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Andrea Casalotti
Attended Insead
Lives in London
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Andrea Casalotti

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 the new bioelectronic device developed by the Karolinska researchers is capable of receiving chemical signals, which it can then relay to organic neurons.
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Milgram in real life:

CIA medical personnel – known as the Office of Medical Services (OMS) – were heavily involved in the torture of detainees in CIA custody. They advised interrogators on the physical and psychological administration of what the agency called “enhanced interrogation techniques”. After observation, the doctors offered perspectives on calibrating them to specific detainees’ resilience.


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This debate on artificial intelligence has been portrayed in a childish black and white manner. It is unlikely that anyone in the field doesn't see the great promise and danger of the technology.
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It is not clear to me why the Basic Income would have to be wholly financed through an increase in income tax. There are many societal "bads" that can be taxed.


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Andrea Casalotti career in Cycling

1995 Board member of London Cycling Campaign. Helped develop the first Cycling Strategy
1997 Introduced cargobikes in London, and consistently promoted them to families and businesses. Now, several thousands used daily
1997 Founded Zero Couriers, first cargobike delivery company in London, offering express and multidrop deliveries - sold to riders in 2007
2000 Partnered with Kensington and Chelsea in offering first residential pick-up/drop-off point in Notting Hill
2003 Started the Velorution blog, the first cycling blog in the UK, with a mixture of advocacy and cycle-chic
2004 Founding member of the London Pedicab Operators Association, which proposed a regulatory regime for the industry
2005 Opened Velorution, the first shop to offer a wide selection of European utility bicycles. Consistently the bicycle shop with the highest female customer ratio in London
2007 Organised Pret-a-Rouler, the first cycle fashion show, replicated worldwide, and showcasing emerging local talent
2007 Introduced the Scorcher, a stylish, well specified city bike.
2013 After selling Velorution, started campaigning again, introducing radical new ideas such as the Clerkenwell Boulevard and the Motoring Grid.
2014 Founded Vision Zero London
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Leadership is listening and then choosing.
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Yonatan Zunger originally shared to Today I Learned::
 
One of the biggest challenges in information retrieval (the branch of computer science that includes search and content recommendation) is how to find good content which humans haven't already found. To date, the most reliable signals have been other human judgments: for example, PageRank is a measure of how "good" a site is based on links people have made to that site (with the challenge being how to separate "meaningful" and trustworthy links from the rest), and collaborative filtering is based on what other users have chosen (with the challenge being how to find users with similar enough taste to be relevant).

The challenge is that, when new material shows up on the scene, you don't yet have any human interactions -- and quite often, good material, things people would love, simply goes unnoticed and never builds up the interaction signals which help. To detect quality in these things requires understanding the content itself, and the aspects of it which matter to people.

There are several hard aspects to this. One is simply understanding the content at the right granularity: "the color of the top-left pixel" or "the frequency of the word 'whenever'" are too fine-grained to give us a hint about whether people will like something, so we need to be able to group the content into more meaningful structures. For images, that might be "an image of a face in 3/4-profile," a certain color balance or contrast, a perspective or a cropping, and advances in image recognition in the past few years have (finally) made it possible to reliably identify such features. For text, it's much harder: there isn't yet even a clear idea of what features both could be measured about text and determine people's tastes. (How do you measure "intellectually meaty" or "hinting at scandal?")

This paper has used the recent advances in image processing, together with recent advances in AI in general, to get a sense of which pictures people will like. It started by taking several thousand images, and having them rated by humans for quality; that was used as "ground truth." Then, those thousands of images are analyzed into meaningful features, and a neural network is trained to find patterns of image features which predict human taste.

This is what neural networks, and other kinds of "supervised" machine learning systems, do in general: they take as inputs a bunch of signals, and combine them using a large number of parameters -- the "weights" -- to produce predictions of some values that you want to measure. The weights are set by taking a large number of test examples ("golden data" or "ground truth") with known values of both the signals and the test values; weights are chosen ("trained") to maximize the quality of the system's predictions for this data. To make sure that the training doesn't just teach it to recognize those specific examples, the golden data is randomly split into two groups; one is used for training, and then it's tested against the other group to make sure that the predictions with the trained weights are good. If they are, then you have a model which can predict -- given any set of measured signals -- the truth values.

