One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and ...

For example, recurrent neural networks (RNNs), a type of ML loosely modeled after the human brain that detects patterns in sequential ... as foundation models (also called general-purpose AI ...

Although Lemonade is still unprofitable, its margins are getting sweeter, with cash flow turning positive. Read why LMND ...

In this important study, the authors use computational modeling to explore how rapid learning can be reconciled with the accumulation of stable memories in the olfactory bulb, where adult neurogenesis ...

What if the next big leap in your portfolio could come from coins the market's just starting to wake up to? As blockchain ...

Deep neural networks formed the core architecture ... primarily on Transformer models. Those models are a 2017 Google Research invention that excels at processing sequential data and capturing ...

There's a lot of data to process, and its sequential order and ... external variables pose significant architectural challenges for the ensuing model and non-trivial computational demands, making ...

Memory requirements are the most obvious advantage of reducing the complexity of a model's internal weights. The BitNet b1.58 ...

The most sophisticated AI models in existence today have scored poorly on a new benchmark designed to measure their progress towards artificial general intelligence (AGI) – and brute-force ...