nhaliday + speedometer   150

performance - What is the difference between latency, bandwidth and throughput? - Stack Overflow
Latency is the amount of time it takes to travel through the tube.
Bandwidth is how wide the tube is.
The amount of water flow will be your throughput

Vehicle Analogy:

Container travel time from source to destination is latency.
Container size is bandwidth.
Container load is throughput.

--

Note, bandwidth in particular has other common meanings, I've assumed networking because this is stackoverflow but if it was a maths or amateur radio forum I might be talking about something else entirely.
q-n-a  stackex  programming  IEEE  nitty-gritty  definition  jargon  network-structure  metrics  speedometer  time  stock-flow  performance 
18 days ago by nhaliday
Reverse salients | West Hunter
Edison thought in terms of reverse salients and critical problems.

“Reverse salients are areas of research and development that are lagging in some obvious way behind the general line of advance. Critical problems are the research questions, cast in terms of the concrete particulars of currently available knowledge and technique and of specific exemplars or models that are solvable and whose solutions would eliminate the reverse salients. ”

What strikes you as as important current example of a reverse salient, and the associated critical problem or problems?
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4 weeks ago by nhaliday
Complexity no Bar to AI - Gwern.net
Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely. (computer science, transhumanism, AI, R)
created: 1 June 2014; modified: 01 Feb 2018; status: finished; confidence: likely; importance: 10
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april 2018 by nhaliday
The first ethical revolution – Gene Expression
Fifty years ago Julian Jaynes published The Origin of Consciousness in the Breakdown of the Bicameral Mind. Seventy years ago Karl Jaspers introduced the concept of the Axial Age. Both point to the same dynamic historically.

Something happened in the centuries around 500 BCE all around the world. Great religions and philosophies arose. The Indian religious traditions, the Chinese philosophical-political ones, and the roots of what we can recognize as Judaism. In Greece, the precursors of many modern philosophical streams emerged formally, along with a variety of political systems.

The next few centuries saw some more innovation. Rabbinical Judaism transformed a ritualistic tribal religion into an ethical one, and Christianity universalized Jewish religious thought, as well as infusing it with Greek systematic concepts. Meanwhile, Indian and Chinese thought continued to evolve, often due to interactions each other (it is hard to imagine certain later developments in Confucianism without the Buddhist stimulus). Finally, in the 7th century, Islam emerges as the last great world religion.

...

Living in large complex societies with social stratification posed challenges. A religion such as Christianity was not a coincidence, something of its broad outlines may have been inevitable. Universal, portable, ethical, and infused with transcendence and coherency. Similarly, god-kings seem to have universally transformed themselves into the human who binds heaven to earth in some fashion.

The second wave of social-ethical transformation occurred in the early modern period, starting in Europe. My own opinion is that economic growth triggered by innovation and gains in productivity unleashed constraints which had dampened further transformations in the domain of ethics. But the new developments ultimately were simply extensions and modifications on the earlier “source code” (e.g., whereas for nearly two thousand years Christianity had had to make peace with the existence of slavery, in the 19th century anti-slavery activists began marshaling Christian language against the institution).
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april 2018 by nhaliday
The Hanson-Yudkowsky AI-Foom Debate - Machine Intelligence Research Institute
How Deviant Recent AI Progress Lumpiness?: http://www.overcomingbias.com/2018/03/how-deviant-recent-ai-progress-lumpiness.html
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:

...

In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom: http://www.overcomingbias.com/2014/07/30855.html
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.

...

In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?

...

In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.

...

Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds: https://sideways-view.com/2018/02/24/takeoff-speeds/
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)
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april 2018 by nhaliday
Eternity in six hours: intergalactic spreading of intelligent life and sharpening the Fermi paradox
We do this by demonstrating that traveling between galaxies – indeed even launching a colonisation project for the entire reachable universe – is a relatively simple task for a star-spanning civilization, requiring modest amounts of energy and resources. We start by demonstrating that humanity itself could likely accomplish such a colonisation project in the foreseeable future, should we want to, and then demonstrate that there are millions of galaxies that could have reached us by now, using similar methods. This results in a considerable sharpening of the Fermi paradox.
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march 2018 by nhaliday
The Coming Technological Singularity
Within thirty years, we will have the technological
means to create superhuman intelligence. Shortly after,
the human era will be ended.

Is such progress avoidable? If not to be avoided, can
events be guided so that we may survive? These questions
are investigated. Some possible answers (and some further
dangers) are presented.

_What is The Singularity?_

The acceleration of technological progress has been the central
feature of this century. I argue in this paper that we are on the edge
of change comparable to the rise of human life on Earth. The precise
cause of this change is the imminent creation by technology of
entities with greater than human intelligence. There are several means
by which science may achieve this breakthrough (and this is another
reason for having confidence that the event will occur):
o The development of computers that are "awake" and
superhumanly intelligent. (To date, most controversy in the
area of AI relates to whether we can create human equivalence
in a machine. But if the answer is "yes, we can", then there
is little doubt that beings more intelligent can be constructed
shortly thereafter.
o Large computer networks (and their associated users) may "wake
up" as a superhumanly intelligent entity.
o Computer/human interfaces may become so intimate that users
may reasonably be considered superhumanly intelligent.
o Biological science may find ways to improve upon the natural
human intellect.

The first three possibilities depend in large part on
improvements in computer hardware. Progress in computer hardware has
followed an amazingly steady curve in the last few decades [16]. Based
largely on this trend, I believe that the creation of greater than
human intelligence will occur during the next thirty years. (Charles
Platt [19] has pointed out the AI enthusiasts have been making claims
like this for the last thirty years. Just so I'm not guilty of a
relative-time ambiguity, let me more specific: I'll be surprised if
this event occurs before 2005 or after 2030.)

What are the consequences of this event? When greater-than-human
intelligence drives progress, that progress will be much more rapid.
In fact, there seems no reason why progress itself would not involve
the creation of still more intelligent entities -- on a still-shorter
time scale. The best analogy that I see is with the evolutionary past:
Animals can adapt to problems and make inventions, but often no faster
than natural selection can do its work -- the world acts as its own
simulator in the case of natural selection. We humans have the ability
to internalize the world and conduct "what if's" in our heads; we can
solve many problems thousands of times faster than natural selection.
Now, by creating the means to execute those simulations at much higher
speeds, we are entering a regime as radically different from our human
past as we humans are from the lower animals.
org:junk  humanity  accelerationism  futurism  prediction  classic  technology  frontier  speedometer  ai  risk  internet  time  essay  rhetoric  network-structure  ai-control  morality  ethics  volo-avolo  egalitarianism-hierarchy  intelligence  scale  giants  scifi-fantasy  speculation  quotes  religion  theos  singularity  flux-stasis  phase-transition  cybernetics  coordination  cooperate-defect  moloch  communication  bits  speed  efficiency  eden-heaven  ecology  benevolence  end-times  good-evil  identity  the-self  whole-partial-many  density 
march 2018 by nhaliday
Existential Risks: Analyzing Human Extinction Scenarios
https://twitter.com/robinhanson/status/981291048965087232
https://archive.is/dUTD5
Would you endorse choosing policy to max the expected duration of civilization, at least as a good first approximation?
Can anyone suggest a different first approximation that would get more votes?

https://twitter.com/robinhanson/status/981335898502545408
https://archive.is/RpygO
How useful would it be to agree on a relatively-simple first-approximation observable-after-the-fact metric for what we want from the future universe, such as total life years experienced, or civilization duration?

