As the name suggests, an AI computer is any computing machine that can do work in the field of artificial intelligence. Thanks to the rapid pace of technological developments, even modest consumer hardware can today be considered an “AI computer,” capable of running cutting-edge ai models.
The past few decades have seen tremendous strides in AI computing technology. In the 1980s and 1990s, for example, when computing power came at a premium, machines had to be specially built and configured to do AI work. Now, the average laptop has three of the most crucial components for any AI computer:
- Graphical processing unit (GPU): Originally created for real-time computer graphics, the GPU excels at any task that requires massively parallel processing, including many types of AI models (such as deep learning).
- Central processing unit (CPU): Work that can’t be offloaded to the GPU on an AI computer is instead run on the CPU, which you can think of as the machine’s “brain.” But technological progress has made CPUs more and more powerful with a higher number of cores capable of handling many tasks that were previously GPU-exclusive.
- Software: The past decade has seen an explosion in the availability of AI and machine learning frameworks and software. Even relative beginners to programming can use these tools to spin up powerful AI models in just a few lines of code.
The choice of operating system is also an important factor when building an AI computer:
- UNIX-based operating systems such as Linux and macOS are significantly more convenient for programmers, and many AI frameworks have been optimized for Linux versions such as Ubuntu and Red Hat.
- Windows computers can present compatibility issues with some AI tools, but the operating system itself is so widespread that it should always be taken into account.
- macOS is convenient in terms of software, but Macintosh hardware still lags behind Linux and Windows when it comes to sheer power (although Apple has been trying to catch up by introducing new chips).
How does AI computing software work?
The dominant form of AI these days is deep learning, which uses an AI model known as the neural network. Machine learning engineers use deep learning software frameworks such as PyTorch, Keras, and TensorFlow to build AI models with just a few keystrokes, and then train them on the GPU.
Composed of many interconnected nodes called “neurons” organized in multiple layers, neural networks are a rough simulation of the structure of the human brain. Each connection between two neurons has a corresponding weight, whose value determines the importance given that connection (larger values represent stronger connections). Neural networks are trained using an algorithm called backpropagation, that automatically adjusts the weights of the neural network when the model makes an incorrect prediction.
The convolutional neural network (CNN) is a special form of neural network optimized for analyzing visual imagery, such as photographs and videos. AI platforms such as Chooch can process and interpret any kind of visual input, from X-rays and sonograms to video cameras and infrared satellite images.
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Why is this AI Computer thing happening now?
Artificial intelligence (AI) and machine learning have never been so widespread or so accessible to the masses – that’s why AI computers have become a thing. To incorporate artificial intelligence into your own workflows, of course, you need an AI computer.
In a 2020 report, the consulting firm McKinsey & Company found that half of organizations “have adopted AI in at least one function.” Also, businesses who derive the most value from AI report that they’ve experienced benefits such as better performance, higher growth rates, and stronger leadership.