Speakers
Description
Red Hen Lab has recently been doing work at the intersection of natural and artificial intelligence, specifically exploring how natural intelligence not only comes up with uniquely appropriate solutions, but does so with a far higher degree of efficiency than the current iterations of AI. Early in the history of computing, a handful of large corporations and government institutions imagined the future of computing as a time share service. The current vision of AI is similar: a set of highly advanced tools dominated by a handful of megacorporations controlling and monetizing these tools as a service.
We suggest that what is needed is an international microbiome of open-source projects that leverage our emerging understanding of the efficiency of natural intelligence to develop a new generation of tools. These tools will undercut the lumbering giants of AI with their multi-billion dollar budgets and dedicated nuclear power stations. The challenge is to generate machine learning strategies and tools that require a fraction of the resources used by current models. All the way from birth, humans are capable of one-shot observational learning, keeping training costs at a minimum. By developing categorical and hierarchical models of the world, we can rapidly prospect possible solutions and their consequences without having to search indiscriminately through astronomically large combinatorial spaces. Implementing such strategies in AI has the potential to dramatically lower training costs and democratize the development of AI tools.
Session author's bio
Mark Turner is Institute Professor and Professor of Cognitive Science at Case Western Reserve University. Before joining the faculty at Case, he was Distinguished University Professor at the University of Maryland and Associate Director of the Center for Advanced Study in the Behavioral Sciences at Stanford. He is Founding Director of the Cognitive Science Network and Co-Director of Red Hen Lab™.
Francis Steen is Professor Emeritus at UCLA and Co-Director of the Red Hen Lab. He has published on topics from computer vision and causal reasoning to cognitive models in microbiology and entertainment.
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