In many fields, the lack of data for training AI on specific targets leaves machine learning unable to produce results with sufficient accuracy for practical use. AI deployment is also hindered by the lack of understanding of the logic behind the final results. Often, despite AI's sufficiently accurate recognition or classification performance, experts and developers cannot explain why the AI produced a certain answer, which makes it difficult to explain the results to potential adopters in the industry.
Fujitsu's so-called "Wide Learning" technology enables judgements to be reached more accurately than was previously possible, claims the researchers, and learning is achieved uniformly, no matter which hypothesis is examined, even when the data is imbalanced.
This is achieved by first extracting hypotheses with a high degree of importance, having made a large set of hypotheses formed by all of the combinations of data items, and then by controlling for the degree of impact of each respective hypothesis based on the overlapping relationships of the hypotheses.
What's more, because the hypotheses are recorded as logical expressions, humans can also understand the reasoning behind a judgement, eliminating the "black box" uncertainty of how the AI judgements are performed. Fujitsu's Wide Learning technology will enable the use of AI even in areas such as healthcare and marketing, where the data needed to make judgements is scarce, supporting operations and promoting the automation of work processes using AI.