AI is permeating nearly every industry, and, as a result, it is a powerful skill for working professionals or those soon entering the workforce to master. To help more people learn AI skills and technologies, Nvidia established the Deep Learning Institute University Ambassador Program, which equips educators with resources to teach state-of-the-art AI workshops. This program is now expanding to a new state. On Monday, Nvidia shared a new AI education initiative in the state of Utah. Through the public-private partnership, educators at universities, community colleges, and adult education programs will gain certification through the Nvidia Deep Learning Institute (DLI) University Ambassador Program, according to the release.

A bill headed to Utah's Senate floor would require police to include disclaimers in any report written with help from artificial intelligence. Introduced by Sen. Stephanie Pitcher, SB180 comes nearly a year after multiple police agencies across the country began testing software like Axon's Draft One, prompting concerns from critics and privacy advocates. Draft One was announced by Axon in April 2024, kicking off a major new phase for the company best known for manufacturing tasers and a popular line of body cameras used by law enforcement. Axon built Draft One using Microsoft's Azure OpenAI platform, and is designed to auto-generate police reports using only an officer's body cam audio records. Once processed, Draft One then crafts "a draft narrative quickly," reportedly cutting down on police officer's paperwork by as much as an hour per day.

The dataset we used was collected by Smith and Kohn [49] and is publicly available at: http://crcns.org/data-sets/vc/pvc-11. In summary, macaque monkeys were anesthetized with Utah arrays placed in the primary visual cortex (V1). In our analysis, we considered population spiking activity of monkey-I in response to a gray-scale natural movie. The movie is about a monkey wading through water. It lasts for 30 seconds (with sampling rate 25Hz) and was played repeatedly for 120 times. Similarly as in [21], we used the last 26 seconds of the movies and recordings. Each frame of the movie has 320 320 pixels and we downsampled them to 27 27 pixels. We used the recording from the 69 neurons with time bins 40ms and considered that there cannot be more than one spike per bin (5% of the time bins had more than one spike).

Despite a growing interest in diffusion-based language models, existing work has not shown that these models can attain nontrivial likelihoods on standard language modeling benchmarks. In this work, we take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models, with the goal of building and releasing a diffusion model which outperforms a small but widely-known autoregressive model. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models. We then study scaling laws for our diffusion models and find compute-optimal training regimes which differ substantially from autoregressive models. Using our methods and scaling analysis, we train and release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent samples in unconditional and zero-shot control settings.

Fox News Flash top entertainment and celebrity headlines are here. Glenn Close acknowledged the ever-changing landscape of the entertainment industry during a stop in Park City, Utah for the Sundance Film Festival. The Academy Award-nominated actress has been trying to keep her "equilibrium" lately, ahead of celebrating Sundance Institute icon Michelle Satter at a gala fundraiser. "I'm very lucky to have a job," Close told The Hollywood Reporter. "There were so many people impacted in LA already, and then now with the fires. I was astounded at how few jobs there are in our profession. I'm a big reader of history, and unfortunately, I think not enough people in this country understand the history and what we've just gotten ourselves into. "On top of that is [artificial intelligence].

Deep latent variable models (DLVMs) are designed to learn meaningful representations in an unsupervised manner, such that the hidden explanatory factors are interpretable by independent latent variables (aka disentanglement). The variational autoencoder (VAE) is a popular DLVM widely studied in disentanglement analysis due to the modeling of the posterior distribution using a factorized Gaussian distribution that encourages the alignment of the latent factors with the latent axes. Several metrics have been proposed recently, assuming that the latent variables explaining the variation in data are aligned with the latent axes (cardinal directions). However, there are other DLVMs, such as the AAE and WAE-MMD (matching the aggregate posterior to the prior), where the latent variables might not be aligned with the latent axes. In this work, we propose a statistical method to evaluate disentanglement for any DLVMs in general. The proposed technique discovers the latent vectors representing the generative factors of a dataset that can be different from the cardinal latent axes. We empirically demonstrate the advantage of the method on two datasets.

The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimizing the VAE objective function is more manageable than other DLVMs. The bottleneck dimension of the VAE is a crucial design choice, and it has strong ramifications for the model's performance, such as finding the hidden explanatory factors of a dataset using the representations learned by the VAE. However, the size of the latent dimension of the VAE is often treated as a hyperparameter estimated empirically through trial and error. To this end, we propose a statistical formulation to discover the relevant latent factors required for modeling a dataset. In this work, we use a hierarchical prior in the latent space that estimates the variance of the latent axes using the encoded data, which identifies the relevant latent dimensions. For this, we replace the fixed prior in the VAE objective function with a hierarchical prior, keeping the remainder of the formulation unchanged. We call the proposed method the automatic relevancy detection in the variational autoencoder (ARD-VAE). We demonstrate the efficacy of the ARD-VAE on multiple benchmark datasets in finding the relevant latent dimensions and their effect on different evaluation metrics, such as FID score and disentanglement analysis.

We study an online linear optimization (OLO) problem in which the learner is provided access to K "hint" vectors in each round prior to making a decision. In this setting, we devise an algorithm that obtains logarithmic regret whenever there exists a convex combination of the K hints that has positive correlation with the cost vectors. This significantly extends prior work that considered only the case K =1. To accomplish this, we develop a way to combine many arbitrary OLO algorithms to obtain regret only a logarithmically worse factor than the minimum regret of the original algorithms in hindsight; this result is of independent interest.

Bayesian optimization (BO) is a popular framework for optimizing black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy. To reduce the optimization cost, many multi-fidelity BO methods have been proposed. Despite their success, these methods either ignore or over-simplify the strong, complex correlations across the fidelities. While the acquisition function is therefore easy and convenient to calculate, these methods can be inefficient in estimating the objective function. To address this issue, we propose Deep Neural Network Multi-Fidelity Bayesian Optimization (DNN-MFBO) that can flexibly capture all kinds of complicated relationships between the fidelities to improve the objective function estimation and hence the optimization performance. We use sequential, fidelity-wise Gauss-Hermite quadrature and moment-matching to compute a mutual information based acquisition function in a tractable and highly efficient way. We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design.

Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both domestically and internationally. While reading newspapers enriches our knowledge, we sometimes come across unnecessary content that isn't particularly relevant to our lives. In this paper, we introduce a neural network model designed to summarize Bangla text into concise and straightforward paragraphs, aiming for greater stability and efficiency.