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Quantified Curiosity
 

Quantified Curiosity

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Presentation at Quantified Self 2012. ...

Presentation at Quantified Self 2012.
This talk explains how I track and organized ideas. The second half contains Gephi graphs of ideas. When I figure out how to edit the transcript, I will add what was said during each slide.

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  • Hi. I am obsessed with thinking about thinking. My name is Amy Robinson and I’m here to share Quantified Curiosity.
  • I work with HealthSterling,WiliLife, BookYourDive and i run the TEDx Global Music Project. I’m very curious about how my mind evolves and how ideas integrate over time to form who I am.
  • A stranger at a TED Conference once walked up to me and said “Hi Amy, so what inspires you?” A stranger. It got me thinking, first about what actually inspires me and then about how the things that inspire me change over time. I’ve referenced this conversation a lot and consequently the 5 seconds that it took to say those words have had a much greater impact than say two hours at a meeting that doesn’t interest me. Could the same be true for ideas? I am curious so I’ve been tracking them.
  • I track  ideas by emailing things to myself. Articles, videos, photos, tweets, notes to self.. Anything that incites the thought “hm, tha’s interesting “
  • I compiled this data into a spreadsheet. Each entry is an “idea.” Each idea has attributes like a date, importance ranking, ngram which is the subject and body, and tags or “topics” From February 1 to Aug 1, 2012, I logged 770 ideas.
  • 6 Months of ideadata I’ve compiled for this talk. It came to 770 entries in 772 primary topics. Or in gephi, there are 772 nodes and over 10,000 edges, which I will get to in a few minutes. First, what constitutes “idea data”?
  • From organized data I am able to decipher insights, starting with this weighted graph of important topics. I weighted entries on a scale of 1 to 5; 1 being low and 5 being high. This graph shows a relative measure of importance by topic among topics that were tagged at least 40 times. The green bar is entries ranked 4 and the blue bar is entries ranked 5. You can see that science is the most popular topic with a weighted score of 728, which more than triples the unweighted score of 198. The unweighted score represents the raw number of entries. The second most popular tag, work, is 150 points lower than science. It’s interesting to note that unweighted, “work” was the largest category by a small margin of 6 (unweighted: 204)
  • You can see that among “important” green bar entries, the top topics are “journal,” “biology,” “neuro,” and “notes” – in this case “journal” is not my own journal but rather indicates peer reviewed literature. When you consider topics ranked “most imporatant,” the breakdown switches up – i.e. “notes” – my personal notes – come in first and are followed by “ideas,” “self,” and “video,”
  • You can also explore most important entries over time. You can see that they often happen in clusters. There’s a large spike in February with 14 items in a 3 day period. What does that mean? When I cross reference this with what I was acutally doing, I saw that these entires are images of notes, which correspond with my starting a new side project
  • PhotosofNotes, a blog where I publish pics of my notes. The spike reflects a significant event: a new side project. Does the same hold for other spikes?
  • Turns out, yes. There’s another spike in March with 21 entries in 12 days. These correspond again with my creating a playful new side project, “life bonus” emails. I send an email to a few friends asking what’s the most beautiful, funny, amazing thing they’ve discovered in the past week. If they respond within 48 hours they get a “lifebonus” – it’s goofy, it’s fun, it rocks the inbox. I still send these. The spike cluster again reflects a new creative development.
  • So how else can we explore the data.
  • Gephi. We’re able to format this data into gephi, a freenetwork graphing program. Note that this data is unweighted. This is a graph of 6 months of my ideas. The circles are called nodes and represent topics. The lines between them are called edges and each one represents an entry, an “idea,” that is tagged with the two topic nodes which it connects. Since each “idea” entry has multiple tags, this graph has 770 nodes and 10,500 edges between them.
  • You can see it is a densely populated graph when all nodes and edges are considered. We can refine the scope and create a more exquisite graph by focusing on singular topics.
  • Larger nodes are used more frequently in tandem. The big blue do is ‘science’. The colors break down the graph into communities. It works by clustering nodes which are more closely linked to each other relative to the overall connectivity of the graph.
  • The communities correspond to similar topics. So blue is science. Red is TED and TED related tags. Note that the gephi statistics link tags by a number of metrics, so the “TED” red tags also include things I most closely relate to TED like “TEDx” and “video.” Green is “self,” it includes the more personal aspects of the ideas, ranging from “notes” to some surprise community members like “play,” and “quora”.Orange is art and while there are not many, they are distinct.Purple is work slash health. I work in health so it makes sense that these are so closely related. If you’ve seen these types of graphs before, you’re probably used to seeing modularized, more separated communities. This is slightly messy looking because the tags are so co-tagged. Red and green infuse as TEDx is a huge part of me; purple and blue overlap because to work in health is to pay attention to the science in your field.
  • Ideas, for example. This is a graph of all tags associated with the tag “ideas” . The yellow dot in the top right is the tag “ideas”Note the green dot towards the center. It’s the tag “me” and it exhibits high betweenness centrality.
  • In network graphs that represent people, those with high betweeness centrality are more likely to bridge gaps and communicate between two or more closely clustered groups. Could the same parallel be true for ideas?
  • A graph of beautiful. Again, the yellow node in the middle of the red cluster is the tag “beautiful.” The dot in the middle is Tech and represents a series of “beautiful” scientific technological videos, which I’ve actually compiled on my blog.We can also see that this graph has a big red cluster.
  • Look deeper and see:Several large nodes for “ted” Others for “side project “ – it’s a good sign of pursuing passion when the things to do you find beautiful.The largest red node for “beautiful” is video - let’s look at that network.
  • Video. Broad in scope. I looked up the top weighted entries for video and have posted them on my blog at amyrobinson.me. This makes me wonder how we could create an interactive Gephi graph where you could see video connectivity and then view videos exhibiting specific properties. Not yet, but someday. That would be rad.
  • Here’s the graph of self. Yellow is self. Again, highly connected. I was curious while looking at this graph which topics had surprisingly large dots. TED and Science are major but don’t surprise me. One, however, did. Quora.
  • This is my Quora network, which was surprisingly well-infused in all topic areas of interest, particularly the green “self” section So what does all this mean. They’re beautiful graphs, but what intelligence do they facilitate?
  • What have I learned?Why am i doing this? By exploring how ideas that matter to me most are related, I’m able to understand myself in new ways.
  • I can see my thoughts in a different context. I can see thoughts that influcence me in important ways which I am not able to fully realize when looking at ideas in an isolated way. It affords me a different degree of relativity. And it greatly stimulates curiosity, my sense of wonder and potential for exploring who I am. Data allows me to think more intelligently about the ideas I love and andhow I employ them. We can go from this messy graph to an elegantly formed network. And then we can think differently about thinking – we can explore our minds in new ways
  • I think there are ideas and discoveries which, like that one line at TED, carry a great impact but that get lost in the massive matrix of awesomeness that is the world. New ideas are everywhere. How do I remember what was new to me 4 years ago? How can I explore how the acquisition and development of such new ideas has influenced who I am today?
  • This is just the beginning. 6 months of data is here..but I’ve been doing this for years. There’s lots missing, from moleskines to autotuned voice memos.  Will long term trends become evident? How can I more objectively explore these data?  Are there internal cycles and sequences indicating that a significant creative accomplishmentis on the horizon?  Could I use this to strategically stimulate creativity?  
  • I don’t know the answers to these questions.  But I think there are answers. Or will be.I want to understand myself, to be able to think about my mind in exquisite detail.
  • So what’s the most important thing I’ve learned? I’ve got to think socially about how to better think about thinking. I know I’m not the only one tracking this type of thing. Do you quantify ideas?  How?  How can we do this better? I want to learn from you. Remember that your mind is extraordinary.  You, the things you think about, the things you find important, create who you are and who you will become. There are endless discoveries to be made in this epic world. It’s beautiful and excites an insatiable curiosity. Imagine, how might you answer the question “what inspires you” if you had a quantified mind in your cognitive toolkit? Thank you.
  • So what’s the most important thing I’ve learned? I’ve got to think socially about how to better think about thinking. I know I’m not the only one tracking this type of thing. Do you quantify ideas?  How?  How can we do this better? I want to learn from you. Remember that your mind is extraordinary.  You, the things you think about, the things you find important, create who you are and who you will become. There are endless discoveries to be made in this epic world. It’s beautiful and excites an insatiable curiosity. Imagine, how might you answer the question “what inspires you” if you had a quantified mind in your cognitive toolkit? Thank you.

