Luke Muehlhauser: A couple years ago you wrote a review article on algorithmic self-assembly, and also created a video introduction to the subject. Your review article begins:

Self-assembly is the process by which small components automatically assemble themselves into large, complex structures. Examples in nature abound: lipids self-assemble a cell’s membrane, and bacteriophage virus proteins self-assemble a capsid that allows the virus to invade other bacteria. Even a phenomenon as simple as crystal formation is a process of self-assembly… Algorithmic self-assembly systems automate a series of simple growth tasks, in which the object being grown is simultaneously the machine controlling its own growth.

As an example, here’s a an electron microscope image of a Sierpinski triangle produced via algorithmic self-assembly of DNA molecules:

Read more »

Read more »

Luke Muehlhauser: Much of your work concerns the question “Why are humans smarter than other animals?” In a series of papers (e.g. 2009, 2012), you’ve argued that recent results show that some popular hypotheses are probably wrong. For example, the so-called “overdeveloped” human cerebral cortex contains roughly the percentage of total brain neurons (19%) as do the cerebral cortices of other mammals. Rather, you argue, the human brain may simply be a “linearly scaled-up primate brain”: primate brains seem to have more economical scaling rules than do other mammals, and humans have the largest brain of any primate, and hence the most total neurons.

Your findings were enabled by a new method for neuron quantification developed at your lab, called “isotropic fractionator” (Herculano-Houzel & Lent 2005). Could you describe how that method works?

Suzana Herculano-Houzel: The isotropic fractionator consists pretty much of turning fixed brain tissue into soup – a soup of a known volume containing free cell nuclei, which can be easily colored (by staining the DNA that all nuclei contain) and thus visualized and counted under a microscope. Since every cell in the brain contains one and only one nucleus, counting nuclei is equivalent to counting cells. The beauty of the soup is that it is fast (total numbers of cells can be known in a few hours for a small brain, and in about one month for a human-sized brain), inexpensive, and very reliable – as much or more than the usual alternative, which is stereology.

Stereology, in comparison, consists of cutting entire brains into a series of very thin slices; processing the slices to allow visualization of the cells (which are otherwise transparent); delineating structures of interest; creating a sampling strategy to account for the heterogeneity in the distribution of cells across brain regions (a problem that is literally dissolved away in the detergent that we use in the isotropic fractionator); acquiring images of these small brain regions to be sampled; and actually counting cells in each of these samples. It is a process that can take a week or more for a single mouse brain. It is more powerful in the sense that spatial information is preserved (while the tissue is necessarily destroyed when turned into soup for our purposes), but on the other hand, it is much more labor-intensive and not appropriate for working on entire brains, because of the heterogeneity across brain parts.
Read more »

Luke Muehlhauser: You lead an ongoing research project aimed at “estimating the global technological capacity to store, communicate and compute information.” Your results have been published in Science and other journals, and we used your work heavily in The world’s distribution of computation. What are you able to share in advance about the next few studies you plan to release through that project?

Read more »

Luke Muehlhauser: Bolander (2003) and some of your subsequent work studies paradoxes of self-reference in the context of logical/computational agents, as does e.g. Weaver (2013). Do you think your work on these paradoxes will have practical import for AI researchers who are designing computational agents, or are you merely using the agent framework to explore the more philosophical aspects of self-reference?

Read more »

Luke Muehlhauser: Since you were a relatively early researcher in the field of covert channel communication, I’d like to ask you about the field’s early days, which are usually said to have begun with Lampson (1973). Do you know when the first covert channel attack was uncovered “in the wild”? My impression is that Lampson identified the general problem a couple decades before it was noticed being exploited in the wild; is that right?

Jonathan Millen: We might never know when real covert channel attacks were first noticed, or when they first occurred. When information is stolen by covert channel, the original data is still in place, so the theft can go unnoticed. Even if an attack is discovered, the victims are as reluctant as the perpetrators to acknowledge it. This is certainly the case with classified information, since a known attack is often classified higher than the information it compromises. The only evidence I have of real attacks before 1999 is from Robert Morris (senior), a pioneer in UNIX security, and for a while the Chief Scientist of the National Computer Security Center, which was organizationally within NSA. He stated at a security workshop that there had been real attacks. He wouldn’t say anything more; it was probably difficult enough to get clearance for that much.
Read more »

Luke Muehlhauser: You co-founded the field of hybrid systems control with Anil Nerode. Anil gave his impressions of the seminal 1990 Pacifica meeting here. What were your own impressions of how that meeting developed? Is there anything in particular you’d like to add to Anil’s account?

Wolf Kohn: The discussion on the first day of the conference centered on the problem of how to incorporate heterogeneous descriptions of complex dynamical systems into a common representation for designing large scale automation. What came almost immediately were observations from Colonel Mettala and others that established as a goal the finding of alternatives to classic approaches based on combining expert systems with conventional control and system identification techniques.

These approaches did not lead to robust designs. More important, they did not lead to a theory for the systematic treatment of the systems DOD was deploying at the time. I was working on control architectures based on constraints defined by rules, so after intense discussions among the participants Nerode and I moved to a corner and came up with a proposal to amalgamate models by extending the concepts of automata theory and optimal control to characterize the evolution of complex dynamical systems in a manifold in which the topology was defined by rules of operation, and behavior constraints and trajectories were generated by variational methods. This was the beginning of what we would be defined later on as “hybrid systems.”
Read more »