Google’s Deep Dream Does Instagram

elsa5, elsa hoesk, instagram, deepdream, deep learning, instagram

@hoeskelsa

Social networks are abuzz with Deep Dream, Google Research’s trippy new inceptionism AI that sequentially enhances what it thinks are key features of images. You can tweak a number of parameters, producing pictures that range from fantastical to nightmarish.

I gave it a try using some of my Instagram pics (@amyleerobinson). But why stop there? With the help of Dreamscope, I gave both my friends and Instagram’s top celebrities the honor of a #deepdream treatment ;) Enjoy the weirdness.

Golden Gate bridge and Destroyer Deep Dream, SF, San Francisco, DeepDream, deep dream, neural nets

@amyleerobinson

beyonce, instagram

@beyonce before

deep dream, deepdream, Beyonce, Google Research, AI,

@beyonce on #deepdream

Before

Original: @mackenzie_rt

deep dream, coffee, San Francisco, M

@mackenzie_rt

Original: @victoriassecret ft Candice Swaenpoel

Original: @victoriassecret

deep dream, deepdream, Google Research, AI, Instagram, Victoria's Secret, Candice Swanepoel

@victoriassecret ft Candice Swanepoel

GoPro, Instagram

Original: GoPro

GoPro, deepdream, AI, instagram

@GoPro

Original: @taylorswift

Original: @taylorswift

deep dream, deepdream, Taylor Swift, Google Research, AI,

@taylorswift and her cat

handstands, bikini, beach, instagram, deep dream

Original: @jen_es_care

deep dream, AI, instagram, yoga

@jen_es_care

yoga, yogi, zen

Original: @kinoyoga

deep dream, AI, yoga, instagram, kinoyoga

@kinoyoga

penguin, national geographic, Christopher Michel

Original: @chris_michel

penguins, deepdream, deep dream, instagram

@chris_michel

instagram, sidewalk, feet, flowers, deep dream

Original: @amyleerobinson

deepdream, deep dream, instagram

@amyleerobinson

espn, bodies issue, deep dream

Original: @espn

deep dream, deepdream, Odell Beckham Jr , ESPN, Bodies Issue, Instagram

@espn ft Odell Beckham Jr

Lily Aldridge, Deep Dream, Instagram, model, supermodel

Original: @LilyAldridge

deep dream, deepdream, AI, neural nets, deep learning, Instagram, Google Research, AI, model, Lily Aldridge

@lilyaldridge

Castle, Scotland, @amyleerobinson, Amyleerobinson, Instagram, deep dream

Original: @amyleerobinson

deep dream, deepdream, AI, instagram

@amyleerobinson

christiano, Ronaldo, deep dream, instagram

Original: @christiano

deep dream, deepdream, AI, neural nets, deep learning, Instagram, Google Research, AI, model, Cristiano Ronaldo, futbol, athlete, Instagram

@cristiano

RedBull,  Instagram, Red Bull, Deep Dream, AI, Google Research

Original: @redbull

deep dream, deepdream, RedBull, Red Bull, Google Research, AI,

@RedBull

Jenner, Kardashian, Deep Dream,

Original: @kendalljenner

Kardashian, Kendall Jenner, Kim Kardashian, Kim Kardashian West, Deep Dream, deepdream, Google AI, AI,

@kendalljenner seems to be sprouting a goatdog with Kim K

@amyleerobinson, Amy Robinson, Costa Rica, Swimming, GoPro, Deep Dream, deepdream, AI

Original: @amyleerobinson

deep dream, deepdream, AI, neural nets, deep learning, Instagram, Google Research, AI, amyleerobinson

@amyleerobinson

Point Lobos, San Francisco, deepdream, deep dream, amyleerobinson, instagram

Original: @amyleerobinson

deep dream, google research, instagram, deepdream, deep learning, instagram

@amyleerobinson – can you tell it’s Point Lobos?

