It’s no secret that artificial intelligence (AI) is rapidly transitioning out of R&D labs and into the mainstream market for enterprise applications.
Leaders in the computer vision space such as those developing autonomous cars have continually made headlines as the new technology breaks ground. Applications beyond computer vision are increasingly gaining recognition as well, including those dealing with non-spatial data such as text and numbers.
The most famous “non-vision” examples include well-known technologies beating the best of the best in highly complex abstract strategy games like ‘GO,’ which is said to hold more possibilities than the total number of atoms in the visible universe. Do the examples set by industry leaders like IBM with Watson mean that AI technology has finally arrived for enterprise applications that have the capability to power business transformation?
The answer is ‘yes’ but it’s not likely with the current technology. The problem is that many existing AI technologies attempt to replicate what has worked in the case of spatial data. This includes the application of computational statistics-based approaches for processing natural language. Such approaches attempt to turn text into “data” in order to look for deep patterns, which – to date – have largely failed.
To ensure the successful future of AI in the enterprise, an approach must be devised that addresses the three primary challenges that AI technologies have to overcome in order to power transformational enterprise applications. This includes language, context and reasoning.
The first challenge posed by many modern AI technologies is the inability to process language as humans do. This is because the large majority of current AI approaches focus on natural language processing (NLP) and are largely driven by computational statistics that treat text as data rather than language — and use the same techniques that work on spatial data, e.g. pattern recognition. These methods do not attempt to understand text but instead simply transform it into data, and attempt to learn from the patterns in that data. During the mechanical process of the conversion of natural language into data, context and meaning of the text is often lost.
The reason that this pattern recognition method doesn’t work is because of the inherent challenge of understanding context. The ultimate goal of AI in this example is to devise mechanisms for comprehending the meaning of written text. To address the language challenge presented by AI for enterprise applications, there needs to be a shift from mechanically converting natural language into data to a word occurrence-based logic that helps the AI technology understand text using its linguistic structure.
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