Artificial intelligence (AI) has come a long way in recent years. Systems can now chat with us, create realistic images and videos, and perform tasks that seem almost human. However, current artificial intelligence services still rely on powerful computers and massive datasets in the cloud. But that is starting to change with the emergence of on-device AI.
In this post, our experts, who also provide brilliant artificial intelligence development services, will explore how new neural processing units (NPUs) enable AI functions directly on phones and computers without needing the cloud. We’ll look at examples from Apple, Samsung, Honor, Microsoft, and Google, showcasing how on-device AI powers features like visual search, image editing, eye contact correction on video calls, and more.
We’ll also discuss the privacy implications of having more AI development services and tools on our devices and how companies address potential concerns. Overall, onboard AI marks an exciting shift that allows for more personalized, private experiences while reducing reliance on the cloud.
The Rise of the NPU
So far, most AI has used graphics processing units (GPUs) for its heavy computations. GPUs excel at the math required for machine learning models. This explains the meteoric rise of Nvidia as a leading AI hardware company.
But in the past couple of years, we’ve seen the emergence of a new chip – the neural processing unit (NPU). As the name suggests, NPUs are explicitly designed for neural networks and AI processing. And because they’re smaller, they can now fit directly inside smartphones.
This on-device processing delivers some key benefits:
- Reduced latency: Rather than having data make multiple trips to the cloud, NPUs enable near-instantaneous results. This allows for smoother AI interactions.
- Increased privacy: With processing occurring locally on the device, less personal data gets shared externally.
- Lower power consumption: NPUs are extremely power efficient compared to GPUs, resulting in longer battery life.
- Offline functionality: On-device AI still works without an internet connection. This means access to AI smarts is available in more places.
With NPUs now embedded in phones, manufacturers like Apple, Samsung, and Honor are actively promoting their advancements in “AI intelligence,” “personal assistants,” and other machine learning capabilities happening directly on devices.
Let’s look at some specific ways these onboard AI chips are enabling smarter smartphones and computers.
Smarter Smartphone Experiences
Modern smartphones boast some seriously capable AI powered by their integrated NPUs. Instead of offloading processing to the cloud, these on-device machine learning models create experiences that are faster, more personalized, and keep data local.
Understanding Messages
Honor phones use their AI to analyze incoming messages and determine the appropriate app to open based on the content. If the message contains an address, it will automatically launch maps. Meeting details will cue the calendar. This contextual understanding helps streamline workflows without compromising privacy.
Taking Notes
Samsung’s phones can transform handwritten notes into structured summaries and task lists. Powered by the onboard NPU, this capability lets people conveniently jot quick notes during meetings or while on the go without needing WiFi or cell service. It’s a more natural user experience using AI in a supporting role.
Real-Time Translation
Google Pixel 6 devices feature direct speech-to-text translation powered by the new Tensor G2 chip. This allows for easy back-and-forth conversations in over 50 languages with sub-second latency, all without an internet connection. The AI runs fully on-device for lightning-fast, private translations.
Summarizing Calls
Building on their real-time translation, Pixel 7 phones go a step further by listening to your phone conversations and providing handy text summaries after you hang up. Rather than recording calls, the on-device AI generates brief overviews of the key discussion points. This allows you to quickly recall details without compromising privacy.
Editing Media
Today’s smartphones also showcase AI photo and video editing capabilities. Using generative algorithms running locally on NPUs, phones can seamlessly remove objects from images or even add new elements into shots. While results aren’t always perfect, it exemplifies the rapid progress of on-device AI.
As these examples demonstrate, shifting AI onto smartphones and away from the cloud enables more personalized, private experiences that feel quicker and more natural. And phone makers are just getting started exploring this burgeoning area.
Smarter Computing with Microsoft and Google
Beyond mobile app development services, we’re also seeing onboard AI deliver clever new computing experiences. Microsoft and Google both showcased concepts of applying AI algorithms locally on devices like laptops and tablets without needing connectivity.
Microsoft Software Gets a Generative Boost
At a recent product unveiling, Microsoft demoed several AI-powered software experiences leveraging their new natural processing capabilities.
