Artificial intelligence: revolutionizing cardiology with large language models
Natural language processing techniques are having an increasing impact on clinical care
from patient, clinician, administrator, and research perspective. Among others are automated …
from patient, clinician, administrator, and research perspective. Among others are automated …
Language, common sense, and the Winograd schema challenge
J Browning, Y LeCun - Artificial Intelligence, 2023 - Elsevier
Since the 1950s, philosophers and AI researchers have held that disambiguating natural
language sentences depended on common sense. In 2011, the Winograd Schema …
language sentences depended on common sense. In 2011, the Winograd Schema …
Deep reinforcement learning based trajectory planning under uncertain constraints
With the advance in algorithms, deep reinforcement learning (DRL) offers solutions to
trajectory planning under uncertain environments. Different from traditional trajectory …
trajectory planning under uncertain environments. Different from traditional trajectory …
[图书][B] From deep learning to rational machines: What the history of philosophy can teach us about the future of artificial intelligence
CJ Buckner - 2023 - books.google.com
" This book provides a framework for thinking about foundational philosophical questions
surrounding machine learning as an approach to artificial intelligence. Specifically, it links …
surrounding machine learning as an approach to artificial intelligence. Specifically, it links …
Abstraction for deep reinforcement learning
M Shanahan, M Mitchell - arXiv preprint arXiv:2202.05839, 2022 - arxiv.org
We characterise the problem of abstraction in the context of deep reinforcement learning.
Various well established approaches to analogical reasoning and associative memory might …
Various well established approaches to analogical reasoning and associative memory might …
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
Striking progress has been made in understanding cognition by analyzing how the brain is
engaged in different modes of information processing. For instance, so-called synergistic …
engaged in different modes of information processing. For instance, so-called synergistic …
The signature-testing approach to mapping biological and artificial intelligences
Making inferences from behaviour to cognition is problematic due to a many-to-one mapping
problem, in which any one behaviour can be generated by multiple possible cognitive …
problem, in which any one behaviour can be generated by multiple possible cognitive …
General intelligence disentangled via a generality metric for natural and artificial intelligence
Success in all sorts of situations is the most classical interpretation of general intelligence.
Under limited resources, however, the capability of an agent must necessarily be limited too …
Under limited resources, however, the capability of an agent must necessarily be limited too …
Unsupervised object-based transition models for 3d partially observable environments
We present a slot-wise, object-based transition model that decomposes a scene into objects,
aligns them (with respect to a slot-wise object memory) to maintain a consistent order across …
aligns them (with respect to a slot-wise object memory) to maintain a consistent order across …
Information optimization and transferable state abstractions in deep reinforcement learning
While humans and animals learn incrementally during their lifetimes and exploit their
experience to solve new tasks, standard deep reinforcement learning methods specialize to …
experience to solve new tasks, standard deep reinforcement learning methods specialize to …