86,199 research outputs found
A Tale of Two Animats: What does it take to have goals?
What does it take for a system, biological or not, to have goals? Here, this
question is approached in the context of in silico artificial evolution. By
examining the informational and causal properties of artificial organisms
('animats') controlled by small, adaptive neural networks (Markov Brains), this
essay discusses necessary requirements for intrinsic information, autonomy, and
meaning. The focus lies on comparing two types of Markov Brains that evolved in
the same simple environment: one with purely feedforward connections between
its elements, the other with an integrated set of elements that causally
constrain each other. While both types of brains 'process' information about
their environment and are equally fit, only the integrated one forms a causally
autonomous entity above a background of external influences. This suggests that
to assess whether goals are meaningful for a system itself, it is important to
understand what the system is, rather than what it does.Comment: This article is a contribution to the FQXi 2016-2017 essay contest
"Wandering Towards a Goal
Computational evolution of decision-making strategies
Most research on adaptive decision-making takes a strategy-first approach,
proposing a method of solving a problem and then examining whether it can be
implemented in the brain and in what environments it succeeds. We present a
method for studying strategy development based on computational evolution that
takes the opposite approach, allowing strategies to develop in response to the
decision-making environment via Darwinian evolution. We apply this approach to
a dynamic decision-making problem where artificial agents make decisions about
the source of incoming information. In doing so, we show that the complexity of
the brains and strategies of evolved agents are a function of the environment
in which they develop. More difficult environments lead to larger brains and
more information use, resulting in strategies resembling a sequential sampling
approach. Less difficult environments drive evolution toward smaller brains and
less information use, resulting in simpler heuristic-like strategies.Comment: Conference paper, 6 pages / 3 figure
The Cognitive Basis of Computation: Putting Computation in Its Place
The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and not the other way around
The Case for a Mixed-Initiative Collaborative Neuroevolution Approach
It is clear that the current attempts at using algorithms to create
artificial neural networks have had mixed success at best when it comes to
creating large networks and/or complex behavior. This should not be unexpected,
as creating an artificial brain is essentially a design problem. Human design
ingenuity still surpasses computational design for most tasks in most domains,
including architecture, game design, and authoring literary fiction. This leads
us to ask which the best way is to combine human and machine design capacities
when it comes to designing artificial brains. Both of them have their strengths
and weaknesses; for example, humans are much too slow to manually specify
thousands of neurons, let alone the billions of neurons that go into a human
brain, but on the other hand they can rely on a vast repository of common-sense
understanding and design heuristics that can help them perform a much better
guided search in design space than an algorithm. Therefore, in this paper we
argue for a mixed-initiative approach for collaborative online brain building
and present first results towards this goal.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014
(arXiv:1406.2507
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data
is indispensable for generating valid and general inferences from patterns
distributed across human brains. The disparities in anatomical structures and
functional topographies of human brains warrant aligning fMRI data across
subjects. However, the existing functional alignment methods cannot handle well
various kinds of fMRI datasets today, especially when they are not
temporally-aligned, i.e., some of the subjects probably lack the responses to
some stimuli, or different subjects might follow different sequences of
stimuli. In this paper, a cross-subject graph that depicts the
(dis)similarities between samples across subjects is used as a priori for
developing a more flexible framework that suits an assortment of fMRI datasets.
However, the high dimension of fMRI data and the use of multiple subjects makes
the crude framework time-consuming or unpractical. To address this issue, we
further regularize the framework, so that a novel feasible kernel-based
optimization, which permits nonlinear feature extraction, could be
theoretically developed. Specifically, a low-dimension assumption is imposed on
each new feature space to avoid overfitting caused by the
highspatial-low-temporal resolution of fMRI data. Experimental results on five
datasets suggest that the proposed method is not only superior to several
state-of-the-art methods on temporally-aligned fMRI data, but also suitable for
dealing `with temporally-unaligned fMRI data.Comment: 17 pages, 10 figures, Proceedings of the Association for the
Advancement of Artificial Intelligence (AAAI-20
Minds, Brains and Programs
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain I assume this is an empirical fact about the actual causal relations between mental processes and brains It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality The main argument of this paper is directed at establishing this claim The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4
Brain-inspired conscious computing architecture
What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human
- …