Today we are proud to publicly launch the Intelligent Agent Foundations Forum (RSS), a forum devoted to technical discussion of the research problems outlined in MIRI’s technical agenda overview, along with similar research problems.

Patrick’s welcome post explains:

Broadly speaking, the topics of this forum concern the difficulties of value alignment- the problem of how to ensure that machine intelligences of various levels adequately understand and pursue the goals that their developers actually intended, rather than getting stuck on some proxy for the real goal or failing in other unexpected (and possibly dangerous) ways. As these failure modes are more devastating the farther we advance in building machine intelligences, MIRI’s goal is to work today on the foundations of goal systems and architectures that would work even when the machine intelligence has general creative problem-solving ability beyond that of its developers, and has the ability to modify itself or build successors.

The forum has been privately active for several months, so many interesting articles have already been posted, including:

• Slepnev, Using modal fixed points to formalize logical causality
• Fallenstein, Utility indifference and infinite improbability drives
• Benson-Tilsen, Uniqueness of UDT for transparent universes
• Christiano, Stable self-improvement as a research problem
• Fallenstein, Predictors that don’t try to manipulate you(?)
• Soares, Why conditioning on “the agent takes action a” isn’t enough
• Fallenstein, An implementation of modal UDT
• LaVictoire, Modeling goal stability in machine learning

Also see How to contribute.

Between 2006 and 2009, senior MIRI researcher Eliezer Yudkowsky wrote several hundred essays for the blogs Overcoming Bias and Less Wrong, collectively called “the Sequences.” With two days remaining until Yudkowsky concludes his other well-known rationality book, Harry Potter and the Methods of Rationality, we are releasing around 340 of his original blog posts as a series of six books, collected in one ebook volume under the title Rationality: From AI to Zombies.

Yudkowsky’s writings on rationality, which were previously scattered in a constellation of blog posts, have been cleaned up, organized, and collected together for the first time. This new version of the Sequences should serve as a more accessible long-form introduction to formative ideas behind MIRI, CFAR, and substantial parts of the rationalist and effective altruist communities.

While the books’ central focus is on applying probability theory and the sciences of mind to personal dilemmas and philosophical controversies, a considerable range of topics is covered. The six books explore rationality theory and applications from multiple angles:

I. Map and Territory. A lively introduction to the Bayesian conception of rational belief in cognitive science, and how it differs from other kinds of belief.

II. How to Actually Change Your Mind. A guide to overcoming confirmation bias and motivated cognition.

III. The Machine in the Ghost. A collection of essays on the general topic of minds, goals, and concepts.

IV. Mere Reality. Essays on science and the physical world, as they relate to rational inference.

V. Mere Goodness. A wide-ranging discussion of human values and ethics.

VI. Becoming Stronger. An autobiographical account of Yudkowsky’s philosophical mistakes, followed by a discussion of self-improvement and group rationality.

These essays are packaged together as a single electronic text, making it easier to investigate links between essays and search for keywords. The ebook is available on a pay-what-you-want basis (link), and on Amazon.com for $4.99 (link). In the coming months, we will also be releasing print versions of these six books, and Castify will be releasing the official audiobook version.

MIRI researcher Benja Fallenstein recently delivered an invited talk at the March 2015 meeting of the American Physical Society in San Antonio, Texas. Her talk was one of four in a special session on artificial intelligence.

Fallenstein’s title was “Beneficial Smarter-than-human Intelligence: the Challenges and the Path Forward.” The slides are available here. Abstract:

Today, human-level machine intelligence is still in the domain of futurism, but there is every reason to expect that it will be developed eventually. A generally intelligent agent as smart or smarter than a human, and capable of improving itself further, would be a system we’d need to design for safety from the ground up: There is no reason to think that such an agent would be driven by human motivations like a lust for power; but almost any goals will be easier to meet with access to more resources, suggesting that most goals an agent might pursue, if they don’t explicitly include human welfare, would likely put its interests at odds with ours, by incentivizing it to try to acquire the physical resources currently being used by humanity. Moreover, since we might try to prevent this, such an agent would have an incentive to deceive its human operators about its true intentions, and to resist interventions to modify it to make it more aligned with humanity’s interests, making it difficult to test and debug its behavior. This suggests that in order to create a beneficial smarter-than-human agent, we will need to face three formidable challenges: How can we formally specify goals that are in fact beneficial? How can we create an agent that will reliably pursue the goals that we give it? And how can we ensure that this agent will not try to prevent us from modifying it if we find mistakes in its initial version? In order to become confident that such an agent behaves as intended, we will not only want to have a practical implementation that seems to meet these challenges, but to have a solid theoretical understanding of why it does so. In this talk, I will argue that even though human-level machine intelligence does not exist yet, there are foundational technical research questions in this area which we can and should begin to work on today. For example, probability theory provides a principled framework for representing uncertainty about the physical environment, which seems certain to be helpful to future work on beneficial smarter-than-human agents, but standard probability theory assumes omniscience about logical facts; no similar principled framework for representing uncertainty about the outputs of deterministic computations exists as yet, even though any smarter-than-human agent will certainly need to deal with uncertainty of this type. I will discuss this and other examples of ongoing foundational work.

Stuart Russell of UC Berkeley also gave a talk at this session, about the long-term future of AI.