nhaliday + automation + speedometer   30

Basic Error Rates
This page describes human error rates in a variety of contexts.

Most of the error rates are for mechanical errors. A good general figure for mechanical error rates appears to be about 0.5%.

Of course the denominator differs across studies. However only fairly simple actions are used in the denominator.

The Klemmer and Snyder study shows that much lower error rates are possible--in this case for people whose job consisted almost entirely of data entry.

The error rate for more complex logic errors is about 5%, based primarily on data on other pages, especially the program development page.
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may 2019 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
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
Reid Hofmann and Peter Thiel and technology and politics - Marginal REVOLUTION
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february 2018 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
China Overtakes US in Scientific Articles, Robots, Supercomputers - The Unz Review
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may 2017 by nhaliday
THE DEMISE OF U.S. ECONOMIC GROWTH: RESTATEMENT, REBUTTAL, AND REFLECTIONS
The United States achieved a 2.0 percent average annual growth rate of real GDP per capita between 1891 and 2007. This paper predicts that growth in the 25 to 40 years after 2007 will be much slower, particularly for the great majority of the population. Future growth will be 1.3 percent per annum for labor productivity in the total economy, 0.9 percent for output per capita, 0.4 percent for real income per capita of the bottom 99 percent of the income distribution, and 0.2 percent for the real disposable income of that group.

The primary cause of this growth slowdown is a set of four headwinds, all of them widely recognized and uncontroversial. Demographic shifts will reduce hours worked per capita, due not just to the retirement of the baby boom generation but also as a result of an exit from the labor force both of youth and prime-age adults. Educational attainment, a central driver of growth over the past century, stagnates at a plateau as the U.S. sinks lower in the world league tables of high school and college completion rates. Inequality continues to increase, resulting in real income growth for the bottom 99 percent of the income distribution that is fully half a point per year below the average growth of all incomes. A projected long-term increase in the ratio of debt to GDP at all levels of government will inevitably lead to more rapid growth in tax revenues and/or slower growth in transfer payments at some point within the next several decades.

There is no need to forecast any slowdown in the pace of future innovation for this gloomy forecast to come true, because that slowdown already occurred four decades ago. In the eight decades before 1972 labor productivity grew at an average rate 0.8 percent per year faster than in the four decades since 1972. While no forecast of a future slowdown of innovation is needed, skepticism is offered here, particularly about the techno-optimists who currently believe that we are at a point of inflection leading to faster technological change. The paper offers several historical examples showing that the future of technology can be forecast 50 or even 100 years in advance and assesses widely discussed innovations anticipated to occur over the next few decades, including medical research, small robots, 3-D printing, big data, driverless vehicles, and oil-gas fracking.

keep in mind, "the world is just atoms" and I think I know some things that Robert J Gordon doesn't
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march 2017 by nhaliday
Futuristic Physicists? | Do the Math
interesting comment: https://westhunt.wordpress.com/2014/03/05/outliers/#comment-23087
referring to timelines? or maybe also the jetpack+flying car (doesn't seem physically impossible; at most impossible for useful trip lengths)?

