For decades, artificial intelligence primarily existed in the cloud. Users would send their data to online servers, have it processed there remotely and receive the results back in return. While that system had its merits, it also had significant trade-offs, mainly related to latency, privacy issues, dependence on connectivity and escalating costs associated with operating server farms to process the collected user data. A series of small, low-key, but ultimately significant changes have been occurring recently in the industry. AI is being brought closer to the end-user's device and now runs directly on their device.
By utilizing the user's device as a processing resource, there is an opportunity for machine learning models to run locally on laptops, smartphones, wearables and edge devices, eliminating the need for the models to rely on cloud-based servers entirely. As more and more hardware has become widely available, machine learning models have become increasingly efficient and, along with the increased demand for privacy-first types of experiences, there has been a shift to running the models entirely locally. The existing applications in consumer-facing applications, or enterprise software and embedded devices are not experimental.
On-dvice AI will change the way we design applications; how we collect, manage and interact with user data; and how we utilize technology overall. Applications will be more responsive, more personalized, and more considerate of the user's privacy. In addition, this transition for developers and product teams will mark a fundamental change in the types of architecture required to support distributed intelligence at the edge, as opposed to centralized intelligence that is hosted by the cloud.
While the current state of this transition may seem understated, the implications of this transition will be extremely significant in the long run.
Why On-Device AI Is Gaining Momentum Now
Three forces are driving this shift. First, advances in hardware have made it possible for today's smartphones and laptops to include specialized neural processing units (NPUs), graphics processing units (GPUs), and AI accelerators that have been specifically designed to operate efficiently in the execution of complex models, while using minimal power from the device's battery.
Second, model optimization techniques, which include quantization, pruning, and distillation, enable developers to take larger models and create smaller, faster versions that can operate on a local machine. There is no longer a requirement for the same cloud-based computing resources to perform many common AI-based tasks.
Third, increased user awareness about the use of their personal information by companies and governments has resulted in greater scrutiny of how data is collected and used. The introduction of tighter regulations regarding the processing of sensitive information means it is now possible for a user to process their data on their device rather than sending it to a remote server for processing, thus posing a lower risk to the user's privacy and simplifying regulatory compliance for the company using the data.
As one can see, on-device AI is no longer just a future technology but is becoming a preferred technology for application developers. On-device AI allows for rapid response, improved cost efficiency, stronger user trust in the applications they use, and has led product teams to reevaluate their existing architectures and implement as much on-device intelligence as possible. The emergence of on-device AI as a viable technology is not a trend that is developing over time. It is a developing reality throughout many industries and across many different platforms today.
How On-Device AI Changes App Performance and Reliability
The introduction of on-device artificial intelligence, or AI, can provide apps with faster performance. Apps using AI for tasks like voice recognition, image analysis and predictive typing will perform more smoothly and reliably as they will no longer have to wait for network transmissions to take place before performing any tasks requested by users.
By providing users with increased performance and reliability, on-device AI also improves the overall reliability of apps in terms of app availability to users. With the introduction of on-device AI, issues related to poor telecommunications connectivity will no longer disrupt the functioning of core features of apps. On-device AI models will continue to operate offline as well as during times of low-bandwidth and no broadband access in areas that do not have reliable internet access, as is often the case with many parts of the world.
By reducing the number of possible failure points that can affect the availability of an app, increased reliability of an app can occur as well. By reducing the number of dependencies that a user has to rely on to use an application, the likelihood of issues stemming from an application due to problems with cloud services, API throttling or latency spikes is diminished.
A shift toward increased usability and trust is also achieved, as users are more likely to recognize apps that respond quickly and work consistently and effectively without continuous access to the internet. This level of recognition leads to users developing new expectations in terms of the quality and performance of apps across the entire mobile device app ecosystem.
Privacy by Design Becomes the Default
The rapid introduction of on-device AI technology has amended the way we think about privacy. With on-device AI, the app can process the user's data without ever sending it to a server. This has resulted in the ability to send general information about the user's activities instead of raw detailed data.
As a result of this new model, personal information such as health records, voice recordings, biometric information, and private messages are stored and processed on the user's device rather than being sent off to a third-party for storage and processing. This ultimately provides greater safety for the user as well as less reliance on compliance regulations for the company.
Additionally, analyzing data and only sending over summary data leads to fewer compliance issues (and lawsuits) for companies. Companies now think of the user's data as "non-trivial" - something to be treated with respect from the start.
In essence, the apps that implement this technology can signal to users that their information is being handled properly, rather than it being an afterthought.
New Possibilities for Personalization
With the introduction of local-based AI, users can personalize their experience without fear of surveillance. Unlike traditional AI, which learns from users' data in the cloud, users will now have access to machines able to understand their individual actions, respond in real-time and also tailor recommendations, interfaces, and interactions based on the user's unique profile.
By creating a personalized experience that feels natural rather than invasive, users will appreciate smarter behavior from th vices without ever feeling uncomfortable about how or where their data was gathered or who has access to that information.
From a product perspective, local-based AI offers manufacturers the opportunity to create fully customizable and instantaneous applications. Users will be able to immediately experience features that are tailored to their current environment, activity, and personal preferences.
Challenges Developers Must Address
While on-device artificial intelligence has the potential for great power, it is not without significant effort to create applications powered by on-device artificial intelligence: developers need to work around their limitations of storages, memory, and compute. Each device type has different performance characteristics, making it difficult to maintain consistent performance between devices of different generations.
It can be difficult to properly version your models for incremental updates. When performing inference on millions of individual devices, debugging will be that much more difficult.
Securing a device means ensuring that your model is not tampered with or used improperly.
Fortunately for developers, the speed and advancement of tools is rapidly increasing. Some frameworks now provide support for cross-device optimization, secure model updates, and hybrid models that combine intelligence between the cloud and the local device.
Although the tradeoffs are significant, the rewards are also substantial.
What This Means for the Future of Apps
Decentralized Intelligence will be the wave of the future for new applications. There will still be use of Cloud-based AI technologies, but it will be used in a more measured way. All complex Coordination, High-Compute Training and Large-Scale Analytic activities still remain within the cloud. However, the intelligence that will be utilized to make day-to-day decisions will be used from devices.
This paradigm change will lead to differences in User Experience (UX), Infrastructure Decisions, application Design Patterns, and User Expectations. The resulting applications will be faster, more Private, and more Resilient than what currently exists.
Teams who understand these differences will create application products which seem years ahead of their time, without any marketing being done to sell the underlying technology. And that’s why this trend is invisible – the users may not see the architecture behind the application, but they will feel the difference created as a result of Decentralized Intelligence.
On-device AIs are not just enhancements of current technologies – Decentralized Intelligence represents a dramatic change in the way an application Thinks, Interacts, and Builds Trust With Users.
