AI PCs are computers specifically designed to support advanced artificial intelligence programs, which use machine learning and data processing to analyze information, deliver new insights, and detect details that human operators might otherwise miss.
This powerful new technology is exceptionally useful in high-demand roles that require accuracy and speed, such as medical and industrial applications. To support this new technology and enable such workflows, however, AI PCs must feature extremely powerful hardware, along with the design features and certifications their intended use-case requires.
How Does an AI PC Work?
On the surface, AI PCs function like conventional computers: they have data storage to save information, RAM, a CPU, and a motherboard to connect everything. Beyond those similarities, AI PCs implement powerful, specialized hardware that enables advanced AI-based tasks.
Graphical Processing Units (GPUs)
The core technology that enables modern AI systems is graphical processing units (GPUs). These specialized chips were originally created to render 3D video graphics for animation and video games by processing multiple tasks at the same time. This is in contrast to CPUs, which are better at handling complex tasks one at a time.
GPUs can run millions of small calculations simultaneously, making them perfect for the type of computing that modern AI programs rely on: machine learning.
Machine Learning
Machine learning is a type of program that, as the name implies, “learns” from patterns found in training data and uses this knowledge to make predictions and inferences about new data.
Different types of machine learning exist, such as:
- Supervised Learning: the machine learning model’s output is compared to an ideal or correct example, such as correctly identifying a manufacturing defect.
- Unsupervised Learning: The model is allowed to work without supervisory signals or guidelines, in order to spot patterns that human observers don’t notice.
- Reinforcement Learning: The model is trained through trial and error and environmental feedback, such as teaching a self-driving car how to drive safely.
For example, a machine learning algorithm can be trained on images of precancerous lesions in a patient’s esophagus to identify those warning signs in other patients. This training involves running thousands, even millions, of data points (in this example, images of lesions) through the model so that it “learns” what a lesion looks like and what it doesn’t. The best way to do this training is through parallel processing.
Parallel Processing
Parallel processing is an approach to computing that divides a task into multiple smaller tasks that are worked on in parallel to each other. This occurs over four steps:
- Decomposition: The task is split into many smaller subtasks. For example, if asked to analyze a dataset, this would involve splitting the dataset evenly across all available processing cores.
- Distribution: The subtasks are distributed throughout the system. This is where GPUs have an advantage: with more processing cores than a CPU, they can support far more parallel processes than a CPU.
- Processing: The individual nodes within the system complete their assigned subtask, such as analyzing an image or piece of information within the dataset.
- Aggregation: After nodes complete their tasks, the results are recombined to produce a unified result from the dataset.
In conclusion: AI PCs use GPUs to support machine learning, which is the basis of modern AI algorithms. GPUs are used for this task because they handle parallel processing most efficiently, making them the fastest way to accomplish machine learning tasks.
What Roles Use AI PCs?
While modern generative AI is being explored for virtually every use case imaginable, these are often cloud-based applications that live on third-party servers. AI PCs are typically owned directly by the operator (such as the hospital or factory where the PC is used) and operate in roles at the “edge” of the network.
Industrial Use Cases
Common uses for industrial AI PCs include:
- Machine Vision: AI enables real-time quality control and tracking by identifying key visual information, such as manufacturing defects and package labels.
- Predictive Maintenance: AI PCs use sensors to track factors like vibration, machine temperature, and sounds to predict equipment breakdown before it actually occurs, letting maintenance teams address these issues before a serious failure happens.
- Robotics: robots and drones can work autonomously or collaboratively with human workers in a factory or warehouse, reacting to changes as needed.
Medical Use Cases
Medical edge AI PCs help deliver better, more accurate care to patients via:
- Medical Imaging: AI can spot details in medical images such as X-rays and camera feeds that providers might otherwise miss.
- Automated Medication Delivery: By tracking medication schedules and patient vital signs, AI can automatically administer medicine when the patient needs it.
- Patient Safety: By receiving information from motion-tracking cameras and sensors worn by the patient, AI computers can track for anomalous movement and falls, preventing injuries.
- Robotic-Assisted Surgery: Thanks to the low latency offered by AI PCs operating in close proximity, surgical robots can handle surgical tasks such as making incisions, tying off sutures, and highlighting surgical sites.
Benefits of Using AI PCs
By using AI PCs instead of other types of AI-based solutions, users in high-stakes environments can enjoy unique benefits like:
Latency Reduction
Conventional AI workflows involve transmitting massive amounts of data back and forth between the end-user and the server hosting the AI software. If that information includes images or video feeds, that information can quickly balloon into gigabytes that take time to transmit and analyze.
In roles that require immediate reaction to changing circumstances, such as quality control or surgical assistance, this simply isn’t acceptable. Edge AI PCs are physically closer to the data source and can use high-speed solutions like GSML cameras to accelerate transmission, reducing latency.
Securing Proprietary Information
Many AI workflows involve confidential or proprietary data, such as production logs, private health information, or algorithms developed in-house. Transmitting and storing these records off-site can introduce serious cybersecurity risks, such as man-in-the-middle attacks and breaches in the cloud server.
AI PCs working under the same roof as the data’s source do not introduce these issues. By relying on wired data transmission to and from devices in the same room, AI PCs drastically reduce the chances of malicious actors accessing proprietary or regulated information.
Less Reliance on Outside Services
Storing and analyzing information on third-party cloud services is convenient, especially if information needs to be accessed from different workstations or sites. However, relying on these third-party services means that if they suffer an outage or failure on their end, your data becomes impossible to access. Cloud services are also a poor choice for mobile roles or areas with spotty internet connections.
Local storage and analysis remove this reliance, ensuring constant access to critical information and AI programs.
AI PCs for High-Demand Roles from Cybernet Manufacturing
AI technology now forms the new cutting edge for the medical and industrial sectors, and businesses must be prepared to embrace it. While partnering with third-party cloud services can be effective in some roles, any task that requires quick reaction times and stringent security is better served by relying on a dedicated AI PC working alongside you.
For over three decades, Cybernet Manufacturing has designed and manufactured computers for high-stakes medical and industrial applications. Now we’re bringing our experience to develop powerful, reliable AI PCs that deliver the performance you need without compromising on reliability. Whether you need…
- Machine vision support for quality control
- Diagnostic imaging assistance
- Robotics control
- Automated medication delivery
- And more…
Our experience as an OEM enables us to develop a custom solution perfectly fit to your needs, backed by a year-over-year 0.5% failure rate. Contact one of our experts today to get started.