In this case, the signals are these features of the image, measured by a second machine learning system; the quantity being predicted is whether people will like it. Because these are all "content-based signals" -- that is, they're based on the contents of the image, and not on people's responses to it -- the resulting model can be applied to any image. 

The team then applied this model to a set of 9 million images from Flickr with fewer than five "favorites." They tested the quality of its picks by having human raters compare that result set with the set of popular images on Flickr; the result was excellent, with its "hidden gems" scoring statistically the same as the most popular images on the site.

I would expect a lot more work on related techniques over the next few years, and for this to have a significant impact on the way that content recommendation is done. The main upshot will be that more little-known works get the spotlight they deserve -- something critical, as more and more people are creating things of value that they want the world to see. 

h/t +Wayne Radinsky and +Daniel Estrada
Beautiful images are not always popular ones, which is where the CrowdBeauty algorithm can help, say computer scientists.
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Andrea Casalotti

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 Huazheng and co are clearly optimistic about future developments. “With appropriate uses of the deep learning technologies, we could be a further step closer to the true human intelligence.”

These techniques are very powerful but are different from how human intelligence works. They are essentially parasitic: they rely on the product of human intelligence to mimic it. They do better than the average human, probably because they are not affected by her bias.

However, can they produce original, creative thoughts, which by definition break rules? There are algorithms that are competent at creating poems, but constrained to what has been done before,

However, people who make their living by charging a lot of money for writing clever text should be warned. It is likely that their skills will be replaced soon.

Which begs the question: if there is no more an incentive to hone certain skills, will we stop learn them? Many people have forgotten (or have never learned) how to do simple arithmetics. How do we make sure that our brains don't atrophy, as machines become better able in solving the majority of our everyday issues?
Computers have never been good at answering the type of verbal reasoning questions found in IQ tests. Now a deep learning machine unveiled in China is changing that.
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Are we mature enough to recognise our biases and develop a justice system where sentencing is done by intelligent machines?
Neuroscience explains why our justice system keeps sending innocent people to prison — and letting guilty ones go
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A wave of experiments is probing the root of quantum weirdness.
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Uncertainty and the Precautionary Principle

We have only one planet. This fact radically constrains the kinds of risks that are appropriate to take at a large scale. Even a risk with a very low probability becomes unacceptable when it affects all of us – there is no reversing mistakes of that magnitude.
It is the degree of opacity and uncertainty in a system, as well as asymmetry in effect, rather than specific model predictions, that should drive the precautionary measures. Push a complex system too far and it will not come back. The popular belief that uncertainty undermines the case for taking seriously the ’climate crisis’ that scientists tell us we face is the opposite of the truth. Properly understood, as driving the case for precaution, uncertainty radically underscores that case, and may even constitute it.
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Have him in circles
284 people
Matthew Michaud's profile photo
Michael Ireland's profile photo
Peter Santos's profile photo
Byron Kidd's profile photo
ROKHMAH FIRDA's profile photo
Julian Bond's profile photo
Totally Driven's profile photo
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Ian Brocklebank's profile photo
Collections Andrea is following
Education
  • Insead
    MBA, 1983 - 1983
  • UCL
    Economics, 1980 - 1982
  • Copenhagen International School
    1974 - 1976
  • Rygaards
    1973 - 1974
  • Liceo Scientifico Enrico Mattei
    1976 - 1979
Basic Information
Gender
Male
Birthday
January 1, 1961
Story
Tagline
Velorutionary
Introduction
Main activities:
I am currently working on these campaigns:

I have three wonderful children.


Bragging rights
Owned the most beautiful bicycle shop in ...
Work
Occupation
UK Agent, Christiania Bikes
Employment
  • Futures trader, present
  • K4RGO - Cargobike evangelist
    2013 - present
    One of the world's foremost expert in cycle logistics, cargobikes and their use.
  • Velorution
    Director, 1997 - 2012
  • UBS
    1993 - 1995
  • PaineWebber
    1986 - 1988
  • Sumitomo
    1983 - 1986
Places
Map of the places this user has livedMap of the places this user has livedMap of the places this user has lived
Currently
London
Previously
Milano - Copenhagen - Norwich - Fointainebleau