This paper details the reconstruction and execution of the Logic Theorist (LT), considered the first artificial intelligence program, originally created in 1955-1956. The authors built a new IPL-V interpreter in Common Lisp and faithfully reanimated LT from code transcribed from a 1963 RAND technical report. The reanimated LT successfully proved 16 of 23 theorems from Principia Mathematica, consistent with the original system's behavior. This work demonstrates "executable archaeology" as a method for understanding early AI systems, highlighting the challenges and insights gained from reconstructing and running historical code.
A new analysis of genetic studies suggests the cognitive capacity for language emerged at least 135,000 years ago, with language likely becoming a social tool around 100,000 years ago. Researchers examined genetic data from Y chromosome, mitochondrial DNA, and whole-genome studies to trace the divergence of human populations, reasoning that all languages share a common origin. The study proposes that language initially developed as an internal cognitive system before evolving into a means of social communication. Archaeological evidence of symbolic behavior around 100,000 years ago supports the idea that language played a key role in the development of modern human behavior.
>- Innovative problem solving can be beneficial, especially for some urban species.
>- Optimal foraging theory can be used to predict exploration–exploitation trade-offs.
>- We found evidence for exploration–exploitation trade-offs in problem solving.
>- Raccoons foraged for information on a multi-access puzzle box.
>- This information foraging followed optimal foraging theory.
>- Optimal foraging theory can be used to predict exploration–exploitation trade-offs.
>- We found evidence for exploration–exploitation trade-offs in problem solving.
>- Raccoons foraged for information on a multi-access puzzle box.
>- This information foraging followed optimal foraging theory.
This article details a five-step process for memorizing and understanding complex concepts, combining mnemonic techniques like the Memory Palace with active learning strategies such as spaced repetition, active recall, and note-taking. It emphasizes memorizing the names of concepts first, then understanding them, and connecting them across multiple fields.
New research introduces Tri-System Theory to explain how we think with AI. It builds on the idea that we have two main thinking styles: System 1 for fast, intuitive thinking. and System 2 for slow, deliberate thinking.
This new theory adds a System 3: thinking with AI. The study found people often "surrender" to AI, meaning they accept AI's answers without much questioning – even if those answers are wrong. This can sometimes improve performance, but often leads to mistakes.
People who trust AI more, and who don't enjoy deep thinking, are more likely to rely on it. In short, we're increasingly letting AI do some of our thinking, and this has both benefits and risks.
This new theory adds a System 3: thinking with AI. The study found people often "surrender" to AI, meaning they accept AI's answers without much questioning – even if those answers are wrong. This can sometimes improve performance, but often leads to mistakes.
People who trust AI more, and who don't enjoy deep thinking, are more likely to rely on it. In short, we're increasingly letting AI do some of our thinking, and this has both benefits and risks.
Large Language Models (LLMs) demonstrate remarkable capabilities, yet their inability to maintain persistent memory in long contexts limits their effectiveness as autonomous agents in long-term interactions. While existing memory systems have made progress, their reliance on arbitrary granularity for defining the basic memory unit and passive, rule-based mechanisms for knowledge extraction limits their capacity for genuine learning and evolution. To address these foundational limitations, we present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles. Nemori's core innovation is twofold: First, its Two-Step Alignment Principle, inspired by Event Segmentation Theory, provides a principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes, solving the critical issue of memory granularity. Second, its Predict-Calibrate Principle, inspired by the Free-energy Principle, enables the agent to proactively learn from prediction gaps, moving beyond pre-defined heuristics to achieve adaptive knowledge evolution. This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks demonstrate that Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.
Study shows humans possess "remote touch," accurately detecting buried objects in sand by sensing subtle mechanical reflections. Humans outperformed a robot using an LSTM algorithm, suggesting remarkable tactile perception.
This Perspective outlines ways in which generative artificial intelligence aligns with and supports the core ideas of generative linguistics, and how generative linguistics can provide criteria to evaluate and improve neural language models.
This study identifies genetic factors associated with quantitative ability, using data from large-scale genome-wide association studies. The research reveals 53 SNPs linked to a latent trait, distinct from general intelligence, and implicates genes involved in neuron projection development and brain function.
A new study published in Psychophysiology used electroencephalography (EEG) and advanced modeling to track brain dynamics during metaphor generation, finding that specific sequences of brain states are associated with more creative metaphors. Early on, higher alpha-band synchronization predicted novelty, while later, alpha-band desynchronization became more prominent.
