AI model from Google's DeepMind reads recipe for life in DNA An AI model developed by Google's DeepMind could transform our understanding of DNA - the complete recipe for building and running the human body - and its impact on disease and medicine discovery, according to researchers. Called AlphaGenome, the model could help scientists discover why subtle differences in our DNA put us at risk of conditions such as high blood pressure, dementia and obesity. It could also dramatically accelerate our understanding of genetic diseases and cancer. The developers of the model acknowledge it's not perfect, but experts have described it as an incredible feat and a major milestone. We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life, says Natasha Latysheva, research engineer at DeepMind.

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architecture, and it enables jointly training from image and video data, which we find to reduce the variance of minibatch gradients and speed up optimization. To generate long and higher resolution videos we introduce a new conditional sampling technique for spatial and temporal video extension that performs better than previously proposed methods. We present the first results on a large text-conditioned video generation task, as well as state-of-the-art results on established benchmarks for video prediction and unconditional video generation. Supplementary material is available at https://video-diffusion.github.io/.

Addressing decision-making problems using sequence modeling to predict future trajectories shows promising results in recent years.In this paper, we take a step further to leverage the sequence predictive method in wider areas such as long-term planning, vision-based control, and multi-task decision-making.To this end, we propose a method to utilize a diffusion-based generative sequence model to plan a series of milestones in a latent space and to have an agent to follow the milestones to accomplish a given task.The proposed method can learn control-relevant, low-dimensional latent representations of milestones, which makes it possible to efficiently perform long-term planning and vision-based control.Furthermore, our approach exploits generation flexibility of the diffusion model, which makes it possible to plan diverse trajectories for multi-task decision-making.We demonstrate the proposed method across offline reinforcement learning (RL) benchmarks and an visual manipulation environment.The results show that our approach outperforms offline RL methods in solving long-horizon, sparse-reward tasks and multi-task problems,while also achieving the state-of-the-art performance on the most challenging vision-based manipulation benchmark.

Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework. Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.

The intrepid spacecraft will cross a major distance milestone in November 2026. Breakthroughs, discoveries, and DIY tips sent every weekday. Voyager 1 is one of humanity's most poignant and remarkable technological achievements. Over the course of its nearly half century odyssey, the probe has glimpsed the gas giant Saturn, passed the threshold for interstellar space, and continually sets the bar for our furthest traveling human-made object. But based on NASA's projections, Voyager 1 is less than a year away from reaching yet another milestone.

Valar Atomics Says It's the First Nuclear Startup to Achieve Criticality A Trump administration pilot program aims for three nuclear startups to reach a key milestone by July 4, 2026. Valar Atomics says it's the first to do so--but it had some help. The El Segundo, California-based startup, which last week announced it had secured a $130 million funding round with backing from Palmer Luckey and Palantir CTO Shyam Sankar, claims that it is the first nuclear startup to create a critical fission reaction. It's also, more specifically, the first company in a special Department of Energy pilot program aiming to get at least three startups to criticality by July 4 of next year to announce it had achieved this reaction. The pilot program, which was formed following an executive order president Donald Trump signed in May, has upended US regulation of nuclear startups, allowing companies to reach new milestones like criticality at a rapid pace.

Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Nvidia has become the first company to reach $5 trillion in market value amid a global artificial intelligence arms race. The chipmaker surge on Wednesday came only three months after the company topped the $4 trillion mark . Since the launch of ChatGPT in 2022, Nvidia's shares have climbed 12-fold as the AI frenzy propelled the S&P 500 to record highs, igniting a debate on whether frothy tech valuations could lead to the next big bubble.

A prospective buyer tries the iPhone 17 Pro in Jakarta, Indonesia, on 17 October. A prospective buyer tries the iPhone 17 Pro in Jakarta, Indonesia, on 17 October. Tech company's stock enters positive territory for first time this year after gaining about 13% since new iPhone launches Apple topped $4tn in market value for the first time on Tuesday, the third tech company to hit the milestone, as robust demand for its latest iPhones allayed fears over its slow progress in the AI race. Microsoft reached a $4tn market cap for the second time the same day as the wider US stock market hit record highs. Apple's shares have gained about 13% since the new launches on 9 September, in a remarkable turnaround that pushed the stock into positive territory for the first time this year.

Recent leaps in large language models (LLMs) caused a revolution in programming tools (like GitHub Copilot) that can help with code generation, debugging, and even performance optimization. In this paper, we focus on the capabilities of the most recent reasoning models to generate optimized CUDA code for predefined, well-known tasks. Our objective is to determine which types of code optimizations and parallel patterns the LLMs can perform by themselves and whether they can be improved by tutoring (providing more detailed hints and guidelines in the prompt). The generated solutions were evaluated both automatically (for correctness and speedup) and manually (code reviews) to provide a more detailed perspective. We also tried an interactive approach where the LLM can fix its previous mistakes within a session. The results indicate that LLMs are quite skilled coders; however, they require tutoring to reach optimized solutions provided by parallel computing experts.

GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap: the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-Judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.