slides
... • The AER communication protocol emulates massive connectivity between cells by time-multiplexing many connections on the same data bus. • For a one-to-one connection topology, the required number of wires is reduced from N to ∼ log2 N . • Each spike is represented by: ◦ Its location: explicitly enc ...
... • The AER communication protocol emulates massive connectivity between cells by time-multiplexing many connections on the same data bus. • For a one-to-one connection topology, the required number of wires is reduced from N to ∼ log2 N . • Each spike is represented by: ◦ Its location: explicitly enc ...
Evolution, Sociobiology, and the Future of Artificial Intelligence
... work. Expert systems and cHess programs are prime examples. • Autonomous robots. The most ambitious version of this goal would be Turing Test AI plus perception, learning, and action. More probable goals are particular classes ...
... work. Expert systems and cHess programs are prime examples. • Autonomous robots. The most ambitious version of this goal would be Turing Test AI plus perception, learning, and action. More probable goals are particular classes ...
Artificial Intelligence Introduction
... – How brains and computers are (dis)similar. • Psychology – How do we think and act? – Cognitive psychology perceives the brain as an information processing machine. – Led to the development of the field cognitive science: how could computer models be used to study language, memory, and thinking fro ...
... – How brains and computers are (dis)similar. • Psychology – How do we think and act? – Cognitive psychology perceives the brain as an information processing machine. – Led to the development of the field cognitive science: how could computer models be used to study language, memory, and thinking fro ...
Towards comprehensive foundations of Computational Intelligence
... Neural information processing in perception and cognition: information compression, or algorithmic complexity. In computing: minimum length (message, description) encoding. Wolff (2006): cognition and computation as compression by multiple alignment, unification and search. Analysis and production o ...
... Neural information processing in perception and cognition: information compression, or algorithmic complexity. In computing: minimum length (message, description) encoding. Wolff (2006): cognition and computation as compression by multiple alignment, unification and search. Analysis and production o ...
Developing Effective Robot Teammates for Human
... focus within Human-Robot Interaction and involves underlying research questions deeply relevant to the Artificial Intelligence community. Especially in domains where modern robots are ineffective, we wish to leverage human-robot teaming to improve the efficiency, ability, and safety of human workers ...
... focus within Human-Robot Interaction and involves underlying research questions deeply relevant to the Artificial Intelligence community. Especially in domains where modern robots are ineffective, we wish to leverage human-robot teaming to improve the efficiency, ability, and safety of human workers ...
Testimony - Eric Horvitz
... A simple definition of AI, drawn from a 1955 proposal that kicked off the modern field of AI, is pursuing how “to solve the kinds of problems now reserved for humans.”1 The authors of the founding proposal on AI also mentioned, “We think that a significant advance can be made in one or more of these ...
... A simple definition of AI, drawn from a 1955 proposal that kicked off the modern field of AI, is pursuing how “to solve the kinds of problems now reserved for humans.”1 The authors of the founding proposal on AI also mentioned, “We think that a significant advance can be made in one or more of these ...
Learning nonlinear functions on vectors: examples and predictions
... This rule belongs to the class of error-modulated learning rules. These rules are typically motivated by the finding that dopamine from the substantia nigra pars compacta and the ventral tegmental area appear to encode something similar to reward prediction error, and related studies that show that ...
... This rule belongs to the class of error-modulated learning rules. These rules are typically motivated by the finding that dopamine from the substantia nigra pars compacta and the ventral tegmental area appear to encode something similar to reward prediction error, and related studies that show that ...
The way we (should?) live now: research, social policy and lived
... accepted as the natural order of things. Learning to age Research into human ageing in the west has produced models based on the concept of ‘adjustment’ which refers to qualitative values regarding morale and general measures of life satisfaction (Coleman, 1993). Two models in particular have been i ...
... accepted as the natural order of things. Learning to age Research into human ageing in the west has produced models based on the concept of ‘adjustment’ which refers to qualitative values regarding morale and general measures of life satisfaction (Coleman, 1993). Two models in particular have been i ...
Lecture 45 - KDD - Kansas State University
... – Selecting relevant data channels from continuous sources (e.g., sensors) – Applications: bioinformatics (genomics, proteomics, etc.), prognostics – See work by: Kohavi, John, Rendell, Donoho, Hsu, Provost ...
... – Selecting relevant data channels from continuous sources (e.g., sensors) – Applications: bioinformatics (genomics, proteomics, etc.), prognostics – See work by: Kohavi, John, Rendell, Donoho, Hsu, Provost ...
Current and Future Trends in Feature Selection and Extraction for
... learning, a relatively new methodology, is explored in the articles by Zelikovitz & Marquez and by Zhong. It is an alternative to the classical supervised setting for machine learning and allows unlabeled data to be used in the training process. Semisupervised learning is becoming popular because it ...
... learning, a relatively new methodology, is explored in the articles by Zelikovitz & Marquez and by Zhong. It is an alternative to the classical supervised setting for machine learning and allows unlabeled data to be used in the training process. Semisupervised learning is becoming popular because it ...
RatCog: A GUI maze simulation tool with plugin “rat brains”
... agents. It is designed using a client-server approach to intelligent agent simulation (e.g., see the RoboCup Soccer Server, Noda, 1995). The software architecture of RatCog is divided into four components: the GUI, the database, the world model, and the “rat brain” plugins. The GUI displays a radial ...
... agents. It is designed using a client-server approach to intelligent agent simulation (e.g., see the RoboCup Soccer Server, Noda, 1995). The software architecture of RatCog is divided into four components: the GUI, the database, the world model, and the “rat brain” plugins. The GUI displays a radial ...