We're Underestimating the Risk of Human Extinction: https://www.theatlantic.com/technology/archive/2012/03/were-underestimating-the-risk-of-human-extinction/253821/
An Oxford philosopher argues that we are not adequately accounting for technology's risks—but his solution to the problem is not for Luddites.

Anderson: You have argued that we underrate existential risks because of a particular kind of bias called observation selection effect. Can you explain a bit more about that?

Bostrom: The idea of an observation selection effect is maybe best explained by first considering the simpler concept of a selection effect. Let's say you're trying to estimate how large the largest fish in a given pond is, and you use a net to catch a hundred fish and the biggest fish you find is three inches long. You might be tempted to infer that the biggest fish in this pond is not much bigger than three inches, because you've caught a hundred of them and none of them are bigger than three inches. But if it turns out that your net could only catch fish up to a certain length, then the measuring instrument that you used would introduce a selection effect: it would only select from a subset of the domain you were trying to sample.

Now that's a kind of standard fact of statistics, and there are methods for trying to correct for it and you obviously have to take that into account when considering the fish distribution in your pond. An observation selection effect is a selection effect introduced not by limitations in our measurement instrument, but rather by the fact that all observations require the existence of an observer. This becomes important, for instance, in evolutionary biology. For instance, we know that intelligent life evolved on Earth. Naively, one might think that this piece of evidence suggests that life is likely to evolve on most Earth-like planets. But that would be to overlook an observation selection effect. For no matter how small the proportion of all Earth-like planets that evolve intelligent life, we will find ourselves on a planet that did. Our data point-that intelligent life arose on our planet-is predicted equally well by the hypothesis that intelligent life is very improbable even on Earth-like planets as by the hypothesis that intelligent life is highly probable on Earth-like planets. When it comes to human extinction and existential risk, there are certain controversial ways that observation selection effects might be relevant.
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march 2018 by nhaliday
Information Processing: US Needs a National AI Strategy: A Sputnik Moment?
FT podcasts on US-China competition and AI: http://infoproc.blogspot.com/2018/05/ft-podcasts-on-us-china-competition-and.html

A new recommended career path for effective altruists: China specialist: https://80000hours.org/articles/china-careers/
Our rough guess is that it would be useful for there to be at least ten people in the community with good knowledge in this area within the next few years.

By “good knowledge” we mean they’ve spent at least 3 years studying these topics and/or living in China.

We chose ten because that would be enough for several people to cover each of the major areas listed (e.g. 4 within AI, 2 within biorisk, 2 within foreign relations, 1 in another area).

AI Policy and Governance Internship: https://www.fhi.ox.ac.uk/ai-policy-governance-internship/

https://www.fhi.ox.ac.uk/deciphering-chinas-ai-dream/
https://www.fhi.ox.ac.uk/wp-content/uploads/Deciphering_Chinas_AI-Dream.pdf
Deciphering China’s AI Dream
The context, components, capabilities, and consequences of
China’s strategy to lead the world in AI

Europe’s AI delusion: https://www.politico.eu/article/opinion-europes-ai-delusion/
Brussels is failing to grasp threats and opportunities of artificial intelligence.
By BRUNO MAÇÃES

When the computer program AlphaGo beat the Chinese professional Go player Ke Jie in a three-part match, it didn’t take long for Beijing to realize the implications.

If algorithms can already surpass the abilities of a master Go player, it can’t be long before they will be similarly supreme in the activity to which the classic board game has always been compared: war.

As I’ve written before, the great conflict of our time is about who can control the next wave of technological development: the widespread application of artificial intelligence in the economic and military spheres.

...

If China’s ambitions sound plausible, that’s because the country’s achievements in deep learning are so impressive already. After Microsoft announced that its speech recognition software surpassed human-level language recognition in October 2016, Andrew Ng, then head of research at Baidu, tweeted: “We had surpassed human-level Chinese recognition in 2015; happy to see Microsoft also get there for English less than a year later.”

...

One obvious advantage China enjoys is access to almost unlimited pools of data. The machine-learning technologies boosting the current wave of AI expansion are as good as the amount of data they can use. That could be the number of people driving cars, photos labeled on the internet or voice samples for translation apps. With 700 or 800 million Chinese internet users and fewer data protection rules, China is as rich in data as the Gulf States are in oil.

How can Europe and the United States compete? They will have to be commensurately better in developing algorithms and computer power. Sadly, Europe is falling behind in these areas as well.

...

Chinese commentators have embraced the idea of a coming singularity: the moment when AI surpasses human ability. At that point a number of interesting things happen. First, future AI development will be conducted by AI itself, creating exponential feedback loops. Second, humans will become useless for waging war. At that point, the human mind will be unable to keep pace with robotized warfare. With advanced image recognition, data analytics, prediction systems, military brain science and unmanned systems, devastating wars might be waged and won in a matter of minutes.

...

The argument in the new strategy is fully defensive. It first considers how AI raises new threats and then goes on to discuss the opportunities. The EU and Chinese strategies follow opposite logics. Already on its second page, the text frets about the legal and ethical problems raised by AI and discusses the “legitimate concerns” the technology generates.

The EU’s strategy is organized around three concerns: the need to boost Europe’s AI capacity, ethical issues and social challenges. Unfortunately, even the first dimension quickly turns out to be about “European values” and the need to place “the human” at the center of AI — forgetting that the first word in AI is not “human” but “artificial.”

https://twitter.com/mr_scientism/status/983057591298351104
https://archive.is/m3Njh
US military: "LOL, China thinks it's going to be a major player in AI, but we've got all the top AI researchers. You guys will help us develop weapons, right?"

US AI researchers: "No."

US military: "But... maybe just a computer vision app."

US AI researchers: "NO."

https://www.theverge.com/2018/4/4/17196818/ai-boycot-killer-robots-kaist-university-hanwha
https://www.nytimes.com/2018/04/04/technology/google-letter-ceo-pentagon-project.html
https://twitter.com/mr_scientism/status/981685030417326080
https://archive.is/3wbHm
AI-risk was a mistake.
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february 2018 by nhaliday
What Peter Thiel thinks about AI risk - Less Wrong
TL;DR: he thinks its an issue but also feels AGI is very distant and hence less worried about it than Musk.

I recommend the rest of the lecture as well, it's a good summary of "Zero to One"  and a good QA afterwards.

