Getting there: Structured data, semantics, robotics, and the future of AI

Getting there: Structured data, semantics, robotics, and the future of AI

Leveraging structure in data is key to making progress in AI, says AI prodigy Gary Marcus. A forward-looking view on Software 2.0, AI chips, robotics, and the future of AI

Deep learning is great, but no, it won’t be able to do everything. The only way to make progress in AI is to put together building blocks that are there already, but no current AI system combines. Adding knowledge to the mix, getting over prejudice against “good old AI“, and scaling it up, are all necessary steps in the long and winding road to reboot AI.

This is a summary of the thesis taken by scientist, best-selling author, and entrepreneur Gary Marcus towards rebooting AI. Marcus, a cognitive scientist by training, has been doing interdisciplinary work on the nature of intelligence — artificial or otherwise — more or less since his childhood.

Marcus, known in AI circles among other things for his critique on deep learning, recently published a 60-page long paper titled “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.” In this work, Marcus goes beyond critique, putting forward concrete proposals to move AI forward.

As a precursor to Marcus’ recent keynote on the future of AI in Knowledge Connexions, ZDNet engaged with him on a wide array of topics. We set the stage by providing background on where Marcus is coming from, and elaborated on the fusion of deep learning and knowledge graphs as an example of his approach.

Read the full article on ZDNet

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