Earth, Space, Astronaut, deep dream, deepdream, AI, Google Research

Original: @AstroSamantha

deep dream, deepdream, AI, neural nets, deep learning, Instagram, Google Research, AI, National Geographic, AstroSamantha

@AstroSamantha – Earth from Space

Still from ISS Timelapse by David Peterson, ISS, timelapse, deep dream, deepdream

Original: David Peterson

deep dream, deepdream, AI, neural nets, deep learning, Instagram, Google Research, AI, National Geographic, ISS, Timelapse, video, space

Still from ISS Timelapse by David Peterson

 

Create your own images using Dreamscope.

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Quantified Curiosity 2.0

ideas, Gephi, "network visualization" "Quantified Curiosity" Amy RobinsonBack in September I gave a talk at Stanford for Quantified Self titled Quantified Curiosity (summarized in this post, which includes slides and a links to videos refrenced in the talk). Below, check out the video complete with a text transcript.

Before you watch it, think about this. Who are you? Seriously, how do you answer that question? Does who you are change over time? How? Why? When? What if you could explore these questions empirically, with data that correlates with significant events in your life, data that collectively integrates to tell a story of who you are? This is what I begin to explore with Quantified Curiosity, a network exploration into the ideas that fuel me. As of March 2013, I’ve connected with a couple academic and corporate network powerhouses to this concept a few orders of magnitude higher and deeper. More on that soon.

Over the coming months, stay tuned for the evolution of questions, new visualizations, and curiosity progress reports. A goal of this side project is to create a platform that allows anyone to explore and graph his or her ideas over time. Here’s to tackling fundamental questions! Ping me if you are interested in brainstorming. Now, on with the evolution of ideas!

Transcript with slide selections:

Quantified Curiosity brainbow Amy RobinsonI am obsessed with thinking about thinking.

My name is Amy Robinson and I am here to share Quantified Curiosity.

I am very curious how the ideas that I encounter and the new things that I discover integrate and infuse to form who I am and who I will become.

A stranger at a TED Conference once walked up to me and said “Hi Amy, What inspires you?” Besides actually making me think about what inspires me, it made me think about how the things that inspire me change over time. I am not a constant, I am very dynamic; however, it’s hard to remember how I change and to keep it in perspective.

Those 5 seconds consequently have mattered much more than just 5 seconds and I wonder if the same is true for ideas. So I’ve been tracking them.

How? I email myself “interestingness.” So when I look at say an article or write notes or watch a cool video; anything that makes me think “hm, that’s interesting,” I email it to myself. For this talk I’ve compiled 6 months of this data into..a pretty big spreadsheet and some beautiful network visualizations.

Each line is an idea, an entry, and the data has attributes like a date, a link, an ngram (which is the subject and body text of the email), it’s tagged with topics and it’s also given an interestingness ranking of 1 being low and 5 being high.

ideas, Gephi, "Quantified Curiosity" Amy RobinsonSix months worth of data came to 770 unique entries – or ideas – in 772 different topics. Once this data was organized into a spreadsheet I was able to analyze it and look at it in a completely new way.

This is a weighted graph  [below] of the most important topics of all topics that were used at least 40 times and weighted either 4, the green bar for “important,” or 5, the blue bar for “most important,” they show up on this graph. You can see based on the importance that the most prevalent topics vary. For example, the green bar most important is “journal,” which is peer reviewed literature, not my personal notes, followed by biology and neuro. Whereas if you look at the blue bar “notes,” my personal notes, come up first.

"Quantified Curiosity" Amy Robinson

photosofnotes, photos, notes, tumblr, amy robinson, quantified curiosity,You can also look at most important entries over time [graph below]. The most important entries  tend to occur in clusters. I wonder do these clusters actually correspond to something? There’s a huge cluster in February, 14 items in 3 days. They actually correspond to my starting a new side project, photos of notes, it’s a tumblr blog where I just publish photos of my notes. In that case, yes, that cluster was something real. And I wondered, is this true for the other clusters?

quantified curiosity, QS, quantified self,

Turns out, yes. In March there’s another one where 21 items occur in a period of 21 days. It corresponds to something kind of goofy that I do — lifebonus emails. I send these out now quite periodically to my friends saying, ya know, share something beautiful, inspiring, intelligent or entertaining that you’ve discovered in the past week and they get a hypothetical lifebonus. It’s goofy, it’s fun, it rocks the inbox but again the data actually corresponds to my doing something new.

