Our paper is out in @natmachintell.nature.com! We trained multiple RNNs to perform free recall. The best-performing ones learned a strategy akin to the memory palace technique. See thread below for more info. www.nature.com/articles/s42...

A neural network model of free recall learns multiple memory strategies - Nature Machine Intelligence

Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an index-based me...

nature.com

A new #AI method for predicting how #cells will #respond to #treatments 💡 What if we could #predict how cells would react to a new drug even before testing it in the laboratory? A radically different and promising approach now on the cover of @natmachintell.nature.com 👉 www.unil.ch/news/en/1782...

A new AI method for predicting how cells will respond to treatments

What if we could predict how cells would react to a new drug even before testing it in the laboratory? A radically different and promising approach now on the cover of Nature Machine Intelligence.

unil.ch

In this new invited commentary in @natmachintell.nature.com , our own Jiyeong Kim, and co-author, Carolyn I. Rodriguez, discuss PsychFound, a domain-specific LLM that demonstrated a potential for LLM grounded in real-world clinical practice in psychiatry. Read more: www.nature.com/articles/s42...

Our new correspondence argues that the impact of conversational AI won’t stay inside the screen. We need to understand how sustained human–LLM interactions shape connection, agency, resilience, and social life, so that these systems strengthen, rather than erode, our relationships with one another

Happy to share that our Developmental Visual Diet (DVD) paper was selected as the cover article for the May issue of Nature Machine Intelligence ( @natmachintell.nature.com)! www.nature.com/natmachintel...

Tim Kietzmann @timkietzmann.bsky.social · 5mo ago

Our NeuroAI study made it onto the cover of Nature Machine Intelligence ❤️. In it, we demonstrate that a developmentally-inspired visual diet can drastically improve the robustness of ANN-based vision systems. www.nature.com/articles/s42... open access, open code, open weights, open science.

Our May issue is live! With a study teaching AI human-like shape-based vision, a domain-adapted LLM to support clinical psychiatrists, an octopus-inspired robot arm for underwater tasks and more. Plus: Our editorial "Stop ‘tokenmaxxing’ and deploy AI sensibly instead"! www.nature.com/natmachintell/

Now out in Nature Machine Intelligence @NatMachIntell “Adopting a human developmental visual diet yields robust and shape-based AI vision”: doi.org/10.1038/s422.... A wonderful case where brain inspiration improved AI. With @martisamuser.bsky.social, Radek Cichy and @timkietzmann.bsky.social .

Just out in @natmachintell.nature.com: Fluid Thinking on Collective Intelligence, a paper comparing #collectiveintelligence in #neuralnetworks with that in swarm robotics and insect colonies from #KempnerInstitute Affiliate Faculty Justin Werfel! Read the paper: rdcu.be/fffak

Our April issue is live! With a Perspective on fluid forms of collective intelligence, a study on mechanisms underlying overconfidence and underconfidence in LLMs, work on deep learning for programmable RNA translation, our editorial on embodied intelligence, and more. www.nature.com/natmachintell/

Geir Kjetil Sandve (U of Oslo) on his dedication to open science "To me, open science is not about whether it’s theoretically possible with unlimited time to build on something but about ensuring it’s open in a way that actually invites reuse, transparency, and reproducibility." tinyurl.com/3743fr5m

Our March issue is live! With a computational framework for human-machine interactions in neural interfaces, benchmarking for neuromorphic soft robots, a ML approach for long-range atomic interactions, and our editorial about reproducibility in times of fast science. www.nature.com/natmachintell/

Ein spannendes, neues Paper aus unserem Cluster, jetzt in @natmachintell.nature.com erschienen! Glückwunsch an die Autoren @mariokrenn.bsky.social und Sören Arlt! Lest die Pressemitteilung von @unituebingen.bsky.social für mehr Infos 👇

Universität Tübingen@unituebingen.bsky.social · 8mo ago

Eine Künstliche Intelligenz, die völlig neue, teils ungewöhnliche Experimente in der #Quantenphysik entwirft und zugleich für Forschende leicht verständlich darstellt, wurde jetzt an der Uni Tübingen entwíckelt uni-tuebingen.de/universitaet... #AI #KI @mariokrenn.bsky.social

Our Feb issue is live! With work on meta-designing quantum experiments, an overview of what works in vision-language models for robots, a foundation model for cardiac health, and our editorial 'AI and the long game', looking back at AlphaGo's breakthrough 10 years ago www.nature.com/natmachintell/

🧵 1/ 🎉New paper alert! Pretrained protein language models (#pLMs) are all the hype, but are they really helping us predict protein- protein interactions? 🤔Dive into our thread to see why you should read the full study @natmachintell.nature.com. ⬇️ 🔗 rdcu.be/e3PGD

🚨 New in @natmachintell.nature.com 🚨 We collected 9000+ annotations of empathic communication in convos from experts, crowds & LLMs across 4 NLP/comms/psych frameworks LLM judgment exceeds crowds' reliability & nearly matches experts Soft skills can now be reliably measured by LLMs 🧵

Our Jan issue is live! With work on solving olympiad maths problems with AI, benchmarking LLMs on safety risks in labs, metasurface structure discovery with a diffusion model. And our editorial on the need for transparency when reporting on multi-agent AI systems! www.nature.com/natmachintell/

