Natural language is often seen as the single factor that explains the cognitive singularity of
the human species. Instead, we propose that humans possess multiple internal languages …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

We survey a current, heated debate in the artificial intelligence (AI) research community on
whether large pretrained language models can be said to understand language—and the …

The power of human language and thought arises from systematic compositionality—the
algebraic ability to understand and produce novel combinations from known components …

The widespread adoption of large language models (LLMs) makes it important to recognize
their strengths and limitations. We argue that in order to develop a holistic understanding of …

This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Do large language models (LLMs) constitute a computational account of how humans
process language? And if so, what is the role of (psycho) linguistic theory in understanding …

This paper discusses the “Get Real Get Better”(GRGB) approach to implementing agile
program management in the US Navy, supported by advanced data analytics and artificial …

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a
framework for computing with distributed representations by exploiting properties of random …

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …