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Why the buildings of the future will be shaped by ... you

TED talk

“according to the EPA, Americans spend 90 percent of their time indoors.”

“these are not just pictures of a building, they're your pictures of a building. And as you use them to tell your story, they become part of your personal narrative, and what you're doing is you're short-circuiting all of our collective memory, and you're making these charged symbols for us to understand”

“digital media hasn't just changed the relationship between all of us, it's changed the relationship between us and buildings.”

smart bots (readings)

Industry - These Five Platforms Will Make Your Bots Language-Intelligent

Academia - Conversational Agents and Mental Health: Theory-Informed Assessment of Language and Affect

Conversational Systems (RecSys literature)

ads in reviews

An associate editor of one of the ACM Transactions sent me a desk reject (which is fine) but then added a link to his project’s site saying “BTW, there are public datasets and codes that might be useful for your research on the following website.” Thank you. That’s quite ethical.

looking at the positive in research

a lot of research is about problems (is, by construction, about solving problems), while little research focuses on the positive side (is, e.g., about preempting problems). a bit like what happens in western medicine or used to happen in psychology (before the introduction of positive psychology). to paraphrase Kenneth Koch, we could say that

“So many researchers have the courage to look into the abyss, but just a few have the courage to look into happiness.”

speed and social network

The author of “The Great Acceleration: How the World is Getting Faster, Faster” wrote this interesting article on Wired Magazine titled “Here’s a cool New Social Network: London”. A few excerpts:

“The larger the community, the faster people move”

“The lives scales up alongside the size of our community, so do levels of innovation, productivity, and income”

“The increase in social interactions ... results in the size of a city’s economy rising more rapidly than its population.”

“they [cities] are so buzzy and creative and exciting that to move to the country, or even to a smaller city, slows your life down.”

when “this great process of urbanisation outpaces economic growth, ... many of the new arrivals dumped in slums and shanty towns.”

A.I. must be transparent: We should be aware of how the technology works and what its rules are. We want not just intelligent machines but intelligible machines. Not artificial intelligence but symbiotic intelligence. The tech will know things about humans, but the humans must know about the machines. People should have an understanding of how the techno

Empathy & texh

From slate A few people are taking the lead on this question. Cynthia Breazeal at the MIT Media Lab has devoted her life to exploring a more humanistic approach to artificial intelligence and robotics. She argues that technologists often ignore social and behavioral aspects of design. In a recent conversation, Cynthia said we are the most social and emotional of all the species, yet we spend little time thinking about empathy in the design of technology. She said, “After all, how we experience the world is through communications and collaboration. If we are interested in machines that work with us, then we can’t ignore the humanistic approach.”

Invent the future

Computer pioneer Alan Kay: "The best way to predict the future is to invent it.” Stop predicting what the future will be like and create it in a principled way

How to compose a successful critical commentary

You should attempt to re-express your target’s position so clearly, vividly, and fairly that your target says, “Thanks, I wish I’d thought of putting it that way.

You should list any points of agreement (especially if they are not matters of general or widespread agreement).

You should mention anything you have learned from your target.

Only then are you permitted to say so much as a word of rebuttal or criticism.

good points !

what comes first

From a previous post, the temporal order is private conflict -> public violence-> public disorder

based on the paper Econometrics in the big data age

the foundation of that work is “Econometrics: towards a science ...”

A couple of related works:

Seeing Disorder

Context and Cognition

Why we need buzz words

At a recent dinner, we were wondering “why do we need buzz words?”. big data, smart city... they are a bit annoying really... If I look back at my research, most of it has been about countering the buzz word of “smart city”. Surprisingly, however, I am not against it. On the contrary, i think we do need buzz words. They make it possible to create a critical mass of people who have very different backgrounds yet are able to work towards the same direction ;)

p.s. I Today, I found this quote from Kafka: “By believing passionately in something that still does not exist, we create it. The nonexistent is whatever we have not sufficiently desired.”

p.s. II Big data doesn’t exist. After you clean it, it becomes fashionably small

there you go ;)

The Death and Life of Great Italian Cities

Marco will present a paper titled “The Death and Life of Great Italian Cities” at WWW 2016. To get the main idea, see a couple of news coverage:

TechReview - Data Mining Reveals the Four Urban Conditions That Create Vibrant City Life

What makes a city great?

