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In practice, EI can manifest in various forms, such as collaborative problem-solving platforms or AI systems that integrate human feedback to improve decision-making. For example, crowdsourcing platforms like Wikipedia demonstrate how collective contributions can lead to a richer repository of knowledge. Moreover, research indicates that diverse teams often outperform homogeneous ones in creative tasks, highlighting the benefits of multiple perspectives in intelligence.
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This interplay between AI and human intelligence can create a more robust framework for understanding intelligence as a collective endeavor rather than a solitary one. So, when we consider modern AI through this lens, it becomes clear that its role is not merely to replicate human thought but to enrich and expand our shared intellectual landscape, ultimately fostering a more interconnected and informed society.
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While some envision a cooperative coexistence, others raise concerns about the risks of AI development, including existential threats. It’s crucial to approach these debates with a balanced perspective, recognizing both the incredible opportunities AI presents and the responsibilities that come with its advancement. Ultimately, fostering open dialogue will be key to navigating this transformative era.
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I see.. Some speculate that AI could become so advanced that it may view humans as either companions or obsolete, leading to fears of existential threats. While these discussions can seem far-fetched, they highlight the importance of ethical considerations and responsible development in AI. As we navigate this landscape, it's crucial to foster a balanced dialogue about the possibilities and challenges that AI presents, ensuring that technology enhances human life rather than diminishes it.
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To contextualize this, the notion of EI suggests that intelligence isn't just something that resides within individuals but is enhanced and extended through our interactions with technology and each other. As we develop more sophisticated tools—be it AI algorithms, collaborative platforms, or data-sharing systems—we're not just amplifying individual capabilities; we're creating a networked intelligence that draws on the strengths and insights of all participants involved.
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As we develop more sophisticated tools—like AI algorithms, data analytics platforms, and collaborative software—we're not just enhancing individual capabilities; we're creating a richer ecosystem where knowledge and insights can flow more freely among all participants. For instance, consider how platforms like GitHub enable developers to collaborate on code in real time, effectively pooling their collective expertise to produce better software faster than any one person could alone.
While a lot of AI funding and research focus on improving statistical machine learning for smarter machines, there is a growing recognition of the importance of leveraging AI to enhance human-machine collaboration and societal impact. By shifting some attention towards supporting the network of minds and our society, we can unlock new opportunities for innovation and positive change.
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It encompasses many important ideas and practices, and thinking about the future of science in the context of design promises to be a fruitful endeavor. geometry dash lite
While we strive for wisdom and ethicality, there's always a concern about potential negative outcomes. The idea of a Borg Collective-like scenario is indeed thought-provoking. It's crucial to explore the intersection of humans and machines to navigate this evolving landscape wisely. Let's continue to approach AI development with caution and mindfulness.
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In essence, as we continue to innovate and develop new technologies that enable us to collaborate and share knowledge across networks, we are indeed expanding the scope of Extended Intelligence. This interconnected web of intelligence underscores the importance of collective efforts in driving innovation and problem-solving.
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It's fascinating to think about intelligence not just as something individual, but as a collective, distributed phenomenon. The concept of Extended Intelligence (EI) that you're proposing at the MIT Media Lab really challenges the traditional notions of what intelligence is and how it operates.
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Extended Intelligence (EI) suggests that intelligence is not solely confined to individual minds but is distributed across interconnected systems and networks. In this context, my advice would be to embrace collaboration, leverage diverse perspectives, and harness the collective intelligence of interconnected entities to enhance problem-solving and decision-making processes. By recognizing the power of distributed intelligence, we can unlock new opportunities for innovation and growth. So, in essence, don't underestimate the potential of collective intelligence and make the most of it in your endeavors!
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Absolutely! It's fascinating to think about how our collective intelligence grows as we connect and collaborate with powerful tools. By expanding our network and sharing information, we're essentially enhancing the Extended Intelligence (EI) that we all contribute to. It's like adding more pieces to a puzzle where each actor plays a crucial role in shaping the bigger picture. Such a collaborative approach truly showcases the beauty of distributed intelligence.
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Extended Intelligence (EI) proposes that intelligence is a distributed phenomenon. It suggests that intelligence is not solely confined to individual entities but can be enhanced and extended through interactions with external systems and environments.
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tree pruning services This blog discusses the concept of Extended Intelligence (EI), which views intelligence as a distributed phenomenon. It raises questions about the nature of intelligence, the relationship between humans and machines, and the potential of collective intelligence. The document outlines various research areas within EI, emphasizing the collaborative interaction between humans and machines in fields like neuroscience, music, and artificial intelligence. It underscores the diverse and interconnected nature of these projects, reflecting the essence of intelligence as a collective and networked concept.
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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
Can you share some links?
Specifically, much of our work has focused on understanding affect to better facilitate human/machine communication
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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