One intriguing example was an AI drawing assistant built into good ol’ Microsoft Paint. By providing a basic sketch, the AI “co-pilot” could then generate a more polished, detailed artwork. Essentially, anyone can produce decent illustrations, regardless of artistic skill.
While the visual results tended to be unpredictable (the demo showed issues with objects randomly floating in space), it exemplifies software tapping into onboard AI. Probably, capabilities will only improve over time.
Video Calls Gain Eye Contact
On their new Surface tablets, Microsoft also revealed subtle AI tweaks to enhance video conferencing. Algorithms running locally can make your eyes appear more focused during calls by digitally adjusting where you’re looking. This helps stimulate greater eye contact even when you’re glancing elsewhere.
The extremely subtle effect aims to make video chats feel more natural. And by processing imagery on-device rather than in the cloud, user privacy remains protected.
Total Recall with AI Search
However, perhaps the most ambitious application of on-device AI comes through a Microsoft concept called “Recall.” Enabled through local processing, Recall continuously captures screenshots as you use apps and websites. It then applies large language models to index these images, making them searchable via natural language queries.
For example, if you vaguely recall seeing a product online days or weeks ago but can’t find it anymore, you describe some key details to Recall. It then scans through your visual history to surface relevant screenshots.
This “photographic memory” for computing demonstrates massive potential. However, it also raises obvious privacy concerns, given the tracking of on-screen activity. Microsoft stated that such personal data never leaves devices and that strict controls will govern Recall’s functionality, aiming to give consumers confidence in AI assisting rather than monitoring.
Google’s AI Lens for Search
Similarly, Google introduced its own local AI search tool for Android devices. Using the Google Lens app, users can search through screenshots using natural language. You describe what you want to find, and it surfaces relevant images complete with handy overlays and links.
Powered entirely by the device itself, this AI search helps reconnect people to moments and information while keeping usage data private. It provides another glimpse into the promising future of on-device machine learning.
The Privacy Balancing Act
As conveyed by Microsoft’s strict Recall controls and Google’s limiting its AI search to local processing, privacy represents a crucial area needing attention as on-device AI expands.
Fundamentally, AI development services rely on data to function. The more data it consumes during training, the better it performs. This creates natural tension with user privacy. People enjoy AI’s conveniences but simultaneously worry about technology overstepping bounds.
Onboard AI offers a compelling solution to help address concerns. With no need for external connectivity, algorithms running locally retain data within devices. This protects privacy far more than cloud-based AI reliant on vast data centers. Users must consent to sharing information beyond their personal gadgets.
Additionally, by focusing processing on individual devices rather than centralized servers, onboard AI delivers more tailored, personalized results catering to individual preferences versus one-size-fits-all cloud intelligence.
Of course, challenges remain to ensure privacy and prevent potential AI overreach. But used judiciously, on-device machine learning offers a promising path to balance utility and transparency. And as companies like Microsoft and Google philosopher over appropriate applications, we’ll likely witness AI progressively support human goals rather than supplant human agency.
The Future with AI Onboard
On-device artificial intelligence marks an important milestone in computing history. With smartphone processors now packing neural grunts equal to early AI supercomputers, the capabilities emerging genuinely feel like science fiction materialized.
Unlike past technology waves centered on software and connectivity, onboard AI unlocks machines with growing awareness and agency. This poses exciting opportunities while necessitating thoughtfulness by creators and caution by consumers as we shape how human and artificial cognition co-evolve.
But make no mistake, offloading AI onto local devices rather than funneling data externally represents pivotal progress. Our phones and computers gain expanded intelligence to serve our wants and needs without compromising privacy or control.
Sure, on-device AI remains imperfect today – don’t expect it to fully replace cloud systems just yet. However, rapid engineering advances could soon yield a global neural network distributed across billions of gadgets rather than concentrated by corporations.
So, while server racks continue churning data to feed the algorithms of AWS and OpenAI for now, increasingly smarter processors embedded in personal gadgets might soon lead to technology’s next paradigm shift. Artificial intelligence services and assistants might become more helpful – and trustworthy – by thinking for themselves rather than about us.