How else can we actually explore this?

We were able to formulate these ideas into Gephi, a free network graphing program. The way this works: the circles are called nodes and they correspond to topics that are tagged with ideas. The size of the nodes indicate how many times they were used in tandem with other nodes. The edges – the lines between them – are the actual ideas that are co-tagged with the two different topics.

ideas, graph, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self", nodes, edges

ideas, graph, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self", nodes, edgesYou can run statistics in Gephi to modularize communities so based on how connected groups of nodes are relative to the overall connectivity of the whole graph and see distinct communities. For example, the blue down at the bottom is science and science-related tags. The purple is work slash health — I work[ed] in health; you can probably actually infer that by looking at the graph. The red section is TED and TED-related tags, including TEDx and video. And then the green section is “self” and there were come cool things in there like playful, curious, ideas and Quora that popped up really close to me. But this is messy. It’s hard to see 10,500 edges so what you can do is you can actually isolate individual topics.

ideas, graph, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self"

The yellow dot here is the tag “ideas” within all my ideas data. You can see the little green dot sort of off to the side. It exhibits what’s called a high “betweenness centrality.” In social network graphs that represent people, those nodes that have a high betweenness centrality are the ones that bridge gaps between distinct communities. They’re interdisciplinary in a way and it made me wonder, could the same be true for ideas? Those “in between” ideas, and how can I decipher this information?

ideas, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self", beautifulWe can look at the graph of “beautiful” for an example. You see there’s a purple dot right in the middle. That’s “tech” and when I actually looked at these tags, there’s a series of beautiful, scientific, technological videos, that I’ve actually compiled on my blog [here!] if you’re curious to see them. You can also zoom in on this red section that were closely tagged with “beautiful” — so “TED”, “TEDx”, “side project”, I guess it’s a good sign that the things I do for free in my spare time incite a sense of awe and beauty. “Video” was the largest in that cluster.

ideas, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self", video

When I actually look at the graph of “video,” it made me wonder how we could take this information and make it interactive. Imagine you were panning through this on a computer and rather than just looking at nodes, you could actually look at the content relative to where they’re tagged and other things

Here is the tag for “self.” A lot of this was intuitive — “TED,” “science,” — I’m geeky, I love TED. But one dot that very much surprised me, closely related to me — the green dot of Quora, Quora the social Q&A network.

ideas, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson

ideas, Gephi, Quora, "network visualization" "Quantified Curiosity" Amy Robinson, "Quantified Self"This [left] is a graph of Quora. It’s highly infused with all the different communities of my ideas.

These are beautiful graphs; they’re elegant and nice to look at but what do they mean? What can you actually learn from exploring ideas in this type of way?

It puts them into context. By being able to see my ideas and see how they’re connected to each other, I’m able to think about myself in new ways. I’m able to see, rather than just the fact that I started a new blog or I sent out a lifebonus email to friends, I can see how that evolve and where it came about. Based on the features of these graphs, I can actually understand more about where my ideas come from and how they change over time. And there’s a lot that can be done in Gephi that I haven’t even gotten to yet.

Really, like that one line at TED, those 5 seconds carried a much greater weight than just 5 seconds. I think the same can be true of ideas. How do I remember what was new to me four years ago? How do I understand how the ideas that i encounter today are influencing me as a function of time? And I really wonder how I can discover more ways to think about myself and how I can explore how my mind looks relative to other people’s. I wonder if there are hidden patterns inside of this.

I don’t know the answers to these questions but I think that there are answers, or can be. I’m very curious to understand who I am and how I exist. Consciousness is my greatest curiosity and in the end I’ve learned that we need to think socially about how to better think about thinking. This was a momentous task to put all this  together and it can certainly be done more efficiently. Remember, you are extraordinary. Your mind is exquisite. You, the things that you think about and the things that are important to you, create who you are and who you will become. So imagine how you might answer the question “what inspires you?” if you had a quantified mind in your cognitive toolkit.

