I was under the impression that unlike previous comments here and the written document, that Extended Intelligence was primarily and objectively about taking lessons learned from AI, interaction design, and cognitive science to /improve ourselves/ through some type of augmentation. 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. 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. See more at: jhupbooks.press.jhu.edu/content/knowledge-games
Always interesting to see the way academic philosophy and engineering/tech interact, or, more often, don't. 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 2005.
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? (do normal rules of morality even apply?) 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. 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: https://medium.com/@ArtificialExperience/creativeai-9d4b2346faf3 curios to hear your thoughts.
Could be relevant to refer to Licklider 1960 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. Some of the more longer term questions are how societies will adapt and our own concept evolves of what it is that makes us human as AI/IA/EI progresses - how will we see ourselves in say 50 years. And what does this mean for related fields such as artificial creativity / creativity augmentation?
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. 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. In other words, we are building data-driven decision-making tools under the assumption that the human minds around us are willing to accept the conclusions. Something akin to the difference between Knowledge and Wisdom. Extended-inteligence sounds great. Perhaps it would also be worth diving into Collective-Wisdom (CW). Anyone interested or am I just rambling?
Thinking about this more, we may have gotten the dystopia to avoid wrong. Everybody thinks AI gone bad as being ‘Terminator’ or maybe even ‘Colossus’ (have you seen that film? If not, I recommend it highly - already from 1969 - link below) or maybe the manipulative ones like HAL or The Matrix. http://www.amazon.com/Colossus-Forbin-Project-Eric-Braeden/dp/B0003JAOO0/ref=sr_1_1?s=movies-tv&ie=UTF8&qid=1455552538&sr=1-1&keywords=collosus
But the way things go bad in the human-in-the-loop scenario runs more along the lines of the Borg Collective from Star Trek (I’m sure you all know that one) or Landru from the original series (http://memory-alpha.wikia.com/wiki/The_Body_of_Landru) - people being essentially teleoperated into an ultimate totalitarianism. The Borg were bad because of their extreme socialism and the desire to endlessly expand. Laudru meant well, but took his mission too seriously and was a narrow ‘autistic’ AI. Hence this ignites a ton of speculation and debate - what is the role of the individual in such a soup of human and machine? What is lost and what is gained - can we somehow protect the role of the individual when we’re all so connected, or will my ego go the way of the dodo?
This may be all wrongheaded - e.g., if we’re destined to become agents running somewhere, the physical manifestation may not matter as much as getting backed up' in enough places - but it's the natural argument where such ideas hit nightmares.
Tried to add this to the third paragraph.
What if we applied some concepts of extended intelligence to the new legal hackers and digital law movement to express statutes and regulations in computational ways. I predict even a little success would be unique, of high impact and (at least apparently) magical. Some thoughts: https://fold.cm/read/dazzagreenwood/lawgometric-code-vjGxW5dv
If you aren't familiar my favorite reference in this space is Roku's Basilisk, similar to Pascal's Wager. http://rationalwiki.org/wiki/Roko's_basilisk
The majority of Affective Computing's work relates to AI and machine learning, but I don't see it on this list.
Can you share some links?
The Center for Bits and Atoms is developing declarative design tools and robotic assemblers of micro to macro discrete continuum materials. Computation and robotic assembly will enable discovery of otherwise unreachable design spaces.
I'm really intruiged by the intersection between Learning (à la LLK & Papert) and AI. Minskian AI was at its core about understanding cognition (building machines to model it). Learning, is also about understanding cognition—building our own minds.
How can both sides benefit by connecting to other areas of inquiries about cognition? (e.g. embodiment, mindfulness, the arts)
Yes! Minsky conceived AI not just as a way to build better machines. He wanted to use machines to understand the mind itself. What makes us human? Who and what are we?
The "extended mind" is an excellent notion, because it acknowledges our embodiment as social, biological and cultural beings, and it emphasizes the interactions between humanity, design, technology and ethics. Of course we are also more than the interface between our tools, society and our therapist. Understanding the mind itself is going to be a crucial part of understanding how we interact with our environments and with each other. I wonder if this should be reflected in this effort, too. For instance, Shoshanna's work in Playful Systems uses games to study motivational traits. The Media Lab also explores art as a way to learn about experience and perception, and many people seem to be interested in cognition itself.
This section must add references to the HITL work with respect to crisis counseling, measuring self-harm and cardiolinguistics currently done at the lab. The AI2 talk and the HITL work at the lab are quite different from each other.
Can you provide the appropriate citations Karthik?
The question of context here is more complicated than that, right? I mean, this is about doing it with greater context, but not merely 'with greater context', the representation of context in the emergent properties of the human-machine system seems, to me, as a key component.
With respect to the method of coactive learning, as presented in the work referenced, it seems that it merely introduces a mechanism for extracting context from user's implicit feedback, but the process is heavily iterative, and relies on some assumed 'fixed' or at least loaclly-fixed of context which, to me, seem like the wrong direction