Category: Artificial Intelligence

In the automotive industry, TIER IV is an innovative and disruptive startup that is transforming the vehicle production process and the future of mobility. Founded in 2015 by Shinpei Kato in Japan, TIER IV builds platforms based on open source software—platforms they manage using AWS—that their partners use for building autonomous vehicles.

When choosing the infrastructure for their ML workloads, startups should consider how to best approach training and inference. Training is process by which a model is built and tuned for a specific task by learning from existing data. Inference is the process of using that model to make predictions based on new input data. Over the last five years, AWS has been investing in our own purpose-built accelerators to push the envelope on performance and compute cost for ML workloads. AWS Trainium and AWS Inferentia accelerators enable the lowest cost for training models and running inference in the cloud.

Startups know firsthand how better technology can improve the quality of life: From AI/ML allowing scientists to better predict patient health outcomes, to cloud computing driving life-saving innovation, and modern apps enhancing accessibility. Fraud.net is one such startup improving quality of life. They use AWS technology to give customers in the banking and fintech industries a serverless modern application that uses artificial intelligence and machine learning to rapidly identify fraud, leading to more efficient operations and higher customer satisfaction.

Holding onto your time—to be a spouse, to be a community member, to be an individual—is difficult, particularly in the world of startups. InsightFinder, an artificial intelligence (AI) startup that uses machine learning (ML) to help customers prevent outages in their cloud infrastructure, is on a mission to change that.

Founded by Helen Gu in 2016, InsightFinder uses unsupervised machine learning to make cloud infrastructure more reliable. The company’s AI-driven predictive observability platform helps companies to predict business-impacting incidents as well as pinpoint the root cause of impending incidents to avoid business loss and brand damage.

Startups are familiar with the importance of creating great customer experiences. Sentiment analysis is one tool that helps with this. It categorizes data as positive, negative, or neutral based on machine learning techniques such as text analysis and natural language processing (NLP). Companies use sentiment analysis to measure the satisfaction of clients for a target product or service. In this blog post, we explain how Widebot uses Amazon Sagemaker to successfully implement a sentiment classifier for Modern Standard Arabic and Egyptian dialect Arabic.

AWS and Epiphany co-curate an AI/ML bootcamp called AI/ML Reactor. This rigorous 5-week virtual program aimed at driving AI/ML awareness and empowering startups in Pakistan and includes exclusive master classes, a group tech mentoring session, and one-on-one mentoring sessions with AWS specialists and thought leaders.

Building a cloud-distributed and scalable artificial intelligence (AI) application is a cross-team effort that requires complicated management of resources and comes with numerous production concerns such as code changes, refactoring, setting up the infrastructure, and complex developer operations (DevOps). These can confuse the development process, slow down time-to-market, and keep developers from focusing on product innovation.

Originally, Fyle’s Data-Extractor service relied on an external service provider for optical character recognition (OCR) and Fyle’s internal machine learning algorithm to detect amount, category, date, currency, and vendor information. Unfortunately, they were receiving some feedback from customers that their tool wasn’t very accurate. As you can imagine, this isn’t the best place to be, so they rewrote their Data-Extractor service to use Amazon Textract because of its intuitive web console for APIs, which allowed them to test APIs in real-time with personalized input. This let them quickly try out an Amazon Textract API, which helped them achieve their goal of turning around a solution in two months. After implementing their new solution, Fyle saw 51.7% improvement in accuracy for the Data-Extractor service.

As with all best forms of innovation, great ideas stem from true need. In Mexico, there is a need for an equitable, efficient, and sustainable healthcare system. Latino startup founders are addressing this need and advancing healthcare equity by leveraging artificial intelligence (AI) to drive better patient outcomes.

Communities are vital to the health of individuals and companies, but they can be sprawling, disjointed, and difficult to grasp—especially online. Enter Common Room, which co-founder and Chief Architect Tom Kleinpeter describes as a community-intelligence platform that provides a single view into everything that’s important in your online community, across all the different places it might be happening.