AI/ML news, top picks, and generated innovation digests.
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Deep Learning Indaba 2025: Africa’s biggest AI Community Gathers 1000 participants in Kigali, Rwanda to Shape the Future KIGALI, RWANDA –July 14th, 2025– The Deep Learning Indaba (DLI), Africa’s premier machine learning and artificial intelligence (AI) event, proudly announces its 7th edition, set to take place in Kigali, Rwanda, under the powerful theme “Urunana – […] The post Press Release DLI 2025 appeared first on Deep Learning Indaba .
Paper: https://research.trychroma.com/context-rot Abstract: Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks. In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows. Authors: Kelly Hong, Anton Troynikov, Jeff Huber Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRj…
Vector’s latest annual report showcases research advancements and industry partnerships that strengthen Canada’s AI leadership Vector bridges AI research and application, translating cutting-edge science into solutions that benefit Canadians. Over […] The post Vector Institute 2024-25 annual report: Where AI research meets real-world impact appeared first on Vector Institute for Artificial Intelligence .
Paper: https://arxiv.org/abs/2507.02092 Code: https://github.com/alexiglad/EBT Website: https://energy-based-transformers.github.io/ Abstract: Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs…
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
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.
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
The European Commission has published a call for applications for a scientific panel of independent experts. The panel focuses on general-purpose AI (GPAI) models and systems. Its tasks include advising the EU AI Office and national authorities on systemic risks, model classification, evaluation methodologies, and cross-border market surveillance. Further, the panel is empowered to alert […]
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
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 .
Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot of fun doing it.
The hub will serve as a catalyst for collaboration and platform for startups in high-potential, emerging markets. Dubai – Dubai Future Foundation (DFF), through the Dubai Centre for Artificial Intelligence (DCAI), has partnered with the South African Artificial Intelligence Association (SAAIA) to help launch a dedicated AI trade & investment hub with the aim of fast-tracking […]
Benchmark results for Qwen3 models using the Aider polyglot coding benchmark.