We set the output of each rule as the majority class (resp.

Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cases, we expect some form of dependency between the draws: the nucleotide at one position in the DNA strand may depend on the preceding nucleotides, children's names are highly correlated from year to year, and topics in text may be correlated and dynamic. These dependencies are not naturally captured by the typical Dirichlet-multinomial formulation. Here, we leverage a logistic stick-breaking representation and recent innovations in P\'{o}lya-gamma augmentation to reformulate the multinomial distribution in terms of latent variables with jointly Gaussian likelihoods, enabling us to take advantage of a host of Bayesian inference techniques for Gaussian models with minimal overhead.

Enhancing reasoning capabilities remains a central focus in the LLM reasearch community. A promising direction involves requiring models to simulate code execution step-by-step to derive outputs for given inputs. However, as code is often designed for large-scale systems, direct application leads to over-reliance on complex data structures and algorithms, even for simple cases, resulting in overfitting to algorithmic patterns rather than core reasoning structures. To address this, we propose TeaR, which aims at teaching LLMs to reason better. TeaR leverages careful data curation and reinforcement learning to guide models in discovering optimal reasoning paths through code-related tasks, thereby improving general reasoning abilities. We conduct extensive experiments using two base models and three long-CoT distillation models, with model sizes ranging from 1.5 billion to 32 billion parameters, and across 17 benchmarks spanning Math, Knowledge, Code, and Logical Reasoning. The results consistently show significant performance improvements. Notably, TeaR achieves a 35.9% improvement on Qwen2.5-7B and 5.9% on R1-Distilled-7B.

When he got his first glimpse of a movie studio, Orson Welles excitedly proclaimed it "the biggest electric train set any boy ever had." But with a reported budget of more than 300 million, Joe and Anthony Russo's The Electric State makes Welles' train set look like a busted caboose. The most expensive movie in Netflix's history, it's also among the costliest of all time, joining a list that includes the brothers' own Avengers: Infinity War and Avengers: Endgame. If the Russos are the most profligate creators in history--their Amazon series Citadel is also one of the most expensive TV shows ever made--they're among the most successful too. And yet for all the money they're making, and all that they're allowed to spend, they don't seem to be enjoying themselves very much.

ChatGPT might hold a special place in your heart if it's the first AI tool you tried, but it's far from the best option now. And we aren't talking about other models like Gemini or Llama, but this other tool that combines them all in one place and eliminates the need for recurring fees: 1min.AI. It's hard to imagine how this is even possible or why everyone doesn't use 1min.AI--and that's exactly why we're sharing it. You can get a 1min.AI lifetime subscription here for 99.99, and you won't find a better price anywhere else (reg. With ChatGPT, you're used to getting only GPT models and communicating through a single text box.

Frank Cottrell-Boyce's programme, broadcast on Christmas Eve, will look at the role reading can play in shaping children's lives and find out at what happens to a child's brain when they are read to. Boxing Day will see Dwayne Fields edit a programme with items on homelessness and the value to communities of volunteering. Sir Sajid Javid's programme on 27 December will include items on the benefits and risks of artificial intelligence (AI) as well as the BBC children's programme Grange Hill. Professor Irene Tracey's programme on 28 December will see her explore developments in chronic pain relief, as well as the role that universities play in society. The quality of children's television, including an examination of the move from public service broadcasters to unregulated online platforms, will be among the topics explored on 30 December by Baroness Floella Benjamin.

Applying the word2vec technique, commonly used in language modeling, to melodies--where notes are treated as words in sentences--enables the capture of pitch information. This study examines two datasets: 20 children's songs and an excerpt from a Bach sonata. The semantic space for defining the embeddings is of very small dimension, specifically 2. Notes are predicted based on the 2, 3 or 4 preceding notes that establish the context. A multivariate analysis of the results shows that the semantic vectors representing the notes have a multiple correlation coefficient of approximately 0.80 with their pitches. Keywords Embedding; Machine Learning; Semantic meaning; Correlation 1. Introduction What kind of meaning can we capture from musical notes using word embedding techniques typically applied in language models? This study addresses this question by modeling various types of music with a relatively simple neural network, commonly used for word embedding. An embedding is a vector representation of an entity (a word, an image, a sound) in a multidimensional space where geometric relationships between vectors reflect semantic relationships between the corresponding entities (Chollet, 2021). This inquiry is not new; numerous statistical and computational models, including neural networks, have been proposed to capture key features of musical pieces and to model music perception. In 2016, Madjiheurem, Qu and Walder compared different embedding techniques to learn musical chord embeddings.

From the very first scene of The Wild Robot, the new animated movie from director Chris Sanders (How to Train Your Dragon), adapted from the first in a trilogy of children's novels by Peter Brown, the viewer is plunged along with the protagonist into a new and alien world. A robot washes up on the shore of a lushly forested island, surrounded by the flotsam of some sort of wrecked vehicle--a plane? a spacecraft?--and immediately begins scanning the area for someone she can help. Rozzum Unit 7134, voiced by Lupita Nyong'o and soon to be known as "Roz," has been designed to, as she puts it, offer "integrated, multifaceted task accomplishment" to whatever human requests it of her. The problem is, the island where she's washed up has no human inhabitants, and the animals witnessing the arrival of this hulking metal biped regard Roz as nothing but a menacing predator to be either fought or fled. A witty time-lapse montage shows the robot powering down for a bit so her software can learn to decode the animal sounds around her, enabling her to communicate with all the island's denizens.

A New Jersey couple woke up to a 67-minute-long voicemail from an'unknown caller' - and discovered it was left by their Amazon Alexa. 'I was checking the message ... and was like, wait, this is me talking in the bedroom,' she said. Alexa can call your smartphone if you trigger the'Find My Phone' feature, but a company spokesperson said the Amazon Echo doesn't record or store conversations unless it hears the'wake word,' prompting a light on the device to turn on to let you know it's listening. Amazon has come under fire for its devices recording conversations and faced two separate privacy violation lawsuits last year, including a claim that it had violated children's privacy rights by refusing to remove the recording history of minors. A judge ruled that the company had to pay out a collective 30.8 million for both violations. 'There wasn't a lot of talking in the message, mostly bleeping,' Creegan said, but added that she could hear snippets of her telling Alexa to'turn the lights off' adding that there was'two or three sentences of me talking to the dog.

The problem with Bluey is there's not enough of it. Even with 151 seven-minute-long episodes of the popular children's animated show out there, parents of toddlers still desperately wait for Australia's Ludo Studio to release another season. The only way to get more Bluey more quickly is if they create their own stories starring the Brisbane-based family of blue heeler dogs. The London-based developer and father used OpenAI's latest tool, customizable bots called GPTs, to create a story generator for his young daughter. The bot, which he calls Bluey-GPT, begins each session by asking people their name, age, and a bit about their day, then churns out personalized tales starring Bluey and her sister Bingo.