Category: data availability

How Navigation Apps Are Changing Municipal Traffic Policies

This post ‘Navigation Apps Changed the Politics of Traffic‘ explains how municipalities and local governments are adapting to the rise of navigation apps.

The issue is that those apps cater for the needs of the drivers, and not for the needs of the wider community. “Driver-first traffic “fixes,” even with the best of intentions, have deleterious effects on transportation networks overall.” There is a collective price of having each driver optimise its own route. “One widely cited 2001 paper by computer scientists at Cornell found that a network of “user-optimized” drivers can experience travel times equivalent to what a network of “system-optimized” drivers would experience with twice as many cars. Transport engineers call the difference between selfish and social equilibria the “price of anarchy.””

There seem to be some debate on the actual effect of those apps, and whether the algorithms also include some more collective constraints (one can remark for example that on two different phone, they may not give the same itinerary, showing that they try to spread congestion).

In any case as apps encourage the usage of smaller roads not normally used for transit, local governments act in restricting speed and transit possibilities in those smaller roads normally not planned for transit traffic. This has given a different priority to municipal policies. Other possible solutions is to install tolls for transit traffic or otherwise price mobility differently, or to change the overall traffic patterns.

It is just the start of the change of our urban landscape and mobility brought by real-time navigation apps. Expect physical changes and changes of usage.

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How Automation Should Not Be Marketed as Intelligent

There is a lot being written these days about putting some limits to the hype of ‘Artificial Intelligence’. In this interesting post on Forbes ‘Automation Is Not Intelligence‘, the point is made that while calling stuff ‘AI-enabled’ is trendy, it does nothing to create more intelligence!

In particular, the article makes the point that automation is not intelligence. Increased automation fosters productivity, but it is only to make repeatable dumb tasks quicker and more efficiently. However there seem to be a trend to mix both aspects in current marketing.

“Vendors that push automation solutions as intelligent are potentially hurting the industry. If customers are lead to believe that various automation solutions are what they can expect out of AI systems and humans are required to add intelligent components on their own to call their systems intelligent, then the industry is heading for a rapid correction.”

The issue is of course that there is excessive hype around everything artificially intelligent (supposedly). “While there is a lot of great, new innovation that’s pushing the industry forward towards more intelligent systems capable of many of the challenging areas that have previously not been able to be solved due to extreme complexity or the need for human labor, there are just as many companies who are using the term AI as more of a marketing ploy or a way to raise money.”

There will be a correction in the industry when people realize what are really the limits of ‘Artificial Intelligence’ technology (read this other interesting post ‘It’s not Artificial Intelligence, it’s a new level of automation‘). Let’s not call everything intelligent, for the moment not a lot is really except humans.

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How Our Phones Are Listening to Everything

Following up on our previous post ‘How We Get Tracked by Our Phones and by People We Don’t Imagine‘, it’s even worse: our phones are constantly listening too! This USA Today column ‘You’re not paranoid: Your phone really is listening in‘ covers some interesting details.

It appears there is a strong suspicion that new marketing approaches is to serve adds based on some key words that are listened by the phone.

“In mid-2018, a reporter for Vice experimented to see just how closely smartphones listen to our conversations. To test his phone, the journalist spoke preselected phrases twice a day for five days in a row. Meanwhile, he monitored his Facebook feed to see if any changes occurred. Sure enough, the changes seemed to arrive overnight.”

Of course there is some convenience in being able to instruct your phone or device by voice (although I still find it odd and prefer to type), but it obviously comes with some drawbacks.

So if you want to have a really important discrete conversation, have your phone somewhere else. Even when it’s off, someone may still listen to you!

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How We Get Tracked by Our Phones and by People We Don’t Imagine

We get permanently tracked by our phones… and they don’t even try to hide it! Like me you probably receive on a regular basis a Google Maps recap of the previous month. At the start I found those emails quite creepy, now I guess I got used to them.

My location data for January 2020

Nevertheless this excellent New York Time visualization ‘ONE NATION, TRACKED: an investigation into the smartphone tracking industry‘ shows the extend it takes when applied to the entire population.

The most interesting part is that although the dataset of the location of 12 million phones provided for research is supposed to be anonymous, it proves quite easy to associate a phone with an individual based on his location pattern. Actually it is not quite possible to anonymise a data set of locations.

