Category: Artificial Intelligence

This introduction to generative artificial intelligence (AI) for startups explains various approaches to build generative AI applications and reviews their key components.

When startups build generative artificial intelligence (AI) into their products, selecting a foundation model (FM) is one of the first and most critical steps. Everything from user experience and go-to-market, to hiring and profitability, can be affected by selecting the right model for your use case. Learn about the most impactful aspects to consider when selecting a foundation model to meet your startup’s needs.

Healthcare and life sciences (HCLS) startups recognize that technology is an impactful vehicle for advancing human health at speed and scale. More importantly, HCLS startups are working to do something about it. C2i Genomics, founded in 2019, is one such startup: C2i Genomics is building a whole genome intelligence platform to improve cancer monitoring. Using artificial intelligence (AI) and machine learning (ML) solutions, C2i Genomics’ platform analyzes sequenced genome data to detect the tumor burden of cancer patients via a simple blood test.

AWS is excited to announce the cohort of startups accepted into the global AWS Generative AI Accelerator. The program kicks off May 24th at our San Francisco AWS Startup Loft and closes on July 27th. Over the course of their 10-week program, participants will receive tailored technical advice, dedicated mentorship, an opportunity to pitch their demos to venture capitalists (VCs) in the AWS network, and up to $300,000 in AWS credits. Critically, they will also have the opportunity to foster lifelong connections with their fellow founders and within AWS. Read on to meet the startups.

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.