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Aug 16, 2022 19 tweets 18 min read Read on X
"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)

Speaking now:

#Solar #Wind #Nuclear #Oil #Gas #Coal
"#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."

- J. Doyne Farmer (SFI, @INETOxford)
"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]?"

- J. Doyne Farmer (SFI, @INETOxford) re: #WrightsLaw
"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...'"

- J. Doyne Farmer (SFI, @INETOxford) re: #WrightsLaw
@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."

- J. Doyne Farmer (SFI, @INETOxford)
"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."

- J. Doyne Farmer on the predicting the evolution of #SolarEnergy #Technology
"Oil extraction technology has gotten better over the last century, but oil has become more difficult to extract, so they cancel each other out."

- J. Doyne Farmer
"Here are examples for what we think will likely be the four key technologies in the #GreenEnergy transition."

"We predicted in 2010 that solar would become cheaper than coal & gas, & [it did]."

- J. Doyne Farmer (SFI+@INETOxford) on #Solar, #Wind, #Batteries, & #Electrolyzers
"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."

- J. Doyne Farmer
Three scenarios for the #CleanEnergy transition:

"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)

For more on "How Can #ComplexityEconomics Give More Insight into #PoliticalEconomy?" — the recent SFI working group co-organized by J. Doyne Farmer (@INET_Complexity) & @EricBeinhocker, read on:
santafe.edu/news-center/ne…
(Then listen to their recent episodes of #ComplexityPodcast!)

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Mar 10, 2023
🧵 "The Smell of Inhibition. A Code in the Nose?"

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":

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"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."
Read 4 tweets
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"Compositionality in Vector Space Models of Meaning"

Today's SFI Seminar by @marthaflinders, streaming:


Follow this 🧵 for highlights!
"Scientists gather here
Santa Fe Institute, oh so near
Inquiring minds seek truth"

#haiku about SFI c/o @marthaflinders & #ChatGPT

...but still, #AI fails at simple tasks:
"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..."
Read 14 tweets
Dec 13, 2022
"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."
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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.”

- Stuart Russell (@UCBerkeley) as quoted by @MelMitchell1 in her latest @QuantaMagazine article
"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."
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Read 6 tweets
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"Training Machines to Learn the Way Humans Do: an Alternative to #Backpropagation"

Today's SFI Seminar by Sanjukta Krishnagopal
(@UCBerkeley & @UCLA)

Starting now — follow this 🧵 for highlights:
Image
"When we learn something new, we look for relationships with things we know already."

"I don't just forget Calculus because I learned something else."

"We automatically know what a 'cat-dog' would look like, if it were to exist."

"We learn by training on very few examples." Image
1, 2) "[#MachineLearning] is fundamentally different from the way humans learn things."

3) Re: #FeedForward #NeuralNetworks

"You choose some loss function...maybe I'm learning the wrong weights. So I define some goal and then I want to learn these weights, these thetas." ImageImageImage
Read 13 tweets

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