occlupanid data writes that the Holotypic Occlupanid Research Group hosts several years of research classifying occlupanids, small ubiquitous objects dotting supermarket aisles and sidewalks, as the most common yet puzzling member of phylum Plasticae within a synthetic taxonomy database.
- The site catalogs dozens of families such as Acutignathidae, Archignathidae, Corrugatidae and Toxodentidae with individual species pages.
- Navigation includes Identification Guide, Publications and Reports, Cartonalia: The Occlupanopsida, and a Guide to symbols for ecological, geographical and taxonomic classification.
- The project also covers morphology, growth and development, origins of the Occlupanida, history of occlupanology, and a Pseudo-occlupanids section.
- The site catalogs dozens of families such as Acutignathidae, Archignathidae, Corrugatidae and Toxodentidae with individual species pages.
- Navigation includes Identification Guide, Publications and Reports, Cartonalia: The Occlupanopsida, and a Guide to symbols for ecological, geographical and taxonomic classification.
- The project also covers morphology, growth and development, origins of the Occlupanida, history of occlupanology, and a Pseudo-occlupanids section.
Samuel K. Ainsworth writes with Jonathan Hayase and Siddhartha Srinivasa that neural network loss landscapes often contain nearly a single basin after accounting for permutation symmetries of hidden units, and they introduce three algorithms to permute one independently trained model into alignment with a reference model so the two can be merged in weight space with near-convex interpolation. Experiments across MLPs, CNNs and ResNets on MNIST, CIFAR-10/100 and ImageNet show the single-basin phenomenon, including the first zero-barrier linear mode connectivity between independently trained ResNets on CIFAR-10, and reveal how mode connectivity emerges with training time and model width. The authors also provide a counterexample showing linear mode connectivity is not universal and discuss limits of the hypothesis.
- Permutation symmetries for a 3-layer 512-width MLP are ~10^3498, far exceeding atoms in the observable universe.
- Weight matching uses coordinate descent on the sum of bilinear assignments problem and runs in seconds without accessing training data.
- Activation matching reduces to a linear assignment problem per layer via ordinary least squares regression on unit activations.
- Straight-through estimator learning of permutations gives the best barriers but is much more expensive than weight or activation matching.
- Merging models trained on disjoint CIFAR-100 splits with weight matching improves test loss and calibration over both inputs while using the compute/memory of one model.
- Permutation symmetries for a 3-layer 512-width MLP are ~10^3498, far exceeding atoms in the observable universe.
- Weight matching uses coordinate descent on the sum of bilinear assignments problem and runs in seconds without accessing training data.
- Activation matching reduces to a linear assignment problem per layer via ordinary least squares regression on unit activations.
- Straight-through estimator learning of permutations gives the best barriers but is much more expensive than weight or activation matching.
- Merging models trained on disjoint CIFAR-100 splits with weight matching improves test loss and calibration over both inputs while using the compute/memory of one model.
Firecrawl introduces pdf-inspector, a high-performance Rust library designed for rapid PDF classification, text extraction, and Markdown conversion. By sampling content streams to quickly distinguish between text-based and scanned documents, the tool enables intelligent routing that bypasses costly OCR services for standard PDFs. It delivers position-aware text extraction, automated table and column detection, and robust encoding handling while maintaining a lightweight footprint with no external ML dependencies or model training requirements.
- Provides bindings for Python, Node.js, and browser WebAssembly environments.
- Achieves sub-200ms processing times on large corpora while outperforming several established local parsers in reading order and table accuracy.
- Features per-page OCR routing suggestions to optimize mixed-format document workflows.
- Handles complex layouts including RTL text, multi-column newspapers, and CID-encoded fonts.
- Released under the MIT license with active community contributions and CI/CD automation.
- Provides bindings for Python, Node.js, and browser WebAssembly environments.
- Achieves sub-200ms processing times on large corpora while outperforming several established local parsers in reading order and table accuracy.
- Features per-page OCR routing suggestions to optimize mixed-format document workflows.
- Handles complex layouts including RTL text, multi-column newspapers, and CID-encoded fonts.
- Released under the MIT license with active community contributions and CI/CD automation.
Rupert Lane writes about Arthur Samuel's checkers program running on the WAITS operating system. Samuel began developing his checkers program in the late 1940s and 1950s, introducing machine learning techniques like rote learning to the field. The article details the program's history from the IBM 7090 to its porting to the PDP-10 at SAIL, where the 1972 version runs on WAITS. It also covers Samuel's work on a home computer version for the VideoBrain and provides instructions for playing the game on WAITS, including board visualization and command syntax.
