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Mars Direct | West Hunter
Send Mr Bezos. He even looks like a Martian.
Throw in Zuckerberg and it’s a deal…
We could send twice as many people half-way to Mars.


I don’t think that the space station has been worth anything at all.

As for a lunar base, many of the issues are difficult and one ( effects of low-gee) is probably impossible to solve.

I don’t think that there are real mysteries about what is needed for a kind-of self-sufficient base – it’s just too hard and there’s not much prospect of a payoff.

That said, there may be other ways of going about this that are more promising.


Venus is worth terraforming: no gravity problems. Doable.


It’s not impossible that Mars might harbor microbial life – with some luck, life with a different chemical basis. That might be very valuable: there are endless industrial processes that depend upon some kind of fermentation.
Why, without acetone fermentation, there might not be a state of Israel.
If we used a reasonable approach, like Orion, I think that people would usefully supplement those robots.
Jeff Bezos isn’t my favorite guy, but he has ability and has built something useful. And an ugly, contested divorce would be harsh and unfair to the children, who have done nothing wrong.

But I don’t care. The thought of tens of billions of dollars being spent on lawyers and PIs offer the possibility of a spectacle that will live forever, far wilder than the antics of Nero or Caligula. It could make Suetonius look like Pilgrim’s Progress.

Have you ever wondered whether tens of thousands of divorce lawyers should be organized into legions or phalanxes? This is our chance to finally find out.
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september 2019 by nhaliday
Moravec's paradox - Wikipedia
Moravec's paradox is the discovery by artificial intelligence and robotics researchers that, contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s. As Moravec writes, "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".[1]

Similarly, Minsky emphasized that the most difficult human skills to reverse engineer are those that are unconscious. "In general, we're least aware of what our minds do best", he wrote, and added "we're more aware of simple processes that don't work well than of complex ones that work flawlessly".[2]


One possible explanation of the paradox, offered by Moravec, is based on evolution. All human skills are implemented biologically, using machinery designed by the process of natural selection. In the course of their evolution, natural selection has tended to preserve design improvements and optimizations. The older a skill is, the more time natural selection has had to improve the design. Abstract thought developed only very recently, and consequently, we should not expect its implementation to be particularly efficient.

As Moravec writes:

Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge. We are all prodigious olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it.[3]

A compact way to express this argument would be:

- We should expect the difficulty of reverse-engineering any human skill to be roughly proportional to the amount of time that skill has been evolving in animals.
- The oldest human skills are largely unconscious and so appear to us to be effortless.
- Therefore, we should expect skills that appear effortless to be difficult to reverse-engineer, but skills that require effort may not necessarily be difficult to engineer at all.
concept  wiki  reference  paradox  ai  intelligence  reason  instinct  neuro  psychology  cog-psych  hardness  logic  deep-learning  time  evopsych  evolution  sapiens  the-self  EEA  embodied  embodied-cognition  abstraction  universalism-particularism  gnosis-logos  robotics 
june 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:

A new recommended career path for effective altruists: China specialist:
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:
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:
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
Gimbal lock - Wikipedia
Gimbal lock is the loss of one degree of freedom in a three-dimensional, three-gimbal mechanism that occurs when the axes of two of the three gimbals are driven into a parallel configuration, "locking" the system into rotation in a degenerate two-dimensional space.

The word lock is misleading: no gimbal is restrained. All three gimbals can still rotate freely about their respective axes of suspension. Nevertheless, because of the parallel orientation of two of the gimbals' axes there is no gimbal available to accommodate rotation along one axis.
Now this is where most people stop thinking about the issue and move on with their life. They just conclude that Euler angles are somehow broken. This is also where a lot of misunderstandings happen so it's worth investigating the matter slightly further than what causes gimbal lock.

It is important to understand that this is only problematic if you interpolate in Euler angles**! In a real physical gimbal this is given - you have no other choice. In computer graphics you have many other choices, from normalized matrix, axis angle or quaternion interpolation. Gimbal lock has a much more dramatic implication to designing control systems than it has to 3d graphics. Which is why a mechanical engineer for example will have a very different take on gimbal locking.

You don't have to give up using Euler angles to get rid of gimbal locking, just stop interpolating values in Euler angles. Of course, this means that you can now no longer drive a rotation by doing direct manipulation of one of the channels. But as long as you key the 3 angles simultaneously you have no problems and you can internally convert your interpolation target to something that has less problems.

Using Euler angles is just simply more intuitive to think in most cases. And indeed Euler never claimed it was good for interpolating but just that it can model all possible space orientations. So Euler angles are just fine for setting orientations like they were meant to do. Also incidentally Euler angles have the benefit of being able to model multi turn rotations which will not happen sanely for the other representations.
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september 2017 by nhaliday
China Overtakes US in Scientific Articles, Robots, Supercomputers - The Unz Review
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may 2017 by nhaliday

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