Edge AI and why it matters
AI has been around for a long time.
The field of AI was officially founded in 1956, and since its inception, it has become a prolific part of people’s everyday lives.
Essentially, AI leverages computers and machines to replicate the decision-making and problem-solving capabilities of the human mind.
One relatively new example of AI can be seen in recommendations.
Maybe you’ve noticed that platforms like YouTube, Netflix, and Spotify seem to have an uncanny ability to suggest content that you just can’t skip past. These curated recommendations are possible thanks to AI.
This type of AI considers what you’ve already seen and liked, then compares it with thousands and thousands of other media pieces. The AI learns from the data you provide, then uses its database to show you content that will keep you engaged with the platform for as long as possible. It’s horrible and genius, really.
Other AI use cases that you might be familiar with include face recognition algorithms, chatbots, smart assistants, and self-driving cars.
If you’ve been staying up to date with everything happening in the tech space, you’ve probably come across the term Edge computing.
To summarize, Edge computing is a distributed computing model that keeps data and computational resources close to the source where the data was initially generated. Often, data processing can even take place on the device itself.
In a nutshell, Edge AI combines Edge computing and artificial intelligence. It enables machine learning tasks to run directly on connected Edge devices, taking full advantage of the many benefits of Edge computing.
The amalgamation of Edge computing and AI also creates new opportunities for AI applications with new services and products.
The main benefits of Edge AI include:
Low latency.
Increased speed for real-time computing.
Privacy and security advantages.
High availability (during a network failure or cyber-attack).
Low costs.
These benefits highlight part of why Edge AI is creating so much buzz right now.
To learn more, look at the articles chosen for you below.
Edge + AI = Edge AI
If you’ve heard of Edge AI but don’t exactly know what it means or is used for, the following article is for you.
The article explains Edge AI simply. It states that Edge AI means running AI algorithms locally on a hardware device using Edge computing (where the AI algorithms are based on the data created on the device itself).
It lists the benefits of Edge AI as reduced costs, improved security, high responsiveness, and easy management. It also explores interesting Edge AI use cases such as surveillance and industrial IoT.
What is Edge AI and What is Edge AI Used For?
The Edge AI explosion
This article kicks off by highlighting that AI is impacting several industries but points out that running AI at the Edge wasn’t possible overnight.
Edge computing wasn’t always able to support AI. However, with the influx of higher computation and the capacity to handle different AI-optimized workloads on Edge devices, Edge AI has really kicked off in recent years.
The benefits discussed here include privacy and security advantages, real-time data processing, the ability to fit AI onto tiny devices, and an optimized user experience.
AI on Edge: Enabling Digital Transformation
Why care about Edge AI?
Rapid advances in AI have made technology necessary for many industries, including microelectronics, finance, energy, and healthcare.
Today, algorithms are primarily run at large cloud data centers. As the following article explains, for this intelligence to be used at the Edge, data must be transmitted to the cloud, analyzed there, and transmitted back to the Edge device.
The problem with the current approach of using AI in the cloud is that it consumes a significant amount of energy, results in data transmission delays, and exposes users to security vulnerabilities.
The article below explores the solution, making the Edge itself more intelligent.
Deploying Artificial Intelligence At The Edge
Some extra resources
The computer vision future
To learn about computer vision and its impact today, check out the following article.
The Future Is Computer Vision – Real-Time Situational Awareness, Better Quality and Faster Insights
One for the environment
I wouldn’t exactly classify this as light reading, but if you want to read about something intriguing, have a look at this article on Energy Harvesting.
For a quick overview, Energy Harvesting technology is a promising, environmentally friendly solution that extends the lifetime of a sensor. It can replace battery power and offers economical and practical advantages thanks to its little energy use and low network maintenance costs.
Energy Harvesting Techniques for Internet of Things (IoT)
Ditch the batteries
If you get annoyed by having to change your batteries all the time, you’ll probably appreciate our final article of the week.
It covers how an air force base in Utah used small, battery-less, low-wattage sensors to monitor the health of steam delivery and mechanical systems, communicating the real-time health and effectiveness of steam traps.
Battery-Less IoT Could Change How, When We Gather Data
Xailient’s newest article
Check out our latest blog post titled Expertly Manage Your Computer Vision AI with Xailient’s Orchestrait Platform.
This post explains how Xailient’s Orchestrait platform makes continual data training and performance monitoring for Edge AI incredibly quick and straightforward.
Thanks for reading,
See you next week!