Thank you.

ideas, Gephi, "network visualization" "Quantified Curiosity" Amy Robinson, Quora, beautiful, video, self, quantified mind

If you sort iPhone photos by size, which images come out on top?

I just got back from a post-TED trip to San Francisco. After importing my SF shots and sorting by size to pull out videos (for a coming-soon time lapse project), I noticed an interesting trend in my photos: the largest ones are taken outside.

Here are the 10 largest pics, ranging from 4.6 MB (green, center) to 3.2 MB (face of curiosity, bottom). Average size images range from 2-2.6 MB. What attributes describe the outliers? Nature. And a bit of art. Try this and share your mobile pics hungriest for data!

San Francisco Street Art The Face of Curiosity, Legion of Honor, San Francisco, Presidio, art Legion of Honor, SF, Linkin Park,
IMG_1639
China Beach, San Francisco, waves IMG_1628
Presidio, Path, Sea, Forest, Green, San Francisco,

Presidio Path San Francisco Forest
Presidio Path, SF, Forest
Presidio Path San Francisco Forest

It’s human nature to want to explore

I have mad respect for RedBull, an energy drink company turned powerhouse of epic. RedBull supports adventure. They embrace risk. They empower people to break bones and boundaries. Here’s their latest video, which is awesome.

I hear you like the wild ones, honey, is that true? Yes, yes it is.  I curate amazing, wild things from Red Bull on a new Facebook page called Be More Epic.

Transcript:

“I think it’s human nature to want to explore.

Find your line and go beyond it.

The only limit is the one you set yourself.”

Images brought to you by RedBull:

See the World Differently with Beautiful Photomicrography

Before you read this, pause and look at your hand.  Imagine that you could see ten, one hundred, a thousand times higher resolution.  What would your hand look like?  What world the world look like?

Photomicrography, the science of imaging through microscopes, is a window into an exotic world.

To illustrate the beautiful new perspectives made possible by advanced imaging technology, I’ve compiled some exquisite images from Nikon Small World.  Can you identify them? You’re doing well if you get even one correct. Answers are at the bottom of this post.

1.

cricket tongue

2.

tapeworm head

3.

compound shrimp eye

4.

red ink mixed with acid, heated

5.

feather of a dove

6.

"fruit fly eye"

7.

"marine diatom"

8.

"moth wing"

9.

"crystallized mix of resorcinal, methylene blue and sulphur"

10.

"fossilized shells"

11.

"soap bubbles"

12.

"wrinkled photoresist"

13.

"actin bundles" image

14.

"cup fern longitudinal section" image

15.

"water crystal" image

16.

"bird of paradise seed"

17.

"Butterfly egg on pink powderpuff bud"

18.

microchip

19.

sand magnified 4x

20.

"mushroom gills"

Answers:

1. Cricket tongue by Christian Gautier

2. Head of a tapeworm by Vigar Zaman

3. Shrimp eye by John Douglass

4. Red ink mixed with acid, heated by Carlos Jimenez Perez

5. Feather of a dove by Leonard Cannone

6. Fruit fly eye by Guichuan Huo

7. Marine diatom by Wim Van Egmond

8. Moth wing by Charles Krebs

9. Crystallized mix of resorcinal, methylene blue and sulphur by John Hart

10. Fossilized shells by Wim van Egmond

11. Soap bubbles by Viktor Syorka

12. Wrinkled photoresist by Pedro Barrios-Perez (what is a photoresist?)

13. Actin bundles by Dennis Breitsprecher

14. Cup fern, longitudinal rhizome section by Stephen Lowry

15. Water crystal by Raul Gonzalez

16. Bird of paradise (plant) seed by Viktor Syorka

17. Butterfly egg on pink powderpuff bud by David Millard

18. Microchip by Alfred Paseika

19. Sand by Yanping Wang

20. Mushroom gills by Charles Krebs

A few more awesome images that may surprise you:

Pollen grains by Shirley Owens

Lysine by Nikolai Vsevolodov

Small intestine of mouse by Paul Appleton

All images sourced from Nikon’s Small World.