Machine Learning
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Imagine an artificial organism living in a complex environment. The organism is not just a passive observer. It can decide which parts of its environment it wants to explore next. It might even be able to actively influence the world around it by taking various actions. We want the organism to learn from its experiences…
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In my previous post about ThoughtNet, an attention-based neural architecture for variable-compute inference, I highlighted two limitations that I encountered with it: Slow and inconsistent convergence during training time Poor generalization on multiplication tasks, despite great performance on addition. While trying to solve the second problem, I stumbled across a surprising way to stabilize training…
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The majority of today’s artificial neural network (ANN) architectures perform a constant amount of computation at inference time regardless of their inputs. This includes all recent GPT-style LLMs1 and other transformer-based architectures. Whether you ask an LLM to complete the series “1, 2, 3, …”, or you ask it to solve a complicated logic riddle,…
