Understanding Intelligence (MIT Press)
Contents:
At the individual level, in the future we may look less like terminators and more like cyborgs; less like isolated individuals, and more like a vast network of humans and machines creating an ever-more-powerful EI.
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Every elements at every scale connected through an increasingly distributed variety of interfaces. Each actor doing what it does best -- bits, atoms, cells and circuits -- each one fungible in many ways, but tightly integrated and part of a complex whole. While we hope that this Extended Intelligence will be wise, ethical and effective, is it possible that this collective intelligence could go horribly wrong, and trigger a Borg Collective hypersocialist hive mind?
Such a dystopia is not averted by either building better machine learning, nor by declaring a moratorium on such research. Such teams dominate the strongest human players as well as the best chess computers. This effect is amplified when the humans themselves play in small groups, together with networked computers.
Connecting electronics to human neurons to augment the brain and our nervous system Synthetic Neurobiology and Biomechatronics. Using machine learning to understand how our brains understand music, and to leverage that knowledge to enhance individual expression and establish new models of massive collaboration Opera of the Future.
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If the best human or computer chess players can be dominated by human-computer teams including amateurs working with laptops, how can we begin to understand the interface and interaction for those teams? How can we get machines to raise analysis for human evaluation, rather than supplanting it? Machine learning is mostly conducted by an engineer tweaking data and learning algorithms, later testing this in the real world. This augments human decision-making and makes the ML training more effective, with greater context.
For example, a wearable system designed to help a person forecast mental health mood or physical health changes will need to sustain a long-term non-annoying interaction with the person in order to get the months and years of data needed for successful prediction. Camera Culture Group is using artificial intelligence and crowdsourcing for understanding and improving the health and well-being of individuals. These tools augment networked intelligence by helping people access the data that large groups of individuals generate, and that are needed to have a panoptic view of large social and economic systems.
Research towards a vision of brain probes that can communicate with external and internal electronic components. The wildly heterogeneous nature of these different projects is characteristic of the Media Lab.
But more than that, it is the embodiment of the very premise of EI: All of these projects are exploring this central idea with different lenses, experiences and capabilities, and in our research as well as in our values, we believe this is how intelligence comes to life. Feb 11, 1 41 1. Discussions 41 Add Share. This work is licensed under a Creative Commons Attribution 4.
- Understanding Intelligence | MIT CogNet.
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New Discussion Discussions 26 Discussions 0 Archived. It's not really about understanding cognition much like AI was agnostic to actual cognitive mechanisms , but about using what we now know as tools to self improve.
Understanding Intelligence
I think the central claim is not on a "distributed" or "cognitive" intelligence ala Rumelhart but on a positivist position that the Media Lab is taking? We could consider Knowledge Games a type of "extended intelligence" -- in situation where the play of a game helps us extend our ability to solve complex problems and produce new knowledge. There's been a ton of work done in philosophy on extended cognition since Clark and Chalmers in '98; I'm not sure who the "we" is in "we propose a kind of extended intelligence" Of course, it's more often that philosophers haven't caught up with technology, but as someone with a foot in both worlds it's a little strange to see an article here that wouldn't look out of place in a copy of Synthese from The most interesting questions in my mind are around all the familiar terms that need to be redefined in light of an extended cognition hypothesis.
Can the self or mind be divorced from intelligence? Is there room for a self at all, or will that dissolve as we communicate at increasingly higher bandwidth with our social networks and machines? Who is to blame when a cognitive network does something immoral? At a certain point, this train of thought leads to viewing the universe as a single, vast network, with any subdivision of interacting parts being arbitrary. Are there any boundaries we should draw on what makes us intelligent? After all, we are constantly interacting with every body in the known universe.
And if not, is intelligence a useful quality to define? Great discussion here, thank you.
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Thinking along similar lines, we just published an extensive piece on "CreativeAI". It includes in-depth analysis, narrative and vision for the space between human, machine and creativity: Could be relevant to refer to Licklider paper on Man Computer Symbiosis. Also the concept of general AI seems to have made more of a comeback with the popularity of general purpose methods such as deep learning.
I very much enjoy how this article attempts to touch base on the wider topics AI.
Always refreshing to hear the MediaLab step up and shake the box a little bit. That being said, I can't help but find some dark humour in the way we speak about "augmented brain" and "smarter computer" with little discussion regarding the metric by which we evaluate those fascinating topics and system.
Understanding Intelligence is comprehensive and highly readable introduction to embodied cognitive science. It will be particularly helpful for people interested. By the mids researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as.
More to the point, despite our many attempts at trying to mimic or extend the human mind, very little attention has been given to the many plague it and consequently we suffer from; namely depression, cognitive biases, lack of compassion, selection of evidence to satisfy previous notion, etc. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science. By the mids researchers from artificial intelligence, Rolf Pfeifer , Christian Scheier. A SelfSufficient Garbage Collector.
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The Principle of SensoryMotor Coordination. A Framework for Embodied Cognitive Science.
Embodied Cognitive Science Basic Concepts. Neural Networks for Adaptive Behavior. Approaches and Agent Examples. Artificial Evolution and Artificial Life.
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- Understanding Intelligence - MIT Technology Review!
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