The Monthly Briefing
We're not statistically minded animals
As countries came to a standstill while COVID-19 was spreading around the world, the pandemic has brought to light some things that had previously gone unnoticed. One of these is the importance of clearly communicating to people the results of scientific research or the implementation of certain social norms that need to be understood by everyone in order to be effective
Within our own modest world, we are also asking ourselves how we can best communicate the output of our algorithms to our customers, so that they can get the most value out of them. This is not an easy task: we humans are not used to working with imperfect results. Especially when it comes to evaluating probability-based scenarios.
This is what is known as the wet bias phenomenon: it has been observed that meteorological services in certain US (United States) media sources were used to deliberately inflate the probability of rain to be much higher than had actually been calculated. In his well-known book “The Signal and the Noise”, the statistician and data disseminator Nate Silver delves into this phenomenon and goes so far as to attribute it to the fact that meteorologists believe that the population, whenever it sees a probability of rain that is too small -say 5%-, will interpret it directly as “it’s not going to rain” -and consequently will be disappointed 5% of the time-. In other words, we humans tend to simplify information for decision-making.
For this reason, we must be very careful not to present the results of a forecast or a prediction as absolute, for example. In this particular case, it is more effective to analyze how confident the algorithm is in each forecast, and perhaps discard the cases where we do not have high confidence. This article by our colleague José A. Rodríguez Serrano goes into more detail about all these considerations
Another crisis that needs to be tracked
On the way to changing our relationship model with the planet towards one that is more sustainable, governments, public and private institutions and society as a whole must be able to manage the emissions of polluting gases warming our atmosphere. And as David Roberts says in this article in Vox, if we want to manage something properly, we have to measure it beforehand.
To tackle climate change, one of the main challenges to be solved is to correctly track where greenhouse gas emissions caused by human activity occur. So far, this has proved to be a complex task, which in turn has made international climate negotiations extremely difficult.
However, the solution to this problem does not seem so far away. The Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions) Coalition “is building a tool that will use artificial intelligence, satellite image processing, machine learning, and other remote sensing technologies to monitor worldwide greenhouse gas (GHG) emissions in real-time”. This tool will be verified independently and will be available to the public for free.
According to Gavin McCormick, executive director of coalition member WattTime, “the Earth is like a medical patient suffering from a condition called climate change. Trying to fix it with only years-late, self-reported emissions data is like asking a doctor to fix a serious disease with no more information than a list of symptoms the patient had years ago”.
One of the most interesting future applications of this tool is to provide crucial visibility to more-easily and accurately meet emissions-reduction goals, direct sustainable investments (and divestments), and assess risk. You can find more information in this articles in Medium (1) (2).
+Using Convolutional Neural Networks for image processing and classification: a case study on Sudan's Darfur region (Citizen Evidence Lab)
+ What is a real data-driven decision and the danger of falling into a confirmation bias. (Hackernoon | Medium)
+Why the path to AI-first banking is a must (McKinsey)
The increased expectations of customers regarding the digital maturity of companies -enhanced by the COVID-19 crisis-, the benefits of adopting AI in parts of the business such as process automation, risk management, fraud detection or customer relationship, and the strong competition in the digital ecosystem, are new elements that banks are facing.
The bank of the future will be AI-first, it will offer propositions and experiences that are intelligent, personalized and truly omnichannel. However, for this to happen, traditional banks will have to overcome certain obstacles. Some of the most important are the lack of a clear strategy for AI, a weak core technology and data backbone, an outmoded operating model or the several weaknesses inherent to legacy systems before they can deploy AI technologies at scale.
+ Facial recognition ban awaiting regulation (c|net)
+ Is facebook amplifying the spread of the messages raised by the right wing? (The Verge)
+ The GPT-3 revolution explained in this video (DotCSV) (in spanish)
+ Time to dataviz (Area Estratégica)
+ The magic of TikTok (TikTok)
+ Another interesting analysis on The Social Dilemma. (Slate)
"Chess engines were initially built to play against humans with the goal of defeating them. Now we see a system like AlphaZero used for creative exploration in tandem with humans rather than opposed to them."
Nenad Tomašev, Senior Research Engineer at Google DeepMind
AlphaZero is a more flexible and powerful successor to AlphaGo, the computer program developed by Google DeepMind that made history in 2016 by beating the world champion in the game of Go, Lee Sedol. Since last March, the complete documentary that tells the story of this tournament is available on youtube (highly recommended).
Now, Vladimir Kramnik, a former world chess champion, teams up with the makers of AlphaZero to test variants on the age-old game that can jolt players into creative patterns. More in this article.
BBVA DATA GALAXY 🌌
Data is vs. data are...
We would like to finish this issue with this amazing little comic. Sometimes the silliest question generates the deepest debate!
See you next month!
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