Edge AI: Intelligence in Every Device
Uncategorized5 min read4 months ago
Loading
Uncategorized5 min read4 months ago
Loading
The world is still coming to terms with how Gen AI and LLMs are going to be drivers for economic growth. Yet, there is already a new technology on the way that is going to represent an even bigger shift in the products we see and will reshape how we interact with AI daily.
Edge AI is a term that few have heard about. This type of AI falls under the umbrella of embedded systems where software runs “on the edge”.
Edge AI models will be able to run directly on smartphones, hospital monitors, and industrial equipment allowing for rapid decision-making. With real-time processing at or near the data source, AI is only going to become more impactful.
Edge AI operates on the idea of processing data “on the edge”, meaning that the inference step is performed on the device. This contrasts with the typical Cloud AI that we have become familiar with, where data is shipped from a device to a centralized server for processing.
By shifting this processing closer to the data source, Edge AI can reduce the reliance on communication between devices and servers.
Edge AI models are optimized to operate on the devices that they will be deployed to. This means that during the development phase, engineers must decide how to train each model so that it can be deployed without performance slipping.
The major drawback of deploying Edge AI is that edge devices do not have the same computing and storage capabilities as does Cloud AI. This means that engineers must either reduce or compress the model weights.
Source: Splunk
One of the biggest advantages of Edge AI is its ability to enable real-time processing. Think about a self-driving car, the amount of time that it takes to send data to a server, have the server process that data, and return an instruction to the car can be upwards of 250ms. While a quarter of a second may not seem like much, it can be essential in stopping accidents.
Compare this to the almost instantaneous processing that could happen if there was no need to send the data to the server and it becomes obvious that Edge AI is a technology of the future.
Another key advantage of Edge AI lies in its scalability. By reducing the strain on centralized servers by running inference tasks on edge devices, applications can scale to larger audiences. In particular, moving AI to the edge opens up entirely new possibilities where even without internet access, anyone can have access.
Perhaps one of the most appealing aspects of Edge AI for businesses is its potential to significantly reduce costs. By minimizing the need for data transfers between edge devices and centralized servers, businesses can cut down on bandwidth usage and its associated costs.
On top of this, maintaining communication infrastructure is notoriously expensive. By shifting the focus of engineers away from maintenance to developing new products and services, businesses can build even more value.
Edge AI is also going to heavily influence the cybersecurity industry by improving data security. Keeping data localized and reducing the need to transmit data, Edge AI mitigates many of the risks associated with data breaches. This is particularly true for businesses that must store personally identifiable information (PII) and must comply with a long list of regulations.
Source: VDC
One of the many promising areas for applications of Edge AI is IoT and smart devices. By embedding AI capabilities directly into connected devices, businesses can offer more intelligent and responsive products to their customers. Imagine a more powerful smartwatch that could analyze your biometric data and help you plan your meals for the week or an app on your phone that can take a picture of your garden and tell you when to water your plants.
Edge AI is going to help transform the automotive industry. By leveraging its real-time decision-making capabilities, widespread adoption of autonomous vehicles may no longer be so far away.
In the healthcare sector, Edge AI has the potential to revolutionize how we imagine patient care. By deploying AI models to edge devices, healthcare providers can deliver more personalized treatments, improved diagnostics, and timely intervention to improve patient outcomes.
For instance, imagine a busy emergency room with dozens of patients waiting to be seen. Edge devices with AI capabilities could be used in this scenario to asynchronously monitor patients while they wait. If there are any early warning signs that a patient’s health is deteriorating, the edge device can alert hospital staff immediately and prompt them to take preventative action.
Beyond these key use cases, the applications of Edge AI are virtually limitless. From manufacturing to logistics and retail to finance, few businesses won’t be able to benefit from Edge AI.
Edge AI is going to be a big driver of change that will reshape how we interact with technology daily. AI will truly be at our fingertips.
Products and services that incorporate Edge AI are likely going to see rapid growth in the coming years. This growth is going to be powered by increasing investments in development and CPUs becoming more powerful over time.
Any business that adopts Edge AI strategically stands to gain a competitive edge by improving the customer experience.
Uncategorized
5 min read
Introduction In the early days of OpenAI their large language models (LLMs) were released open-source, and the company operated as a non-profit. However, as the models improved, access to these models became restricted and OpenAI is now on pace to generate over $1 Billion in revenue over the next 12 months. Big corporations like Microsoft, […]