Self-Embedded Agent

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Challenges with Breaking into MIRI-Style Research

Unclear. Some things that might be involved

  • a somewhat anti/non academic vibe
  • a feeling that they have the smartest people anyway, only hire the elite few that have a proven track record
  • feeling that it would take too much time and energy to educate people
  • a lack of organisational energy
  • .... It would be great if somebody from MIRI could chime in.

I might add that I know a number of people interested in AF who feel somewhat afloat/find it difficult to contribute. Feels a bit like a waste of talent

Challenges with Breaking into MIRI-Style Research

Agreed. Thank you for writing this post. Some thoughts:

As somebody strongly on the Agent Foundations train it puzzles me that there is so little activity outside MIRI itself. We are being told there are almost limitless financial resources, yet - as you explain clearly - it is very hard for people to engage with the material outside of LW. 

At the last EA global there was some sort of AI safety breakout session. There were ~12 tables with different topics. I was dismayed to discover that almost every table was full with people excitingly discussing various topics in prosaic AI alignment and other things the AF table had just 2 (!) people.

In general, MIRI has a rather insular view of itself. Some of it is justified. I do think they have done most of the interesting research, are well-aligned, employ many of the smartest & creative people etc.

But the world is very very big. 

I have spoken with MIRI people arguing for the need to establish something like a PhD apprentice-style system. Not much interest.

Just some sort of official & long-term& OFFLINE study program that would teach some of the previous published MIRI research would be hugely beneficial for growing the AF community. 

Finally, there needs to be way more interaction with existing academia. There are plenty of very smart very capable people in academica that do interesting things with Solomonoff induction, with Cartesian Frames (but they call them Chu Spaces), with Pearlian causal inference, with decision theory, with computational complexity & interactive proof systems, with post-Bayesian probability theory etc etc. For many in academia AGI safety is still seen as silly, but that could change if MIRI and Agent Foundations people would be able to engage seriously with academia. 

One idea could be to organize sabbaticals for prominent academics + scholarships for young people. This seems to have happened with the prosaic AI alignment field but not with AF. 

Self-Embedded Agent's Shortform

Failure of convergence to social optimum in high frequency trading with technological speed-up

Possible market failures in high-frequency trading are of course a hot topic recently with various widely published Flash Crashes. There has a loud call for a reign in of high frequency trading and several bodies are moving towards heavier regulation. But it is not immediately clear whether or not high-frequency trading firms are a net cost to society. For instance, it is sometimes argued that High-Frequency trading firms as simply very fast market makers. One would want a precise analytical argument for a market failure.

There are two features that make this kind of market failure work: the first is a first-mover advantage in arbitrage, the second is the possibility of high-frequency trading firms to invest in capital, technology, or labor that increases their effective trading speed.

The argument runs as follows.

Suppose we have a market without any fast traders. There are many arbitrage opportunities open for very fast traders. This inaccurate pricing inflicts a dead-weight loss D on total production P. The net production N equals P-D. Now a group of fast traders enter the market. At first they provide for arbitrage which gives more accurate pricing and net production rises to N=P. 

Fast traders gain control of a part of the total production S.  However there is a first-mover advantage in arbitrage so any firm will want to invest in technology, labor, capital that will speed up their ability to engage in arbitrage. This is a completely unbounded process, meaning that trading firms are incentived to trade faster and faster beyond what is beneficial to real production. There is a race to the bottom phenomenon. In the end a part A of S is invested in 'completely useless' technology, capital and labor. The new net production is N=P-A and the market does not achieve a locally maximal Pareto efficient outcome.

As an example suppose the real economy R consults market prices every minute. Trading firms invest in technology, labor and capital and eventually reach perfect arbitrage within one minute of any real market movement or consult (so this includes any new market information, consults by real firms etc). At this point the real economy R clearly benefits from more accurate pricing. But any one trading firm is incentivized to be faster than the competition. By investing in tech, capital, suppose trading firms can achieve perfect arbitrage within 10 microseconds of any real market movement. This clearly does not help the real economy R in achieving any higher production at all since it does not consult the market more than once every minute but there is a large attached cost.

Self-Embedded Agent's Shortform

Measuring the information-theoretic optimizing power of evolutionary-like processes

Intelligent-Design advocates often argue that the extraordinary complexity that we see in the natural world cannot be explained simply by a 'random process' as natural selection, hence a designer. Counterarguments include:

  • (seemingly) Complicated and complex phenomena can often be created by very simple (mathematical) rules [e.g. Mandelbrott sets etc]
  • Our low-dimensional intuition may lead us astray when visualizing high-dimensional evolutionary landscapes: there is much more room for many long 'ridges' along which evolution can propagate a species.
  • Richard Dawkins has a story about how eyes evolved in step-like manner from very primitive photo-receptors etc. In this case he is mostly able to explain the exact path.

To my eyes these are good arguments but they certainly are not conclusive. Compare:

  • Stuart Kauffmann has a lot of work ( see 'At Home in The Universe') on which kind of landscapes allow for a viable 'evolution by natural selection'. Only some landscapes are suitable, with many being either 'too flat' or 'too mountainous'. Somewhat hilly is best. Most systems are also too chaotic to say anything useful
  • Population geneticists know many (quantatitive!) things about when different evolutionary forces (Drift, Mutation, Sexual Recombination) may overwhelm natural selection
  • Alan Grafen has a fairly precise mathematical framework that he says is able to determine when Darwinian-Wallacian evolution is maximizing a 'fitness function'. Importantly, not all situations/ecosystems can support the traditional 'survival of the fittest' interpretation of evolution

The take-away message is that we should be careful when we say that evolution explains biological complexity. This might certainly be true - but can we prove it?

Search-in-Territory vs Search-in-Map

A very basic yet, to my mind, novel and profound distinction. Thank you, John!

Timeline of AI safety

The effort is commendable. I am wondering why you started at 2013?

Debatably it is the things that happened prior to 2013 that is especially of interest.

I am thinking of early speculations by Turing, Von Neumann and Good continuing on to the founding of SI/MIRI some twenty years ago and much more in between I am less familiar with - but would like to know more about!