Jane Jacobs Was Put to the Test in 6 Italian Cities

European researchers prove Jane Jacobs was right

Here is the pdf of the original paper. Room 518b.

who benefits from airbnb

We have recently worked on "algorithmic regulation" in the context of airbnb in London. The paper (to be presented at WWW 2016) is here, the work was covered by CityLab, and a rebuttal to that article is here.

Culture Analytics Beyond Text

Dawn of the Selfie Era: The Whos, Wheres, and Hows of Selfies on Instagram by Meeyoung Cha. Mia presented our work on selfies (paper, press, project site) and showed how the use of selfies is linked to a country’s socio-cultural aspects. It might be beneficial to see when the use of selfies does not match a country’s official socio-economic indexes.

Data Mining beyond Text in the Vogue Archive by Lindsay King. She pointed out an interesting way of exploring a museum collection by colors.

A Global Selfie : Producing images of our world

Yesterday I went to a seminar by Googler Ed Parsons. The interesting bit is that Google Street Views are captured by mobile phone users now (not only by the google cars). Of course, the engagement problem is still there - it is unclear how to convince people to go around and capture urban scenes ;) Abstract A picture paints a thousand words, or perhaps a thousand maps ? For ten years Google has been supplementing cartography with images of our planet giving our uses both the "ultimate level of zoom" and an image of the fragile "Blue Marble"that is our planet. This talk will cover the development of the Streetview and Earth Observation programmes and look into the future of Geoimage machine learning

Bio Ed Parsons is the Geospatial Technologist of Google, with responsibility for evangelising Google’s mission to organise the world’s information using geography. In this role he maintains links with Universities, Research and Standards Organisations which are involved in the development of Geospatial Technology. He is currently co-chair of the W3C/OGC Spatial Data on the Web Working Group.

Texts Come from People

Today I went to a seminar titled "Texts Come from People - How Demographic Factors Influence NLP Models" by @Dirk_Hovy

The abstract of the talk is here. Dirk’s scholar profile is here and his personal site here.

Live blogging below - hope you can extract something meaningful out of my confusing notes ;)

Big problem in NLP: part-of-speech (POS) tagging is racist and ageist (represent a certain segment of the population)

Q1: How to partly tackle this problem? Get training data! On trustpilot.com, people write reviews (and they provide their gender, age, location). The outcome of this research is a web site under http://languagevariation.com/index.html This is a web interface that allows the user to query for lexical phenomena in several languages, and to get both statistical analysis and map representations of the results along several demographic factors.

Fun Finding: In NYC, there is an anti-correlation between the prestige of a clothing store and the number of dropped r’s by the store’s typical customer (more on the "smiley" paper titled “User Review Sites as a Resource for Large-Scale Sociolinguistic Studies” pdf)

Q2: How *syntax* varies over age and gender? Feature selection is done with a randomized logistic regression (see Meinshausen and Buhlmann (2010) & Johannsen/Hovy/Sogaard (CoNLL 2015)) Cross-lingual syntactic variation over age and gender. pdf

Q3: How much sexism Hillary Clinton faces on Twitter ?

Q4: Can "regio-lects" be derived from geographic studies of social media?

Q5: How tagging differ in terms of gender and age? The tag “machine” is used differently by males vs. females. It might be useful to incorporate sex and age into NLP models for personalized NLP (e.g., to improve disambiguation)

They Killed Spiderman

Usual dead night in the Bronx In the club they're playing a blues piece by the Stones Shady individuals in the bar's balconi Filled with whiskey and margaritas

All of a sudden, the door goes "slam"! The squinter/one-eyed man enters with news It's a reliable whisper that The cannons have gone "bang"!

They killed Spiderman No one knows who did it Maybe someone from the bad part of town Maybe the advertisers They killed Spiderman No one even knows why He must have done wrong To some coffee industry

At the police station The chief says what's the big deal? What has happened won't stop us Crime won't win

But there's panic in the streets No one leaves their house No one wants trouble And to all the appeals for calm on the TV Who believes anymore?

They killed Spiderman No one knows who did it Maybe someone from the bad part of town Maybe the advertisers They killed Spiderman No one even knows why He must had done wrong To some coffee industry

Down in the streets gangs can be seen Made up of stressed accountants in double breasted suits If I don't sell you out, you'll sell me out For a few bucks or maybe more

The faces of Vogue are legends to us Too-beautiful actors are out only heroes But him Yes, he was a star But he won't come back

They killed Spiderman No one knows who did it Maybe someone from the bad part of town Maybe the advertisers They killed Spiderman No one even knows why He must have done wrong To some coffee industry

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