Technology is developing at such a rapid pace that once you believe you have in-depth knowledge of the essentials for the moment, something else begins making headway. The concepts themselves may not be entirely innovative and revolutionary, but the applications and developments are. In the world of artificial intelligence, that’s a more common occurrence. For example, have you ever put much thought into how the autopilot setting works on vehicles that support self-driving? In this lifetime, it’s impossible to have not experienced a patchy or terrible Internet connection. Imagine your car is on autopilot, and the Internet connection is disrupted, leading to widespread chaos. Believe it or not, that is not an issue you need to worry about with our modern-day technology. All of that is thanks to something known as edge AI. Let’s understand what edge machine learning exactly is, how it functions, and more prominent real-life examples of edge AI.
In this article, you’ll receive an in-depth look into:
- What is edge AI?
- How it works
- Edge computing vs. edge AI vs. IoT
- The benefits of edge AI
- Edge AI solutions and applications
- Key takeaways
What is edge AI?
Edge artificial intelligence, otherwise known as edge AI, is a merge of edge computing and AI. It’s vital to understand what edge computing entails in order to effectively comprehend edge AI. We will take a closer look at edge computing in another section below, but to briefly define edge computing, it’s important to mention that it is often referred to as the alternative to cloud computing. As opposed to cloud computing, where data is stored, and algorithms are run on the cloud, edge computing refers to those processes carried out locally. That “location” is typically referred to as on the “edge.” While info processing with cloud computing typically is carried out in remote centralized locations, in edge computing, it is carried out on local Internet of Things devices such as smartphones or servers close to the source of the data.
Now that we have briefly established edge computing let’s understand what artificial intelligence on edge is. With edge AI, we can store and process data with machine learning algorithms all on local hardware (device) without connection to the internet—and all of that in real-time. In which type of situation would it make sense to use edge computing and AI? There are circumstances when edge AI is not only preferable, but it is a necessity. Such an instance is the case of self-driving vehicles, as we mentioned in the beginning. Additionally, self-operated drones are another optimal example since latency will increase if pattern recognition on a drone is carried out via the cloud, along with the possibility of it losing control and crashing if the connection is lost.
How it works
With a steady grasp on what edge AI is specifically, we can dive deeper to understand how it works. Let’s go to the very beginning of the life cycle for a typical ML model. In order to receive efficient and accurate results, each model requires data training, first, on an initial test dataset that can be one of the dozens of free, public datasets available and then trained on new, unprocessed datasets to verify that the algorithm is optimized before it can be deployed for production. Hence, the training of the algorithms with the data is not carried out on the edge, contrary to the popular misconception of edge AI. Only after, the trained model can be deployed to the separate devices, most commonly onto the edge device. If you are wondering about devices that conduct constant training and learning on the local device while keeping its data private, then that requires a whole introduction to something known as federated learning.
In order to have a fully-functioning edge AI device, you will need to optimize the hardware-software association to arrive at your desired result. A great example to look at is the use case when edge AI is used for security surveillance cameras. Let’s assume you want it to carry out the computer vision task of object detection, with bounding boxes and everything. Not only does the model need to be trained prior to the deployment, but you will need to determine what type of hardware (camera) will be used, along with ensuring it has the proper viewpoints, cables, lighting, and anything else required to meet your desired end result.
Edge computing vs. edge AI vs. IoT
It’s easy to be tangled in the web and not quite pinpoint the differences between edge computing, edge AI, and IoT, especially if you are newly emerging into the industry. Let’s briefly define all three to ensure there aren’t any blurred lines when referring to them going forward.
- Edge computing — An alternative medium for computing services to the cloud that is carried out on a local device as opposed to a centralized one. Edge computing is not synonymous with edge AI, but rather, edge AI operates via edge computing.
- Internet of Things — Physical objects/devices that are equipped with the technology to process data and connect with other systems via available connections such as the Internet. Best known as “smart” appliances from smart homes to smartwatches.
- Edge AI — Real-time data processing without the cloud or mandatory dependability on available Internet. Edge AI itself is not a physical, tangible entity. If you want to refer to specific hardware, then you need to mention edge AI devices or an IoT device with edge AI.
The benefits of edge AI
Besides the evident benefits that edge AI brings in terms of technological advancements, it’s necessary to highlight key advantages of utilizing edge AI in the product designing stage. These primary advantages are mutually beneficial for those creating a product with the use of edge AI and the user themselves.
Decreased latency
Edge AI devices are able to execute tasks on a real-time basis. An AI device carrying out a function with cloud computing will require sending a signal to the cloud and then receiving a response back (roughly speaking) in about a second. That seems sufficiently fast, doesn’t it? However, edge AI devices decrease that latency even more to a response time of about 400 milliseconds, according to closer observation. Those extra milliseconds of spared time not only boost the user experience and satisfaction levels but ensure greater safety in devices where those milliseconds can be determining moments.
Greater cost-efficiency
The costs associated with sending, processing, analyzing, and receiving data from the cloud for copious amounts of data are significantly high. Not to mention the bandwidth you would need to acquire in order to execute a task like image classification on the cloud with terabytes worth of data. According to research conclusions from Analysis Mason, businesses will notice anywhere from 10% to 20% savings on operational costs on average when switching from the cloud to the edge. Utilizing edge AI is without a doubt the cost-effective choice with this consideration in mind.
Efficient data security
Opting for edge computing has a one-up on cloud computing from the perspective of data security. With the cloud, your data is stored on a centralized cloud and can be tampered with in case of a single breach to the cloud in question. For example, if you store your photos on Google Drive, which is a cloud-based storage solution, then that content is under some degree of data breach risk if a threat targets Drive. Additionally, your data is not transported from one area to another; it remains on the edge device and is, therefore, more secure.
Edge AI solutions and applications
A lot of common applications of edge AI were not only discussed above but may have also popped into your mind once its premise was established. It is no secret that edge AI is becoming prevalent so let’s take a look at a few more use cases where edge AI plays an integral role in everyday life.
- Self-driving cars
- Smart speakers and assistants
- Surveillance cameras utilizing computer vision
- Self-operating drones
- Robots (utilizing machine vision)
- Smartphones and smartwatches
- Facial and fingerprint recognition
- Text-to-speech
- Body monitoring (for health use)
- Medical imaging
Key takeaways
Edge AI is quickly becoming not only a preference but a necessity for new products and services emerging in the market, from self-driving vehicles to smart home appliances. Instead of running algorithms and processing computer vision tasks such as image segmentation via the cloud, edge computing allows all of that to be carried out on a local IoT device. The benefits of edge AI currently outweigh the disadvantages, providing significant gains such as better security, cost-efficiency, and reliability on the system it is integrated with. Edge artificial intelligence offers a fail-safe approach to computing that is unaffected by network inconsistencies and data breaches that are more prone to the cloud. Living life on the edge seems to be the go-to choice for those who want to create innovative and exceptional products or software from now and into the future. Now you know all about what edge AI is and how it’s taking the industry by storm.
