By Tom Strange

We are far from AI-based systems that can reason the way humans do.

Once associated with negative connotations - such as unemployment due to job automation and industry redundancy, or sci-fi movie plot-lines to ‘destroy the world’ - AI is now widely accepted, adopted and better understood by people outside of the technology sector. An array of accessible mainstream AI applications means it has been seamlessly integrated into many elements of our daily lives. Whether through a virtual assistant that helps to book doctor appointments; an email service that can accurately suggest the end to a sentence or predict the next word in text; or a system that suggests content we might like to stream and watch - artificial intelligence is now pervasive and growing in capability.

As a result of mainstream AI applications like these, there can be a false perception that the technology has, for the most part, mastered human language. In truth, AI has proven to be successful in natural language-based use cases, where it is designed and trained for a specific purpose. This is because the problem definition is clear and narrowly defined in scope, which enables the use of scripted techniques, and or statistical neural network-based approaches. But there has been less success with general conversation capability, or with techniques to understand the meaning that humans express through their use of natural language. We are far from AI-based systems that can reason the way humans do.

The current nascent state of the technology is evident even in the leading consumer products. Alexa Siri, and other voice and digital assistants use AI to interact with users. These products, though widely adopted, can execute only a limited number of tasks and are incapable of offering meaningful user experiences based around conversation. Instead, they enable users to execute tasks and actions through them but not to engage in dialogue with them or grow their understanding over time. The limitations of approaches used by these products are glaringly obvious. Delivering the value and experience consumers want from natural language-based experiences, requires us as practitioners and industry leaders to make new breakthroughs. It seems that cognitive development may reveal answers.

Last year, Josh Tenenbaum, who leads the Computational Cognitive Science Lab at MIT, announced plans to study the minds of children to inspire ‘the next big innovation in AI’. Tenenbaum claims that by studying how young children see the world and learn, we can apply similar techniques to develop an AI that will be truly ‘intelligent’ - or, at least, intelligent in a way that is more useful to consumers...

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