And the scariest bit – the data did not originate from a phone network provider or a GAFA. It “originated from a location data company, one of dozens quietly collecting precise movements using software slipped onto mobile phone apps. You’ve probably never heard of most of the companies — and yet to anyone who has access to this data, your life is an open book.“

I encourage you to read and watch the infographics of this paper to really understand what we have all accepted to get into. It would be quite easy for non scrupulous users of the database – or some surveillance state – to know exactly what we are up to.

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How to Address the Challenge of Weak Signal Detection

As mentioned in our previous post ‘How to Explain Covid-19 Blindness‘, the Covid-19 situation illustrates the more general challenge in complex systems to identify weak signals early and specifically, those that can, with some probability, develop into a crisis of significant consequences.

It is a challenge many organisations are regularly facing. For example in my professional field, project management in complex projects, the challenge to detect weak signals early and act on them is addressed by advanced project control approaches.

Nevertheless, it remains a difficult issue. This monitoring is prone to generate many false alarms; and some actions taken early will also avoid some of those weak signals develop into a situation or a crisis. Therefore, there is a risk that responsible bodies become fed up by too many weak signals and lose their vigilance. Still, maintaining this detection capability remains obviously essential.

In the Covid-19 situation as in some other challenges of humanity, the weak signal was identified and clearly delineated at least in some pockets of medical specialists, and even in some strategic analysis by the military. What was not anticipated was the consequential impact on the economy. This was probably because of a lack of pluri-discipline linkage and scenario planning. In addition there has been a lack of anticipation as soon as the first signs of a possible looming scenario appeared.

As a learning point, it is probably worth as in all complex system issues to setup a multi-disciplinary weak signal challenge team to review on a regular basis those signals and recommend actions.

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How to Explain Covid-19 Blindness

In this post “COVID-19’s General Blindness is Also a Journalistic Failure“, Frederic Filloux explains the reasons for a lack of anticipation of journalism on the epidemics.

The observation is that while the possibilities of a pandemics were exposed in many scientific publications and widely available, journalists have not raised the alarm early. Of course this blindness is not limited to journalists, but they could have played a significant role.

According to Frederic Filloux this can be explained by loss of in-house expertise due to newsroom shrinking in the current economic situation of the press. When there is a situation, external experts are asked to help, but the very possibility of detecting a situation is lost.

According to him “Newsrooms harboring experts — in house, or more realistically, on retainers — would have been more likely to read low-noise signals or even connect the dots of apparently unrelated facts, to put together a true picture of what is unfolding.“

It is well known that it is always difficult to detect low-noise signals and raise the awareness of a wider group. But in that case, the low noise signal was apparently not even identified, which is a concern.

What could be the solution? Frederic Filloux is currently supporting the development of an AI-based content editor, and is quite confident that such solutions could help. In my mind, in an ever-accelerating world, keeping more emphasis on memory and long term approaches is also important: countries that had been exposed to SARS 15 years ago did remember what had to be done.

Lack of memory and general loss of expertise in groups that could relay the issues we are facing are certainly important culprits.

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How to Simply Understand Machine Learning

I love this simple introduction to machine learning described in ‘Taking Stealing To The Next Level: Baseball And Machine Learning‘ and the YouTube video ‘I used machine learning to hack baseball‘. In a simple mundane context (secret signals players exchange during baseball games), the power of machine learning is demonstrated in a very simple educational manner. I encourage you to watch!

Even myself having no clue about baseball and discovering there are secret signs exchanged during the game managed to understand the AI approach to this problem, so don’t fear if you know nothing about this game.

One interesting point here is of course that AI can be applied to many interesting problems in our daily lives to give us breakthrough insights into many aspects that repeat sufficiently to provide a sufficiently broad set of data points. However as the example shows, AI will provide reasonable answers even after a reasonable sample.

We are probably just starting to identify all the mundane applications that AI could have!

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How Smart City Data Should Not Be Made Free

In this article ‘A smart city should serve its users, not mine their data‘, Cory Doctorow verbalizes one of the important issues facing infrastructure digitalization.

The first aspect, following our post ‘How Data Really is the New Oil, and Better‘, is that whatever data a smart city gathers should not be left available for free to service suppliers. It belongs to the community and should be valued if it is to be made available.

Further than that, the risk of the smart city in terms of data management and privacy is that the system decides how to change the city instead of the citizens.

“What if people were the things that smart cities were designed to serve, rather than the data that smart cities lived to process?” Cory Doctorow goes on to suggest that the flow of data access should be reversed, the individual having the opportunity to tap into the collective data, not letting know of his final choice, rather than data being collected independently of his will.

