It could be argued that Geoffrey Hinton’s recent success with neural networks is the Big Bang moment for artificial intelligence. Deep learning has enabled today’s AI systems to beat Go world champions and translate data into innovation for industries like finance and energy. But the field still struggles in areas requiring broader intelligence, and the question remains whether a series of incremental innovations to the current foundation will lead AI to a new level of sophistication — one that can outpace human ingenuity.

Current AI Can Dominate In A Game Of Go But Is Flustered By Nursery Rhymes

Today, the AI research agenda is focused on deep learning, which processes large data sets to solve narrow and specific tasks at hand. Deep neural networks can learn complex functions to solve intricate problems but only within certain parameters. For example, Google’s AI-based AlphaGo is easily able to beat the best human player at the incredibly sophisticated game Go because it is played using set rules.

But outside of situations with set parameters, the real question is if data-driven AI can outperform humans in the complex world we live in.

Deep learning is very good at solving a particular set of problems but does not directly address many important issues, such as long-term planning. While deep learning can solve the problem of finding human experts for time-consuming data input by automating perception and knowledge acquisition, there is more work required to recreate human-like semantic understanding for complex actions.

Natural language processing is an excellent example of this. Though the complexities of language seem beyond statistical correlation, machine learning researchers have successfully used their techniques to manipulate language to complete tasks. However, the techniques don’t extend to natural language understanding — to a computer, this is just another algorithm, not a sentence with meaning.

To become genuinely intelligent, AI systems need to have human-like reasoning skills combined with a machine scale to process data. Consider the nursery rhyme “Jack and Jill went up the hill to fetch a pail of water.” A modern system can answer, “Where did Jack and Jill go?” AI is learning to answer questions like, “Are Jack and Jill still at the top of the hill?” demonstrating inductive reasoning. However, an AI system would be unable to describe how Jack and Jill retrieved the water, as it requires background information not specified in the rhyme.

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