As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.
Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. Their study is published in the journal Nature Communications.
As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (memory) from data processing (processors), resulting in significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of in-memory computing.
In this second episode of the AI Bros podcast Bruce and John go completely unscripted and just letting the conversation flow where it goes.
They talk about everything from Sam Altman’s controversial TED interview to John’s Animated Action Figure that came to life and broke out of its packaging. It’s interesting, fun and informative.
Subscribe, Like, Follow, and Share the AI Bros to your favorite social media platforms. The AI Bros Podcast is where artificial intelligence meets real talk.
Both Bruce and John share their hot takes, perspective and journey with AI as they learn it and use it themselves in their own day to day workflow.
The AI Bros podcast is a weekly series, and a part of the Neural News Network, and the (A)bsolutely (I)ncredible podcast channel. John Lawson III is the CEO of ColderICE Media and is highly recognized in e-commerce, and AI.
John is an Amazon #1 best-selling author, IBM Futurist, and an eBay Influencer.
He’s celebrated as one of the Top 100 Small Business Influencers in America, and crowned “Savviest in Social Medial” by StartUp Nation. You can connect with John directly on his website at www.johnlawson. comJohn is an internationally-recognized keynote speaker, a “Commerce Evangelist” and an absolute wealth of knowledge on all things e-retail, and online marketing strategy.
John is a pioneer in the online retail vertical space, and founder of The E-commerce Group, a global community of e-commerce vendors, and online marketers.
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.