For context, in case anyone doesn't realize: Thiel has been MIRI's top donor throughout its history.

other stuff:
nice interview question: "thing you know is true that not everyone agrees on?"
"learning from failure overrated"
cleantech a huge market, hard to compete
software makes for easy monopolies (zero marginal costs, network effects, etc.)
for most of history inventors did not benefit much (continuous competition)
ethical behavior is a luxury of monopoly
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february 2018 by nhaliday
Reid Hofmann and Peter Thiel and technology and politics - Marginal REVOLUTION
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february 2018 by nhaliday
Information Processing: Mathematical Theory of Deep Neural Networks (Princeton workshop)
"Recently, long-past-due theoretical results have begun to emerge. These results, and those that will follow in their wake, will begin to shed light on the properties of large, adaptive, distributed learning architectures, and stand to revolutionize how computer science and neuroscience understand these systems."
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january 2018 by nhaliday
Books 2017 | West Hunter
Arabian Sands
The Aryans
The Big Show
The Camel and the Wheel
Civil War on Western Waters
Company Commander
Double-edged Secrets
The Forgotten Soldier
Genes in Conflict
Hive Mind
The horse, the wheel, and language
The Penguin Atlas of Medieval History
Habitable Planets for Man
The genetical theory of natural selection
The Rise of the Greeks
To Lose a Battle
The Jewish War
Tropical Gangsters
The Forgotten Revolution
Egil’s Saga
Shapers
Time Patrol

Russo: https://westhunt.wordpress.com/2017/12/14/books-2017/#comment-98568
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december 2017 by nhaliday
If Quantum Computers are not Possible Why are Classical Computers Possible? | Combinatorics and more
As most of my readers know, I regard quantum computing as unrealistic. You can read more about it in my Notices AMS paper and its extended version (see also this post) and in the discussion of Puzzle 4 from my recent puzzles paper (see also this post). The amazing progress and huge investment in quantum computing (that I presented and update  routinely in this post) will put my analysis to test in the next few years.
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november 2017 by nhaliday
Genome Editing
This collection of articles from the Nature Research journals provides an overview of current progress in developing targeted genome editing technologies. A selection of protocols for using and adapting these tools in your own lab is also included.
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october 2017 by nhaliday
[1709.01149] Biotechnology and the lifetime of technical civilizations
The number of people able to end Earth's technical civilization has heretofore been small. Emerging dual-use technologies, such as biotechnology, may give similar power to thousands or millions of individuals. To quantitatively investigate the ramifications of such a marked shift on the survival of both terrestrial and extraterrestrial technical civilizations, this paper presents a two-parameter model for civilizational lifespans, i.e. the quantity L in Drake's equation for the number of communicating extraterrestrial civilizations. One parameter characterizes the population lethality of a civilization's biotechnology and the other characterizes the civilization's psychosociology. L is demonstrated to be less than the inverse of the product of these two parameters. Using empiric data from Pubmed to inform the biotechnology parameter, the model predicts human civilization's median survival time as decades to centuries, even with optimistic psychosociological parameter values, thereby positioning biotechnology as a proximate threat to human civilization. For an ensemble of civilizations having some median calculated survival time, the model predicts that, after 80 times that duration, only one in 1024 civilizations will survive -- a tempo and degree of winnowing compatible with Hanson's "Great Filter." Thus, assuming that civilizations universally develop advanced biotechnology, before they become vigorous interstellar colonizers, the model provides a resolution to the Fermi paradox.
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october 2017 by nhaliday
The First Men in the Moon | West Hunter
But what about the future? One generally assumes that space colonists, assuming that there ever are any, will be picked individuals, somewhat like existing astronauts – the best out of hordes of applicants. They’ll be smarter than average, healthier than average, saner than average – and not by just a little.

Since all these traits are significantly heritable, some highly so, we have to expect that their descendants will be different – different above the neck. They’d likely be, on average, smarter than any existing ethnic group. If a Lunar colony really took off, early colonists might account for a disproportionate fraction of the population (just as Puritans do in the US), and the Loonies might continue to have inordinate amounts of the right stuff indefinitely. They’d notice: we’d notice. We’d worry about the Lunar Peril. They’d sneer at deluded groundlings, and talk about the menace from Earth.

https://westhunt.wordpress.com/2014/09/29/the-first-men-in-the-moon/#comment-58473
Depends on your level of technical expertise. 2 million years ago, settlement of the Eurasian temperate zone was bleeding-edge technology – but it got easier. We can certainly settle the Solar system with near-term technology, if we choose to. And you’re forgetting one of the big payoffs: gafia.
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october 2017 by nhaliday
Biopolitics | West Hunter
I have said before that no currently popular ideology acknowledges well-established results of behavioral genetics, quantitative genetics, or psychometrics. Or evolutionary psychology.

What if some ideology or political tradition did? what could they do? What problems could they solve, what capabilities would they have?

Various past societies knew a few things along these lines. They knew that there were significant physical and behavioral differences between the sexes, which is forbidden knowledge in modern academia. Some knew that close inbreeding had negative consequences, which knowledge is on its way to the forbidden zone as I speak. Some cultures with wide enough geographical experience had realistic notions of average cognitive differences between populations. Some people had a rough idea about regression to the mean [ in dynasties], and the Ottomans came up with a highly unpleasant solution – the law of fratricide. The Romans, during the Principate, dealt with the same problem through imperial adoption. The Chinese exam system is in part aimed at the same problem.

...

At least some past societies avoided the social patterns leading to the nasty dysgenic trends we are experiencing today, but for the most part that is due to the anthropic principle: if they’d done something else you wouldn’t be reading this. Also to between-group competition: if you fuck your self up when others don’t, you may be well be replaced. Which is still the case.

If you were designing an ideology from scratch you could make use of all of these facts – not that thinking about genetics and selection hands you the solution to every problem, but you’d have more strings to your bow. And, off the top of your head, you’d understand certain trends that are behind the mountains of Estcarp, for our current ruling classes : invisible and unthinkable, That Which Must Not Be Named. .

https://westhunt.wordpress.com/2017/10/08/biopolitics/#comment-96613
“The closest…s the sort of libertarianism promulgated by Charles Murray”
Not very close..
A government that was fully aware of the implications and possibilities of human genetics, one that had the usual kind of state goals [ like persistence and increased power] , would not necessarily be particularly libertarian.

https://westhunt.wordpress.com/2017/10/08/biopolitics/#comment-96797
And giving tax breaks to college-educated liberals to have babies wouldn’t appeal much to Trump voters, methinks.

It might be worth making a reasonably comprehensive of the facts and preferences that a good liberal is supposed to embrace and seem to believe. You would have to be fairly quick about it, before it changes. Then you could evaluate about the social impact of having more of them.

Rise and Fall: https://westhunt.wordpress.com/2018/01/18/rise-and-fall/
Every society selects for something: generally it looks as if the direction of selection pressue is more or less an accident. Although nations and empires in the past could have decided to select men for bravery or intelligence, there’s not much sign that anyone actually did this. I mean, they would have known how, if they’d wanted to, just as they knew how to select for destriers, coursers, and palfreys. It was still possible to know such things in the Middle Ages, because Harvard did not yet exist.

A rising empire needs quality human capital, which implies that at minimum that budding imperial society must not have been strongly dysgenic. At least not in the beginning. But winning changes many things, possibly including selective pressures. Imagine an empire with substantial urbanization, one in which talented guys routinely end up living in cities – cities that were demographic sinks. That might change things. Or try to imagine an empire in which survival challenges are greatly reduced, at least for elites, so that people have nothing to keep their minds off their minds and up worshiping Magna Mater. Imagine that an empire that conquers a rival with interesting local pathogens and brings some of them home. Or one that uses up a lot of its manpower conquering less-talented subjects and importing masses of those losers into the imperial heartland.

If any of those scenarios happened valid, they might eventually result in imperial decline – decline due to decreased biological capital.