Relational Learning as Search in a Critical Region
... problems, is referred to as the phase transition (PT) framework (Hogg et al., 1996a), and it considers computational complexity as a random variable that depends on some order parameters of the problem class at hand. Computational complexity is thus modeled as a distribution over problem ...
... problems, is referred to as the phase transition (PT) framework (Hogg et al., 1996a), and it considers computational complexity as a random variable that depends on some order parameters of the problem class at hand. Computational complexity is thus modeled as a distribution over problem ...
START of day 1
... Naïve Bayes Learning (I) • Large or infinite H make the above algorithm impractical still • Naive Bayes learning is a practical Bayesian learning method: – Applies directly to learning tasks where instances are conjunction of attribute values and the target function takes its values from some ...
... Naïve Bayes Learning (I) • Large or infinite H make the above algorithm impractical still • Naive Bayes learning is a practical Bayesian learning method: – Applies directly to learning tasks where instances are conjunction of attribute values and the target function takes its values from some ...
Systems that act like humans
... • Knowledge tracing extends model tracing to assess probability that a student knows domain rules given observed actions • These models showed good fit with student performance, indicating value of the ACT-R theory • Also, the Cognitive Tutors based on this model are great examples of AI success – u ...
... • Knowledge tracing extends model tracing to assess probability that a student knows domain rules given observed actions • These models showed good fit with student performance, indicating value of the ACT-R theory • Also, the Cognitive Tutors based on this model are great examples of AI success – u ...
- BTechSpot
... generate the output value. Supervised learning assumes that a set of training data (the training set) has been provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model that attempts to meet two sometimes c ...
... generate the output value. Supervised learning assumes that a set of training data (the training set) has been provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model that attempts to meet two sometimes c ...
Introduction: What is AI?
... Critic: gives feedback to learning about success Problem generator: suggests actions to find new states ...
... Critic: gives feedback to learning about success Problem generator: suggests actions to find new states ...
Great Challenge in Building Intelligent Systems – Quo Vadis
... What if the user observes the error of UPRS – how will the UPRS create the feedback and ‘correction’ in this situation? ...
... What if the user observes the error of UPRS – how will the UPRS create the feedback and ‘correction’ in this situation? ...
Vita - CIS Users web server
... September 2003 – August 2009: Research assistant for Pedro Domingos, University of Washington. Developed faster machine learning algorithms, more flexible probabilistic models, and methods for learning models with efficient inference. Summer 2008: Intern at SmartDesktop division of Pi Corporation, S ...
... September 2003 – August 2009: Research assistant for Pedro Domingos, University of Washington. Developed faster machine learning algorithms, more flexible probabilistic models, and methods for learning models with efficient inference. Summer 2008: Intern at SmartDesktop division of Pi Corporation, S ...
Sebastiaan Terwijn
... Sampling from models M first-order model, D arbitrary probability distribution over M. Want to decide with high probability the approximate truth of sentences ϕ on the basis of samples of atomic data taken from M. We have the following assymmetry between ∃ and ∀: • On seeing an atomic truth R(a), w ...
... Sampling from models M first-order model, D arbitrary probability distribution over M. Want to decide with high probability the approximate truth of sentences ϕ on the basis of samples of atomic data taken from M. We have the following assymmetry between ∃ and ∀: • On seeing an atomic truth R(a), w ...
AMAM Conference 2005
... Increase challenge: skill level already too high Decrease challenge: performance could not be reached ...
... Increase challenge: skill level already too high Decrease challenge: performance could not be reached ...
A bio-inspired learning signal for the cumulative learning - laral
... What we propose in this paper is that providing artificial agents with a learning signal that resembles the characteristic of the phasic DA signal, determined both by intrinsic and extrinsic reinforcements, would be an advancement in the development of more autonomous and versatile systems. In parti ...
... What we propose in this paper is that providing artificial agents with a learning signal that resembles the characteristic of the phasic DA signal, determined both by intrinsic and extrinsic reinforcements, would be an advancement in the development of more autonomous and versatile systems. In parti ...
Document
... In contrast to supervised learning, unsupervised or self-organised learning does not require an external teacher. During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data i ...
... In contrast to supervised learning, unsupervised or self-organised learning does not require an external teacher. During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data i ...
CS382 Introduction to Artificial Intelligence
... “The art of creating machines that action... and studies the design of perform functions that require rational agents. A rational agent intelligence when performed by acts so as to achieve the best people” expected outcome” (Kurzweil, 1990) (S.R. & P.N., 1995) Acting ...
... “The art of creating machines that action... and studies the design of perform functions that require rational agents. A rational agent intelligence when performed by acts so as to achieve the best people” expected outcome” (Kurzweil, 1990) (S.R. & P.N., 1995) Acting ...
Intelligent Behavior in Humans and Machines
... the complex forms of cognition observed in humans. Some researchers took human intelligence as an inspiration and source of ideas without attempting to model its details. Other researchers, including Herbert Simon and Allen Newell, generally viewed as two of the field’s co-founders, viewed themselve ...
... the complex forms of cognition observed in humans. Some researchers took human intelligence as an inspiration and source of ideas without attempting to model its details. Other researchers, including Herbert Simon and Allen Newell, generally viewed as two of the field’s co-founders, viewed themselve ...
Document
... Hidden Markov Models http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html Links recommended by PCAI http://www.ics.uci.edu/~mlearn/MLOther.html CMU’s research areas (scroll down): http://www.ri.cmu.edu/people/kanade_takeo.html MIT’s Media Lab: http://www.media.mit.edu/ Computer vision links: http://www ...
... Hidden Markov Models http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html Links recommended by PCAI http://www.ics.uci.edu/~mlearn/MLOther.html CMU’s research areas (scroll down): http://www.ri.cmu.edu/people/kanade_takeo.html MIT’s Media Lab: http://www.media.mit.edu/ Computer vision links: http://www ...