12Technology
We provide machines with an end, and they provide us with the means.
—Iain Banks (science fiction author)
AI technology is composed of hardware and software capabilities used to develop, implement, and operate AI solutions. Computers, servers, storage, and networking equipment are all hardware resources for these systems, used to store and process vast amounts of data required for AI applications and to train and run AI algorithms. For example, graphics processing units (GPUs) are preferred for training deep learning algorithms due to their speed and efficiency.
On the software side, specialised tools for machine learning, natural language processing, computer vision, and other AI applications enable organisations to build, train, and deploy AI solutions that are able to learn, reason, and make decisions with data. Examples of such software tools include TensorFlow and PyTorch for deep learning, spaCy and NLTK for natural language processing, and OpenCV for computer vision. This chapter addresses which technology capabilities are required to create value with AI.
The financial investment in AI technology capabilities vary along an organisation's AI journey. When companies start out, they usually invest more than 50% of their AI budget on technology, and those further down the journey usually reduce it below that threshold (Celi and Miles 2020).
In recent years, many AI initiatives have turned to cloud services to reduce the cost and complexity of investing in ...
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