Right now this is speculation. If we knew enough about the GWAS hits for intelligence, and had enough ancient DNA, we might be able to observe that rise and fall, just as we see dysgenic trends in contemporary populations. But that won’t happen for a long time. Say, a year.

hmm: https://westhunt.wordpress.com/2018/01/18/rise-and-fall/#comment-100350
“Although nations and empires in the past could have decided to select men for bravery or intelligence, there’s not much sign that anyone actually did this.”

Maybe the Chinese imperial examination could effectively have been a selection for intelligence.
--
Nope. I’ve modelled it: the fraction of winners is far too small to have much effect, while there were likely fitness costs from the arduous preparation. Moreover, there’s a recent
paper [Detecting polygenic adaptation in admixture graphs] that looks for indications of when selection for IQ hit northeast Asia: quite a while ago. Obvious though, since Japan has similar scores without ever having had that kind of examination system.

decline of British Empire and utility of different components: https://westhunt.wordpress.com/2018/01/18/rise-and-fall/#comment-100390
Once upon a time, India was a money maker for the British, mainly because they appropriate Bengali tax revenue, rather than trade. The rest of the Empire was not worth much: it didn’t materially boost British per-capita income or military potential. Silesia was worth more to Germany, conferred more war-making power, than Africa was to Britain.
--
If you get even a little local opposition, a colony won’t pay for itself. I seem to remember that there was some, in Palestine.
--
Angels from on high paid for the Boer War.

You know, someone in the 50’s asked for the numbers – how much various colonies cost and how much they paid.

Turned out that no one had ever asked. The Colonial Office had no idea.
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october 2017 by nhaliday
Definite optimism as human capital | Dan Wang
I’ve come to the view that creativity and innovative capacity aren’t a fixed stock, coiled and waiting to be released by policy. Now, I know that a country will not do well if it has poor infrastructure, interest rate management, tax and regulation levels, and a whole host of other issues. But getting them right isn’t sufficient to promote innovation; past a certain margin, when they’re all at rational levels, we ought to focus on promoting creativity and drive as a means to propel growth.

...

When I say “positive” vision, I don’t mean that people must see the future as a cheerful one. Instead, I’m saying that people ought to have a vision at all: A clear sense of how the technological future will be different from today. To have a positive vision, people must first expand their imaginations. And I submit that an interest in science fiction, the material world, and proximity to industry all help to refine that optimism. I mean to promote imagination by direct injection.

...

If a state has lost most of its jobs for electrical engineers, or nuclear engineers, or mechanical engineers, then fewer young people in that state will study those practices, and technological development in related fields slow down a little further. When I bring up these thoughts on resisting industrial decline to economists, I’m unsatisfied with their responses. They tend to respond by tautology (“By definition, outsourcing improves on the status quo”) or arithmetic (see: gains from comparative advantage, Ricardo). These kinds of logical exercises are not enough. I would like for more economists to consider a human capital perspective for preserving manufacturing expertise (to some degree).

I wonder if the so-called developed countries should be careful of their own premature deindustrialization. The US industrial base has faltered, but there is still so much left to build. Until we’ve perfected asteroid mining and super-skyscrapers and fusion rockets and Jupiter colonies and matter compilers, we can’t be satisfied with innovation confined mostly to the digital world.

Those who don’t mind the decline of manufacturing employment like to say that people have moved on to higher-value work. But I’m not sure that this is usually the case. Even if there’s an endlessly capacious service sector to absorb job losses in manufacturing, it’s often the case that these new jobs feature lower productivity growth and involve greater rent-seeking. Not everyone is becoming hedge fund managers and machine learning engineers. According to BLS, the bulk of service jobs are in 1. government (22 million), 2. professional services (19m), 3. healthcare (18m), 4. retail (15m), and 5. leisure and hospitality (15m). In addition to being often low-paying but still competitive, a great deal of service sector jobs tend to stress capacity for emotional labor over capacity for manual labor. And it’s the latter that tends to be more present in fields involving technological upgrading.

...

Here’s a bit more skepticism of service jobs. In an excellent essay on declining productivity growth, Adair Turner makes the point that many service jobs are essentially zero-sum. I’d like to emphasize and elaborate on that idea here.

...

Call me a romantic, but I’d like everyone to think more about industrial lubricants, gas turbines, thorium reactors, wire production, ball bearings, underwater cables, and all the things that power our material world. I abide by a strict rule never to post or tweet about current political stuff; instead I try to draw more attention to the world of materials. And I’d like to remind people that there are many things more edifying than following White House scandals.

...

First, we can all try to engage more actively with the material world, not merely the digital or natural world. Go ahead and pick an industrial phenomenon and learn more about it. Learn more about the history of aviation, and what it took to break the sound barrier; gaze at the container ships as they sail into port, and keep in mind that they carry 90 percent of the goods you see around you; read about what we mold plastics to do; meditate on the importance of steel in civilization; figure out what’s driving the decline in the cost of solar energy production, or how we draw electricity from nuclear fission, or what it takes to extract petroleum or natural gas from the ground.

...

Here’s one more point that I’d like to add on Girard at college: I wonder if to some extent current dynamics are the result of the liberal arts approach of “college teaches you how to think, not what to think.” I’ve never seen much data to support this wonderful claim that college is good at teaching critical thinking skills. Instead, students spend most of their energies focused on raising or lowering the status of the works they study or the people around them, giving rise to the Girardian terror that has gripped so many campuses.

College as an incubator of Girardian terror: http://danwang.co/college-girardian-terror/
It’s hard to construct a more perfect incubator for mimetic contagion than the American college campus. Most 18-year-olds are not super differentiated from each other. By construction, whatever distinctions any does have are usually earned through brutal, zero-sum competitions. These tournament-type distinctions include: SAT scores at or near perfection; being a top player on a sports team; gaining master status from chess matches; playing first instrument in state orchestra; earning high rankings in Math Olympiad; and so on, culminating in gaining admission to a particular college.

Once people enter college, they get socialized into group environments that usually continue to operate in zero-sum competitive dynamics. These include orchestras and sport teams; fraternities and sororities; and many types of clubs. The biggest source of mimetic pressures are the classes. Everyone starts out by taking the same intro classes; those seeking distinction throw themselves into the hardest classes, or seek tutelage from star professors, and try to earn the highest grades.

Mimesis Machines and Millennials: http://quillette.com/2017/11/02/mimesis-machines-millennials/
In 1956, a young Liverpudlian named John Winston Lennon heard the mournful notes of Elvis Presley’s Heartbreak Hotel, and was transformed. He would later recall, “nothing really affected me until I heard Elvis. If there hadn’t been an Elvis, there wouldn’t have been the Beatles.” It is an ancient human story. An inspiring model, an inspired imitator, and a changed world.

Mimesis is the phenomenon of human mimicry. Humans see, and they strive to become what they see. The prolific Franco-Californian philosopher René Girard described the human hunger for imitation as mimetic desire. According to Girard, mimetic desire is a mighty psychosocial force that drives human behavior. When attempted imitation fails, (i.e. I want, but fail, to imitate my colleague’s promotion to VP of Business Development), mimetic rivalry arises. According to mimetic theory, periodic scapegoating—the ritualistic expelling of a member of the community—evolved as a way for archaic societies to diffuse rivalries and maintain the general peace.

