A fact that is only relevant if those properties can capture the desired feature. You'll recall that defining the desired feature is a major goal of MIRI.
No that presumes what is being checked against is the friendly goal system. What I'm talking about is checking that e.g. all actions being taken by the AI are in search of solutions to a compact goal description, also extracted from the machine in the form of a bayesian concept net. Then both the goal set and stochastic samplings of representative mental processes are checked by humans for anomalous behavior (and a much larger subset frequency mined to determine what's representative).
You're not testing that the machine obeys some as-of-yet-not-figured-out friendly goal set, but that the extracted goals and computational traces are representative, and then manually inspecting those.
Giving the AI zero power to affect our behavior, in the strict sense, would mean not running it (or not letting it produce even one bit of output and not expecting any).
That's a legalistic definition which belongs only in philosophy debates.
Utility maximization today seems like the best-formalized part of human general intelligence
I disagree. Much of human behavior is not utility maximizing. Much of it is about fulfilling needs, which is often about eliminating conditions. You have hunger? You eliminate this condition by eating a reasonable amount of food. You do not maximize your lack of hunger by turning the whole planet into a food-generating system and force-feeding the products down your own throat.
Anyway, in my own understanding general intelligence has to do with concept formation and system 1/system 2 learned behavior. There's not much about utility maximization there.
It doesn't seem like you even want to focus on uploading.
Do you count intelligence augmentation as uploading? Because that's my path throughthe singularity.
despite being mathematically equivalent to some utility function
Gah, no no no. Not every program is equal to a utility maximizer. Not if utility and utility maximization is to have any meaning at all. Sure you can take any program and call it a utility maximizer by finding some super contrived function which is maximized by the program. But if that goal system is more complex than the program that supposidly maximizes it, then all you've done is demonstrate the principle of overfitting a curve.
I'm pleased to announce the release of Aligning Superintelligence with Human Interests: A Technical Research Agenda written by Benja and I (with help and input from many, many others). This document summarizes and motivates MIRI's current technical research agenda.
I'm happy to answer questions about this document, but expect slow response times, as I'm travelling for the holidays. The introduction of the paper is included below. (See the paper for references.)
The characteristic that has enabled humanity to shape the world is not strength, not speed, but intelligence. Barring catastrophe, it seems clear that progress in AI will one day lead to the creation of agents meeting or exceeding human-level general intelligence, and this will likely lead to the eventual development of systems which are "superintelligent'' in the sense of being "smarter than the best human brains in practically every field" (Bostrom 2014). A superintelligent system could have an enormous impact upon humanity: just as human intelligence has allowed the development of tools and strategies that let humans control the environment to an unprecedented degree, a superintelligent system would likely be capable of developing tools and strategies that give it extraordinary power (Muehlhauser and Salamon 2012). In light of this potential, it is essential to use caution when developing artificially intelligent systems capable of attaining or creating superintelligence.
There is no reason to expect artificial agents to be driven by human motivations such as lust for power, but almost all goals can be better met with more resources (Omohundro 2008). This suggests that, by default, superintelligent agents would have incentives to acquire resources currently being used by humanity. (Can't we share? Likely not: there is no reason to expect artificial agents to be driven by human motivations such as fairness, compassion, or conservatism.) Thus, most goals would put the agent at odds with human interests, giving it incentives to deceive or manipulate its human operators and resist interventions designed to change or debug its behavior (Bostrom 2014, chap. 8).
Care must be taken to avoid constructing systems that exhibit this default behavior. In order to ensure that the development of smarter-than-human intelligence has a positive impact on humanity, we must meet three formidable challenges: How can we create an agent that will reliably pursue the goals it is given? How can we formally specify beneficial goals? And how can we ensure that this agent will assist and cooperate with its programmers as they improve its design, given that mistakes in the initial version are inevitable?
This agenda discusses technical research that is tractable today, which the authors think will make it easier to confront these three challenges in the future. Sections 2 through 4 motivate and discuss six research topics that we think are relevant to these challenges. Section 5 discusses our reasons for selecting these six areas in particular.
We call a smarter-than-human system that reliably pursues beneficial goals "aligned with human interests" or simply "aligned." To become confident that an agent is aligned in this way, a practical implementation that merely seems to meet the challenges outlined above will not suffice. It is also necessary to gain a solid theoretical understanding of why that confidence is justified. This technical agenda argues that there is foundational research approachable today that will make it easier to develop aligned systems in the future, and describes ongoing work on some of these problems.
Of the three challenges, the one giving rise to the largest number of currently tractable research questions is the challenge of finding an agent architecture that will reliably pursue the goals it is given—that is, an architecture which is alignable in the first place. This requires theoretical knowledge of how to design agents which reason well and behave as intended even in situations never envisioned by the programmers. The problem of highly reliable agent designs is discussed in Section 2.
The challenge of developing agent designs which are tolerant of human error has also yielded a number of tractable problems. We argue that smarter-than-human systems would by default have incentives to manipulate and deceive the human operators. Therefore, special care must be taken to develop agent architectures which avert these incentives and are otherwise tolerant of programmer error. This problem and some related open questions are discussed in Section 3.
Reliable, error-tolerant agent designs are only beneficial if they are aligned with human interests. The difficulty of concretely specifying what is meant by "beneficial behavior" implies a need for some way to construct agents that reliably learn what to value (Bostrom 2014, chap. 12). A solution to this "value learning'' problem is vital; attempts to start making progress are reviewed in Section 4.
Why these problems? Why now? Section 5 answers these questions and others. In short, the authors believe that there is theoretical research which can be done today that will make it easier to design aligned smarter-than-human systems in the future.