AI Appliances are Simplifying Food Preparation
AI powered technology is making storing and preparing food simpler and more efficient. Computer vision based AI models are beginning to become commonplace on our roads and in public spaces, but increasingly the benefits of this technology are being felt in our homes.
By helping people to manage their food inventories, and to prepare delicious meals consistently, smart appliances can empower home cooks and reduce food waste. In order for this technology to continue to develop it is important that developers have access to precisely annotated AI training images. Providers, like Keymakr, can support innovation with responsive and annotation services.
This blog will look at two specific applications for AI in the kitchen: smart fridges, and AI powered ovens. Both of these use cases are supported by image annotation, outsourcing to experienced providers offers innovators in this sector a range of advantages.
Storing food with the help of smart fridges
Storing food and managing food waste can be a struggle for many people. It can be difficult to know what food supplies are running low and to manage meals and shopping accordingly. Smart fridges may provide a solution to these common problems. These devices are capable of autonomously recognizing and monitoring the consumption items that are stored inside them.
This allows smart fridges to create real-time inventories, that in turn allows owners to plan their shopping lists and work out what meals to make. On a day to day basis smart fridges mean that items can be looked for in the fridge without opening it, leading to less energy usage. By linking devices smart fridges can become part of an integrated kitchen system that provides owners with a wealth of information and food preparation options.
This technology is enabled by image annotation. By labeling images of different food items human annotators create training datasets that enable smart fridge machine learning models to recognize a wide range of items quickly and reliably.
Cooking with AI
Computer vision technology is also helping people to cook food accurately and consistently. It can be difficult for inexperienced home cooks to know how long to cook different meals, and when exactly a particular item is ready to eat. Smart ovens are capable of recognizing what food needs to be cooked, and selecting the appropriate cooking temperature and time.
In-built sensors can then monitor food as it cooks, turning off the heat when a perfect level of doneness is achieved. This takes the pressure off home cooks, and allows them to complete other cooking tasks without constantly checking and worrying about food in the oven. By ensuring that only correct cooking times are followed, smart ovens also help to reduce energy waste, and food waste from burnt and discarded meals.
At the core of complex food preparation systems is image recognition. Identifying different types of food allows smart ovens to apply pre-programmed cooking procedures to right food items. Being able to distinguish between steak and fish is a consequence of accurate image annotation, leading to effective training datasets.
Data annotation supports smart appliances
Outsourcing data annotation to dedicated annotation services can help to streamline development of companies in the smart kitchen sector:
- Varied datasets: In order for final models to work consistently it is essential that training datasets reflect a wide variety of consumption items, from around the world. Annotation providers are experts at collecting varied training images from open sources, and can even create bespoke images to order.
- Validation services: Annotation services can play an important role when it comes to verifying algorithmic outputs. This can help machine learning engineers to refine and improve their models.
- Flexible data provision: Annotation providers, like Keymakr, allow AI companies to operate with flexibility. Training data needs can change, and outsourcing allows companies to respond to these changes without significant cost, or lost time.