- The 1952 checkers program was written in raw machine code as the IBM system lacked an assembler at the time.
- Samuel introduced rote learning to the program on the IBM 704 in 1954, enabling interesting gameplay.
- A 1976 match against Duke University was abandoned due to excessive CPU time, though analysis suggests a draw was possible.
- The VideoBrain home console version operated within 1KB of RAM and offered four difficulty levels.
- Users can visualize the board on WAITS with the `BD` command or observe the AI declare wins based on search depths.
- Oldest surviving source file `CKP2.MAC 2,ALS » ` dates to 1971, while the running binary is from 1972.
- The 1952 checkers program was written in raw machine code as the IBM system lacked an assembler at the time.
- Samuel introduced rote learning to the program on the IBM 704 in 1954, enabling interesting gameplay.
- A 1976 match against Duke University was abandoned due to excessive CPU time, though analysis suggests a draw was possible.
- The VideoBrain home console version operated within 1KB of RAM and offered four difficulty levels.
- Users can visualize the board on WAITS with the `BD` command or observe the AI declare wins based on search depths.
- Oldest surviving source file `CKP2.MAC 2,ALS » ` dates to 1971, while the running binary is from 1972.
Supervision provides essential building blocks for computer vision applications, such as data loading and real-time zone counting. The toolkit remains model agnostic, enabling easy integration of various machine learning models via specialized connectors.
- Supports multiple dataset formats including YOLO, COCO, and Pascal VOC
- Offers utilities to split, merge, and convert datasets
- Includes capabilities for speed estimation and dwell time analysis
- Supports multiple dataset formats including YOLO, COCO, and Pascal VOC
- Offers utilities to split, merge, and convert datasets
- Includes capabilities for speed estimation and dwell time analysis
Ashish Vaswani et. al. introduce Transformers and Attention in this classic 2017 paper.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
Anurag Singh writes that Cursor’s strength is its capacity for deep project understanding through efficient context management rather than simple model speed. Instead of pushing a whole repository into an LLM's window, the IDE employs search tools to provide only relevant code segments as tasks arise. This curated approach ensures models receive high-quality information and instructions tailored to their specific capabilities, allowing them to maintain existing architectural patterns more effectively.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
The Bitter Lesson (2919) by Rich Sutton explores a recurring pattern in the history of AI research, arguing that general methods leveraging massive computation are ultimately more effective than those relying on human-encoded domain knowledge. While incorporating human intuition can provide short-term gains, long-term breakthroughs are consistently driven by scaling computational power through search and learning as described by Moore's Law.
Key observations include:
- The historical shift in chess, Go, speech recognition, and computer vision from rule-based or human-centric models toward massive computation.
- The tendency for researchers to favor methods that reflect human understanding, which often plateaus compared to scalable learning processes.
- The necessity of developing meta-methods capable of discovering complex patterns rather than hardcoding existing human perceptions into agents.
Key observations include:
- The historical shift in chess, Go, speech recognition, and computer vision from rule-based or human-centric models toward massive computation.
- The tendency for researchers to favor methods that reflect human understanding, which often plateaus compared to scalable learning processes.
- The necessity of developing meta-methods capable of discovering complex patterns rather than hardcoding existing human perceptions into agents.
An exploration into the history of conversational technology, tracing its roots from Joseph Weizenbaum's 1966 ELIZA experiment at MIT to modern large language models like ChatGPT and Claude. The article examines how the evolution from rule-based symbolic AI to probabilistic deep learning has changed human interaction with machines, often leading users to attribute human qualities to code. It specifically addresses the risks of "chatbot psychosis" and the danger of individuals relying on general-purpose generative models for mental health support when these systems are prone to hallucinations or reinforcing delusional beliefs.
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
Researchers demonstrate that recurrent cortical circuits generate traveling waves acting as a computational engine in the visual cortex. These neural traveling waves allow the brain to build internal representations of the external world, enabling the prediction of upcoming sensory inputs and the replay of temporal memories. This biological mechanism is functionally analogous to how large language models learn statistical structures from text; instead, these waves encode environmental regularities into synaptic networks through experience.
- Traveling waves modulate moment-to-moment perception.
- They enable the inference of causes for noisy sensory inputs.
- These dynamics allow for short-term predictions and memory replay.
- Traveling waves modulate moment-to-moment perception.
- They enable the inference of causes for noisy sensory inputs.
- These dynamics allow for short-term predictions and memory replay.
