AI Nationalism

&gt;&gt; This arms race will potentially speed up the pace of AI development and shorten the timescale for getting to AGI.<p>... or it will all fizzle out once it becomes clear that classifiers trained from immense datasets can&#x27;t get to AGI.<p>In the last few months I&#x27;ve noticed a sudden uptick in papers and articles on the limitations of deep learning and even a few conferences discussing the way to overcome them (e.g. Logic and Learning at the Alan Turing Institute). Eventually, the hype will die down, people in the field will feel more confident discussing the weaknesses of deep learning and the general public (including the industry and the military) will catch on. Then we&#x27;ll move forward again when the next big thing comes along.

I added up the number of university researchers who publish in top AI conferences (according to csrankings.org) from Jan 2017 to late May 2018.<p><a href="http:&#x2F;&#x2F;csrankings.org&#x2F;#&#x2F;fromyear&#x2F;2017&#x2F;toyear&#x2F;2018&#x2F;index?ai&amp;vision&amp;mlmining&amp;nlp&amp;ir&amp;world" rel="nofollow">http:&#x2F;&#x2F;csrankings.org&#x2F;#&#x2F;fromyear&#x2F;2017&#x2F;toyear&#x2F;2018&#x2F;index?ai&amp;v...</a><p>Here are approximate numbers (with 2 significant digits) of faculty members&#x2F;university researchers who published as above in each country&#x2F;continent:<p>US 770<p>Canada 92<p>Asia (including China) 340<p>China 240<p>Europe 280<p>Australia + New Zealand 86<p>South America 12<p>The world excluding the US 810<p>So the US is still far ahead of other nations&#x2F;regions, but it now has a bit below 50% of the world&#x27;s university researchers who recently published in top AI conferences. China as a country is close to Europe as a continent and its number of published university researchers has increased rapidly in recent years.<p>The number of researchers is not weighted by the number of papers published but this number is useful since it counts how many people are capable of advising graduate students to produce world-class research. Using the number of papers is complicated by how likely highly capable international graduate students would choose to study in each program (in addition to the researcher&#x27;s capability), i.e. university&#x27;s reputation would have an additional impact beyond its research capability.

Something tells me people that write these articles have never read a paper from e.g. NIPS or pick any top tier conference on cutting edge research, heck I would go so far as to say they don’t even know how to write an image classifier for MNIST using Keras if their life depended on it.<p>Universal function approximators are not about to take over the world.

&gt; [I]f most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers...This kind of dependency would be tantamount to a new kind of colonialism.<p>This is an interesting observation, it is actually already happening now with Internet companies, and to a lesser degree physical product companies with global reach (like Apple and Amazon). Money flow from local economies into those companies who don&#x27;t pay much local tax nor creating much local employment. That could eventually drain the well dry.<p>Countries suffered from colonialism have been catching up to the developed world in terms of standards of living in the past 100 years. I wonder if the above effect will reverse that course.

I prefer to think about this in a manner similar to Robin Hanson’s Foresight Institute presentation regarding models for AGI timescales [0].<p>Basically, the component of this that says “but machine learning is different” is still not convincing. The same nationalistic divides and concerns about geopolitical backing for warfare tech that happened in response to nuclear weaponry and chemical weaponry are likely to be high-fidelity models of whatever geopolitical divide for machine learning weaponry.<p>I agree it will be a significant policy issue, but I do not agree it is very related to the topic of AGI. Reasoning about it by studying how various other tech arms races have unfolded in history will be a good, but not perfect, model for how it unfolds for ML too. And the pieces where this time is different will be far more understated than the amount of hype about it.<p>[0]: &lt; <a href="https:&#x2F;&#x2F;vimeo.com&#x2F;9508131" rel="nofollow">https:&#x2F;&#x2F;vimeo.com&#x2F;9508131</a> &gt;

So you&#x27;re saying we are going to digitize emotional bias and pretend it&#x27;s more intelligent decision making, and then hand that over to a corporation. Sounds like a winner.

