29 research outputs found
Toward a general logicist methodology for engineering ethically correct robots,”
Abstract It is hard to deny that robots will become increasingly capable, and that humans will increasingly exploit this capability by deploying them in ethically sensitive environments; i.e., in environments (e.g., hospitals) where ethically incorrect behavior on the part of robots could have dire effects on humans. But then how will we ensure that the robots in question always behave in an ethically correct manner? How can we know ahead of time, via rationales expressed in clear English (and/or other so-called natural languages), that they will so behave? How can we know in advance that their behavior will be constrained specifically by the ethical codes selected by human overseers? In general, it seems clear that one reply worth considering, put in encapsulated form, is this one: "By insisting that our robots only perform actions that can be proved ethically permissible in a human-selected deontic logic." (A deontic logic is simply a logic that formalizes an ethical code.) This approach ought to be explored for a number of reasons. One is that ethicists themselves work by rendering ethical theories and dilemmas in declarative form, and by reasoning over this declarative information using informal and/or formal logic. Other reasons in favor of pursuing the logicist solution are presented in the paper itself. To illustrate the feasibility of our methodology, we describe it in general terms free of any committment to particular systems, and show it solving a challenge regarding robot behavior in an intensive care unit
Taking Turing by Surprise? Designing Digital Computers for morally-loaded contexts
There is much to learn from what Turing hastily dismissed as Lady Lovelace s
objection. Digital computers can indeed surprise us. Just like a piece of art,
algorithms can be designed in such a way as to lead us to question our
understanding of the world, or our place within it. Some humans do lose the
capacity to be surprised in that way. It might be fear, or it might be the
comfort of ideological certainties. As lazy normative animals, we do need to be
able to rely on authorities to simplify our reasoning: that is ok. Yet the
growing sophistication of systems designed to free us from the constraints of
normative engagement may take us past a point of no-return. What if, through
lack of normative exercise, our moral muscles became so atrophied as to leave
us unable to question our social practices? This paper makes two distinct
normative claims:
1. Decision-support systems should be designed with a view to regularly
jolting us out of our moral torpor.
2. Without the depth of habit to somatically anchor model certainty, a
computer s experience of something new is very different from that which in
humans gives rise to non-trivial surprises. This asymmetry has key
repercussions when it comes to the shape of ethical agency in artificial moral
agents. The worry is not just that they would be likely to leap morally ahead
of us, unencumbered by habits. The main reason to doubt that the moral
trajectories of humans v. autonomous systems might remain compatible stems from
the asymmetry in the mechanisms underlying moral change. Whereas in humans
surprises will continue to play an important role in waking us to the need for
moral change, cognitive processes will rule when it comes to machines. This
asymmetry will translate into increasingly different moral outlooks, to the
point of likely unintelligibility. The latter prospect is enough to doubt the
desirability of autonomous moral agents
Towards A Measure Of General Machine Intelligence
To build general-purpose artificial intelligence systems that can deal with
unknown variables across unknown domains, we need benchmarks that measure how
well these systems perform on tasks they have never seen before. A prerequisite
for this is a measure of a task's generalization difficulty, or how dissimilar
it is from the system's prior knowledge and experience. If the skill of an
intelligence system in a particular domain is defined as it's ability to
consistently generate a set of instructions (or programs) to solve tasks in
that domain, current benchmarks do not quantitatively measure the efficiency of
acquiring new skills, making it possible to brute-force skill acquisition by
training with unlimited amounts of data and compute power. With this in mind,
we first propose a common language of instruction, a programming language that
allows the expression of programs in the form of directed acyclic graphs across
a wide variety of real-world domains and computing platforms. Using programs
generated in this language, we demonstrate a match-based method to both score
performance and calculate the generalization difficulty of any given set of
tasks. We use these to define a numeric benchmark called the generalization
index, or the g-index, to measure and compare the skill-acquisition efficiency
of any intelligence system on a set of real-world tasks. Finally, we evaluate
the suitability of some well-known models as general intelligence systems by
calculating their g-index scores.Comment: 31 pages, 15 Figures, 3 Tables; Sample Data and g-index Reference
Code at https://github.com/mayahq/g-index-benchmark; g-index toy environment
at https://github.com/mayahq/flatland; version 2 added a section about the
toy environment; version 3 compressed images to reduce file size; version 4
updated description of flatland toy environmen
Turingův test: filozofické aspekty umělé inteligence
Disertační práce se zabývá problematikou připisování myšlení jiným entitám, a to pomocí imitační hry navržené v roce 1950 britským filosofem Alanem Turingem. Jeho kritérium, známé v dějinách filosofie jako Turingův test, je podrobeno detailní analýze. Práce popisuje nejen původní námitky samotného Turinga, ale především pozdější diskuse v druhé polovině 20. století. Největší pozornost je věnována těmto kritikám: Lucasova matematická námitka využívající Gödelovu větu o neúplnosti, Searlův argument čínského pokoje konstatující nedostatečnost syntaxe pro sémantiku, Blockův návrh na použití brutální síly pro řešení imitační hry, Frenchova teorie subkognitivních informací a Michieho skepticismus ohledně možnosti umělého vědomí. Závěr práce zachycuje současný stav recepce Turingova testu a představuje pokusy o jeho praktickou realizaci, například v každoroční soutěži o Loebnerovu cenu. Autor práce zastává názor, že ani po více než šedesáti letech od uveřejnění Turingova paradigmatického eseje stále neexistují žádné vážné důvody pro zamítnutí jeho tvrzení. Tradiční komputační funkcionalismus možná není ideální teorií vysvětlující činnost myslí a jako slibnější se může jevit vývoj v neurálních vědách, ale Turingův test je přesto užitečným a snad i jediným nástrojem pro detekci inteligence u lidmi vytvořených strojů
Foundations of Trusted Autonomy
Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie