15 Jun · 6 min read
Although, the cloud has played a significant role in expanding AI applications towards the mainstream. But the emergence of embedded artificial intelligence (embedded AI) has opened new opportunities beyond cloud AI, which transfers the significant tasks from cloud-based AI to device-level AI.
Embedded AI implements Machine and Deep Learning in software at the device level. The software can be programmed to deliver both predictive and reactive intelligence based on the collected and analyzed data. Both Cloud-based AI processing and device-based AI processing have pros and cons regarding tasks, data, and results.
Let’s discuss the differences between the two in detail:
One of the key differences between cloud AI and on-device AI is their efficiency in processing data. Cloud servers are meant to process centralized data, whereas on-devices perform data processing locally. For instance, to manage and control the data of a retail shop chain, it is efficient to use the cloud as cloud servers would help coordinate and collect data from multiple store locations. Similarly, on-device is better suited for intelligence platforms such as Qualcomm’s Snapdragon 845. Users can directly process facial recognition instead of waiting for data for cloud transmission to unlock their devices.
Both cloud AI and device-level AI have significant security concerns regarding privacy. Cloud is meant to store sensitive data on the Internet, making it more prone to cyberattacks, whereas keeping this data on local devices can make it less prone to cyberattacks. However, local devices don’t ensure 100% security with regard to internet connectivity. E.g., If an employee’s smartphone is lost, a malicious user can potentially access the local device to compromise the entire company network.
Ease of Comfort
Another critical point of comparing Cloud-based AI and on-device AI is convenience. Most users find cloud AI convenient as it allows them to access large AI applications without the further requirement of building massive local IT infrastructure. But, in some cases, like in autonomous cars, it is more convenient to use on-device AI to ensure smooth real-time data processing.
A tremendous shift has occurred from cloud-level to device-level processing of AI tasks, data, and results. This significant transition has resulted in the emergence of embedded AI or Edge AI.
Initially, complex AI computations such as creating search engine results were performed at the cloud data center. But with the introduction of AI models on Graphics Processing Units (GPUs), System on Chips (SOCs), and Session Border Controller (SBCs), there has been less reliance on the Cloud for AI data processing. The introduction of embedded AI has made it possible for devices to implement AI models on the device level and use those results to perform specific tasks or actions. Cloud storage can still be helpful to store data for security reasons as data is temporarily stored at the device level.
1. Better Decision-Making
The emergence of embedded AI will help businesses in better decision-making. With predictive machine learning features, it becomes easy to make optimal moves to witness the growth in their businesses. For example, in ERP, device-level processing can be used to eradicate manual processes such as budgeting, inventory management, and pricing. Moreover, implementing embedded AI will allow companies to manage inventory and manufacture more efficiently based on the global market data.
2. Valuable insights through predictive analytics
The vast amount of data is essential for machine learning to provide important user insights. It becomes easy to determine employee engagement with the help of advanced and predictive analytics. Especially for individuals in industries such as Human Resources, it becomes easy to churn data in a specific order. This data-driven approach will help companies plan their hiring process more efficiently to retain employees and avoid potential barriers.
3. Seamless User Experience
The strong foundation of ML and AI has managed to improve the customer experience to the best of its potential. The introduction of a chatbot for B2C companies has served as a critical player in streamlining the customer experience, whereas B2B companies are still on the verge of utilizing various artificial methods to improve user experience. In fact, it would be exciting to see how embedded AI can further mold the game of customer experience with real-time customer data.
4. Automation of day-to-day tasks and businesses processes
While looking for AI software, businesses often tend to look out for solutions that regularly automate their day-to-day tasks. Effective implementation of AI in CRM software has made the lives of sales professionals relatively easier with automated lead scoring and optimization of email content. The utilization of embedded AI has helped employees save enough time and money to utilize it on the tasks that demand their presence on a more frequent basis. Such in-depth insights related to sales not help them drive more leads but would also add up to a significant increase in revenue.
Healthcare and Safety
Consider an example where a patient is in a critical condition and is being taken from home to the hospital in an ambulance. In such situations, the transmission of patient data to the cloud can be challenging. Therefore, embedded AI can prove to be a blessing as it can help process and analyze data immediately, followed by implementing relevant actions.
Transportation and Logistics
Embedded AI can be helpful in real-time data processing for autonomous vehicles. With no further requirement for data transmission to the cloud, it becomes more convenient to process data locally through on-device AI platforms.
The use of embedded AI can be seen in space exploration to automate image processing for the categorization of galaxies, planets, or stars. It also involves the development of autonomous spacecraft that can avoid space debris without human involvement and employing an AI-based cognitive radio to provide efficient and distortion-free communication networks.
Embedded AI can be helpful in the manufacturing industry for edge analytics. In edge analytics, manufacturers collect and evaluate data on edge to reduce the time to insight. The insights are gained closer to the devices to gather data instead of relying on a central location at a distance apart.
Due to a significant increase in smart sensing, the use of embedded AI in sensing data and IoT applications has accelerated Machine Learning to encourage real-time interaction. This will enable a large amount of sensing data to be processed to give valuable insights for aiding smart devices in making informed decisions. With advancements in embedded AI, businesses in industries like manufacturing, logistics & supply chain, etc., are likely to become more proactive and predictive in their approach toward digital transformation.
Moreover, businesses in B2B industries are more likely to take advantage of embedded intelligence to develop predictive products. Let’s take the example of a tractor embedded with AI that would alert owners to take appropriate actions towards concerns that are likely to happen. This would save the utilization of resources by farmers and prevent them from irrelevant cost overruns. Furthermore, there are several challenges in integrating Artificial Intelligence into smart sensing and IoT applications, such as memory footprint, maintaining the computational complexity, and making the evolution of embedded AI an ongoing trend.
As the technology behind embedded AI continues to evolve, the development of embedded AI is expected to add significant value to most industries. This will result in the evolution of businesses and serve as a breakthrough in offering a better customer experience. We hope that the insights mentioned above about embedded AI helped you take an informative dive into the topic. Contact DAC digital for your next embedded AI project!