AI has become one of the most common terms in the marketing of modern televisions and smartphones. Manufacturers advertise AI processors, AI picture enhancement, AI cameras, AI sound, AI recommendations, and AI assistants. But what is actually behind these claims?
The answer is not simply “real” or “fake.” Modern devices can use genuine machine learning and neural networks, but many functions described as AI are still based on conventional algorithms, sensors, statistics, or predefined settings. The important question is not whether a product carries an AI label, but what technology is actually used and what it does.
AI in smartphones
Smartphones are well suited to AI because they combine powerful processors, cameras, microphones, sensors, and cloud services.
Camera processing is a good example. A modern phone can recognize faces and objects, reduce noise, improve dynamic range, combine several frames, and enhance colors. Some of these operations use traditional image-processing algorithms, while others can use machine-learning models.
Voice assistants work in a similar way. Speech must first be recognized, converted into data, and interpreted before the system can respond. Modern speech recognition and language processing can use neural networks. Some processing happens on the phone, while more complex tasks may be performed on remote servers.
Recommendations and personalization can also use machine learning. A smartphone can analyze which applications you use, when you use them, and which functions are important to you. However, not every form of personalization requires machine learning. Some functions simply follow predefined rules.
Facial recognition is another example. Modern systems can use neural networks to identify and compare facial patterns. This is different from simpler biometric systems based on conventional pattern matching.
AI in televisions
Television manufacturers use the term AI particularly heavily, especially when describing picture and sound processing.
Modern TVs can analyze video and adjust noise reduction, color, contrast, sharpness, and resolution. Some of these functions use conventional algorithms, while more advanced systems can use machine-learning models.
Upscaling demonstrates the difference particularly well. A conventional scaler enlarges an image using mathematical calculations. A neural-network-based system can analyze image patterns and estimate details using a model trained on large amounts of visual data. Both perform upscaling, but the underlying technology is different.
Manufacturers also advertise AI processors. This does not mean that the processor is an intelligent entity. It means that the chip contains hardware optimized for the mathematical calculations used by machine-learning models. Similar neural-processing hardware is now common in smartphones.
Sound processing can also combine traditional digital signal processing with machine learning. A TV may enhance dialogue, adjust volume, or optimize sound depending on the content or environment. The exact technology depends on the model.
Where marketing becomes misleading
The biggest problem is that manufacturers often use AI as a general marketing term.
Automatic brightness adjustment, for example, can be performed simply with a light sensor and a predefined algorithm. Calling such a function AI does not necessarily mean that machine learning is involved.
The same applies to many automatic picture adjustments, power-saving functions, and basic recommendations.
At the same time, it would be wrong to say that all AI features are merely marketing. Neural networks are genuinely used today for image recognition, speech recognition, computational photography, image processing, recommendations, and other tasks.
The difference is that a product can contain both genuine AI technologies and ordinary automation.
On-device and cloud AI
Another important distinction is where the processing takes place.
On-device AI runs directly on the smartphone or television. Cloud AI runs on remote servers operated by the service provider. Modern products often combine both approaches.
For example, a smartphone may perform some image or speech processing locally while sending more demanding tasks to the cloud. A television can process video locally while receiving recommendations from an online service.
Therefore, cloud processing does not make a technology less real. It simply means that the processing is performed outside the device.
What consumers should look for
The word AI should not be the main reason to choose a smartphone or television.
Instead, look at the specific function. If a manufacturer advertises AI upscaling, find out whether it actually improves picture quality. If a TV has an AI processor, check which features use its neural-processing capabilities. If a phone advertises an AI camera, look at what it actually improves — low-light photography, noise reduction, HDR, object recognition, or image processing.
The technology behind the marketing label matters much more than the label itself.
