7,170 research outputs found
A Minimal Architecture for General Cognition
A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International
Joint Conference on Neural Network
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Although information workers may complain about meetings, they are an
essential part of their work life. Consequently, busy people spend a
significant amount of time scheduling meetings. We present Calendar.help, a
system that provides fast, efficient scheduling through structured workflows.
Users interact with the system via email, delegating their scheduling needs to
the system as if it were a human personal assistant. Common scheduling
scenarios are broken down using well-defined workflows and completed as a
series of microtasks that are automated when possible and executed by a human
otherwise. Unusual scenarios fall back to a trained human assistant who
executes them as unstructured macrotasks. We describe the iterative approach we
used to develop Calendar.help, and share the lessons learned from scheduling
thousands of meetings during a year of real-world deployments. Our findings
provide insight into how complex information tasks can be broken down into
repeatable components that can be executed efficiently to improve productivity.Comment: 10 page
The varieties of the psychedelic experience: A preliminary study of the association between the reported subjective effects and the binding affinity profiles of substituted phenethylamines and tryptamines
Classic psychedelics are substances of paramount cultural and neuroscientific importance. A distinctive feature of psychedelic drugs is the wide range of potential subjective effects they can elicit, known to be deeply influenced by the internal state of the user (“set”) and the surroundings (“setting”). The observation of cross-tolerance and a series of empirical studies in humans and animal models support agonism at the serotonin (5-HT)2A receptor as a common mechanism for the action of psychedelics. The diversity of subjective effects elicited by different compounds has been attributed to the variables of “set” and “setting,” to the binding affinities for other 5-HT receptor subtypes, and to the heterogeneity of transduction pathways initiated by conformational receptor states as they interact with different ligands (“functional selectivity”). Here we investigate the complementary (i.e., not mutually exclusive) possibility that such variety is also related to the binding affinity for a range of neurotransmitters and monoamine transporters including (but not limited to) 5-HT receptors. Building on two independent binding affinity datasets (compared to “in silico” estimates) in combination with natural language processing tools applied to a large repository of reports of psychedelic experiences (Erowid’s Experience Vaults), we obtained preliminary evidence supporting that the similarity between the binding affinity profiles of psychoactive substituted phenethylamines and tryptamines is correlated with the semantic similarity of the associated reports. We also showed that the highest correlation was achieved by considering the combined binding affinity for the 5-HT, dopamine (DA), glutamate, muscarinic and opioid receptors and for the Ca+ channel. Applying dimensionality reduction techniques to the reports, we linked the compounds, receptors, transporters and the Ca+ channel to distinct fingerprints of the reported subjective effects. To the extent that the existing binding affinity data is based on a low number of displacement curves that requires further replication, our analysis produced preliminary evidence consistent with the involvement of different binding sites in the reported subjective effects elicited by psychedelics. Beyond the study of this particular class of drugs, we provide a methodological framework to explore the relationship between the binding affinity profiles and the reported subjective effects of other psychoactive compounds.Fil: Zamberlan, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Sanz, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Martínez Vivot, Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Pallavicini, Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Erowid, Fire. Grass Valley; Estados UnidosFil: Erowid, Earth. Grass Valley; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Institut du cerveau et de la moelle épinière; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin
Comparing virtual patients with synthesized and natural speech
Virtual Patient (VP) simulations are often designed to use pre-recorded
speech in order to provide more realism and immersion. However, using actors for
recording these utterances has certain downsides. It can add to the cost during
implementation, can take considerable time especially when a large number of
VPs have to be created, and is not very flexible for example when sentences or
words have to be added frequently. This study aims to explore the use of
synthesized speech as an alternative to pre-recorded speech for VPs. Two medical
scenarios have been prepared for this study, and both have been implemented
using a VP with natural language or with synthesized speech. In a pilot study we
explored students' retention rates of the symptoms reported by the VP under both
conditions to investigate whether synthesized speech can serve as a good enough
alternative
On the Linguistic and Computational Requirements for Creating Face-to-Face Multimodal Human-Machine Interaction
In this study, conversations between humans and avatars are linguistically,
organizationally, and structurally analyzed, focusing on what is necessary for
creating face-to-face multimodal interfaces for machines. We videorecorded
thirty-four human-avatar interactions, performed complete linguistic
microanalysis on video excerpts, and marked all the occurrences of multimodal
actions and events. Statistical inferences were applied to data, allowing us to
comprehend not only how often multimodal actions occur but also how multimodal
events are distributed between the speaker (emitter) and the listener
(recipient). We also observed the distribution of multimodal occurrences for
each modality. The data show evidence that double-loop feedback is established
during a face-to-face conversation. This led us to propose that knowledge from
Conversation Analysis (CA), cognitive science, and Theory of Mind (ToM), among
others, should be incorporated into the ones used for describing human-machine
multimodal interactions. Face-to-face interfaces require an additional control
layer to the multimodal fusion layer. This layer has to organize the flow of
conversation, integrate the social context into the interaction, as well as
make plans concerning 'what' and 'how' to progress on the interaction. This
higher level is best understood if we incorporate insights from CA and ToM into
the interface system
Towards sociable virtual humans : multimodal recognition of human input and behavior
One of the biggest obstacles for constructing effective sociable virtual humans lies in the failure of machines to recognize the desires, feelings and intentions of the human user. Virtual humans lack the ability to fully understand and decode the communication signals human users emit when communicating with each other. This article describes our research in overcoming this problem by developing senses for the virtual humans which enables them to hear and understand human speech, localize the human user in front of the display system, recognize hand postures and to recognize the emotional state of the human user by classifying facial expression. We report on the methods needed to perform these tasks in real-time and conclude with an outlook on promising research issues of the future
Autonomous Acquisition of Natural Language
An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora
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