Selmer Bringsjord

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Selmer Bringsjord

Cognitive Nanorobotics for Killing Viruses

Abstract:

Bleak as things may still seem, our world will eventually surmount the current pandemic. This will happen because (assuming no sudden, separate cataclysm befalls humanity) both (conventionally developed) vaccines and therapies will progress to the point of !rm and widespread e"cacy. However, when looking at the history of our species, it’s not just death and taxes that are inevitable: viruses are as well, and as much as I hate taxes, some viruses are even worse. A future virus with a fatality rate of 5% distributed across all ages, with a period of one month during which asymptomatic transmission is possible, would essentially mean that 40M people from young to old would die. To put things in perspective, as I write this sentence, the JHU dashboard says that the current pandemic has resulted in 703,330 deaths — a great tragedy, but as you can see, we can easily calculate carnage of an entirely higher order. But what if there was a way to harness AI and robotics so that viruses can be directly, and preemptively, vanquished? What if, speci!cally, a kind of nanobot (or at least a microbot; for ease of exposition I hereafter refer only to the former) could be released into our bodies (and for that matter into the bodies of animals) so as to kill viruses directly, before any compromise of health? As to the kind of tiny robot I have in mind, it is: a cognitive nanobot. (There are already some nanobots starting to appear, and speci!cally in medicine:anti-cancer, but they have no capacity for cognition, at all, and are pre-programmed; this is a de!cient approach, as I explain in the presentation.)

We de!ne a cognitive robot as a robot whose signi!cant actions are the result of its autonomous reasoning (by deduction, e.g.) over the declarative information it perceives, believes, and knows.** As a result, everything signi!cant that such a robot does is the conclusion of a proof or — if some non-deductive reasoning is used, e.g. reasoning by analogy, or reasoning by enumerative induction — formal argument that it !nds itself. (Since these proofs and arguments can be mechanically checked, we have full explanation and veri!cation, unlike today’s machine learning.) Hence, to engineer a cognitive robot we must base it upon an automated reasoner; in the deductive case, speci!cally with an automated theorem prover. These types of intelligent logic-based systems go back to the dawn of modern AI in 1956 (when nobelist Herbert Simon revealed the automated theorem prover Logic Theorist), and engineering of these systems have been repeatedly carried out by researchers in the Rensselaer AI & Reasoning (RAIR) Lab and in other labs. This work, I think, has met with considerable success. But, and this is a big ‘but,’ this work has invariably been carried out with big robots.

Given the foregoing, then, a question:

Q: Can cognitive nanobots be engineered, so that they might be able to directly combat viruses such as SARS-Cov-2?

This is an intoxicatingly rich question, intellectually speaking. One reason is that such cognitive nanobots would presumably need to be self- contained, so as not to have to rely continuously upon processing taking place in remote macroscopic CPUs to which they have a communication channel. But we simply don’t know, at present, how small an automated reasoner can be. We don’t know the ultimate mathematical and physical limits that bound the relevant kind of logic-based AI needed for cognitive nanobots. (We know a thing or two about very small universal Turing machines, and this is relevant, as I explain.) And here’s another thing: Cognitive robots use knowledge of a domain expressed as axioms in formal logic; hence the nanobots we want must be supplied with an axiom system for cellular biology. At present, though there is work in this direction (which I summarize), it’s primitive, and we don’t know how compressed such an axiom system could be, so that it can ride on board a cognitive nanobot.

I explore these issues in service of seeking an answer to Q, and include presentation and discussion of a remarkable family of tiny robots (catabots) introduced by NS Govindarajulu. One class in this family appears to point the way toward the self-contained cognitive nanobots we want.

**This is in line with and extends “Cognitive Robotics” by Levesque and Lakemeyer (2007).

Biography:

Selmer Bringsjord specializes in the logico-mathematical and philosophical foundations of AI/ML, cognitive robots, and cognitive science, in collaboratively building AI/cognitive robots on the basis of computational logic, and in the logic-based modeling and simulation of rational, human-level-and-above cognition. Though he spends considerable engineering time in pursuit of ever-smarter computing machines, he claims that a priori reasoning time has enabled him to deduce that the human mind will forever be superior to such machines. At RPI, Bringsjord is Director of the Rensselaer AI & Reasoning Laboratory, and Professor of Cognitive Science, Computer Science, Logic & Philosophy, and Management & Technology. Funding for Bringsjord’s R&D has come from the Luce Foundation, the National Science Foundation, the Templeton Foundation, AT&T, IBM, Apple, AFRL, ARDA/DTO/IARPA, ONR, DARPA, AFOSR, and other sponsors. Among his current ONR grants is found one devoted to a new form of logic-based machine learning, and one devoted to creating and implementing in AIs and cognitive robots a capacity for ethically correct reasoning and decision-making. Bringsjord has consulted to and advised many companies in the general realm of intelligent systems, and continues to do so. His next book will be Gödel’s Great Theorems, from Oxford University Press; it’s last chapter is devoted to whether future AI will match or even exceed the intellectual power of human persons, an issue Gödel sought to reduce to the question of whether such persons are able to solve Diophantine equations in the general case.