Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they're dependent upon the quality and amount of data used in model development. This post explains a deep learning-based approach developed by the Amazon Machine Learning Solutions Lab for sports event detection using Amazon SageMaker. Our solution uses a multimodal architecture utilizing video, static images, audio, and optical flow data to develop and fine-tune a model, followed by boosting and a postprocessing algorithm.

Google has released TensorFlow 3D, a library that adds 3D deep-learning capabilities to the TensorFlow machine-learning framework. The new library brings tools and resources that allow researchers to develop and deploy 3D scene understanding models. TensorFlow 3D contains state-of-the-art models for 3D deep learning with GPU acceleration. These models have a wide range of applications from 3D object detection (e.g. For instance, 3D object detection is a hard problem using point cloud data due to high sparsity.

In this issue: we look at Neural Architecture Search (NAS) and how it relates to AutoML; we explain the research paper “A Survey on Neural Architecture Search” and how it helps to understand NAS; we speak about Uber’s Ludwig toolbox that lowers the entry point for developers by enabling the training and testing of ML models that can be done without writing code.

Deepfakes have started to appear everywhere – from viral celebrity face swaps to impersonations of political leaders. Millions got their first taste of the technology when they saw former US president Barack Obama using an expletive to describe then-president Donald Trump, or actor Bill Hader shape shifting on a late-night talk show. Earlier this week, social media went into a frenzy after deepfakes surfaced of actor Tom Cruise in a series of TikTok videos that appear to show him doing a magic trick and playing golf, all with a smoothness that was unsettlingly realistic. This isn't even a super high quality deepfake and I'm willing to bet that it could fool most people. Now imagine the quality of deepfake a government agency could produce.https://t.co/wMFMarEtAi

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images. In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn normal variability of the brain from MR scans of healthy individuals.

Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However, for accuracy, random feature methods require more measurements than trainable parameters, limiting their use for data-scarce applications or problems in scientific machine learning. This paper introduces the sparse random feature method that learns parsimonious random feature models utilizing techniques from compressive sensing. We provide uniform bounds on the approximation error for functions in a reproducing kernel Hilbert space depending on the number of samples and the distribution of features. The error bounds improve with additional structural conditions, such as coordinate sparsity, compact clusters of the spectrum, or rapid spectral decay.

Deep learning is advancing at lightning speed, and Alexander Amini '17 and Ava Soleimany '16 want to make sure they have your attention as they dive deep on the math behind the algorithms and the ways that deep learning is transforming daily life. Last year, their blockbuster course, 6.S191 (Introduction to Deep Learning) opened with a fake video welcome from former President Barack Obama. This year, the pair delivered their lectures "live" from Stata Center -- after taping them weeks in advance from their kitchen, outfitted for the occasion with studio lights, a podium, and a green screen for projecting the blackboard in Kirsch Auditorium on their Zoom backgrounds. "It's hard for students to stay engaged when they're looking at a static image of an instructor," says Amini. "We wanted to recreate the dynamic of a real classroom." Amini is a graduate student in MIT's Department of Electrical Engineering and Computer Science (EECS), and Soleimany a graduate student at MIT and Harvard University.

The solution- Use GNY's LSTM neural network to better understand the multiple systems that converge in ground water systems. These include weather patterns, domestic and industrial water usage, non-weather climate events (ie. The LSTM could efficiently predict both the demands on water and the changing resources available to meet those needs. Extending these predictions into the future would allow the Department of Agriculture's NWI to predict when shortages will occur and develop plans that can prepare individuals and businesses accordingly. Better prediction of different annual, and seasonal patterns would increase preparedness and extend the amount of time available to respond meaningfully to potentially life threatening challenges.

With ever-growing data generation and its usage, the demand for machine learning models is multiplying. As ML systems encompass algorithms and rich ML libraries, it helps analyze data and make decisions. There is no wonder that machine learning is gaining more visibility as ML applications are dominating almost every aspect of the modern-day world. With rapidly increasing exploration and adoption of this technology in businesses, it is setting the ground for ample employment opportunities. However, landing a career in this disruptive field, you must be well-equipped and familiar with some of the best machine learning tools to create efficient and functional ML algorithms. Here are the 10 best machine learning tools to look for in 2021.

Markets are subject to fads and the embedded-control sector is far from immune to them. In the 1990s, fuzzy logic seemed to be the way forward and microcontroller (MCU) vendors scrambled to put support into their offerings only to see it flame out. Embedded machine learning (ML) is seeing a far bigger feeding frenzy as established MCU players and AI-acceleration start-ups try to demonstrate their commitment to the idea, which mostly goes under the banner of TinyML. Daniel Situnayake, founding TinyML engineer at software-tools company Edge Impulse and co-author of a renowned book on the technology, says the situation today is very different to that of the 1990s. "The exciting thing about embedded ML is that machine learning and deep learning are not new, unproven technologies - they've in fact been deployed successfully on server-class computers for a relatively long time, and are at the heart of a ton of successful products. Embedded ML is about applying a proven set of technologies to a new context that will enable many new applications that were not previously possible."