Topic Mean % pessim. median disposition
1. Autopilot Cars 1.4 (125 yr) 4 likely within 50 years
15. Real Robots 2.2 (800 yr) 10 likely within 500 years
13. Fusion Power 2.4 (1300 yr) 8 likely within 500 years
10. Lunar Colony 3.2 18 likely within 5000 years
16. Cloaking Devices 3.5 32 likely within 5000 years
20. 200 Year Lifetime 3.3 16 maybe within 5000 years
11. Martian Colony 3.4 22 probably eventually (>5000 yr)
12. Terraforming 4.1 40 probably eventually (> 5000 yr)
18. Alien Dialog 4.2 42 probably eventually (> 5000 yr)
19. Alien Visit 4.3 50 on the fence
2. Jetpack 4.1 64 unlikely ever
14. Synthesized Food 4.2 52 unlikely ever
8. Roving Astrophysics 4.6 64 unlikely ever
3. Flying “Cars” 3.9 60 unlikely ever
7. Visit Black Hole 5.1 74 forget about it
9. Artificial Gravity 5.3 84 forget about it
4. Teleportation 5.3 85 forget about it
5. Warp Drive 5.5 92 forget about it
6. Wormhole Travel 5.5 96 forget about it
17. Time Travel 5.7 92 forget about it
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march 2017 by nhaliday
Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation
Several recent theories emphasize the negative effects of an aging population on economic growth, either because of the lower labor force participation and productivity of older workers or because aging will create an excess of savings over desired investment, leading to secular stagnation. We show that there is no such negative relationship in the data. If anything, countries experiencing more rapid aging have grown more in recent decades. We suggest that this counterintuitive finding might reflect the more rapid adoption of automation technologies in countries undergoing more pronounced demographic changes, and provide evidence and theoretical underpinnings for this argument.
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january 2017 by nhaliday
Will Your Job Be Done By A Machine? : Planet Money : NPR
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january 2017 by nhaliday
China invents the digital totalitarian state | The Economist
PROGRAMMING CHINA: The Communist Party’s autonomic approach to managing state security: https://www.merics.org/sites/default/files/2017-12/171212_China_Monitor_44_Programming_China_EN__0.pdf
- The Chinese Communist Party (CCP) has developed a form of authoritarianism that cannot be measured through traditional political scales like reform versus retrenchment. This version of authoritarianism involves both “hard” and “soft” authoritarian methods that constantly act together.
...
- To describe the social management process, this paper introduces a new analytical framework called China’s “Autonomic Nervous System” (ANS). This approach explains China’s social management process through a complex systems engineering framework. This framework mirrors the CCP’s Leninist way of thinking.
- The framework describes four key parts of social management, visualized through ANS’s “self-configuring,” “self-healing,” “self-optimizing” and “self-protecting” objectives.

China's Social Credit System: An Evolving Practice of Control: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3175792

https://news.ycombinator.com/item?id=12771302
https://twitter.com/Aelkus/status/873584698655735808
http://infoproc.blogspot.com/2017/06/face-recognition-applied-at-scale-in.html
The Chinese government is not the only entity that has access to millions of faces + identifying information. So do Google, Facebook, Instagram, and anyone who has scraped information from similar social networks (e.g., US security services, hackers, etc.).

In light of such ML capabilities it seems clear that anti-ship ballistic missiles can easily target a carrier during the final maneuver phase of descent, using optical or infrared sensors (let alone radar).

https://www.wsj.com/articles/the-all-seeing-surveillance-state-feared-in-the-west-is-a-reality-in-china-1498493020
https://twitter.com/0xa59a2d/status/880098750009659392
https://archive.is/zHmmE
China goes all-in on technology the US is afraid to do right.
US won't learn its lesson in time for CRISPR or AI.

https://www.acast.com/theeconomistasks/theeconomistasks-howdoyouwintheairace-
Artificial intelligence is developing fast in China. But is it likely to enable the suppression of freedoms? One of China's most successful investors, Neil Shen, has a short answer to that question. Also, Chinese AI companies now have the potential to overtake their Western rivals -- we explain why. Anne McElvoy hosts with The Economist's AI expert, Tom Standage

the dude just stonewalls when asked at 7:50, completely zipped lips

http://www.indiatimes.com/technology/science-and-future/this-scary-chinese-surveillance-video-is-serious-cause-for-concern-but-just-not-why-you-think-330530.html
What you’re looking at above is the work of SenseTime, a Chinese computer vision startup. The software in question, called SenseVideo, is a visual scenario analytics system. Basically, it can analyse video footage to pinpoint whether moving objects are humans, cars, or other entities. It’s even sophisticated enough to detect gender, clothing, and the type of vehicle it’s looking at, all in real time.

https://streamable.com/iyi3z

Even China’s Backwater Cities Are Going Smart: http://www.sixthtone.com/news/1001452/even-chinas-backwater-cities-are-going-smart

https://twitter.com/ctbeiser/status/913054318869217282
https://archive.is/IiZiP
remember that tweet with the ML readout of Chinese surveilance cameras? Get ready for the future (via @triviumchina)