As civilization matured, social institutions evolved to prevent conflict. To Girard, sacrificial religious ceremonies first arose as imitations of earlier scapegoating rituals. From the mimetic worldview healthy social institutions perform two primary functions,

They satisfy mimetic desire and reduce mimetic rivalry by allowing imitation to take place.
They thereby reduce the need to diffuse mimetic rivalry through scapegoating.
Tranquil societies possess and value institutions that are mimesis tolerant. These institutions, such as religion and family, are Mimesis Machines. They enable millions to see, imitate, and become new versions of themselves. Mimesis Machines, satiate the primal desire for imitation, and produce happy, contented people. Through Mimesis Machines, Elvis fans can become Beatles.

Volatile societies, on the other hand, possess and value mimesis resistant institutions that frustrate attempts at mimicry, and mass produce frustrated, resentful people. These institutions, such as capitalism and beauty hierarchies, are Mimesis Shredders. They stratify humanity, and block the ‘nots’ from imitating the ‘haves’.
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october 2017 by nhaliday
[1709.06560] Deep Reinforcement Learning that Matters
https://twitter.com/WAWilsonIV/status/912505885565452288
I’ve been experimenting w/ various kinds of value function approaches to RL lately, and its striking how primitive and bad things seem to be
At first I thought it was just that my code sucks, but then I played with the OpenAI baselines and nope, it’s the children that are wrong.
And now, what comes across my desk but this fantastic paper: (link: https://arxiv.org/abs/1709.06560) arxiv.org/abs/1709.06560 How long until the replication crisis hits AI?

https://twitter.com/WAWilsonIV/status/911318326504153088
Seriously I’m not blown away by the PhDs’ records over the last 30 years. I bet you’d get better payoff funding eccentrics and amateurs.
There are essentially zero fundamentally new ideas in AI, the papers are all grotesquely hyperparameter tuned, nobody knows why it works.

Deep Reinforcement Learning Doesn't Work Yet: https://www.alexirpan.com/2018/02/14/rl-hard.html
Once, on Facebook, I made the following claim.

Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. I think this is right at least 70% of the time.
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september 2017 by nhaliday
New Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine
A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

sounds like he's just talking about autoencoders?
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september 2017 by nhaliday
Accurate Genomic Prediction Of Human Height | bioRxiv
Stephen Hsu's compressed sensing application paper

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.

https://infoproc.blogspot.com/2017/09/accurate-genomic-prediction-of-human.html

http://infoproc.blogspot.com/2017/11/23andme.html
I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits

Here's how people + robots handle your spit sample to produce a SNP genotype:

https://drive.google.com/file/d/1e_zuIPJr1hgQupYAxkcbgEVxmrDHAYRj/view
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september 2017 by nhaliday
Beijing’s uneasy deals with overseas car groups under strain
The new EV joint ventures are part of a Chinese effort to master the technology for electric vehicles — and rely on a tried and tested model of working with the global car industry since the 1980s. In a nutshell, joint ventures are the only way for foreign groups to access the world’s largest and most lucrative market. China gives the overseas companies the right to sell cars in exchange for their technology, management expertise and a share of their profits. 

“China’s central planners said ‘how can we basically force global automakers to participate and bring their very best electric vehicle technology to China?’” says Michael Dunne, president of Dunne Automotive, a Hong Kong-based car consultancy. 

Since 1984, starting with Jeeps, foreign carmakers have been allowed to produce cars in China — but only in concert with a local partner holding at least 50 per cent of the venture. In practice, this is almost always one of six anointed state companies. 

While widely criticised as a trade barrier, the JV law managed to survive China’s entry into the World Trade Organisation in 2001 — testament to Beijing’s bargaining power. Now China is using an updated version of the JV law to once again dangle access to its car market in exchange for technology — this time for new electric vehicles. 

The results of the three-decade-old policy have been mixed. Rather than transforming Chinese car companies into technology giants, the joint venture companies have arguably made Chinese carmakers complacent, according to Chinese policymakers. He Guangyang, a former minister of industry, controversially described the JVs as “like opium” in an interview five years ago.

...

This has created fears that their proprietary technology could be stolen. Over the past two decades, foreign makers of everything from high-speed trains to fighter planes have licensed the technology to local Chinese partners only to find a few years later that their partner is a major international competitor. 

In order to keep this from happening, foreign carmakers are trying to give away as little as possible — and keep sensitive items, such as software codes, outside of China. In the past, foreign companies have managed to evade similar requirements simply by bringing in outdated technology, which has angered Chinese policymakers. 

...

Weeks later Miao Wei, minister of industry and information technology, told a press conference that the notion foreign companies would have to transfer technology to Chinese companies was a “misunderstanding”. 
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september 2017 by nhaliday
Superintelligence Risk Project Update II
https://www.jefftk.com/p/superintelligence-risk-project-update

https://www.jefftk.com/p/conversation-with-michael-littman
For example, I asked him what he thought of the idea that to we could get AGI with current techniques, primarily deep neural nets and reinforcement learning, without learning anything new about how intelligence works or how to implement it ("Prosaic AGI" [1]). He didn't think this was possible, and believes there are deep conceptual issues we still need to get a handle on. He's also less impressed with deep learning than he was before he started working in it: in his experience it's a much more brittle technology than he had been expecting. Specifically, when trying to replicate results, he's often found that they depend on a bunch of parameters being in just the right range, and without that the systems don't perform nearly as well.

The bottom line, to him, was that since we are still many breakthroughs away from getting to AGI, we can't productively work on reducing superintelligence risk now.

He told me that he worries that the AI risk community is not solving real problems: they're making deductions and inferences that are self-consistent but not being tested or verified in the world. Since we can't tell if that's progress, it probably isn't. I asked if he was referring to MIRI's work here, and he said their work was an example of the kind of approach he's skeptical about, though he wasn't trying to single them out. [2]

https://www.jefftk.com/p/conversation-with-an-ai-researcher
Earlier this week I had a conversation with an AI researcher [1] at one of the main industry labs as part of my project of assessing superintelligence risk. Here's what I got from them:

They see progress in ML as almost entirely constrained by hardware and data, to the point that if today's hardware and data had existed in the mid 1950s researchers would have gotten to approximately our current state within ten to twenty years. They gave the example of backprop: we saw how to train multi-layer neural nets decades before we had the computing power to actually train these nets to do useful things.

Similarly, people talk about AlphaGo as a big jump, where Go went from being "ten years away" to "done" within a couple years, but they said it wasn't like that. If Go work had stayed in academia, with academia-level budgets and resources, it probably would have taken nearly that long. What changed was a company seeing promising results, realizing what could be done, and putting way more engineers and hardware on the project than anyone had previously done. AlphaGo couldn't have happened earlier because the hardware wasn't there yet, and was only able to be brought forward by massive application of resources.

https://www.jefftk.com/p/superintelligence-risk-project-conclusion
Summary: I'm not convinced that AI risk should be highly prioritized, but I'm also not convinced that it shouldn't. Highly qualified researchers in a position to have a good sense the field have massively different views on core questions like how capable ML systems are now, how capable they will be soon, and how we can influence their development. I do think these questions are possible to get a better handle on, but I think this would require much deeper ML knowledge than I have.
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july 2017 by nhaliday
Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? | Scientific Reports
As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest – and hope – that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited – in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.
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july 2017 by nhaliday
My review of Robert Gordon's *Rise and Fall of American Growth* - Marginal REVOLUTION
And here is the “oops” aspect of the book:

What Gordon neglects to mention, however, is that he is also the author of a 2003 Brookings essay titled “Exploding Productivity Growth,” in which he optimistically predicted that productivity in the United States would grow by 2.2 to 2.8 percent for the next two decades, most likely averaging 2.5 percent a year; he even suggested that a three percent rate was possible.

