This paper discusses the relation between intelligence and motivation in artificial agents, developing and briefly arguing for two theses. The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having any of a wide range of final goals will pursue similar intermediary goals because they have instrumental reasons to do so. In combination, the two theses help us understand the possible range of behavior of superintelligent agents, and they point to some potential dangers in building such an agent.
Yampolskiy & Fox (2012a). Safety engineering for artificial general intelligence.
Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we challenge the scientific community to develop intelligent systems that have humanfriendly values that they provably retain, even under recursive self-improvement.
Yampolskiy & Fox (2012b). Artificial general intelligence and the human mental model.
When the first artificial general intelligences are built, they may improve themselves to far-above-human levels. Speculations about such future entities are already affected by anthropomorphic bias, which leads to erroneous analogies with human minds. In this chapter, we apply a goal-oriented understanding of intelligence to show that humanity occupies only a tiny portion of the design space of possible minds. This space is much larger than what we are familiar with from the human example; and the mental architectures and goals of future superintelligences need not have most of the properties of human minds. A new approach to cognitive science and philosophy of mind, one not centered on the human example, is needed to help us understand the challenges which we will face when a power greater than us emerges.
- Uziel Awret - Introduction
- Susan Blackmore - She Won’t Be Me
- Damien Broderick - Terrible Angels: The Singularity and Science Fiction
- Barry Dainton - On Singularities and Simulations
- Daniel Dennett - The Mystery of David Chalmers
- Ben Goertzel - Should Humanity Build a Global AI Nanny to Delay the Singularity Until It’s Better Understood?
- Susan Greenfield - The Singularity: Commentary on David Chalmers
- Robin Hanson - Meet the New Conflict, Same as the Old Conflict
- Francis Heylighen - Brain in a Vat Cannot Break Out
- Marcus Hutter - Can Intelligence Explode?
- Drew McDermott - Response to ‘The Singularity’ by David Chalmers
- Jurgen Schmidhuber - Philosophers & Futurists, Catch Up!
- Frank Tipler - Inevitable Existence and Inevitable Goodness of the Singularity
- Roman Yampolskiy - Leakproofing the Singularity: Artificial Intelligence Confinement Problem
Luke Muehlhauser and Anna Salamon of the Singularity Institute have released a draft version of their forthcoming book chapter “Intelligence Explosion: Evidence and Import.”
Humans may create human-level artificial intelligence (AI) this century. Shortly thereafter, we may see an “intelligence explosion” or “technological singularity” — a chain of events by which human-level AI leads, fairly rapidly, to intelligent systems whose capabilities far surpass those of biological humanity as a whole.
How likely is this, and what will the consequences be? Others have discussed these questions previously…; our aim is to provide a brief review suitable both for newcomers to the topic and for those with some familiarity with the topic but expertise in only some of the relevant fields.
MIT’s Paul Christiano has written many substantive blog posts related to Friendly AI theory on his blog, Ordinary Ideas.
A new website, Friendly-AI.com, provides a quick introduction to the concept of Friendly AI.
Luke Muehlhauser and Louie Helm have posted a draft of their forthcoming article The Singularity and Machine Ethics:
Many researchers have argued that a self-improving artificial intelligence (AI) could become so vastly more powerful than humans that we would not be able to stop it from achieving its goals. If so, and if the AI’s goals differ from ours, then this could be disastrous for humans. One proposed solution is to program the AI’s goal system to want what we want before the AI self-improves beyond our capacity to control it. Unfortunately, it is difficult to specify what we want. After a brief digression concerning human intuitions about intelligence, we offer a series of “intuition pumps” in moral philosophy for our conclusion that human values are complex and difficult to specify. We then survey the evidence from the psychology of motivation, moral psychology, and neuroeconomics that supports our position. We conclude by recommending ideal preference theories of value as a promising approach for developing a machine ethics suitable for navigating the Singularity.
The open problems he lists are:
- Describe a general decision system that can completely rewrite itself without decreasing the strength of its proof system each time.
- Prove blackmail-free equilibrium among timeless strategists.
- Avoid proving contradiction inside Q’s counterfactual.
- Better formalize hybrid of causal and mathematical inference.
- Fair division by continuous / multiparty agents (required for EU agents to divide a benefit).
- Theory of logical uncertainty in temporal bounded agents. If part of you assigns 60% probability to P and part of you assigns 60% probability to ~P it requires a specific operation to notice the contradiction. It’s okay to be outperformed by a smarter agent who noticed first, it’s not okay to assign 20% probability to everything being true after you notice.
- Making hypercomputation conceivable – extension of Solomonoff induction to anthropic reasoning and higher-order logic – why ideal rational agents still seem to need anthropic assumptions.
- AIXI’s reward button will kill you – challenge of extending AIXI to non-Cartesian embedding and a utility function over environments with known ontologies.
- Shifting ontologies – general problem of expressing resolvable uncertainty in utility functions.
- How do you construe a utility function from a psychologically realistic detailed model of a human’s decision process? May end up being 90% morality and 10% math, or what we really want may be formalish statements of desiderata for how to teach a young AI this at the same time as it’s learning about humans. But worth throwing out there for any ethical philosophers who can understand the difference between computable and non-constructive specifications, on the off-chance that it’s an interesting enough problem that some of them will help save the world.
- Microeconomic models of self-improving systems – it would be helpful if we could get any further information about how fast self-improving AIs go FOOM, or more powerful/formal arguments to convince anyone open to math that they do go FOOM, for all non-contrived curves of cumulative optimization pressure vs. optimization output that fit human evolution & economics to date.
He also notes:
Most things you need to know to build Friendly AI are rigorous understanding of AGI rather than Friendly parts per se – contrary to what people who dislike the problem would have you believe, we don’t spend all our time pondering morality.
There is no strong reason to believe human level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed goals or motivation systems. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in the world except by answering questions. Even this narrow approach presents considerable challenges and we analyse and critique various methods of control. In general this form of limited AI might be safer than unrestricted AI, but still remains potentially dangerous.
Systems with the computational power of the human brain are likely to be cheap and ubiquitous within the next few decades. As technology becomes more intelligent, we need to ensure that it remains safe and beneficial. This paper describes a rational framework for analyzing intelligent systems and a strategy for developing them safely. The analysis is based on von Neumann’s model of rational economic behavior. We introduce the “Rationally-Shaped Minds” model of intelligent systems with bounded computation. We show that as computational resources increase, there is a natural progression through stimulus-response systems, learning systems, reasoning systems, self-improving systems, to fully rational systems. We show that rational systems are subject to “drives” toward self-protection, resource acquisition, replication, goal preservation, efficiency, and self-improvement. Several of these drives are anti-social and need to be counteracted with analogs of human cooperativeness and compassion. We analyze the three basic strategies for controlling the behavior of intelligent systems. We describe the “Safe-AI Scaffolding” strategy which builds intentionally limited but safe systems to use in the construction of more powerful systems.
The piece builds on his earlier work, “The Nature of Self-Improving Artificial Intelligence” (2007) and “The Basic AI Drives” (2008). The latter was cited in the latest edition of Russell and Norvig’s famous AI textbook.