XI praised the organization and promised to help it beef up its operations (China
Daily):
- "China will 'help ... 100 developing countries build or upgrade communication systems and crime labs in the next five years'"
- "The Chinese government will establish an international law enforcement institute under the Ministry of Public Security which will train 20,000 police for developing nations in the coming five years"

The Chinese connection to the Zimbabwe 'coup': http://www.cnn.com/2017/11/17/africa/china-zimbabwe-mugabe-diplomacy/index.html

China to create national name-and-shame system for ‘deadbeat borrowers’: http://www.scmp.com/news/china/economy/article/2114768/china-create-national-name-and-shame-system-deadbeat-borrowers
Anyone who fails to repay a bank loan will be blacklisted and have their personal details made public

China Snares Innocent and Guilty Alike to Build World’s Biggest DNA Database: https://www.wsj.com/articles/china-snares-innocent-and-guilty-alike-to-build-worlds-biggest-dna-database-1514310353
Police gather blood and saliva samples from many who aren’t criminals, including those who forget ID cards, write critically of the state or are just in the wrong place

Many of the ways Chinese police are collecting samples are impermissible in the U.S. In China, DNA saliva swabs or blood samples are routinely gathered from people detained for violations such as forgetting to carry identity cards or writing blogs critical of the state, according to documents from a national police DNA conference in September and official forensic journals.

Others aren’t suspected of any crime. Police target certain groups considered a higher risk to social stability. These include migrant workers and, in one city, coal miners and home renters, the documents show.

...

In parts of the country, law enforcement has stored DNA profiles with a subject’s other biometric information, including fingerprints, portraits and voice prints, the heads of the DNA program wrote in the Chinese journal Forensic Science and Technology last year. One provincial police force has floated plans to link the data to a person’s information such as online shopping records and entertainment habits, according to a paper presented at the national police DNA conference. Such high-tech files would create more sophisticated versions of paper dossiers that police have long relied on to keep tabs on citizens.

Marrying DNA profiles with real-time surveillance tools, such as monitoring online activity and cameras hooked to facial-recognition software, would help China’s ruling Communist Party develop an all-encompassing “digital totalitarian state,” says Xiao Qiang, adjunct professor at the University of California at Berkeley’s School of Information.

...

A teenage boy studying in one of the county’s high schools recalled that a policeman came into his class after lunch one day this spring and passed out the collection boxes. Male students were told to clean their mouths, spit into the boxes and place them into envelopes on which they had written their names.

...

Chinese police sometimes try to draw connections between ethnic background or place of origin and propensity for crime. Police officers in northwestern China’s Ningxia region studied data on local prisoners and noticed that a large number came from three towns. They decided to collect genetic material from boys and men from every clan to bolster the local DNA database, police said at the law-enforcement DNA conference in September.

https://twitter.com/nils_gilman/status/945820396615483392
China is certainly in the lead in the arena of digital-biometric monitoring. Particularly “interesting” is the proposal to merge DNA info with online behavioral profiling.

https://twitter.com/mr_scientism/status/949730145195233280
https://archive.is/OCsxs

https://www.techinasia.com/china-citizen-scores-credit-system-orwellian
https://www.theglobeandmail.com/amp/news/world/chinese-blacklist-an-early-glimpse-of-sweeping-new-social-credit-control/article37493300/

https://twitter.com/mr_scientism/status/952263056662384640
https://archive.is/tGErH
This is the thing I find the most disenchanting about the current political spectrum. It's all reheated ideas that are a century old, at least. Everyone wants to run our iPhone society with power structures dating to the abacus.
--
Thank God for the forward-thinking Chinese Communist Party and its high-tech social credit system!

https://en.wikipedia.org/wiki/Social_Credit_System

INSIDE CHINA'S VAST NEW EXPERIMENT IN SOCIAL RANKING: https://www.wired.com/story/age-of-social-credit/
http://www.wired.co.uk/article/chinese-government-social-credit-score-privacy-invasion

http://foreignpolicy.com/2017/05/24/chinese-citizens-want-the-government-to-rank-them/
The government thinks "social credit" will fix the country's lack of trust — and the public agrees.