This would be quite a different model from the one that develops currently where our data is reaped by giant organizations without our consent. The data should not be made free, both economically and in terms of availability. Quite an interesting avenue to investigate: the final equilibrium of the Collaborative Age will probably be somewhere in the middle.

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How Easy Modern Technology Makes It For Spies

In this excellent Foreign Policy article ‘the Spycraft Revolution‘ (recommended read although a bit long) the changes in the world of espionnage are described, as well as the challenges faced by those involved in this activity to adapt.

Cover identities are now much harder to forge, as we leave many traces of our past on internet. Closed data societies such as China would seem to have an edge on open data societies like the western world (and authoritarian governments over liberal democracies that limit spying). Counterintelligence can leverage the internet to resist deception. Mobile phones are the most spy-friendly device that has ever been invented, it is an incredible tracking tool, and they can even listen to what’s happening remotely.

“The cloak of anonymity is steadily shrinking“. Still, western intelligence agencies are facing legal hurdles but they may have to be partly removed to allow competition with opponents that don’t have this type of issues. Which leaves society more open to intrusive spying. The right balance has not been defined yet.

“Most of us don’t want to live in a country […] where the intelligence and security agencies are at the heart of public life and political decision-making.” Still we need to be realistic enough to defend ourselves against undue foreign influence. This balance will take time to establish and there will be blunders along the way.

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How Night Lights Illustrate Economic Development

This excellent YouTube Video shows how night light illustrates the level of economic development.

The illustration around how lights went off in Syria during the civil war, or the contrast between North and South Korea, or the evolution of India’s lighting levels over a short decade are great illustrations of how to witness economic development in a qualitative manner.

At the same time it reminds us that even the billion humans we are are but a few spots on the entire earth surface, and how quickly that little flicker of light can get extinguished should keep us quite humble!

Hat tip to Alex Tabarrok in the Marginal Revolution blog.

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How Anonymous Datasets Turn-Out to be Not So Anonymous

This paper (‘anonymous data too easy to identify ‘in French, refering to this scientific paper published in Nature ‘Estimating the success of re-identifications in incomplete datasets using generative models‘) exposes how easy it is to identify people even in the midst of anonymous datasets.

In fact the paper states that any individual can be identified by only 15 demographic attributes, where common marketing databases can have 5,000 attributes per person (“15 demographic attributes would render 99.98% of people in Massachusetts unique“).

Therefore, while “de-identification, the process of anonymizing datasets before sharing them, has been the main paradigm used in research and elsewhere to share data while preserving people’s privacy“, it does not seem to work so well. Several methods are often used to improve anonymity such as for example, only using a subset of the dataset so that it is never possible to be really sure about a correct identification. However the nature paper concludes that “the likelihood of a specific individual to have been correctly re-identified can be estimated with high accuracy even when the anonymized dataset is heavily incomplete.”

Some frightening examples are quoted such as “In 2016, journalists re-identified politicians in an anonymized browsing history dataset of 3 million German citizens, uncovering their medical information and their sexual preferences“.

There is thus still some way to go to have really anonymized databases for data research, which shows again that privacy is now quite virtual! We certainly need to be aware of it.

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How Data Really is the New Oil, and Better

We hear more and more often this expression, and it has been made popular by a few papers and books. We have quoted a quite noted Economist article on the topic in a previous post ‘How User-Generated Data Should be Better Valued‘. In this interesting post ‘Fueling Alpha: Data Is Powering The New World‘, this view is explained in a quite straightforward manner.

“Data can be seen as the fuel to the information economy and oil to the industrial economy. The amount of power someone has can be correlated to their control of and access to these resources … and, leaking of these resources can lead to extreme consequences.”

It is important to realize that the amount of data we are generating is staggering and ever increasing: “A staggering 90% of all the world’s data (2.5 quintillion bytes per day) has been created in the past two years alone … and its value is rapidly rising. With IoT growing from 2 billion devices in 2006 to a projected 200 billion by 2020 you can expect to see that growth continue to explode.“

Thus of course, data is better than oil because it is renewable and is currently created at a much higher rate every month. It is also quite accessible without any issue of geographical border and limited legal issues. Therefore it won’t deplete soon and we can expect to have limited access risks (with some exceptions).

Data is what will soon drive fully our economies. Our institutions have not yet realized it, and while we can expect like for oil a temporary domination by some large companies, we can also expect that in the long term there will be a more regulated usage framework.

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