AI models are growing ever-more capable, accurate and impressive. The question of if they represent “general intelligence” is increasingly moot.
How does the human brain learn to see? Research from Max Planck Florida Institute for Neuroscience, Frankfurt Institute for Advanced Studies, and Goethe University Frankfurt explores how early visual experiences restructure circuits in the visual cortex. This research sheds light on the fundamental mechanisms of brain development and visual perception.
Explore the research.
How early visual experience builds reliable brain circuits Because it does it so well, we often take for granted how our brain creates reliable visual representations of our surroundings that are critical for guiding our behavior. While scientists understand a lot about how mature neural circuits support reliable vision, the sequence of developmental events before and after birth that build these circuits is not clear. Collaborating scientists at Max Planck Florida Institute for Neuroscience, the Frankfurt Institute for Advanced Studies, and Goethe University Frankfurt have discovered how early visual experience dramatically changes the brain networks that process vision – changes that are essential for establishing reliable visual perception.
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An AI model can scan your brain with non-invasive equipment and convert your thoughts into typed sentences — with no implants required.
Each is calculated to be just 20 to 30% the mass of Earth and completes one full trip around the star in only a few days.
These findings have caught many people’s attention because they point to greater precision in detecting smaller, more elusive planets.
“It’s a really exciting find – Barnard’s Star is our cosmic neighbor, and yet we know so little about it,” said Ritvik Basant, Ph.D. student at the University of Chicago and first author on the study. “It’s signaling a breakthrough with the precision of these new instruments from previous generations.”
For the first time, it has been confirmed that individual neurons represent the concepts we learn, regardless of the context in which they are encountered, challenging previous beliefs.
A study led by Dr. Rodrigo Quian Quiroga, head of the Neural Mechanisms of Perception and Memory Research Group at the Hospital del Mar Research Institute, has provided the first direct evidence of how neurons in the human brain store memories independently of the context in which they are acquired.
Published in Cell Reports.
Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just mimicking patterns? A new study from the University of Amsterdam and the Santa Fe Institute reveals that while GPT models perform well on some analogy tasks, they fall short when the problems are altered, highlighting key weaknesses in AI’s reasoning capabilities. The work is published in Transactions on Machine Learning Research.
Analogical reasoning is the ability to draw a comparison between two different things based on their similarities in certain aspects. It is one of the most common methods by which human beings try to understand the world and make decisions. An example of analogical reasoning: cup is to coffee as soup is to??? (the answer being: bowl)
Large language models (LLMs) like GPT-4 perform well on various tests, including those requiring analogical reasoning. But can AI models truly engage in general, robust reasoning or do they over-rely on patterns from their training data? This study by language and AI experts Martha Lewis (Institute for Logic, Language and Computation at the University of Amsterdam) and Melanie Mitchell (Santa Fe Institute) examined whether GPT models are as flexible and robust as humans in making analogies.
A research team has identified different subtypes of white matter (WM) astrocytes, including a unique type with the ability to multiply and potentially aid in brain repair. Using single-cell RNA sequencing and spatial transcriptomics, the scientists mapped astrocyte diversity across different brain regions and species, providing the first detailed molecular profile of WM astrocytes.
The team was led by Dr. Judith Fischer-Sternjak from Helmholtz Munich and Ludwig-Maximilians-Universität (LMU) München, alongside Prof. Magdalena Götz from Helmholtz Munich, LMU and the Munich Cluster for Systems Neurology (SyNergy). The research is published in the journal Nature Neuroscience.
Unveiling white matter astrocyte diversity Astrocytes, known for their crucial role in supporting neurons and maintaining brain health, have been predominantly studied in gray matter (GM), which is involved in information processing. However, white matter astrocytes, which support long-range neural connections, remain poorly understood. This study fills a major knowledge gap by showing that WM astrocytes are not a uniform population but consist of distinct subtypes with specialized roles.
A little over a year after releasing two open Gemma AI models built from the same technology behind its Gemini AI, Google is updating the family with Gemma 3.
According to the blog post, these models are intended for use by developers creating AI applications capable of running wherever they’re needed, on anything from a phone to a workstation with support for over 35 languages, as well as the ability to analyze text, images, and short videos.
The company claims that it’s the world’s best single-accelerator model, outperforming competition from Facebook’s Llama, DeepSeek, and OpenAI for performance on a host with a single GPU, as well as optimized capabilities for running on Nvidia’s GPUs and dedicated AI hardware.
Gemma 3’s vision encoder is also upgraded, with support for high-res and non-square images, while the new ShieldGemma 2 image safety classifier is available for use to filter both image input and output for content classified as sexually explicit, dangerous, or violent.
To go deeper into those claims, you can check out the 26-page technical report.
Last year it was unclear how much interest there would be in a model like Gemma, however, the popularity of DeepSeek and others shows there is interest in AI tech with lower hardware requirements.
Sustaining growth in storage and computational needs is increasingly challenging. For over a decade, exponentially more information has been produced year after year while data storage solutions are pressed to keep up. Soon, current solutions will be unable to match new information in need of storage. Computing is on a similar trajectory, with new needs emerging in search and other domains that require more efficient systems. Innovative methods are necessary to ensure the ability to address future demands, and DNA provides an opportunity at the molecular level for ultra-dense, durable, and sustainable solutions in these areas.
In this webinar, join Microsoft researcher Karin Strauss in exploring the role of biotechnology and synthetic DNA in reaching this goal. Although we have yet to achieve scalable, general-purpose molecular computation, there are areas of IT in which a molecular approach shows growing promise. These areas include storage as well as computation.
Learn how molecules, specifically synthetic DNA, can store digital data and perform certain types of special-purpose computation, like large-scale similarity search, by leveraging tools already developed by the biotechnology industry. Starting with some background on DNA and its storage potential, you’ll explore the advantages of using DNA for this application. Then, you’ll get a closer look at an end-to-end system, including encoding, synthesizing, reading, and decoding DNA. We’ll also look at an affordable full-stack digital microfluidics platform for wet lab preparations and conclude with a discussion of future hybrid systems.
Together, you’ll explore:
■ The intersection between technology and science of DNA data storage and computation.
■ The many advantages for using DNA to store data compared with other methods.
■ A detailed walkthrough of an end-to-end DNA storage system and its stages.
■ How DNA can be used for image similarity search.