Most of the technical problems in the agenda have this property, including:
On my understanding of CogPrime, its answers to these questions are of the form "we'll just keep adding/refining heuristics and heuristic-learning mechanisms and heuristic-selection methods until it's smart, and so we don't need to answer these problems ourselves." To me, this seems like a Person 1-type answer ("we'll just add more gears until it can think") rather than a Person 2-type answer ("here is how these things could be done in theory").
It is possible to design a chess-playing machine by simply adding more and more gears -- the trick is to add the right gears in the right places. In practice, this turns out to be really difficult. It seems implausible that someone could put the gears in the right places on purpose before having a conceptual understanding of trees and backtracking.
Similarly, I think it is possible to design an intelligent system by developing better collections of heuristics, learning methods, and heuristic-selection methods -- but this is in fact pretty difficult, and it doesn't seem likely to me that someone can get the right heuristics and learning methods before they can generate Person 2-type answers to questions such as the above.
(Well, actually, I worry that if we take the "add heuristics until it seems intelligent" route before we can give Person 2-type answers to questions such as the above, then we may be able to succeed, but we will be much more likely to end up with UFAI.)
On my understanding of CogPrime, its answers to these questions are of the form "we'll just keep adding/refining heuristics and heuristic-learning mechanisms and heuristic-selection methods until it's smart, and so we don't need to answer these problems ourselves”
Have you read Engineering General Intelligence[1]? CogPrime is not a hack job -- there is significant theory going into the architectural design.
But I won’t belabor that point because even if CogPrime / OpenCog were as you describe, that pretty much also describes how human intelligence w...
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.