AI/ML news, top picks, and generated innovation digests.
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The Future of Life Institute’s 2025 summer update to its AI Safety Index shows some companies making incremental progress, but dangerous gaps remain in key categories such as risk assessment and controlling the systems they plan to build.
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Confronting the production-progress paradox
On July 11, the Tübingen AI Center hosted the 2025 edition of the Tübingen AI Summer Poster Event
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تحت الياسمينة This month’s newsletter was written by two community members. Amal Nammouchi, originally from Tunisia 🇹🇳, is a co-founder of AfriClimate AI, a Pan-African non-profit using AI to tackle climate challenges across Africa. She is a PhD candidate affiliated with SOLVE and Karlstad University, Sweden, and is a long time organiser for the Deep Learning […] The post Under the Jasmine Tree appeared first on Deep Learning Indaba .
AI can help, or hurt, our thinking
I have two binary classifiers and would like to check whether there is a statistically significant difference between the area under the ROC curve (AUROC). I have reason to opt for AUROC as my evaluation metric of choice. For each classifier, I have 15 runs as I do 5-fold cross-validation and use 3 random seeds for initialisation. For evaluation, I have used unseen/independent test data. This means that for both classifiers I have 15 paired AUROC values. According to this article on Nature , DeLong test is (often) used for significance testing with AUROCs. However, as this depends on the variance and covariance I suspect that I cannot use DeLong test with these 15 AUROC values. In order to use DeLong test, I should concatenate all predictions on the test data across the 15 unique versions of each classifier. Would this be correct? Would it be a good idea to use paired t-test on these 15 AUROC pairs (assuming the differences between these pairs values are normally distributed)? Are there any arguments favouring either DeLong test or paired t-test?
A topic-organized collection of 200+ LLM research papers from 2025
× Predicting Ego-centric Video from human Actions (PEVA) . Given past video frames and an action specifying a desired change in 3D pose, PEVA predicts the next video frame. Our results show that, given the first frame and a sequence of actions, our model can generate videos of atomic actions (a), simulate counterfactuals (b), and support long video generation (c). Recent years have brought significant advances in world models that learn to simulate future outcomes for planning and control. From intuitive physics to multi-step video prediction, these models have grown increasingly powerful and expressive. But few are designed for truly embodied agents. In order to create a World Model for Embodied Agents, we need a real embodied agent that acts in the real world. A real embodied agent has a physically grounded complex action space as opposed to abstract control signals. They also must act in diverse real-life scenarios and feature an egocentric view as opposed to aesthetic scenes and stationary cameras. 💡 Tip: Click on any image to view it in full resolution. Why It’s Hard Action and vision are heavily context-dependent. The same view can lead to different movements and vice versa. This is because humans act in complex, embodied, goal-directed environments. Human control is high-dimensional and structured. Full-body motion spans 48+ degrees of freedom with hierarchical, time-dependent dynamics. Egocentric view reveals intention but hides the body. First-person vision reflects…
Tübingen AI Center is proud to be a partner in ELLlOT, a Horizon Europe-funded project aiming to develop next-generation Multimodal Generalist Foundation Models.
Johannesburg, South Africa, 30 June 2025 – Cassava Technologies, a global technology leader of African heritage, is pleased to announce that it has signed a Memorandum of Understanding (MOU) with the South African AI Association (SAAIA), an industry body focused on growing responsible AI adoption, to deliver artificial intelligence (AI) solutions and GPU-as-a-Service (GPUaas) across the […]
"Google’s rollout of tools like AI Overviews and AI Mode—chatbots that answer users’ search queries—has begun shifting online behavior from browsing links to reading AI-generated responses, drastically reducing traffic to news sites. The resulting drop in organic traffic is forcing many media organizations to rethink their sustainability models in an ecosystem increasingly dominated by tech […] The post Google’s AI search features slash traffic to news sites, deepening sustainability crisis appeared first on LatAm Journalism Review by the Knight Center .
"Google’s rollout of tools like AI Overviews and AI Mode—chatbots that answer users’ search queries—has begun shifting online behavior from browsing links to reading AI-generated responses, drastically reducing traffic to news sites. The resulting drop in organic traffic is forcing many media organizations to rethink their sustainability models in an ecosystem increasingly dominated by tech […] The post Google’s AI search features slash traffic to news sites, deepening sustainability crisis appeared first on LatAm Journalism Review by the Knight Center .
Securing the invisible paths: How cross-account event flows can become security blind spots
ByteDance introduces Astra, an innovative dual-model architecture revolutionizing robot navigation in complex indoor environments. The post ByteDance Introduces Astra: A Dual-Model Architecture for Autonomous Robot Navigation first appeared on Synced .
Which AIs to use, and how to use them
Research update on on applying local volume measurement to downstream tasks
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
I'm analyzing open-source software development data and trying to determine whether the number of core maintainers per quarter influences the PR abandonment rate (specifically, contributor-abandoned pull requests) across several projects. Here's what my data looks like: For each quarter of the year (e.g., Q1, Q2, Q3, Q4), per project: core_maintainers: Number of core maintainers, identified using a refined commit-based heuristic (developers contributing ≥5% of total commits). abandonment_rate: Ratio of contributor-abandoned PRs to total PRs in that quarter. Goal: I want to test whether the number of core maintainers significantly affects the PR abandonment rate per quarter.
