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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/

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.

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.”

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."

AI-risk was a mistake.
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february 2018 by nhaliday
Deliberate Practice and Performance in Music, Games, Sports, Education, and Professions: A Meta-Analysis
We found that deliberate practice explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions. We conclude that deliberate practice is important, but not as important as has been argued.
pdf  study  psychology  cog-psych  social-psych  teaching  tutoring  learning  studying  stylized-facts  metabuch  career  long-term  music  games  sports  education  labor  data  list  expert-experience  ability-competence  roots  variance-components  top-n  meta-analysis  practice  quixotic 
december 2017 by nhaliday
Learn Difficult Concepts with the ADEPT Method – BetterExplained
Make explanations ADEPT: Use an Analogy, Diagram, Example, Plain-English description, and then a Technical description.
thinking  education  learning  teaching  tutoring  better-explained  analogy  visual-understanding  examples 
july 2016 by nhaliday
Teachers: Much More Than You Wanted To Know | Slate Star Codex
Random Thoughts on the Idiocy of VAM: https://educationrealist.wordpress.com/2016/05/20/random-thoughts-on-the-idiocy-of-vam/
Scott Alexander reviews the research on value-added measurement of teacher quality. While Scott’s overview is perfectly fine, any such effort is akin to a circa 1692 overview of the research literature on alchemy. Quantifying teacher quality will, I believe, be understood in those terms soon enough.

Value-Added and Social Desirability Bias, Bryan Caplan: http://econlog.econlib.org/archives/2016/09/value-added_and.html
The policy that dramatically passes the cost-benefit test is "deselection," better known as firing bad teachers.

What's up? I once again point my accusatory finger at Social Desirability Bias. Rewarding good teachers sounds a lot nicer than firing bad teachers. So when research comes along that potentially recommends both, pundits and politicians don't coolly crunch the numbers. They leap to the recommendation that's pleasing to the ear. So what if the original researchers find that firing bad teachers wins with flying colors? Move along folks, nothing to see here...
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may 2016 by nhaliday

bundles : ed

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