Quantified Curiosity Quantified Curiosity Presentation Transcript

  • Quantified Curiosity Amy Robinson | @amyleerobinson Image Source: Tamily Weissman, Harvard University
  • Image Source: Tamily Weissman, Harvard University
  • What inspires you?
  • 6 months770 entries772 topics
  • 60.00% Most Important Topics, Weighted50.00%40.00%30.00%20.00%10.00% 0.00% Important | Most important
  • 60.00% Most Important Topics, Weighted50.00%40.00%30.00%20.00%10.00% 0.00% Important | Most important
  • Important Entries Over Time10 9 8 7 6 5 4 3 2 1 01/12/2012 2/1/2012 2/21/2012 3/12/2012 4/1/2012 4/21/2012 5/11/2012 5/31/2012 6/20/2012 7/10/2012 7/30/2012 8/19/2012
  • PhotosofNotes.tumblr.com
  • playful curiosityImage source: Good-Wallpapers.com
  • playful curiosityImage source: Good-Wallpapers.com
  • ScienceTEDSelfArtWork/Health
  • NeuroScience
  • ScienceTEDSelfArtWork/Health
  • ScienceTEDSelfArtWork/Health
  • Science TED Self Art Work/Healthideas
  • Science TED Self Art Work/Healthideas
  • Science TED Self Art Work/Healthbeautiful
  • TEDx Side Project TED Videobeautiful
  • Science TED Self Art Work/HealthVideo
  • Science TED Self Art Work/HealthSelf
  • Science TED Self Art Work/HealthQuora
  • Video Beautiful Ideas Science TED Self Art Work/Health Quora Self
  • Video Beautiful Ideas Science TED Self Art Work/Health Quora Self
  • Image courtesy of TEDxAmsterdam
  • Image courtesy of TEDxAmsterdam
  • @amyleerobinson
  • @amyleerobinson