"The key question around the #GreenEnergy transition is technological progress, and which technologies would improve..."
- SFI Prof J. Doyne Farmer (@INETOxford)
Follow this 🧵 for insights from today's seminar, streaming now on our YouTube channel:
"We're tracking the evolution of the global #energy landscape over about 140 years: which source is providing our energy, and how much is it providing us with?"
- SFI Prof J. Doyne Farmer (@INETOxford)
"#FossilFuels cost about the same now as they did a century ago. #Renewables have been dropping in price at rates of about 10% a year and deployment has been shooting up at about 30% a year, and this has been continuing for several decades."
"Some products, especially computers and electronics, go down in price by as much 15% per year every year since the 1950s. How can we make use of this [kind of observation] to understand the future of [#CleanEnergy]?"
"I asked #KenArrow, 'I would like examples of empirical regularities that are predicted by economic theory.' He said, 'Here's a good one. It's not really predicted by economic theory...'"
@INETOxford "The data's not great, but there are 50 technologies here, which is enough to pretend to be at a given time in the past & then forecast each 'future' date. We tested 7 different models & showed #WrightsLaw tied #MooresLaw on that data set."
"We're predicting the change in cost from the change in cumulative production. We showed a lot of predictions in that flavor and showed it's roughly [the same as] #MooresLaw. And based on the noise, you can predict the accuracy of your predictions."
- J. Doyne Farmer
"One of the things you see empirically is lots of 'wiggles'; there tend to be a lot of 'wiggles' in the future, too, [but] we're still within the error bars we predicted."
"We put anchor points in the predictions and see them going forward. ALL of them didn't predict costs [of #Solar] going down as fast as they did. They could have just put the past curve on paper."
+
"We have never seen any evidence for floor costs, but they put them in anyway."
"We made conservative projections. We did not forecast deployment. And we don't assume, as all these other models do, that we manage to get more efficiency or use less energy; we assume energy use continues to go up 2%/year, as it has for the last 50 years."
"We don't specify [the reason] that the use of #FossilFuels goes down. We just use costs."
- J. Doyne Farmer (SFI, @INETOxford)
"If we just extrapolate this curve, #solar and #wind take over in less than a decade. The push you make when the naysayers say, 'Solar is only 3%' is, 'Just wait a few years.'"
- J. Doyne Farmer
"The fast transition is saving us $17.5T relative to NO transition, at ANY discount rate."
- J. Doyne Farmer
"In a traditional investment portfolio, they assume that the things you're investing in have no effect on the other things. They say you should always diversify. But if you're dealing with #WrightsLaw you want to pick a few baskets, 4 or 5 or 6, & put your eggs in THOSE baskets."
"All we have to do is keep with the existing trends for the next decade and we'll have dealt with 75% of #ClimateChange. What's blocking improvement is grid capacity. There are some bottlenecks but they're [technologically speaking] relatively small."
- J. Doyne Farmer
"Petrostates are going to collapse in the 25 years. We're going to face a lot of transition risk; I think it's actually bigger than climate risk."
- J. Doyne Farmer (@INETOxford, SFI)
ICYMI, this week's SFI Seminar by Fractal Faculty Stuart Firestein (@Columbia) on "what started out ass a very simple-seeming problem [re: #olfaction] and turned out to be very complicated":
"Everything we know about the world comes through these little holes in our head and the skin covering our body, processed through tissue specialized to interpret it."
"The thing to notice about [sight and hearing] is that they're [processing] fairly low-dimensional stimuli."
"Even a simple smell is composed of a VARIETY of molecules, and these are high-dimensional from a chemical point of view. And it's also a somewhat discontinuous stimulus. How do we get from this bunch of molecules to this unitary perception of something like a rose?"
"A key feature of this is talk is that we make sense of what each other are saying IN PART by what they say, but ALSO by what we expect of them."
"Language transmits info against a background of expectations – syntactic, semantic, and this larger cultural spectrum. It's not just the choices of make but [how] we set ourselves up to make later choices."
"I think what really drives [the popularity of the #multiverse in #scifi] is regret... There's a line in @allatoncemovie where #MichelleYeoh is told she's the worst version of herself."
"I don't think we should resist melting brains. I think we should just bite the bullet."
"When you measure the spin of an electron, or the position...what happened to all of the other things you could have seen? Everett's idea is that they're all real. They all become real in that measurement."
- SFI Fractal Faculty @seanmcarroll at @guardian theguardian.com/science/audio/…
"At the level of the equations there is zero ambiguity, but the metaphors break down. The two universes it splits into aren't as big as the original universe. The thickness of the two new universes adds up to the thickness of the original universe."
"One way to represent the kind of #compositionality we want to do is with this kind of breakdown...eventually a kind of representation of a sentence. On the other hand, vector space models of #meaning or set-theoretical models put into a space have been very successful..."
"Humans are prone to giving machines ambiguous or mistaken instructions, and we want them to do what we mean, not what we say. To solve this problem we must find ways to align AI with human preferences, goals & values."
- @MelMitchell1 at @QuantaMagazine: quantamagazine.org/what-does-it-m…
“All that is needed to assure catastrophe is a highly competent machine combined with humans who have an imperfect ability to specify human preferences completely and correctly.”
"It’s a familiar trope in #ScienceFiction — humanity threatened by out-of-control machines who have misinterpreted human desires. Now a not-insubstantial segment of the #AI research community is concerned about this kind of scenario playing out in real life."
- @MelMitchell1