Making decisions in a real computer - an argument for meta-decision theory research

post by whpearson · 2017-05-13T23:18:42.407Z · LW · GW · Legacy · 8 comments

Contents

  Decisions at 10Gbps
    Enter Meta-decision theory
  More human decisions
  Conclusion
None
8 comments

Decision theory is being used as the basis for AI safety work. This currently involves maximising expected utility of specific actions. Maximising expected utility is woefully inefficient for performing very rapid paced unimportant decisions, which occur frequently in computing. But these fast paced decisions will still need to be made in a way that is purpose oriented in an AI.

This article presents an argument that we should explore meta-decision theories to allow the efficient solution of these problems. Meta-decision theories are also more human-like and could have a different central problems to first order decision theories.

Decision theory tends to be the domain of economists, mathematicians, ethicists and other philosophers. However now it is being used in arguments about artificial intelligence and how to make it safe. If we are to try to implement an AI that follows any specific decision theory, it is brought into the real world domain of the computer. Now it has to deal with slightly less abstract world of bits and bytes. We have to make decision theories that take notice of the costs and consequences of making a decision.

AI is often seen through a dual lens, as both an agent making reasoned decisions about goals and as a computer able to process data and act at astounding speeds. These views have not entirely been integrated and this needs to be done for safe AI work.

AIs will still have to send data over the network and it will display images on a screen. These boring things are still up for optimisation, they still need to be decided upon. We will want these actions to be correctable if they are going badly wrong.

Decisions at 10Gbps

The example that will be followed through is networking. Consider a simplified network interface where the possible actions are outputting a 0 or a 1 across a wire.

We have an AI in control of this network interface, part of it’s job to send a file as quickly as possible to the other side. It has a 10 Gbps ethernet connection. It gets the optimal utility if it can send a 10 Gb file in 1 second. It also has to pick and chose which files to send, some are more important that others. Imagine doing an expected utility calculations for each bit. If you can have a machine with 300 GIPs (top of the range CPUs are in that order), and under ideal conditions could only use 30 instructions to do an expected utility calculation for each bit. This might work for a dead simple expected utility function that just checks whether a 0 or 1 matches the current bit of the current file it is sending and if it matches the utility is 1 and if it doesn’t  match the utility is 0.

However we are expecting better things from our AI, it should be able to figure out if it should interrupt the current file upload and start a different one, because that file has become more important. 30 instructions is slim pickings for something that might want to do a complex simulation of the state of the world to figure out what it should do. An option to try and optimise things might be to batch up bits and do one utility calculation per set of bits. Batching things up into sets of 64bits doesn’t seem to gain you much if any, while you divide the number of utility calculations you need to do by 64, each decision has 2^64 different possible actions instead of only 2 and you need to calculate the expected utility of each action.

No one is seriously proposing to do expected utility calculations for control of a network card, but then no one is talking about what it means to intelligently control a network interface at all.

So what do we do about it? Currently to achieve this task we would have a dumb program that just copied bits across the network and then a higher level program that modeled the world and interrupted the low level program to change it’s behaviour to copying a different file, if it found it would get more utility. So divorcing utility from the low-level code entirely.  This seems, at first glance, like an elegant solution.

However now we add a wrinkle, we want to stream some video from a webcam for an important conference call, this gains the AI some utility. We have to decide if streaming from the webcam or sending a file gets us more utility or multiplexing the streams of each. This brings us right back to where we started, making 10 000 000 000 decisions per second .

This would be solved in normal computing by implementing quality of service (QoS) software that makes decisions about sending the data based on its importance. And our expected utility maximiser could reason about the expected utility of different QoS settings. However the QoS software has some overhead, it takes more than 30 instructions per bit on average to do QoS and process the data, so it degrades the maximal throughput rate.

So now we come to another decision, should we use QoS software or not. Note that this is not at all about an action, it is purely about an internal change to the system, which program has control of the network interface. To rescue the correctable aspect of this, we can look at the expected utility of having QoS program have control of the system and we can predict the expected utility at having a simpler program with less overhead in control of the system. If we expect to have lots of contention then we want QoS, if we expect to be monomaniacally doing one action then we should have no QoS. We are making a decision about how decisions are made.

People do bypass useful networking features in the kernels to get better throughput rates, see for example this cloudflare blog so this is not an unmotivated example.

Making this choice about to have QoS software or not doesn’t seem interesting; unless you see that it is not an isolated example. The same arguments can be made for all processing, power, memory and memory bandwidth usage in the computer. You need to be making choices about the expected utility of which programs get allocations of all these things, when they might impact the expected utility.

Enter Meta-decision theory

This view leads to an interesting side effect, our decision theories are encoded in programs. How much memory and processing power should we allocate to them? Some decision theories might lead to less utility purely on how they function, we might not get an answer from them in our lifetimes if they are trying to do a formal proof of something unprovable. So perhaps we should allocate control of making decisions to the decision theory that is expected to net the most utility for the system. This is a form of meta decision theoretic thinking as defined in Smokers, Psychos, and Decision-Theoretic Uncertainty by William Macaskill. Resource constraints gives us different motivations for wanting to choose between decision theories, and we would want decision theories to have control over the system for longer periods of time, rather than single actions. But this work shows that their might be benefits above and beyond the resource constraint arguments.  I’ve not yet decided if his preference for Causal Meta-decision theory carries over to the scenarios I’ve been talking about.

As I’m not a decision theorist by training I stray even further from the fold, I’m interested in using meta-decision theory systems that can pick non-utility maximising rule sets for making decisions. This might allow me to program or train the computer system into having deontological ethics in control of the certain types of decisions, so that the system would not wirehead, lie or murder or prevent itself from being turned off.

If we could have these rules and could make sure that they persist (by making sure they achieve the most utility) we could solve some of the longstanding problems of first order decision theories as they relate to AI.

More human decisions

Another interesting feature of meta-decision theoretic systems, is that they share the stance we as humans apply to decision theories. We adopt design different decision theories to use, we make and keep rules in different circumstances. So too could meta-decision theoretic computer systems pick deontological rules for consequential reasons.

Conclusion

AI control theory should not just use rely on abstract maths. Real world computing concerns can and should inform the way that decisions are made. Meta-decision theories are currently under explored and should be favoured when trying to build systems that work in the real world in resource constrained situations.

There is so much work to do here. There are many open questions

• We need programs to make decisions about control. Where do these programs come from?
• How often should decisions about control should be made?
• How should they be made?

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