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Hot nights: US in July sets new record for overnight warmth

Associated Press

Talk about hot nights, America got some for the history books last month. The continental United States in July set a record for overnight warmth, providing little relief from the day's sizzling heat for people, animals, plants and the electric grid, meteorologists said. The average low temperature for the Lower 48 states in July was 63.6 degrees (17.6 Celsius), which beat the previous record set in 2011 by a few hundredths of a degree. The mark is not only the hottest nightly average for July, but for any month in 128 years of record keeping, said National Oceanic and Atmospheric Administration climatologist Karin Gleason. July's nighttime low was more than 3 degrees (5.4 Celsius) warmer than the 20th century average. Scientists have long talked about nighttime temperatures -- reflected in increasingly hotter minimum readings that usually occur after sunset and before sunrise -- being crucial to health.

Composing the future of banks - FinTech Futures


In my last two posts, I've defined what composable banking is and its potential scope. In both these posts, I've highlighted BIAN as a way of defining the composable modules. One thing I'd like to add though is that BIAN represents the business model of banking first and then how it can be supported by technology. It's not about the modularisation of software components that can be easily interchanged, it is very much about creating flexibility and agility in banking by creating a canonical representation of the business of banking. The biggest challenge for any bank is how do they reach such a vision of composable banking when over decades of investment in technology automation they have hundreds or thousands of systems, with some sharing data through extraction, some integrated through technical bridges and maybe a few more modern solutions through APIs?

ML@GT Awards First-Ever Doctorate in Machine Learning from Georgia Tech


In early December, Harsh Shrivastava became the first person to be awarded a doctorate in machine learning from Georgia Tech. "It's a special, happy feeling to be the first recipient of a ML Ph.D. degree. I am much obliged to my advisor, thesis committee members, friends and academic staff of Georgia Tech for their help and support throughout my Ph.D. years," said Shrivastava. Though machine learning has long been a research interest of the Institute, it wasn't until 2016 that the Machine Learning Center at Georgia Tech (ML@GT) was created. The center began offering a doctorate program in 2017.

Remote Machine Learning Engineers openings in Boston on August 13, 2022 – Data Science Jobs


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Is AI an Opportunity or a Threat?


Artificial intelligence is rapidly evolving and growing more sophisticated every day. With its ability to learn, adapt and even innovate, AI has the potential to reconstruct our world in ways we can only imagine. But as AI continues to evolve, it also raises important questions about its impact on our society, economy, and even our security. Is AI an opportunity or a threat? There are many reasons to believe that AI presents a huge opportunity for humanity.

Cross-Validation in Machine Learning


The model performance is based on dividing the known data into two parts, one to train the model and the other to test the prediction performance, thus obtaining the model accuracy and adjusting it according to the results. However, accuracy depends on how we slip the data, which can lead to possible biases in the model that prevent accuracy from generalizing to unseen data. Cross-validation is used to combat the random split of the data. This is a method that allows testing the performance of a predictive machine learning model, based on the same principle of the Train-Test split technique but with the difference that it must be performed k times and obtain the accuracy of each attempt. This technique is known as k-folds, where each fold is a specific division of the data different from the rest.

Meta Learning


This is a part of the series of blog posts related to automated creation of Machine Learning Models, and Datasets used for training Neural Networks, and Model Agnostic Meta-Learning. If you are interested in the background of the story, you may scroll to the bottom of the post to get the links to previous blog posts. You may also head to Use SERP Data to Build Machine Learning Models page to get a clear idea of what kind of automated Machine Learning Models you can create, or how to utilize it for meta-learning. In previous weeks, I have showcased an example of a form to create machine learning algorithms. It was possible to our storage of meta-data of a machine learning algorithms.

How artificial intelligence could lower nuclear energy costs


Argonne scientists are building systems to streamline operations and maintenance at reactors. Nuclear power plants provide large amounts of electricity without releasing planet-warming pollution. But the expense of running these plants has made it difficult for them to stay open. If nuclear is to play a role in the U.S. clean energy economy, costs must come down. Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory are devising systems that could make nuclear energy more competitive using artificial intelligence.

Xiaomi's shuffling humanoid robot is here to cheer you up


Developed by Xiaomi, CyberOne is a humanoid robot that can supposedly comfort you in times of sadness. The robot was revealed in a sort of self deprecating promo video in which CyberOne can be seen clumsily falling face first multiple times. According to Xiaomi, CyberOne can recognize 85 different environmental sounds and 45 types of human emotion.