Advanced machine learning systems require far more energy and processing power than conventional computer programmes. The International Energy Agency predicts the electricity consumption from data centres and artificial intelligence could reach more than 1,000TWh by 2026. Improving the efficiency of machine learning tools so they can run locally on individual devices or networks rather than requiring large data centres will be one important direction of travel in the future.
As AI is inserted into decision making processes and used in scientific research, it will also require machine learning algorithms that are more explainable. This means overcoming the “black box” problem that many machine learning systems have – where it is not clear how or why they arrive at the answers they do. Using such a system in the legal system or in healthcare means users need to understand why an algorithm makes the prediction it does. Similarly, for any scientific research using an AI, it will be essential that an experiment is reproducible, which requires an understanding of how the AI arrives at its answers.
There is now also growing research in developing AI’s that work as an extension of human capabilities rather than a replacement. These would enable human-AI hybrid systems to solve problems that would otherwise be beyond both individually.
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