Category: Artificial Intelligence

AWS had the great privilege of partnering with SomosVC, an extraordinary group of Latino investors, for a panel discussion on building with generative artificial intelligence (AI). Panelists from the venture capital industry share their insights on how to secure funding for your startup, when to incorporate generative AI into your product, and the competitive advantages of being a startup in the generative AI industry.

Sign-Speak is an innovative startup whose language software recognizes American Sign Language (ASL) and translates it into spoken words (and vice versa) with machine learning. Their platform offers real-time ASL recognition, avatar, and transcription to facilitate communication with Deaf and Hard of Hearing individuals. Sign-Speak leveraged the AWS Impact Accelerator Latino Founders cohort to gain technical and business support as they make the world a more accessible and inclusive place.

STIGMA is an asynchronous messaging app that connects people who are struggling with strangers who share their lived experience to give them living proof that what they’re going through is something they will get through. Participating in the AWS Impact Accelerator Latino Founder cohort helped STIGMA to come up with solutions that would improve the product experience for their members, while also helping the STIGMA team to hone skills such delivering a compelling pitch to investors.

Learn which AWS services can help you to build a generative AI application. The approaches discussed here can help your startup get its product to market as quickly as possible, while maintaining cost efficiency and high performance.

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.