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DIY Tech Hacks iOS 26 adds a new app to your iPhone. Here's how to use it. You're not imagining it--there is a new app on your iPhone. Breakthroughs, discoveries, and DIY tips sent every weekday. Apple's big iOS 26 software update for 2025 has now reached millions of iPhones, and brought with it a bunch of new features and an updated visual interface.

To facilitate the wider adoption of robotics, accessible programming tools are required for non-experts. Observational learning enables intuitive human skills transfer through hands-on demonstrations, but relying solely on visual input can be inefficient in terms of scalability and failure mitigation, especially when based on a single demonstration. This paper presents a human-in-the-loop method for enhancing the robot execution plan, automatically generated based on a single RGB video, with natural language input to a Large Language Model (LLM). By including user-specified goals or critical task aspects and exploiting the LLM common-sense reasoning, the system adjusts the vision-based plan to prevent potential failures and adapts it based on the received instructions. Experiments demonstrated the framework intuitiveness and effectiveness in correcting vision-derived errors and adapting plans without requiring additional demonstrations. Moreover, interactive plan refinement and hallucination corrections promoted system robustness.

Breakthroughs, discoveries, and DIY tips sent every weekday. If you own an iPhone, you're never far away from a major or minor software update. So while iOS 18.4 doesn't introduce as many features as the main iOS 18 release, it does come with a few interesting new tricks. Specifically, there's now the option to open your favorite chat in Messages, right from the lock screen. It might be the group chat with your friends, for example, or the one-to-one conversation you've got going with your partner.

Apple typically pushes out major iOS upgrades every September, alongside new iPhones--you may recall the launch of iOS 18. Those big software upgrades are followed by'point' releases that squash outstanding bugs, improve security and stability, and occasionally introduce new features. That's the case with iOS 18.4, which brings more with it than most minor iOS updates. From extra tools in Image Playground, to ambient music to relax you or send you off to sleep, here's what's new in the iOS 18 update. Apple Intelligence can now prioritize iPhone notifications, selecting which you need to see first and create AI-written summaries.

The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory for their ability to achieve a goal. However, when feedback is delayed and sparse, the learning signal is poor, and action evaluation becomes harder. Canonical solutions, such as reward shaping and options, require extensive domain knowledge and manual intervention, limiting their scalability and applicability. In this work, we lay the foundations for Credit Assignment with Language Models (CALM), a novel approach that leverages Large Language Models (LLMs) to automate credit assignment via reward shaping and options discovery. CALM uses LLMs to decompose a task into elementary subgoals and assess the achievement of these subgoals in state-action transitions. Every time an option terminates, a subgoal is achieved, and CALM provides an auxiliary reward. This additional reward signal can enhance the learning process when the task reward is sparse and delayed without the need for human-designed rewards. We provide a preliminary evaluation of CALM using a dataset of human-annotated demonstrations from MiniHack, suggesting that LLMs can be effective in assigning credit in zero-shot settings, without examples or LLM fine-tuning. Our preliminary results indicate that the knowledge of LLMs is a promising prior for credit assignment in RL, facilitating the transfer of human knowledge into value functions.

Specifying rewards for reinforcement learned (RL) agents is challenging. Preference-based RL (PbRL) mitigates these challenges by inferring a reward from feedback over sets of trajectories. However, the effectiveness of PbRL is limited by the amount of feedback needed to reliably recover the structure of the target reward. We present the PRIor Over Rewards (PRIOR) framework, which incorporates priors about the structure of the reward function and the preference feedback into the reward learning process. Imposing these priors as soft constraints on the reward learning objective reduces the amount of feedback required by half and improves overall reward recovery. Additionally, we demonstrate that using an abstract state space for the computation of the priors further improves the reward learning and the agent's performance.

Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural model that takes Open-StreetMap representations as input and learns to generate navigation instructions that contain visible and salient landmarks from human natural language instructions. Routes on the map are encoded in a location-and rotation-invariant graph representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7,672 crowd-sourced instances that have been verified by human navigation in Street View. Our evaluation shows that the navigation instructions generated by our system have similar properties as human-generated instructions, and lead to successful human navigation in Street View. Current navigation services provided by the automotive industry or by Google Maps generate route instructions based on turns and distances of named streets. In contrast, humans naturally use an efficient mode of navigation based on visible and salient physical objects called landmarks. Route instructions based on landmarks are useful if GPS tracking is poor or not available, and if information is inexact regarding distances (e.g., in human estimates) or street names (e.g., for users riding a bicycle or on a bus). In our framework, routes on the map are learned by discretizing the street layout, connecting street segments with adjacent points of interest - thus encoding visibility of landmarks, and encoding the route and surrounding landmarks in a location-and rotation-invariant graph representation. Based on crowd-sourced natural language instructions for such map representations, a graph-to-text mapping is learned that decodes graph representations into natural language route instructions that contain salient landmarks. Our work is accompanied by a dataset of 7,672 instances of routes rendered on OpenStreetMap and crowd-sourced natural language instructions. The navigation instructions were generated by workers on the basis of maps including all points of interest, but no street names. Furthermore, the timenormalized success rate of human workers finding the correct goal location on Street View is at 66%. Since these routes can have a partial overlap with routes in the training set, we further performed an evaluation on completely unseen routes. The rate of produced landmarks drops slightly compared to human references, and the time-normalized success rate also drops slightly to 63%. While there is still room for improvement, our results showcase a promising direction of research, with a wide potential of applications in various existing map applications and navigation systems. Mirowski et al. (2018) published a subset of Street View covering parts of New York City and Pittsburgh.

Given the need for compliance and the growing number of digital services that need to have more inclusive access, web accessibility is no longer a trivial or low-priority concern for business websites. According to one Pew Internet Project survey, 54% of adults with disabilities use the internet. However, based on a recent analysis of the top 1 million websites by WebAIM, 98.1 percent of home pages have compliance issues with the Web Accessibility Guidelines 2 (WCAG 2). Also, the study reports that 97.8% of internal pages do not pass WCAG 2 standards. "Significant work remains to be done to make the web accessible to everyone," the study concludes. The most common failures detected were low contrast text, missing alternative text for images, empty links, absence of form input labels, and missing document language. These failures are not difficult to address. The problem is that many website owners fail to pay attention to such issues.

This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies. All this material is available for free, and consists of content mostly created in 2019 and 2018, by various top experts in their respective fields. A few of these documents are available on LinkedIn: see last section on how to download them. Move your mouse on the LinkedIn post in question. Now the document will open in full screen mode.