…Gordon offers a brief history of the evolution of his views on productivity. Yet he does not mention the 2003 essay, nor does he explain why he has changed his mind so dramatically. He also fails to cite other proponents of the stagnation thesis, even though…their work predates his book.
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june 2017 by nhaliday
Logic | West Hunter
All the time I hear some public figure saying that if we ban or allow X, then logically we have to ban or allow Y, even though there are obvious practical reasons for X and obvious practical reasons against Y.

No, we don’t.

http://www.amnation.com/vfr/archives/005864.html
http://www.amnation.com/vfr/archives/002053.html

compare: https://pinboard.in/u:nhaliday/b:190b299cf04a

Small Change Good, Big Change Bad?: https://www.overcomingbias.com/2018/02/small-change-good-big-change-bad.html
And on reflection it occurs to me that this is actually THE standard debate about change: some see small changes and either like them or aren’t bothered enough to advocate what it would take to reverse them, while others imagine such trends continuing long enough to result in very large and disturbing changes, and then suggest stronger responses.

For example, on increased immigration some point to the many concrete benefits immigrants now provide. Others imagine that large cumulative immigration eventually results in big changes in culture and political equilibria. On fertility, some wonder if civilization can survive in the long run with declining population, while others point out that population should rise for many decades, and few endorse the policies needed to greatly increase fertility. On genetic modification of humans, some ask why not let doctors correct obvious defects, while others imagine parents eventually editing kid genes mainly to max kid career potential. On oil some say that we should start preparing for the fact that we will eventually run out, while others say that we keep finding new reserves to replace the ones we use.

...

If we consider any parameter, such as typical degree of mind wandering, we are unlikely to see the current value as exactly optimal. So if we give people the benefit of the doubt to make local changes in their interest, we may accept that this may result in a recent net total change we don’t like. We may figure this is the price we pay to get other things we value more, and we we know that it can be very expensive to limit choices severely.

But even though we don’t see the current value as optimal, we also usually see the optimal value as not terribly far from the current value. So if we can imagine current changes as part of a long term trend that eventually produces very large changes, we can become more alarmed and willing to restrict current changes. The key question is: when is that a reasonable response?

First, big concerns about big long term changes only make sense if one actually cares a lot about the long run. Given the usual high rates of return on investment, it is cheap to buy influence on the long term, compared to influence on the short term. Yet few actually devote much of their income to long term investments. This raises doubts about the sincerity of expressed long term concerns.

Second, in our simplest models of the world good local choices also produce good long term choices. So if we presume good local choices, bad long term outcomes require non-simple elements, such as coordination, commitment, or myopia problems. Of course many such problems do exist. Even so, someone who claims to see a long term problem should be expected to identify specifically which such complexities they see at play. It shouldn’t be sufficient to just point to the possibility of such problems.

...

Fourth, many more processes and factors limit big changes, compared to small changes. For example, in software small changes are often trivial, while larger changes are nearly impossible, at least without starting again from scratch. Similarly, modest changes in mind wandering can be accomplished with minor attitude and habit changes, while extreme changes may require big brain restructuring, which is much harder because brains are complex and opaque. Recent changes in market structure may reduce the number of firms in each industry, but that doesn’t make it remotely plausible that one firm will eventually take over the entire economy. Projections of small changes into large changes need to consider the possibility of many such factors limiting large changes.

Fifth, while it can be reasonably safe to identify short term changes empirically, the longer term a forecast the more one needs to rely on theory, and the more different areas of expertise one must consider when constructing a relevant model of the situation. Beware a mere empirical projection into the long run, or a theory-based projection that relies on theories in only one area.

We should very much be open to the possibility of big bad long term changes, even in areas where we are okay with short term changes, or at least reluctant to sufficiently resist them. But we should also try to hold those who argue for the existence of such problems to relatively high standards. Their analysis should be about future times that we actually care about, and can at least roughly foresee. It should be based on our best theories of relevant subjects, and it should consider the possibility of factors that limit larger changes.

And instead of suggesting big ways to counter short term changes that might lead to long term problems, it is often better to identify markers to warn of larger problems. Then instead of acting in big ways now, we can make sure to track these warning markers, and ready ourselves to act more strongly if they appear.

Growth Is Change. So Is Death.: https://www.overcomingbias.com/2018/03/growth-is-change-so-is-death.html
I see the same pattern when people consider long term futures. People can be quite philosophical about the extinction of humanity, as long as this is due to natural causes. Every species dies; why should humans be different? And few get bothered by humans making modest small-scale short-term modifications to their own lives or environment. We are mostly okay with people using umbrellas when it rains, moving to new towns to take new jobs, etc., digging a flood ditch after our yard floods, and so on. And the net social effect of many small changes is technological progress, economic growth, new fashions, and new social attitudes, all of which we tend to endorse in the short run.

Even regarding big human-caused changes, most don’t worry if changes happen far enough in the future. Few actually care much about the future past the lives of people they’ll meet in their own life. But for changes that happen within someone’s time horizon of caring, the bigger that changes get, and the longer they are expected to last, the more that people worry. And when we get to huge changes, such as taking apart the sun, a population of trillions, lifetimes of millennia, massive genetic modification of humans, robots replacing people, a complete loss of privacy, or revolutions in social attitudes, few are blasé, and most are quite wary.

This differing attitude regarding small local changes versus large global changes makes sense for parameters that tend to revert back to a mean. Extreme values then do justify extra caution, while changes within the usual range don’t merit much notice, and can be safely left to local choice. But many parameters of our world do not mostly revert back to a mean. They drift long distances over long times, in hard to predict ways that can be reasonably modeled as a basic trend plus a random walk.

This different attitude can also make sense for parameters that have two or more very different causes of change, one which creates frequent small changes, and another which creates rare huge changes. (Or perhaps a continuum between such extremes.) If larger sudden changes tend to cause more problems, it can make sense to be more wary of them. However, for most parameters most change results from many small changes, and even then many are quite wary of this accumulating into big change.

For people with a sharp time horizon of caring, they should be more wary of long-drifting parameters the larger the changes that would happen within their horizon time. This perspective predicts that the people who are most wary of big future changes are those with the longest time horizons, and who more expect lumpier change processes. This prediction doesn’t seem to fit well with my experience, however.

Those who most worry about big long term changes usually seem okay with small short term changes. Even when they accept that most change is small and that it accumulates into big change. This seems incoherent to me. It seems like many other near versus far incoherences, like expecting things to be simpler when you are far away from them, and more complex when you are closer. You should either become more wary of short term changes, knowing that this is how big longer term change happens, or you should be more okay with big long term change, seeing that as the legitimate result of the small short term changes you accept.

https://www.overcomingbias.com/2018/03/growth-is-change-so-is-death.html#comment-3794966996
The point here is the gradual shifts of in-group beliefs are both natural and no big deal. Humans are built to readily do this, and forget they do this. But ultimately it is not a worry or concern.

