CLEVELAND, Ohio (WOIO) -School districts across the country spent much of their summer break re-examining their safety measures and security plans, in the wake of the devastating shooting in Uvlade, Texas. The newest trend in school security, is artificial intelligence. Iterate.ai is applying the same kind tech that helps track down criminals with license plate and face recognition, to detect weapons on people who are approaching a school. Iterate founder Brian Santhianathan says they trained their platform in a large catalog of weapons, using 25,000 images. "We have also trained it to detect knives, sharp objects and things like Kevlar vests. We have also trained it to detect masks, both robbery masks and medical masks," he said.

In part 1, I covered an overview and some of the primary challenges in doing MLOps. Implementing models at scale can be a difficult exercise due to the changing nature of data, business, and code. In this part, I'll show how you can build data pipeline components using TensorFlow Extended (TFX). This will follow the work and skills taught in the Machine Learning Engineering (MLOps) in Production Specialization by DeepLearning.ai, I'll go through the final assignment here, but I'll be applying it to a new dataset.

I am currently a PhD student in Graduate School of AI at KAIST. I am in Intelligent mobile-manipulation (IM 2) lab directed by Beomjoon Kim. I am interested in all the things related with creating an intelligent movement of the robot arm including physics simulation, rendering, vision, computational hardware, reinforcement learning, trajectory optimization and actuator. Previously, I obtained my Master Degree in mechanical engineering from Seoul National University under the guidance of Dongjun Lee, and my Bachelor Degree in mechanical engineering from Seoul National University. I also had worked full-time at Samsung Research, and Hanwha Techwin.

Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally, but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks (GANs) to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient ESM CM2Mc-LPJmL. Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the GAN can generalize to future climate scenarios unseen during training. Feature attribution shows that the GAN identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational costs.

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.

As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application-focused fields like Music Information Retrieval. In this work, we explore cross-modal learning in an attempt to bridge audio and language in the music domain. To this end, we propose MusCALL, a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences, producing multimodal embeddings that can be used for text-to-audio and audio-to-text retrieval out-of-the-box. Thanks to this property, MusCALL can be transferred to virtually any task that can be cast as text-based retrieval. Our experiments show that our method performs significantly better than the baselines at retrieving audio that matches a textual description and, conversely, text that matches an audio query. We also demonstrate that the multimodal alignment capability of our model can be successfully extended to the zero-shot transfer scenario for genre classification and auto-tagging on two public datasets.

We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test accuracy on a validation set, that may not exist in practice. In this work, we focus on task-agnostic data valuation without any validation requirements. The data buyer has access to a limited amount of data (which could be publicly available) and seeks more data samples from a data seller. We formulate the problem as estimating the differences in the statistical properties of the data at the seller with respect to the baseline data available at the buyer. We capture these statistical differences through second moment by measuring diversity and relevance of the seller's data for the buyer; we estimate these measures through queries to the seller without requesting raw data. We design the queries with the proposed approach so that the seller is blind to the buyer's raw data and has no knowledge to fabricate responses to queries to obtain a desired outcome of the diversity and relevance trade-off.We will show through extensive experiments on real tabular and image datasets that the proposed estimates capture the diversity and relevance of the seller's data for the buyer.

While French is one of the most popular GCSEs in the UK, many Brits are nervous when it comes to using their language skills later in life. But a new suggests there's nothing to fear - even if it has been decades since you last studied a foreign language. Researchers from the University of York have shown that people tested on foreign languages 50 years after they last sat any exam perform just as well as recent students. 'We often say if you don't use a language, you will lose it, but this doesn't seem to be the case,' said Professor Monika Schmid, Head of the University of York's Department of Language and Linguistics. During recent tests, experts from Abertay University in Dundee, found that speaking more than one language didn't have any cognitive benefit.

Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don't have the tools and capabilities to extract knowledge and make decisions from that data. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.

Created by Jose Portilla 10.5 hours on-demand video course Welcome to the best online course to take your SQL skills to the next level! This course is designed to take you from basic SQL knowledge to a SQL Developer Professional. After completing this course you will have a better understanding of how tables and databases work as well as advanced capabilities in querying the information that is stored in a SQL database effectively. SQL is a critical skill for the modern workforce and we've created an online course specifically designed to take your current SQL knowledge to an advanced level. We'll begin the course with a deep dive into understanding how to construct subqueries and common table expressions, afterwards we'll move on to discussing window functions and advanced Join operations.