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Found 16 papers, 4 papers with code
Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning
We therefore argue that the value system of an AI should be culturally attuned: just as a child raised in a particular culture learns the specific values and norms of that culture, we propose that an AI agent operating in a particular human community should acquire that community's moral, ethical, and cultural codes.
Expressive probabilistic sampling in recurrent neural networks
We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution.
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century.
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning
Here we propose a new framework for predictive coding called active predictive coding which can learn hierarchical world models and solve two radically different open problems in AI: (1) how do we learn compositional representations, e. g., part-whole hierarchies, for equivariant vision?
Hyper-Universal Policy Approximation: Learning to Generate Actions from a Single Image using Hypernets
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment given only a single glimpse?
Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies
Human vision involves parsing and representing objects and scenes using structured representations based on part-whole hierarchies.
Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies
We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a parse tree?
Predictive Coding Theories of Cortical Function
Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities.
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes
Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural computations that support plume tracking.
Brain Co-Processors: Using AI to Restore and Augment Brain Function
Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function.
Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
We show results from our approach applied to data collected for 12 human subjects over 7--9 days for each subject.
Learning a Convolutional Bilinear Sparse Code for Natural Videos
Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner.
Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces
The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device.
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Two of the three subjects are "Senders" whose brain signals are decoded using real-time EEG data analysis to extract decisions about whether to rotate a block in a Tetris-like game before it is dropped to fill a line.
Human-Computer Interaction Neurons and Cognition
Transformational Sparse Coding
Instead of discarding the rich and useful information about feature transformations to achieve invariance, we argue that models should learn object features conjointly with their transformations to achieve equivariance.
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs.
