When people talk about AI in the home, they usually picture voice assistants or smart plugs. But it's more interesting to look at how major manufacturers embed algorithms directly into fridges, washing machines, and air conditioners – and what this actually looks like in practice.
Samsung recently published an interview with its Vice President, Miyoung Yoo, who is responsible for home appliance reliability. The conversation turned out to be not marketing-focused but quite technical – about how they structure the development process and why they believe AI in appliances shouldn't operate in isolation but should work in tandem with hardware and software.
Reliability Isn't Just About Things Not Breaking
Miyoung begins by saying that reliability, in her view, is not just about the device's lifespan. It is also about operational predictability, feature stability, and the ability to adapt to real-world usage conditions.
Simply put: a fridge might run for 15 years, but if it becomes noisier or fails to maintain temperature properly after three years, that's already a reliability issue. And if its built-in AI, intended to adapt modes to user habits, behaves unpredictably, trust evaporates even faster.
Samsung focuses on ensuring that devices don't just perform basic functions but also maintain performance quality throughout their entire lifecycle. Here, AI becomes not a mere interface flourish but part of the system influencing physical processes inside the appliance.
Why Hardware, Software, and AI Must Be Designed Together
The main idea Miyoung conveys is that you can't just add AI to a finished device as if it were an app. You need to plan from the very beginning how algorithms will control components and what data they will require.
For example, an AI washing machine can determine fabric type, load weight, and soil level on its own – and adjust temperature, drum speed, and water amount accordingly. But for this to work reliably, you need to:
- properly place and calibrate sensors;
- train the model to interpret sensor data;
- map the model's outputs to physical actuators – motors, valves, heaters;
- ensure all this works for years, not just the first month after purchase.
At Samsung, this is called an integrated approach: teams responsible for hardware, software, and AI work together from the very start of the project. Miyoung emphasizes that if you simply train a model on data and embed it into a device, something will likely go wrong.
What Data Is Used and How It Is Collected
One crucial point is where the data for training models comes from. Samsung uses several sources:
- lab testing – devices are run through various modes under controlled conditions;
- field trials – appliances are placed in real homes to see how they behave in different climates and usage scenarios;
- anonymized telemetry from active devices – collected with user consent.
This data helps train models, but more importantly, it helps identify where a model might make mistakes. For instance, if the AI in an air conditioner incorrectly estimates room temperature, this directly impacts comfort and energy consumption. That's why it's important to identify edge cases in advance and teach the model to handle them.
Testing: Not Just Functionality, But Long-Term Stability
Miyoung devotes a separate section to how they verify the reliability of AI-enabled devices. Usually, home appliances are tested for fault tolerance: turned on and off repeatedly, subjected to heavy loads under extreme conditions, and checked for vibration and voltage surges.
But when a device has AI, another layer of checks is added: engineers must ensure the model doesn't degrade over time, that it works correctly when external conditions change, and that its decisions don't cause components to wear out faster than designed.
For example, if the fridge's AI cycles the compressor too often while trying to hold temperature more precisely, it could shorten the compressor's life. Or if the washing machine algorithm ramps up the drum too aggressively, this will take a toll on the bearings.
Therefore, Samsung uses accelerated lifecycle tests: devices operate in modes that mimic several years of usage while engineers track how the AI behaves and how that affects mechanical components.
Updates and Post-Purchase Support
Another point Miyoung touches on is the ability to update algorithms after the device reaches the user. Unlike standard appliances, where firmware is updated rarely and mostly to fix bugs, smart devices can receive model improvements.
This opens up possibilities: the model can be fine-tuned on new data, support for new modes can be added, and recognition accuracy can be improved. But it also creates risks: an update shouldn't worsen device performance, break hardware compatibility, or introduce new vulnerabilities.
According to the interview, Samsung approaches this cautiously: updates go through several testing stages before reaching users, and there is always an option to roll back if something goes wrong.
What This Means for the Industry
The interview is interesting because it articulates things that often remain behind the scenes. When we see an announcement for a «smart fridge with AI», we are usually shown an attractive interface and a list of features. But questions about how AI is integrated with hardware, how it is tested, and how it will perform five years from now often remain unanswered.
Samsung, it seems, is genuinely investing in development and testing infrastructure so that AI in appliances doesn't remain a «marketing label» but actually works in practice. This requires more time and resources than simply adding a model to the firmware.
On the other hand, it's not yet clear how scalable these approaches are across an entire product lineup. It's one thing for flagship devices sold at a high price point and another for mass-market models where the development budget is tighter.
Open Questions
A few points that weren't covered in the interview but are worth asking:
- How does Samsung handle the data privacy issue? If devices collect telemetry, how is it stored and processed? Is it possible to completely disable data transmission without losing functionality?
- How open is the architecture for integration with other systems? Does the AI work only within the Samsung ecosystem, or can these devices be integrated with third-party smart home platforms?
- What happens if the internet goes down? Part of the models likely runs locally, but are there features that become unavailable without a cloud connection?
These questions are important because they affect how independent and controllable the devices remain for users.
In Short
Samsung demonstrates a fairly mature approach to embedding AI into home appliances: not as a separate module, but as part of a complex system where aligning hardware, software, and algorithms is key. Reliability, in its understanding, is not just the lifespan but the stability of AI performance throughout the device's entire lifecycle.
This requires more effort at the design and testing stages, but a rigorous approach like this may help smart appliances become truly useful rather than merely trendy.
