Hyundai Motor Company, headquartered in Seoul, South Korea, is one of the largest car manufacturers in the world. They have been heavily investing human and material resources in the race to develop self-driving cars, also known as autonomous vehicles. One of the algorithms often used in autonomous driving is semantic segmentation, which is a task to annotate every pixel of an image with an object class. These classes could be road, person, car, building, vegetation, sky, and so on. In a typical development cycle, the team at Hyundai Motor Company tests the accuracy periodically, and gathers additional images to correct for the insufficient predictive performance in specific situations.

Do you ever wonder what goes behind bringing various levels of autonomy to vehicles? What the vehicle sees (perception) and how the vehicle predicts the actions of different agents in the scene (behavior prediction) are the first two steps in autonomous systems. In order for these steps to be successful, large-scale driving datasets are key. Driving datasets typically comprise of data captured using multiple sensors such as cameras, LIDARs, radars, and GPS, in a variety of traffic scenarios during different times of the day under varied weather conditions and locations. The Amazon Machine Learning Solutions Lab is collaborating with the Laboratory of Intelligent and Safe Automobiles (LISA Lab) at the University of California, San Diego (UCSD) to build a large, richly annotated, real-world driving dataset with fine-grained vehicle, pedestrian, and scene attributes. This post describes the dataset label taxonomy and labeling architecture for 2D bounding boxes using Amazon SageMaker Ground Truth. Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning (ML) workflows. These workflows support a variety of use cases, including 3D point clouds, video, images, and text.

This post is co-written by Nikunj Agarwal, lead data scientist at Edelweiss Tokio Life Insurance. Edelweiss Tokio Life Insurance Company Ltd is a leading life insurance company in India. Its broad spectrum of offerings includes life insurance, health insurance, retirement policies, wealth enhancement schemes, education funding, and more. How are you being recommended a credit card based on your savings account behavior? How about a life insurance product when you buy car insurance, or a side dish when you order a main course on your food ordering app?

In today's world, being able to quickly bring on-premises machine learning (ML) models to the cloud is an integral part of any cloud migration journey. This post provides a step-by-step guide for launching a solution that facilitates the migration journey for large-scale ML workflows. This solution was developed by the Amazon ML Solutions Lab for customers with streaming data applications (e.g., predictive maintenance, fleet management, autonomous driving). Some of the AWS services used in this solution include Amazon SageMaker, which is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly, and Amazon Kinesis, which helps with real-time data ingestion at scale. Being able to automatically refresh ML models with new data can be of high value to any business when an ML model drifts.

There have been breakthroughs in understanding COVID-19, such as how soon an exposed person will develop symptoms and how many people on average will contract the disease after contact with an exposed individual. The wider research community is actively working on accurately predicting the percent population who are exposed, recovered, or have built immunity. Researchers currently build epidemiology models and simulators using available data from agencies and institutions, as well as historical data from similar diseases such as influenza, SARS, and MERS. It's an uphill task for any model to accurately capture all the complexities of the real world. Challenges in building these models include learning parameters that influence variations in disease spread across multiple countries or populations, being able to combine various intervention strategies (such as school closures and stay-at-home orders), and running what-if scenarios by incorporating trends from diseases similar to COVID-19.

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

"F1 and Amazon Machine Learning Solutions Lab took a full year to build the algorithm that led to the fastest driver." Formula 1 has been working with Amazon Web Services (AWS) to rank their racers. After a year of algorithmic heavy lifting, the results are out now. Ayrton Senna, the three-time world champion from Brazil came out on top, followed by the seven-time champion, Michael Schumacher with a time differential of 0.114 second. Whereas current World Champion Lewis Hamilton featured at 3rd position with a relative time of 0.275 seconds.

Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions. In IDG's 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. "We've seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML. Amazon is a prime example of how ML can impact every area of the business.

AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The purpose for these real-time analytics was to provide context and more in-depth experience with top competitors' strategies and tactics. This helped viewers tangibly interpret how specific model strategy translated to on-track performance, which further demystified ML development and demonstrated its real-world application. This enhancement enabled fans to monitor the performance and driving style of competitors from around the world.

New Delhi: French multinational oil and gas company Total announced it has signed an agreement with Google Cloud to jointly develop Artificial Intelligence (AI) solutions for subsurface data analysis for oil and gas exploration and production. "Total is convinced that applying artificial intelligence in the oil and gas industry is a promising avenue to be explored for optimizing our performance, particularly in subsurface data interpretation. We are excited to work with Google Cloud towards this goal. This builds on the strategy being developed at Total, where A.I. is already used, for example, in predictive maintenance at facilities," said Marie-Noëlle Semeria, Senior Vice President, Group CTO at Total. The agreement will focus on the development of AI programs which will make it possible to interpret subsurface images, notably from seismic studies (using Computer Vision technology) and automate the analysis of technical documents (using Natural Language Processing technology), the company said.