in washing machines, washers can autonomously regulate the washing strength and detergent to be used according to the load weight and the type of fabric.

For instance, if the user sets a gym workout on his/her calendar, the washing machine will set gym clothes setting on when he/she returns.

A smart fridge can also let the user create shopping lists, upload photos using smartphones, and check inside the fridge to get an idea about things that are out of stock.

 Smart speakers can be controlled using voice commands to do various tasks such as create a playlist, turn on reminders, make grocery lists, and even search the web.

Also, the motion detection feature of smart cameras personalizes and alerts any movements in the vision zone, based on the availability or non-availability of residents in the home.

Energy Saving The feasibility of remote connectivity and access through smart meters provides the functionality to track the electricity consumption and send information in real-time to a smartphone.

For example, a user can remotely control a smart washing machine via an app that alerts the user about the washing cycle progress, errors or threats, and the energy rate on demand.

In the washing and drying process, a user can monitor the cycle of a smart washer, energy consumption, and can get notified when the drying process is finished, using a remote application.

Using the smart grid technology, homeowners can save money by operating appliances during off-peak times, eliminating the need for the power plant to generate more electricity.

Currently, there are many standards, networks, and devices used to connect the smart home, creating interoperability problems and making it confusing for the user to set up and control multiple devices.

Demystifying Artificial Intelligence for the Product-Led Company

In this article, we’ll demystify machine learning for product managers and explore an MVP framework for product-led companies.  Artificial intelligence, a buzzword that is synonymous with machine learning, combines ideas from computer science, mathematics, and statistics to allow computer programs to generate insights and predictions without being explicitly programmed.

Below are three examples of machine learning use cases that help drive revenue and productivity.  Use cases: Converting customers from free trials to paid subscriptions, upsells, and customer retention Examples:  Use cases: Productivity gains, smoother user experience Examples: Use cases: Customer self-service, extracting insights from text Examples:  Given the rapid evolution of the machine learning field, novel use cases for ML come up every day.

As a result, ML consumers must implicitly, or explicitly, validate model results so algorithms can learn and evolve over time.  As product teams identify new use cases for machine learning (or augment existing use cases), consider the following MVP framework:  The words “machine learning”