Now out in @natmachintell.nature.com TCRT5 is a rapid generator of target-conditioned CDR3b, leads SoTA, and yields the first AI-designed self-tolerant binder to an OOD non-viral epitope (w val) 📑: www.nature.com/articles/s42... 🤗: huggingface.co/dkarthikeyan1 👨‍💻: github.com/pirl-unc/tcr_translate

Dhuvi Karthikeyan@dkarthikey1.bsky.social · 2y ago

My first Skeetorial! 💻🧬TCR-TRANSLATE - A new framework for thinking about the TCR:pMHC specificity problem. TLDR: We pretrained LLMs on ~8M TCR & pMHC seqs Finetuned on sparse pMHC->TCR pair data Validated CDR3b sequences to unseen antigens >> random performance on IMMREP2023 "private" antigens

A brain-computer interface co-piloted by AI improved how a person with paralysis complete tasks, such as moving a computer cursor or operating a robotic arm, by up to four times, according to research in @natmachintell.nature.com: spklr.io/63322BHjsK #Neuroscience #Neuroskyence #AI

Brain–computer interface control with artificial intelligence copilots - Nature Machine Intelligence

AI copilots are integrated into brain–computer interfaces, enabling a paralysed participant to achieve improved control of computer cursors and robotic arms. This shared autonomy approach offers a promising path to increase BCI performance and clinical viability.

spklr.io

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...

Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence

An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...

nature.com

🚨 new paper alert! 🚨 Excited to share our latest paper in @natmachintell.nature.com , we tested 27 large language models to see if any could generate a publication-ready Citation Diversity Report… and several (free) LLMs could! Read paper for free at link: rdcu.be/eCfwJ @natureportfolio.nature.com

LLMs as all-in-one tools to easily generate publication-ready citation diversity reports

Nature Machine Intelligence - LLMs as all-in-one tools to easily generate publication-ready citation diversity reports

rdcu.be

What complexity of algorithms can AI compute? In a new paper with colleagues at IBM Research, we explore how circuit complexity theory can help quantify the degree of algorithmic generalization in AI systems. www.nature.com/articles/s42... @natmachintell.nature.com #ML #AI #MLSky 1/n

#AI "Ultimately, this is a step forward in understanding how the human brain understands meaning from the visual world." #LLMs @mila-quebec.bsky.social @adriendoerig.bsky.social @timkietzmann.bsky.social @natmachintell.nature.com nouvelles.umontreal.ca/en/article/2...

Using AI to 'see' what we see

Fed the right information, large language models can match what the brain sees when it takes in an everyday scene such as children playing or a big city skyline, a new study led by Ian Charest finds.

nouvelles.umontreal.ca

🚨 Finally out in Nature Machine Intelligence!! "Visual representations in the human brain are aligned with large language models" 🔗 www.nature.com/articles/s42...

High-level visual representations in the human brain are aligned with large language models - Nature Machine Intelligence

Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain activity to caption-based embeddings from lar...

nature.com

#Chatbots are increasingly used as #MentalHealth supports and companions but this can be risky for ppl due to bots' abilities to manipulate users, an issue that providers and regulators must be more proactive about, argues @natmachintell.nature.com www.nature.com/articles/s42... #AI

Emotional risks of AI companions demand attention - Nature Machine Intelligence

The integration of AI into mental health and wellness domains has outpaced regulation and research.

nature.com

Our July issue is live! Read our editorial about the emotional risks of companion chatbots, a Perspective on LLMs in real-world materials, research on AI-design of mechanical metamaterials with nonlinear responses, a new robot grasping mechanism and more: www.nature.com/natmachintell/

Our April issue is live! With a review article on AI safety research, an editorial on the emerging use of LLMs in robotics planning, a deep learning method for generating transitions states in chemical reactions, a wearable multimodal visual assistance system and more: www.nature.com/natmachintell/

🚨Our April issue is now live and includes a model to unravel plant behavior for functional devices, a method to efficiently screen compound libraries, a call for papers on generative molecular design and discovery, and much more! www.nature.com/natcomputsci...

'AI Safety for Everyone' is out now in @natmachintell.nature.com! Through an analysis of 383 papers, we find a rich landscape of methods that cover a much larger domain than mainstream notions of AI safety. Our takeaway: Epistemic inclusivity is important, the knowledge is there, we only need use it

Check out our new piece in @natmachintell.bsky.social @natureportfolio.nature.com, featuring AI-driven biomaterials discovery by Daniela Kalafatovic & Goran Mauša through resource-efficient deep learning to generate self-assembling peptides. Huge kudos to Tianang Leng! @upenn.bsky.social

AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning - Nature Machine Intelligence

Recurrent neural networks are efficient and capable agents for discovering new peptides with strong self-organizing capabilities.

nature.com

What are goals? Can we model them as programs that produce rewards? In particular, can we model free-form creativity in game design this way? And learn to generate games like humans do? Our new paper in @natmachintell.bsky.social, led by @guydav.bsky.social and Graham Todd, shows that yes, we can!

Guy Davidson ✈️ NeurIPS 2025@guydav.bsky.social · 2y ago

Out today in Nature Machine Intelligence! From childhood on, people can create novel, playful, and creative goals. Models have yet to capture this ability. We propose a new way to represent goals and report a model that can generate human-like goals in a playful setting... 1/N