Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In the very different tradition of reinforcement learning, researchers have described an intrinsic reward signal called "empowerment" which maximizes mutual information between actions and their outcomes. "Empowerment" may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of childrens causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations, and design effective causal interventions.

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Smart homes don't need to be complicated or expensive. Take a methodical approach and keep an eye out for bargains and you can enjoy many of the conveniences of living in a smart home without spending much at all. The secret is to start slow, and to invest in a few solid products that will have the most impact, I'm talking electrical outlets, lighting, climate control, and the like. In other words, you can probably get by without a smart washing machine, but installing a smart thermostat will be a game changer for both your comfort and living expenses. One of the keys to successfully getting a smart home up and running is to ensure everything you install is fully compatible and interoperable. Many smart home ecosystems were originally designed around a central hub and wireless protocols that were supposed to hit it big, but that either never really took off or have faded in importance and mainstream appeal.

On paper, the up-and-coming Matter standard appears to be an ideal solution for smart homes: a protocol that enables the competing Alexa, Apple Home, Google Home, and other major smart-home platforms to collaborate effectively. In reality, Matter is still plagued with issues. Adding new devices to your Matter network can be a pain, and products connected via Matter sometimes "expose" only a fraction of the functionality to Matter controllers. And while Matter supports everything from smart bulbs and smart shades to robot vacuums and thermostats, one key smart home category still isn't part of the specification. I'm discussing security cameras, which are a crucial part of the smart home but still cannot connect to a Matter network.

My in-laws own a little two-bedroom beach bungalow. It's part of a condo development that hasn't changed much in fifty years. The units are connected by brick paths that wind through palm trees and tiki shelters to a beach. Nearby, developers have built big hotels and condo towers, and it's always seemed inevitable that the bungalows would be razed and replaced. But it's never happened, probably because, according to the association's bylaws, eighty per cent of the owners have to agree to a sale of the property.

Smart LED light bulbs are one of the easiest ways to get into the IoT space. These smart lighting solutions let you control your home's illumination from your phone and other connected devices, and in addition to that practicality, they also inject some fun into your space. Color-changing bulbs have a plethora of RGB options for you to customize the lighting mood for your next movie night, date night or game day, or you can opt for cozy warm white light when you need to unwind at the end of a long day. It goes without saying that many of these smart LED light bulbs work with Amazon's Alexa and the Google Assistant, so if you already have a smart home setup in the works, you can find one that fits into your chosen ecosystem. And arguably the best thing about these devices is that they can fit into any budget; affordable and advanced options have flooded the space over the past few years. We've tested out a bunch of smart lights over the years, and these are our current favorites. If you've done any research into smart lights, you've probably come across Philips Hue bulbs.

Recent research increasingly focuses on training vision-language models (VLMs) with long, detailed image captions. However, small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-tuning. In this paper, we explore how well VLMs adapt to such captions. To quantify caption quality, we propose Decomposed NLI (DNLI), an evaluation framework that breaks down generated captions into individual propositions, assessing each in isolation. This fine-grained analysis reveals a critical balance between capturing descriptive details and preventing hallucinations. Our findings show that simply reducing caption complexity or employing standard data curation techniques does not effectively resolve this issue. To tackle this challenge, we introduce Knowledge Adapted (KnowAda) fine-tuning, a data-centric approach that automatically adapts training data with the model's existing knowledge and visual understanding. KnowAda minimizes hallucinations while preserving high descriptiveness. We validate this approach across several small-scale VLMs (up to 7B parameters) and dense caption datasets, demonstrating that KnowAda effectively balances hallucination reduction and descriptiveness. Our results show that KnowAda outperforms various baselines in both automatic metrics and human evaluations. We will release our code and models.

What makes a good Large Language Model (LLM)? That it performs well on the relevant benchmarks -- which hopefully measure, with some validity, the presence of capabilities that are also challenged in real application. But what makes the model perform well? What gives a model its abilities? We take a recently introduced type of benchmark that is meant to challenge capabilities in a goal-directed, agentive context through self-play of conversational games, and analyse how performance develops as a function of model characteristics like number of parameters, or type of training. We find that while there is a clear relationship between number of parameters and performance, there is still a wide spread of performance points within a given size bracket, which is to be accounted for by training parameters such as fine-tuning data quality and method. From a more practical angle, we also find a certain degree of unpredictability about performance across access methods, possible due to unexposed sampling parameters, and a, very welcome, performance stability against at least moderate weight quantisation during inference.

The fourth-gen Amazon Echo smart speaker in white is on sale for 65, and the deal includes a Sengled Bluetooth smart light bulb. This is a discount of 40 percent. This is 40 percent off. This Echo easily made our list of the best smart speakers. We really appreciate just how loud this thing can get, especially when compared to competing speakers.

Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like $\textit{tabula rasa}$ reinforcement learning (RL) agents. Large Language Models (LLMs), with a wealth of world knowledge, can help RL agents learn quickly and adapt to distribution shifts. In this work, we introduce Language Guided Exploration (LGE) framework, which uses a pre-trained language model (called GUIDE ) to provide decision-level guidance to an RL agent (called EXPLORER). We observe that on ScienceWorld (Wang et al.,2022), a challenging text environment, LGE outperforms vanilla RL agents significantly and also outperforms other sophisticated methods like Behaviour Cloning and Text Decision Transformer.