Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.
They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.
The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).
Large language models (LLMs) are at the forefront of artificial intelligence (AI) and have been widely used for conversational interactions. However, assessing the personality of a given LLM remains a significant challenge.
A new brain-inspired AI model called TopoLM learns language by organizing neurons into clusters, just like the human brain. Developed by researchers at EPFL, this topographic language model shows clear patterns for verbs, nouns, and syntax using a simple spatial rule that mimics real cortical maps. TopoLM not only matches real brain scans but also opens new possibilities in AI interpretability, neuromorphic hardware, and language processing.
Join our free AI content course here 👉 https://www.skool.com/ai-content-acce… the best AI news without the noise 👉 https://airevolutionx.beehiiv.com/ 🔍 What’s Inside: • A brain-inspired AI model called TopoLM that learns language by building its own cortical map • Neurons are arranged on a 2D grid where nearby units behave alike, mimicking how the human brain clusters meaning • A simple spatial smoothness rule lets TopoLM self-organize concepts like verbs and nouns into distinct brain-like regions 🎥 What You’ll See: • How TopoLM mirrors patterns seen in fMRI brain scans during language tasks • A comparison with regular transformers, showing how TopoLM brings structure and interpretability to AI • Real test results proving that TopoLM reacts to syntax, meaning, and sentence structure just like a biological brain 📊 Why It Matters: This new system bridges neuroscience and machine learning, offering a powerful step toward *AI that thinks like us. It unlocks better interpretability, opens paths for **neuromorphic hardware*, and reveals how one simple principle might explain how the brain learns across all domains. DISCLAIMER: This video covers topographic neural modeling, biologically-aligned AI systems, and the future of brain-inspired computing—highlighting how spatial structure could reshape how machines learn language and meaning. #AI #neuroscience #brainAI
🔍 What’s Inside: • A brain-inspired AI model called TopoLM that learns language by building its own cortical map. • Neurons are arranged on a 2D grid where nearby units behave alike, mimicking how the human brain clusters meaning. • A simple spatial smoothness rule lets TopoLM self-organize concepts like verbs and nouns into distinct brain-like regions.
🎥 What You’ll See: • How TopoLM mirrors patterns seen in fMRI brain scans during language tasks. • A comparison with regular transformers, showing how TopoLM brings structure and interpretability to AI • Real test results proving that TopoLM reacts to syntax, meaning, and sentence structure just like a biological brain.
It’s obvious when a dog has been poorly trained. It doesn’t respond properly to commands. It pushes boundaries and behaves unpredictably. The same is true with a poorly trained artificial intelligence (AI) model. Only with AI, it’s not always easy to identify what went wrong with the training.
Research scientists globally are working with a variety of AI models that have been trained on experimental and theoretical data. The goal: to predict a material’s properties before taking the time and expense to create and test it. They are using AI to design better medicines and industrial chemicals in a fraction of the time it takes for experimental trial and error.
But how can they trust the answers that AI models provide? It’s not just an academic question. Millions of investment dollars can ride on whether AI model predictions are reliable.
New research shows that the adult brain can generate new neurons that integrate into key motor circuits. The findings demonstrate that stimulating natural brain processes may help repair damaged neural networks in Huntington’s and other diseases.
“Our research shows that we can encourage the brain’s own cells to grow new neurons that join in naturally with the circuits controlling movement,” said a senior author of the study, which appears in the journal Cell Reports. “This discovery offers a potential new way to restore brain function and slow the progression of these diseases.”
It was long believed that the adult brain could not generate new neurons. However, it is now understood that niches in the brain contain reservoirs of progenitor cells capable of producing new neurons. While these cells actively produce neurons during early development, they switch to producing support cells called glia shortly after birth. One of the areas of the brain where these cells congregate is the ventricular zone, which is adjacent to the striatum, a region of the brain devastated by Huntington’s disease.
Human cyborgs are individuals who integrate advanced technology into their bodies, enhancing their physical or cognitive abilities. This fusion of man and machine blurs the line between science fiction and reality, raising questions about the future of humanity, ethics, and the limits of human potential. From bionic limbs to brain-computer interfaces, cyborg technology is rapidly evolving, pushing us closer to a world where humans and machines become one.
ChatGPT and alike often amaze us with the accuracy of their answers, but unfortunately, they also repeatedly give us cause for doubt. The main issue with powerful AI response engines (artificial intelligence) is that they provide us with perfect answers and obvious nonsense with the same ease. One of the major challenges lies in how the large language models (LLMs) underlying AI deal with uncertainty.
Until now, it has been very difficult to assess whether LLMs designed for text processing and generation base their responses on a solid foundation of data or whether they are operating on uncertain ground.
Researchers at the Institute for Machine Learning at the Department of Computer Science at ETH Zurich have now developed a method that can be used to specifically reduce the uncertainty of AI. The work is published on the arXiv preprint server.
This Deep Dive AI podcast discusses my book The Physics of Time: D-Theory of Time & Temporal Mechanics, an insightful exploration into one of the most profound mysteries of existence: the nature of time. As part of the Science and Philosophy of Information series, this book presents a radical reinterpretation of time grounded in modern physics and digital philosophy. It questions whether time is a fundamental aspect of reality or an emergent property of consciousness and information processing. Drawing on quantum physics, cosmology, and consciousness studies, this work invites readers (and listeners) to reimagine time not as a linear, absolute entity, but as a dynamic, editable dimension intertwined with the fabric of reality itself. It challenges traditional views, blending scientific inquiry with metaphysical insights, aimed at both the curious mind and the philosophical seeker.
In this episode, we dive deep into The Physics of Time: D-Theory of Time & Temporal Mechanics by futurist-philosopher Alex M. Vikoulov. Explore the profound questions at the intersection of consciousness, quantum and digital physics, and the true nature of time. Is time fundamental or emergent? Can we travel through it? What is Digital Presentism?
The Physics of Time: D-Theory of Time & Temporal Mechanics by Alex M. Vikoulov is an insightful exploration into one of the most profound mysteries of existence: the nature of time. As part of the Science and Philosophy of Information series, this book presents a radical reinterpretation of time grounded in modern physics and digital philosophy. It questions whether time is a fundamental aspect of reality or an emergent property of consciousness and information processing.
The book introduces the D-Theory of Time, or Digital Presentism, which suggests that all moments exist as discrete, informational states, and that our perception of time’s flow is a mental construct. Vikoulov explores theoretical models of time travel, the feasibility of manipulating time, and the concept of the Temporal Singularity, a proposed point where temporal mechanics may reach a transformative threshold.
Artificial intelligence (AI) shows tremendous promise for analyzing vast medical imaging datasets and identifying patterns that may be missed by human observers. AI-assisted interpretation of brain scans may help improve care for children with brain tumors called gliomas, which are typically treatable but vary in risk of recurrence.
Investigators from Mass General Brigham and collaborators at Boston Children’s Hospital and Dana-Farber/Boston Children’s Cancer and Blood Disorders Center trained deep learning algorithms to analyze sequential, post-treatment brain scans and flag patients at risk of cancer recurrence.
