How AI is shaping the smart home

Artificial intelligence (AI) is impacting every corner of computing and every type of technology on the planet. Smart home technology is no different. AI has been used throughout the smart home for smaller tasks over the past several years. However, the rise of more efficient large language models (LLMs) and AI at the edge – processing AI workloads on the device rather than the cloud – will enable exciting future use cases and experiences.

The smart home as a “smart assistant”

In the age of AI, the whole smart home can effectively become a smart assistant. Today’s AI-powered virtual assistants respond to your voice to perform an action when prompted. However, future virtual assistants will understand when a person walks into the home and then adapt the internal environment automatically based on their preferences. This could be anything from adjusting the temperature or light settings, playing music, managing access to the home, or generating automated grocery lists.

This will be enabled by various consumer and IoT devices being connected and talking with one another, as well an increase in centralized processing. For example, wearable devices, like smart watches or fitness trackers, could collect biometric data from the user and then make meal recommendations based on their current levels of health, previous fitness activities, dietary requirements, and even the food that is available in the smart fridge.

The rise of AI workloads at the edge will bring a whole new level of experiences and personalization for content and applications that manage the smart home environment. In fact, the TV, which sits at the center of the smart home experience, will become AI-powered.

AI in the Smart TV

Today’s smart TVs use AI for picture quality enhancements, and voice command for control and content selection. Also streaming services – which play a pivotal role in today’s TV experience – use powerful machine learning (ML) algorithms to recommend relevant content to users.

Additionally, as cameras start re-appearing in TVs, there is a significant increase in AI-based smart camera use cases, such as health and fitness, gaming and video calling. Home fitness is an interesting use case that is becoming more prevalent on TVs, with applications using AI for body tracking and then making subsequent relevant recommendations for a specific workout or for motion interactive gaming.

Looking ahead, AI in the TV could enable a range of future uses cases. Some examples include: acting as a smart mirror for people to virtually try on clothes; actively managing energy usage across the smart home through being the central processing hub for heating, lighting and home appliances; and helping children with their homework through on-device LLM-based smart assistants.

The engineering opportunity

These exciting use cases present an interesting engineering opportunity, with increasing computing demands across all smart home devices. Central processing hubs, like the TV, will need far more general compute, neural processing capabilities and security as more AI workloads take place at the edge.

The momentum towards AI at the edge is being supported by devices and AI models becoming more power efficient, as the industry drives AI to be deployed at scale across a variety of technology touchpoints in the IoT and consumer technology markets.

For security, AI at the edge has positive implications. From the consumer perspective, it means potentially sensitive personal data can be handled and processed on the actual device. Meanwhile, for the smart home ecosystem, powerful AI and ML algorithms and models, like those used by streaming services and for picture quality enhancements, are better protected against attackers.

There is already an ecosystem of services and applications that add huge value to the smart home experience, with significant opportunities for further developer innovation fueled by the age of AI. However, developers require a stable, flexible, and consistent compute platform that works across a wide variety of AI frameworks and libraries that are available to them.

CPU and GPUs are popular AI targets as they can run a variety of network types in many different data formats, while also being pervasive processor technologies. This provides differentiation and scalability opportunities for developers across the many devices in the smart home.

Universal standards

Universal standards benefit developers, as they deliver connected and consistent smart home AI experiences across a wide variety of connected devices. Matter is a universal IoT standard already in place today that increases the compatibility of diverse IoT devices in the smart home. For consumers, this means seamless, secure and reliable home technology experiences, with all smart home devices operating together.

However, while Matter is the standard protocol for controlling and connecting devices, there is no such protocol for managing data. This has implications for AI where vast volumes of personal data will be collected by smart home devices. It will be interesting to see how protocols like Matter could be extended to standardize how data is shared.

Smart home’s AI future

AI is already prevalent in the smart home and there are plenty of smart home devices, like the TV, that already have AI workloads at the heart of use cases used by consumers. However, the future will see AI accelerating across the smart home, both in terms of the quantity and quality of the experiences.

This will require an unprecedented leap in performance, power efficiency and security, particularly as more AI workloads move to the edge. The growing smart home ecosystem will need a compute platform that matches these requirements, while also being ubiquitous across IoT and consumer technology devices.

The growing role of AI promises exciting innovation opportunities as it accelerates the capabilities of smart home devices.

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