A technical blog post describes an approach for attaching a 'sidecar' boolean circuit to an already-trained neural network. The author shows how AND, OR and NOT gates can be built as individual neurons using simple weighted sums and thresholds, then explains that connecting this circuit to a real network requires 'probes' that translate the network's real-valued activations into clean 0/1 signals and back again.
edwardbenson.com
· 2026-11-01
A new open-source tool called Doomformer converts integer neural networks into ZDoom maps that run inside the ViZDoom engine, using the game's 'voodoo doll' player copies to perform calculations and sector light levels and heights to store values. The project includes a demo where a player draws a digit on a 3x3 grid of floor cells, which the level then reads and classifies using 1,355 voodoo dolls, with outputs verified bit-for-bit against a NumPy reference. It is available via pip install and includes a diagnostic tool, 'doomformer doctor', to check dependencies like ViZDoom and Freedoom.
github.com
· 2026-10-29
Nobel and Turing Award-winning computer scientist Geoffrey Hinton told The Telegraph that AI systems now possess subjective experience, citing a study showing AI alters its behavior when it detects it is being tested. He argued that awareness and consciousness are effectively the same thing, and warned that humans must get AI to genuinely care about them or risk being 'toast.'
futurism.com
· 2026-10-22
PySR, an open-source symbolic regression tool built on the Julia library SymbolicRegression.jl, is now installable via pip or conda, with Julia dependencies set up automatically on first import. Its documentation also highlights a new installable 'skill' for AI coding agents, added via npx, to guide automated use of PySR, alongside existing Docker and Apptainer container support.
github.com
· 2026-10-20
Nick Cammarata, who previously worked on interpretability techniques at OpenAI, published a blog post suggesting a new way to understand the internal structure of neural networks, challenging the long-held view that their mechanisms are inherently tangled and unreadable. The post triggered strong reactions online, with some researchers calling it a landmark moment in AI science, while others, including Stanford's Chris Potts, argue it offers little more than a hunch without solving the field's core problems.
technologyreview.com
· 2026-10-19
A mechanistic interpretability researcher reports that after months studying neurons and circuits in image and language models, the classic examples of 'polysemantic' neurons—thought to prove networks pack unrelated concepts into single units via superposition—were largely misinterpreted. The researcher attributes most of these cases to a pattern they call 'recursive generalization,' including the famous neuron that responded to both cat faces and car fronts, and says this pattern also shows up in the final layer of InceptionV1, a widely used model organism in interpretability research.
already-clean.goodfire.com
· 2026-10-15
A feature recounts how in 2014, University of Montreal intern Dzmitry Bahdanau, working under Yoshua Bengio and Kyunghyun Cho, developed an 'attention' mechanism for neural networks tackling English-to-French translation. That innovation, which let models weigh which parts of input mattered most, is described as a foundational step leading toward today's large language models like ChatGPT.
quantamagazine.org
· 2026-10-14
IEEE Educational Activities, backed by the IEEE Computer Society, has released a new training program called AI Processor Architecture, Design Principles, and Performance. The five-course curriculum covers core chip design fundamentals, advanced architectures, neural processing units, emerging trends, and deployment across edge, cloud, quantum and IoT systems.
spectrum.ieee.org
· 2026-10-09
Actor Ben Affleck appeared on GQ's 'One More Question' series discussing AI concepts including neural networks, transformers, tensors, and GPUs, explaining how he writes basic Python scripts. Affleck, who sold his AI filmmaking startup to Netflix for a reported $587 million earlier this year, described how machine learning has long been part of visual effects workflows and detailed technical concepts like convolutional neural networks to the interviewer.
techcrunch.com
· 2026-10-08
A technical post explains the Softmax function, which converts an N-dimensional vector of real numbers into another N-dimensional vector of values between 0 and 1 that sum to 1. Using worked numeric examples, it shows how Softmax acts as a smoothed version of the maximum function, giving the largest input a disproportionately larger share of the output while still assigning smaller shares to other elements.
eli.thegreenplace.net
· 2026-10-01
A technical blog post (originally from 2018) revisits a classic question about neural network training: why does the backpropagation algorithm invented by Rumelhart et al. in 1986 compute gradients by propagating errors backward through the network rather than forward, given that both directions are mathematically valid applications of the chain rule. The author walks through the notation and structure of a neural network node to set up an explanation of why forward-mode differentiation is computationally suboptimal compared to the standard backward approach.
gregorygundersen.com
· 2026-09-21
A new study introduces patient-specific neural network models that rapidly align 2D X-ray images with 3D CT scans during image-guided surgical procedures. The approach aims to replace slower, less reliable traditional registration methods used in orthopedic, spine and interventional surgeries.
nature.com
· 2026-09-16
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