But radical shifts that are big, whether near or far, portend strife and conflict. Either between groups or within them. If the shift is big enough, our intuition tells us our in-group will be in a fight. Alarms go off.
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may 2017 by nhaliday
[1705.08807] When Will AI Exceed Human Performance? Evidence from AI Experts
Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans.

https://www.reddit.com/r/slatestarcodex/comments/6dy6ex/arxiv_when_will_ai_exceed_human_performance/
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may 2017 by nhaliday
GDP Per Capita Growth -- 1980s Growth Patterns & Economic Policy | National Review
When we compare the post-2000 era to 1947–2000, a huge divergence in GDP growth per capita emerges. Between 1947 and 2000, inflation-adjusted GDP per capita grew a little over 2 percent a year. Since 2000, it has grown at about 0.9 percent a year. So, GDP growth per person has more than halved since Y2K. This slowdown in per capita GDP growth is unprecedented in the postwar era. In no 15-year period between 1947 and 2000 did average annual GDP growth per capita fall below 1.5 percent, so a 15-year stretch of under 1 percent is a radical departure from recent history.
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may 2017 by nhaliday
Lucio Russo - Wikipedia
In The Forgotten Revolution: How Science Was Born in 300 BC and Why It Had to Be Reborn (Italian: La rivoluzione dimenticata), Russo promotes the belief that Hellenistic science in the period 320-144 BC reached heights not achieved by Classical age science, and proposes that it went further than ordinarily thought, in multiple fields not normally associated with ancient science.

La Rivoluzione Dimenticata (The Forgotten Revolution), Reviewed by Sandro Graffi: http://www.ams.org/notices/199805/review-graffi.pdf

Before turning to the question of the decline of Hellenistic science, I come back to the new light shed by the book on Euclid’s Elements and on pre-Ptolemaic astronomy. Euclid’s definitions of the elementary geometric entities—point, straight line, plane—at the beginning of the Elements have long presented a problem.7 Their nature is in sharp contrast with the approach taken in the rest of the book, and continued by mathematicians ever since, of refraining from defining the fundamental entities explicitly but limiting themselves to postulating the properties which they enjoy. Why should Euclid be so hopelessly obscure right at the beginning and so smooth just after? The answer is: the definitions are not Euclid’s. Toward the beginning of the second century A.D. Heron of Alexandria found it convenient to introduce definitions of the elementary objects (a sign of decadence!) in his commentary on Euclid’s Elements, which had been written at least 400 years before. All manuscripts of the Elements copied ever since included Heron’s definitions without mention, whence their attribution to Euclid himself. The philological evidence leading to this conclusion is quite convincing.8

...

What about the general and steady (on the average) impoverishment of Hellenistic science under the Roman empire? This is a major historical problem, strongly tied to the even bigger one of the decline and fall of the antique civilization itself. I would summarize the author’s argument by saying that it basically represents an application to science of a widely accepted general theory on decadence of antique civilization going back to Max Weber. Roman society, mainly based on slave labor, underwent an ultimately unrecoverable crisis as the traditional sources of that labor force, essentially wars, progressively dried up. To save basic farming, the remaining slaves were promoted to be serfs, and poor free peasants reduced to serfdom, but this made trade disappear. A society in which production is almost entirely based on serfdom and with no trade clearly has very little need of culture, including science and technology. As Max Weber pointed out, when trade vanished, so did the marble splendor of the ancient towns, as well as the spiritual assets that went with it: art, literature, science, and sophisticated commercial laws. The recovery of Hellenistic science then had to wait until the disappearance of serfdom at the end of the Middle Ages. To quote Max Weber: “Only then with renewed vigor did the old giant rise up again.”

...

The epilogue contains the (rather pessimistic) views of the author on the future of science, threatened by the apparent triumph of today’s vogue of irrationality even in leading institutions (e.g., an astrology professorship at the Sorbonne). He looks at today’s ever-increasing tendency to teach science more on a fideistic than on a deductive or experimental basis as the first sign of a decline which could be analogous to the post-Hellenistic one.

Praising Alexandrians to excess: https://sci-hub.tw/10.1088/2058-7058/17/4/35
The Economic Record review: https://sci-hub.tw/10.1111/j.1475-4932.2004.00203.x

listed here: https://pinboard.in/u:nhaliday/b:c5c09f2687c1

Was Roman Science in Decline? (Excerpt from My New Book): https://www.richardcarrier.info/archives/13477
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may 2017 by nhaliday
Genetically engineered humans will arrive sooner than you think. And we're not ready. - Vox
lol "epigenetics" makes an appearance ofc

https://www.theatlantic.com/science/archive/2017/06/the-moral-question-that-stanfords-bioengineering-students-get/531876/

For now, that’s prohibitively expensive, but it won’t always be that way. In 2003, it cost 4 dollars to press one of the keys on Endy’s hypothetical synthesizer. This month, it costs just two cents—a 200-fold decrease in price in just 14 years. In the same time frame, the cost of tuition at Stanford has doubled, and is now around $50,000. Given all of that, the first question that Stanford’s budding bioengineers get is this:

At what point will the cost of printing DNA to create a human equal the cost of teaching a student in Stanford?
And the answer is: 19 years from today.

But the follow-up question is a little more complicated:

If you and your future partner are planning to have kids, would you start saving money for college tuition, or for printing the genome of your offspring?
The question tends to split students down the line, says Endy. About 60 percent say that printing a genome is wrong, and flies against what it means to be a parent. They prize the special nature of education and would opt to save for the tuition. But around 40 percent of the class will say that the value of education may change in the future, and if genetic technology becomes mature, and allows them to secure advantages for them and their lineage, they might as well do that.

https://www.nytimes.com/2016/05/14/science/synthetic-human-genome.html
http://www.nature.com/news/plan-to-synthesize-human-genome-triggers-mixed-response-1.20028

https://ipscell.com/2017/06/crispr-human-genetic-modification-a-needed-course-correction/
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may 2017 by nhaliday
One more time | West Hunter
One of our local error sources suggested that it would be impossible to rebuild technical civilization, once fallen. Now if every human were dead I’d agree, but in most other scenarios it wouldn’t be particularly difficult, assuming that the survivors were no more silly and fractious than people are today.  So assume a mild disaster, something like the effect of myxomatosis on the rabbits of Australia, or perhaps toe-to-toe nuclear combat with the Russkis – ~90%  casualties worldwide.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69221
Books are everywhere. In the type of scenario I sketched out, almost no knowledge would be lost – so Neolithic tech is irrelevant. Look, if a single copy of the 1911 Britannica survived, all would be well.

You could of course harvest metals from the old cities. But even if if you didn’t, the idea that there is no more copper or zinc or tin in the ground is just silly. “recoverable ore” is mostly an economic concept.

Moreover, if we’re talking wiring and electrical uses, one can use aluminum, which makes up 8% of the Earth’s crust.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69368
Some of those book tell you how to win.

Look, assume that some communities strive to relearn how to make automatic weapons and some don’t. How does that story end? Do I have to explain everything?