To be Chinese today is to live in a society of distrust, where every opportunity is a potential con and every act of generosity a risk of exploitation. When old people fall on the street, it’s common that no one offers to help them up, afraid that they might be accused of pushing them in the first place and sued. The problem has grown steadily since the start of the country’s economic boom in the 1980s. But only recently has the deficit of social trust started to threaten not just individual lives, but the country’s economy and system of politics as a whole. The less people trust each other, the more the social pact that the government has with its citizens — of social stability and harmony in exchange for a lack of political rights — disintegrates.

All of which explains why Chinese state media has recently started to acknowledge the phenomenon — and why the government has started searching for solutions. But rather than promoting the organic return of traditional morality to reduce the gulf of distrust, the Chinese government has preferred to invest its energy in technological fixes. It’s now rolling out systems of data-driven “social credit” that will purportedly address the problem by tracking “good” and “bad” behavior, with rewards and punishments meted out accordingly. In the West, plans of this sort have tended to spark fears about the reach of the surveillance state. Yet in China, it’s being welcomed by a public fed up of not knowing who to trust.

It’s unsurprising that a system that promises to place a check on unfiltered power has proven popular — although it’s… [more]
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january 2017 by nhaliday
Edge.org: 2016 : WHAT DO YOU CONSIDER THE MOST INTERESTING RECENT [SCIENTIFIC] NEWS? WHAT MAKES IT IMPORTANT?
highlights:
- quantum supremacy [Scott Aaronson]
- gene drive
- gene editing/CRISPR
- carcinogen may be entropy
- differentiable programming
- quantitative biology
soft:
- antisocial punishment of pro-social cooperators
- "strongest prejudice" (politics) [Haidt]
- Europeans' origins [Cochran]
- "Anthropic Capitalism And The New Gimmick Economy" [Eric Weinstein]

https://twitter.com/toad_spotted/status/986253381344907265
https://archive.is/gNGDJ
There's an underdiscussed contradiction between the idea that our society would make almost all knowledge available freely and instantaneously to almost everyone and that almost everyone would find gainful employment as knowledge workers. Value is in scarcity not abundance.
--
You’d need to turn reputational-based systems into an income stream
technology  discussion  trends  gavisti  west-hunter  aaronson  haidt  list  expert  science  biotech  geoengineering  top-n  org:edge  frontier  multi  CRISPR  2016  big-picture  links  the-world-is-just-atoms  quantum  quantum-info  computation  metameta  🔬  scitariat  q-n-a  zeitgeist  speedometer  cancer  random  epidemiology  mutation  GT-101  cooperate-defect  cultural-dynamics  anthropology  expert-experience  tcs  volo-avolo  questions  thiel  capitalism  labor  supply-demand  internet  tech  economics  broad-econ  prediction  automation  realness  gnosis-logos  iteration-recursion  similarity  uniqueness  homo-hetero  education  duplication  creative  software  programming  degrees-of-freedom  futurism  order-disorder  flux-stasis  public-goodish  markets  market-failure  piracy  property-rights  free-riding  twitter  social  backup  ratty  unaffiliated  gnon  contradiction  career  planning  hmm  idk  knowledge  higher-ed  pro-rata  sociality  reinforcement  tribalism  us-them  politics  coalitions  prejudice  altruism  human-capital  engineering  unintended-consequences 
november 2016 by nhaliday
Overcoming Bias : Lognormal Jobs
could be the case that exponential tech improvement -> linear job replacement, as long as distribution of jobs across automatability is log-normal (I don't entirely follow the argument)

Paul Christiano has objection (to premise not argument) in the comments
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november 2016 by nhaliday

bundles : dismalityeconfrontierprops

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