Recent Discussion
We're excited to announce our November book discussion featuring Deirdre McCloskey's The Bourgeois Virtues: Ethics for an Age of Commerce as part of our ongoing book series dedicated to exploring the ideas of Progress Studies.
Pathways to Progress is a community of individuals committed to understanding and contributing to human prosperity. Through our discussions, we examine technological and scientific innovation, economic development, and their role in advancing human prosperity.
Each month, we read selected book(s), followed by a Q&A event with the author. In October, we discussed J. Storrs Hall's Where's My Flying Car?, followed by a Q&A with Hall. Previous books include Tyler Cowen's Stubborn Attachments and Ed Glaeser's Triumph of the City. We also host speaker events with guests such as Jason Crawford, Matt Clancy, and Heidi...
Sharing from my blog: https://spiralprogress.com/2024/10/22/scenario/
Following Kuhn, normal progress is when things get better along the values you already hold and society feels improved without becoming less recognizable. Cars get safer, screens have more pixels, food gets cheaper.
Normal progress does not mean “trivial” or “frivolous”. Decreasing infant mortality is hugely important and also a kind of “normal” progress.
There are however, rare moments of revolutionary progress where society changes in a more profound way. I am thinking of the advent of democracy, the slow death of religious fundamentalism, the shift to agriculture. In these moments, along the values and priorities held by the pre-revolutionary people, society may actually seem to be in decline. It is more difficult to say along the same axes that were important pre-revolution, that these...
The first practical steam engine was built by Thomas Newcomen in 1712. It was used to pump water out of mines.
An astute observer might have looked at this and said: “It’s clear where this is going. The engine will power everything: factories, ships, carriages. Horses will become obsolete!”
This person would have been right—but they might have been surprised to find, two hundred years later, that we were still using horses to plow fields.
In fact, it took about a hundred years for engines to be used for transportation, in steamships and locomotives, both invented in the early 1800s. It took more than fifty years just for engines to be widely used in factories.
What...
The history of humanity can be summarized as a long series of “fuck around and find out.”[1]
It’s the cycle of innovation and consequence. We see a problem X, we invent a solution, we discover that solution creates a new problem, we can't stop doing X, and we have to invent another solution. And so on. This is our philosophy: to seek, to solve, to stumble anew.
We invented fire, which kept us warm and cooked our food, and also burned down our villages and killed us in wildfires. We invented knives and arrows and saws, which helped us hunt and build, and also cut off our fingers and stabbed us in the gut. We invented agriculture, providing food surplus, yet it sowed seeds of war, famine, and environmental...
Most development interventions are considered “good buys” (for donors or governments) if they return $15 in social benefits for every $1 spent.
Every $1 spent on reading glasses could result in $46 more in earnings.
Say you have a time machine. You can only use it once, to send a single idea back to 2005. If you wanted to speed up the development of AI, what would you send back? Many people suggest attention or transformers. But I’m convinced that the answer is “brute-force”—to throw as much data at the problem as possible.
AI has recently been improving at a harrowing rate. If trends hold, we are in for quite a show. But some suggest AI progress might falter due to a “data wall”. Current language models are trained on datasets fast approaching “all the text, ever”. What happen when it runs out?
Many argue this data wall won’t be a problem, because humans have excellent language and reasoning despite seeing far less language data. They say that humans must be leveraging visual data and/or using a more data-efficient learning algorithm. Whatever trick humans are using, they say, we can copy it and avoid the data wall.
I am dubious of these arguments. In this post, I will explain how you can be dubious, too.
Ok. Firstly I do think your "Embodied information" is real. I just think it's pretty small. You need the molecular structure for 4 base pairs of DNA, and for 30 ish protiens. And this wikipedia page. https://en.wikipedia.org/wiki/DNA_and_RNA_codon_tables
That seems to be in the kilobytes. It's a rather small amount of information compared to DNA.
Epigenetics is about extra tags that get added. So theoretically the amount of information could be nearly as much as in the DNA. For example, methyization can happen on A and C, so that's 1 bit per base pair, in th... (read more)
This essay is cross-posted from https://thegreymatter.substack.com/p/the-ai-responsibility-dilemma
In an article in The Conversation, Professor of Philosophy David Danks and Professor of Computing Mike Kirby present the following scenario:
A self-driving taxi has no passengers, so it parks itself in a lot to reduce congestion and air pollution. After being hailed, the taxi heads out to pick up its passenger – and tragically strikes a pedestrian in a crosswalk on its way.
Who or what deserves praise for the car’s actions to reduce congestion and air pollution? And who or what deserves blame for the pedestrian’s injuries?
They tell us:
...One possibility is the self-driving taxi’s designer or developer. But in many cases, they wouldn’t have been able to predict the taxi’s exact behavior. In fact, people typically want artificial intelligence to discover some
This essay is cross-posted here: https://open.substack.com/pub/cleanenergyreview/p/the-uber-ization-of-electricity
Before Uber, taxis were all we had. They operated on a fixed rate model, with the same metered fare if it was a sunny holiday morning or raining during rush hour. This drove prices up and kept drivers circling for fares, only to leave taxis hard to find at any price during peak periods.
Uber changed this. By recruiting drivers outside the regulated taxi structure, and implementing surge prices during periods of high demand, they were able to flex supply to better match demand. Their app-based platform also gave riders much improved transparency into the price they would be paying, and the expected length and route of their ride.
The result: a revolution in intra-city transport that is still reverberating.
We are at the beginning...
This is a linkpost for https://amistrongeryet.substack.com/p/alphaproof-and-openai-o1
The latest advances in AI reasoning come from OpenAI's o1 and Google's AlphaProof. In this post, I explore how these new models work, and what that tells us about the path to AGI.
Interestingly, unlike GPT-2 -> GPT-3 -> GPT-4, neither of these models rely on increased scale to drive capabilities. Instead, both systems rely on training data that shows, not just the solution to a problem, but the path to that solution. This opens a new frontier for progress in AI capabi... (read more)