I’m developing a tree-based model classifier (XGBoost) using some healthcare (patient visits) data. The data has a time dimension, and I want to observe if there is a longitudinal effect for the prediction of the target feature. To predict the target for the current visit (Timepoint n), it should incorporate information from the previous visit(s) (T0 to T(N-1)). The input shape is visit_time, features, and target/label. Let’s say, I have a patient with 5 visits (T1 – T5). The idea is that the first prediction (T1) will be just based on the features for this timepoint. To predict T2, I want to add information from T1. Then, for T3, it will be T1 + T2, and so on, T5 (T1+ T2 + T3 + T4). The number of timepoints (visits) vary for each patient. I read that I can add lags and rolling windows. But still couldn’t figure out the best way to do it. Any thoughts on what and how to do it for my scenario?
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
MIT introduces SEAL, a framework enabling large language models to self-edit and update their weights via reinforcement learning. The post MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI first appeared on Synced .
"Automated failure attribution" is a crucial component in the development lifecycle of Multi-Agent systems. It has the potential to transform the challenge of identifying "what went wrong and who is to blame" from a perplexing mystery into a quantifiable and analyzable problem The post Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution first appeared on Synced .
In this post, we will study inductive biases of the parameter-function map of random neural networks using star domain volume estimates. This builds on the ideas introduced in Estimating the Probability of Sampling a Trained Neural Network at Random and Neural Redshift: Random Networks are not Random Functions (henceforth NRS). Inductive biases To understand generalization in deep neural networks, we must understand inductive biases. Given a fixed architecture, some tasks will be easily learnable, while others can take an exponentially long time to learn (see here and here).
Our best practices for quickly identifying, resolving, and preventing issues at scale. The post How GitHub engineers tackle platform problems appeared first on The GitHub Blog .
With the conclusion of its inaugural round, the DEEP X Tübingen AI cooperation has established a successful sciencepreneurship initiative in artificial intelligence.
Announcing the Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
In many resources, I see that repeated measures analyses—such as mixed-effects models or GEE—are commonly applied when the outcome is measured multiple times within the same individuals. However, I was wondering: Can these models also be used when it’s the exposure that is repeatedly measured (e.g. at several time points), and the outcome is measured only once (e.g. at a later time point)? For example, suppose I measure maternal blood pressure at 3 time points during pregnancy (repeated exposure), and I want to study its association with birthweight (a single outcome). Can I use a mixed-effects model or GEE to account for within-subject correlation in the exposures? I’m curious which modeling approaches are most appropriate in this context, and if there are any recommended papers or examples. So for example in R Studio: model
"In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of human
TLDR: We enhance the reliability and efficiency of language model evaluation by introducing IRT-based adaptive testing, which has been integrated into the HELM framework.
Recsys & search are converging with LLMs via semantic IDs, data augmentation, and unified foundation models.
Our latest annual report maps the current state of play with the AI market, interrogates the industry’s key sources of power, and provides an actionable strategy to reclaim public agency over the future of AI. The post Artificial Power: 2025 Landscape Report appeared first on AI Now Institute .
This month’s letter is presented by Professor AZA Allsop: artist, neuroscientist, and psychiatrist who conducts research at the intersection of social cognition, music mindfulness, and psychedelics. AZA’s intersectional research is motivated by the desire to decode methods for treating mental suffering and enhancing the evolution of society at large. I am honored to have this […] The post Letter to the Community: Prof. AZA Allsop appeared first on Deep Learning Indaba .
Using Product Key Memories to encode sparse coder features
AI Singapore (AISG) and the United Nations Development Programme (UNDP) today signed a new Memorandum of Understanding (MOU) to expand access to AI learning in six pilot countries from...
Interviews are not just about improving hiring outcomes - they are about strengthening the entire DS function
By combining State-Space Models (SSMs) for efficient long-range dependency modeling with dense local attention for coherence, and using training strategies like diffusion forcing and frame local attention, researchers from Adobe Research successfully overcome the long-standing challenge of long-term memory in video generation. The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced .
As organisations across Europe navigate the implementation of the EU AI Act — including Article 4, which addresses the importance of AI literacy — there is growing interest in accessible and practical training resources. This document presents a non-exhaustive selection of AI literacy programs that may be useful for companies, institutions, and professionals seeking to better understand […]
What makes a good leader? What do good leaders do? And commando, soldier, and police leadership.
AAAI today announced a pilot program that strategically incorporates Large Language Models (LLMs) to enhance the academic paper review process for the AAAI-26 conference. The post AAAI Launches AI-Powered Peer Review Assessment System appeared first on AAAI .
A newly released 14-page technical paper from the team behind DeepSeek-V3, with DeepSeek CEO Wenfeng Liang as a co-author, sheds light on the “Scaling Challenges and Reflections on Hardware for AI Architectures.” The post DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design first appeared on Synced .