Right:
Each colored point-cloud is a country
Each point (x,y) is 1 hour of electricity production with x=energy produced in kWh; y=CO2 emission in g/kWh.
Left:
bar graphs of the mix of production methods for select countries.
1/N
France: low overall CO2 emissions, low variance on emissions, relying essentially on nuclear energy with a bit of hydro [reminder: nuclear produce essentially no CO2].
2/N
Germany: despite having a large proportion of renewables, has high emissions and a high variance of emissions: when there is no wind nor sun, it has to rely on fossil fuel, having abandoned and phased out nuclear production.
3/N
Poland has very high emissions, relying essentially on coal and other fossil fuels.
Sweden, like France has a high proportion of nuclear, and a favorable geography for hydro and wind power, and hence has low emissions.
4/N
Portugal, like Sweden, relies heavily on hydro and wind, but unlike Sweden relies on fossil fuels instead of nuclear to pick up the slack when there is no wind nor sun. Hence their CO2 emission variance is huge, like Germany's.
5/N
I put the diagram together from the following sources:
- The scatter plot comes from this article in Le Monde yesterday: lemonde.fr/.../electricit…
- The energy mix bar graphs (from 2018) come from the European Commission website: ec.europa.eu/.../infographs…
6/N, N=6
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I came first to work at Bell Labs on a J-1 visa, because I thought I'd stay only a year or two.
But I stayed longer and got an H1-B visa.
Then I got a green card.... 1/N
I hesitated to take up citizenship during the GW Bush years, waiting for the country to become respectable again.
But after Bush's re-election, I just wanted to be able to vote and kick out the neocon bastards.
So I became a citizen just in time to vote for Barack Obama.
2/N
As an immigrant, scientist, academic, liberal, atheist, and Frenchman, I am a concentrate of everything the American Right hates.
3/N
@timnitGebru If I had wanted to "reduce harms caused by ML to dataset bias", I would have said "ML systems are biased *only* when data is biased".
But I'm absolutely *not* making that reduction.
1/N
@timnitGebru I'm making the point that in the *particular* *case* of *this* *specific* *work*, the bias clearly comes from the data.
2/N
@timnitGebru There are many causes for *societal* bias in ML systems
(not talking about the more general inductive bias here). 1. the data, how it's collected and formatted. 2. the features, how they are designed 3. the architecture of the model 4. the objective function 5. how it's deployed
We often hear that AI systems must provide explanations and establish causal relationships, particularly for life-critical applications.
Yes, that can be useful. Or at least reassuring....
1/n
But sometimes people have accurate models of a phenomenon without any intuitive explanation or causation that provides an accurate picture of the situation. In many cases of physical phenomena, "explanations" contain causal loops where A causes B and B causes A.
2/n
A good example is how a wing causes lift. The computational fluid dynamics model, based on Navier-Stokes equations, works just fine. But there is no completely-accurate intuitive "explanation" of why airplanes fly.
3/n
Some folks still seem confused about what deep learning is. Here is a definition:
DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization.... facebook.com/722677142/post…
This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.
Don't say "DL can't do X" when what you really mean is "supervised learning needs too much data to do X"....
....Extensions (dynamic networks, differentiable programming, graph NN, etc) allow the network architecture to change dynamically in a data-dependent way.