The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network output and instructive signals, thus afflicting not only inference, but also learning. We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components which avoids these issues by harnessing the ability of biological neurons to phase-advance their output with respect to their membrane potential. This principle enables quasi-instantaneous inference independent of network depth and avoids the need for phased plasticity or computationally expensive network relaxation phases. We jointly derive disentangled neuron and synapse dynamics from a prospective energy function that depends on a network's generalized position and momentum. The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active, local plasticity. We demonstrate successful learning of standard benchmark datasets, achieving competitive performance using both fully-connected and convolutional architectures, and show how our principle can be applied to detailed models of cortical microcircuitry. Furthermore, we study the robustness of our model to spatio-temporal substrate imperfections to demonstrate its feasibility for physical realization, be it in vivo or in silico.

Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length. In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks. It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity. Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (e.g., a moderate size of 55.8M model solely trained on 224x224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images.

AI struggles to write a good jingle. You'd be forgiven if you can't hum the 18th-century Cumbrian folk song "Do Ye Ken John Peel." But in 1942, a version of that tune, reworked with lyrics about Pepsi-Cola, was the most recognized melody in America. Three years earlier, two men walked into the office of Pepsi-Cola's president, carrying a phonograph. They played a demo of what would become one of America's earliest advertising jingles.

Is It Really Worth It? Apple, Michigan taxpayers, and one of Detroit's wealthiest families spent roughly $30 million training hundreds of people to build iPhone apps. Two years ago, Lizmary Fernandez took a detour from studying to be an immigration attorney to join a free Apple course for making iPhone apps . The Apple Developer Academy in Detroit launched as part of the company's $200 million response to the Black Lives Matter protests and aims to expand opportunities for people of color in the country's poorest big city. But Fernandez found the program's cost-of-living stipend lacking--"A lot of us got on food stamps," she says--and the coursework insufficient for landing a coding job. "I didn't have the experience or portfolio," says the 25-year-old, who is now a flight attendant and preparing to apply to law school. "Coding is not something I got back to."

Supervised learning methods trained with maximum likelihood objectives often overfit on training data. Most regularizers that prevent overfitting look to increase confidence on additional examples (e.g., data augmentation, adversarial training), or reduce it on training data (e.g., label smoothing). In this work we propose a complementary regularization strategy that reduces confidence on self-generated examples. The method, which we call RCAD (Reducing Confidence along Adversarial Directions), aims to reduce confidence on out-of-distribution examples lying along directions adversarially chosen to increase training loss. In contrast to adversarial training, RCAD does not try to robustify the model to output the original label, but rather regularizes it to have reduced confidence on points generated using much larger perturbations than in conventional adversarial training. RCAD can be easily integrated into training pipelines with a few lines of code. Despite its simplicity, we find on many classification benchmarks that RCAD can be added to existing techniques (e.g., label smoothing, MixUp training) to increase test accuracy by 1-3% in absolute value, with more significant gains in the low data regime. We also provide a theoretical analysis that helps to explain these benefits in simplified settings, showing that RCAD can provably help the model unlearn spurious features in the training data.

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When you purchase through links in our articles, we may earn a small commission. Upscayl uses AI technology to scale images to a larger format. The open source tool adds sharpness and brings details to light. There are several modes and models to choose from. The Upscayl tool uses several AI models to enhance your images.

The science of human touch - and why it's so hard to replicate in robots Robots now see the world with an ease that once belonged only to science fiction. They can recognise objects, navigate cluttered spaces and sort thousands of parcels an hour. But ask a robot to touch something gently, safely or meaningfully, and the limits appear instantly. As a researcher in soft robotics working on artificial skin and sensorised bodies, I've found that trying to give robots a sense of touch forces us to confront just how astonishingly sophisticated human touch really is. My work began with the seemingly simple question of how robots might sense the world through their bodies.

Things aren't great, but there are a few positive signs, we promise. Climate news hasn't been great in 2025. Global greenhouse-gas emissions hit record highs (again). This year is set to be either the second or third warmest on record. Climate-fueled disasters like wildfires in California and flooding in Indonesia and Pakistan devastated communities and caused billions in damage. In addition to these worrying indicators of our continued contributions to climate change and their obvious effects, the world's largest economy has made a sharp U-turn on climate policy this year.

Big AI companies courted controversy by scraping wide swaths of the public internet. With the rise of AI agents, the next data grab is far more private. For years, the cost of using "free" services from Google, Facebook, Microsoft, and other Big Tech firms has been handing over your data. Uploading your life into the cloud and using free tech brings conveniences, but it puts personal information in the hands of giant corporations that will often be looking to monetize it. Now, the next wave of generative AI systems are likely to want more access to your data than ever before. Over the past two years, generative AI tools--such as OpenAI's ChatGPT and Google's Gemini--have moved beyond the relatively straightforward, text-only chatbots that the companies initially released.