&gt; Machine learning will enable new modes of warfare<p>Bucky was confident that we could use computers to solve our problems. We could enter all relevant data and the machine could compute the optimal solutions for us.<p>The issue has always been ensuring we ask them to solve the <i>right</i> problems.<p>If we use AI to tell us how long to imprison people (already happening) rather than how to decrease recidivism, that&#x27;s a meta-computer choice that <i>we</i> made, not the AI.<p>If we use AI to kill people, rather than to figure out how not to have to kill them in the first place, that&#x27;s also our choice.<p>Cf. &quot;Wargames&quot; <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;WarGames" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;WarGames</a> This was in &#x27;83!

AI will have the biggest impact on who makes money and controls wealth. Before any nation-state tries to take over the world with some kind of ultra-dominant weapon, most large states will have to deal with their own populations, as the rich control more resources.<p>The imaginary graph of ML technology that can be developed for destruction or defense is fraught with inter-dependent paranoid scenarios. The use of ML for the increase of human happiness is apparent and obvious. An ML arms race that invokes conflict is going to be a huge waste of a nation&#x27;s ML resources.<p>It would be much more productive to think about how ML&#x2F;AI can be used to for egalitarian human prosperity (a la post-scarcity, etc).

A general critique: it&#x27;s not <i>just</i> about AI.<p>Computation, in general, is capable of solving many problems that afflict the world - disease, hunger, resource allocation, etc. Some of these problems have &quot;conventional&quot; computational solutions.<p>Fundamentally, there are two problems that must be solved. First, the actual ability to actually compute needs perfected. This means that massive computations (i.e. the computations that solve massive, game-changing problems) can easily be performed. Things like public clouds are solving that problem. Second, computation needs applied to a problem. Statistical learning approaches have become popular because they are relatively simple to apply and are relatively successful. AI researches tend to believe that AI is all that matters, but obviously the success of AI is only possible with efficient computation. Similarly, efficient computation alone is useless if that computation cannot be used to solve actual problems.<p>Computation is to the 21st century as energy is the 20th. The ramifications of that statement are immediately obvious: consider the petrodollar. Soon, computation will become a currency.

Maybe it&#x27;s not about AI as a strategic asset, so much as it&#x27;s about private sector data collection of the kind that AI can utilise as a strategic asset. The ability to perform image recognition against user photos would be limited to countries that host the headquarters of a large social network, for example.

This is strangely relevant.<p><a href="https:&#x2F;&#x2F;archive.fo&#x2F;zP1F1" rel="nofollow">https:&#x2F;&#x2F;archive.fo&#x2F;zP1F1</a><p>- The economics surrounding AI development favor those who can commoditize data to the cheapest price. (Silicon Valley, militaries, and finance have AND MUST MAINTAIN their influence over this commoditization) This commoditization requirement was once previously thought as irreversible, allowing dumb money to buy into the idea that “data is the new oil”, but Butterfly War shows how to unexpectedly drive up the liability of a mass accumulation of data commodities.<p>- Foreign actors and short sellers can now use derivations of the Butterfly War to become market makers of the data economy, forcing the theory of “AI Winters” to be replaced with a more predictive “AI Business Cycle”. (Do you now understand why I went to Soros-influenced actors first?)<p>- This undesirable pressure, when paired with the institutional dependencies of established AI infrastructure, will force a deeper consolidation of Silicon Valley, military, and financial “cognitive assets”, which in turn will skew the funding and purposes behind additional AI development to be more risk-averse and conservative (from an power preservation standpoint).<p>- The pressures to embrace “cognitive mercantilism” become irreversible. Nations will aggressively retain talent and technologies for themselves to improve their collective bargaining power on the international stage. &#x2F;pol&#x2F;-tier nationalism finally has the footing to stifle their material humanist opposition.<p>- AI development will enter an artificially induced “deep freeze” period, similar to what happened to space exploration after the Space Race.<p>- The doctrine of Gnostic Warfare we develop today dominates in this period, focusing primarily on the epistemological limitations of Deep Belief Networks and, more precisely, how these cognitive assets define emotion.

the idea that politicians are welcome in theology is ridiculous<p>&gt; what is intelligent<p>&gt; what is the set of all x such that x is intelligent<p>not political questions