I guess so!

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69334
Well, perhaps having a zillion times more books around would make a difference. That and all the “X for Dummies” books, which I think the Romans didn’t have.

A lot of Classical civ wasn’t very useful: on the whole they didn’t invent much. On the whole, technology advanced quite a bit more rapidly in Medieval times.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69225
How much coal and oil are in the ground that can still be extracted with 19th century tech? Honest question; I don’t know.
--
Lots of coal left. Not so much oil (using simple methods), but one could make it from low-grade coal, with the Fischer-Tropsch process. Sasol does this.

Then again, a recovering society wouldn’t need much at first.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69223
reply to: https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69220
That’s more like it.

#1. Consider Grand Coulee Dam. Gigawatts. Feeling of power!
#2. Of course.
#3. Might be easier to make superconducting logic circuits with MgB2, starting over.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69325
Your typical biker guy is more mechanically minded than the average Joe. Welding, electrical stuff, this and that.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69260
If fossil fuels were unavailable -or just uneconomical at first- we’d be back to charcoal for our Stanley Steamers and railroads. We’d still have both.

The French, and others, used wood-gasifier trucks during WWII.

https://westhunt.wordpress.com/2015/05/17/one-more-time/#comment-69407
Teslas are of course a joke.
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may 2017 by nhaliday
Chinese innovations | West Hunter
I’m interested in hearing about significant innovations out of contemporary China. Good ones. Ideas, inventions, devices, dreams. Throw in Outer China (Taiwan, Hong Kong, Singapore).

super nationalistic dude ("IC") in the comments section (wish his videos had subtitles):
https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91378
https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91378
https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91382
https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91292
https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91315

on the carrier-killer missiles: https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91280
You could take out a carrier task force with a nuke 60 years ago.
--
Then the other side can nuke something and point to the sunk carrier group saying “they started first”.

Hypersonic anti-ship cruise missiles, or the mysterious anti-ship ballistic missiles China has avoid that.
--
They avoid that because the law of physics no longer allow radar.

https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91340
I was thinking about the period in which the United States was experiencing rapid industrial growth, on its way to becoming the most powerful industrial nation. At first not much science, buts lots and lots of technological innovation. I’m not aware of a corresponding efflorescence of innovative Chinese technology today, but then I don’t know everything: so I asked.

I’m still not aware of it. So maybe the answer is ‘no’.

hmm: https://westhunt.wordpress.com/2017/05/10/chinese-innovations/#comment-91389
I would say that a lot of the most intelligent faction is being siphoned over into government work, and thus not focused in technological innovation. We should expect to see societal/political innovation rather than technological if my thesis is true.

There’s some evidence of that.
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may 2017 by nhaliday
China Overtakes US in Scientific Articles, Robots, Supercomputers - The Unz Review
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may 2017 by nhaliday
Talks
Quantum Supremacy: Office of Science and Technology Policy QIS Forum, Eisenhower Executive Office Building, White House Complex, Washington DC, October 18, 2016. Another version at UTCS Faculty Lunch, October 26, 2016. Another version at UT Austin Physics Colloquium, Austin, TX, November 9, 2016.

Complexity-Theoretic Foundations of Quantum Supremacy Experiments: Quantum Algorithms Workshop, Aspen Center for Physics, Aspen, CO, March 25, 2016

When Exactly Do Quantum Computers Provide A Speedup?: Yale Quantum Institute Seminar, Yale University, New Haven, CT, October 10, 2014. Another version at UT Austin Physics Colloquium, Austin, TX, November 19, 2014; Applied and Interdisciplinary Mathematics Seminar, Northeastern University, Boston, MA, November 25, 2014; Hebrew University Physics Colloquium, Jerusalem, Israel, January 5, 2015; Computer Science Colloquium, Technion, Haifa, Israel, January 8, 2015; Stanford University Physics Colloquium, January 27, 2015
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may 2017 by nhaliday
What is the likelihood we run out of fossil fuels before we can switch to renewable energy sources? - Quora
1) Can we de-carbon our primary energy production before global warming severely damages human civilization? In the short term this means switching from coal to natural gas, and in the long term replacing both coal and gas generation with carbon-neutral sources such as renewables or nuclear. The developed world cannot accomplish this alone -- it requires worldwide action, and most of the pain will be felt by large developing nations such as India and China. Ultimately this is a political and economic problem. The technology to eliminate most carbon from electricity generation exists today at fairly reasonable cost.

2) Can we develop a better transportation energy storage technology than oil, before market forces drive prices to levels that severely damage the global economy? Fossil fuels are a source of energy, but primarily we use oil in vehicles because it is an exceptional energy TRANSPORT medium. Renewables cannot meet this need because battery technology is completely uncompetitive for most fuel consumers -- prices are an order of magnitude too high and energy density is an order of magnitude too low for adoption of all-electric vehicles outside developed-world urban centers. (Heavy trucking, cargo ships, airplanes, etc will never be all-electric with chemical batteries. There are hard physical limits to the energy density of electrochemical reactions. I'm not convinced passenger vehicles will go all-electric in our lifetimes either.) There are many important technologies in existence that will gain increasing traction in the next 50 years such as natural gas automobiles and improved gas/electric hybrids, but ultimately we need a better way to store power than fossil fuels. _This is a deep technological problem that will not be solved by incremental improvements in battery chemistry or any process currently in the R&D pipeline_.

Based on these two unresolved issues, _I place the odds of us avoiding fossil-fuel-related energy issues (major climate or economic damage) at less than 10%_. The impetus for the major changes required will not be sufficiently urgent until the world is seeing severe and undeniable impacts. Civilization will certainly survive -- but there will be no small amount of human suffering during the transition to whatever comes next.

- Ryan Carlyle
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may 2017 by nhaliday
[1502.05274] How predictable is technological progress?
Recently it has become clear that many technologies follow a generalized version of Moore's law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future.

model:
- p_t = unit price of tech
- log(p_t) = y_0 - μt + ∑_{i <= t} n_i
- n_t iid noise process
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april 2017 by nhaliday
Sequencing a genome for less than the cost of an X-ray? Not quite yet
A $100 genome will cost $100 in the same way that the $1,000 genome costs $1,000. As in, it won’t, at least not soon. “The $1,000 genome” — which sequencer makers began promising about five years ago — “costs us $3,000,” said Richard Gibbs, founder of the Baylor College of Medicine Human Genome Sequencing Center and one of the leaders of the original Human Genome Project in the 1990s.
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april 2017 by nhaliday
Overview of current development in electrical energy storage technologies and the application potential in power system operation
- An overview of the state-of-the-art in Electrical Energy Storage (EES) is provided.
- A comprehensive analysis of various EES technologies is carried out.
- An application potential analysis of the reviewed EES technologies is presented.
- The presented synthesis to EES technologies can be used to support future R&D and deployment.

Prospects and Limits of Energy Storage in Batteries: http://pubs.acs.org/doi/abs/10.1021/jz5026273
study  survey  state-of-art  energy-resources  heavy-industry  chemistry  applications  electromag  stock-flow  wonkish  frontier  technology  biophysical-econ  the-world-is-just-atoms  🔬  phys-energy  ideas  speedometer  dirty-hands  multi 
april 2017 by nhaliday
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