AWS AI: Human-in-the-loop machine learning and annotation call for proposals — Spring 2022
Sharing learnings and ML capabilities as fully managed services, and putting them into the hands of every scientist and developer.
About this CFP
AWS offers a broad and deep set of tools for businesses to create impactful machine learning solutions faster. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every scientist and developer. AWS AI aims to advance machine learning research by funding development of open-source tools and research that benefit the machine learning community at large, or impactful research that uses machine learning tools on AWS, including AWS AI Services, AWS ML Services (Amazon SageMaker, Amazon SageMaker Ground Truth, Amazon SageMaker Neo, and Amazon Augmented AI), and Apache MXNet on AWS.
AWS AI is soliciting funding proposals related to Human-in-the-Loop Machine Learning and Annotation. We specifically aim to solicit funding proposals that focus on innovation related to supporting annotators with machine learning and artificial intelligence. Proposals should be attached to one (or more) of the following themes:
- Tools and Techniques for Instructional Clarity and Accelerated Onboarding in Annotation Tasks.
- Interactive Tools and Techniques for Real-Time AI-Assisted Annotation.
- ML models for auto-labeling in low data regime that work across tasks and modalities, e.g. vision and language.
- 3D deep learning for autonomous driving for auto-labeling and data selection.
- Submission period: June 3 — July 15, 2022
- Decision letters will be sent out October 2022
Selected Principal Investigators (PIs) may receive the following:
- Unrestricted funds, no more than $50,000 USD on average
- AWS Promotional Credits, no more than $40,000 USD on average
- Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers
Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.
Please refer to the ARA Program rules on the FAQ page.
PIs are encouraged to exemplify how their proposed techniques or research studies will apply to tasks related to human-in-the-loop machine learning and data annotation. PIs should either include plans for open source contributions or state that they do not plan to make any open source contributions (data or code) under the proposed effort. Proposals for this CFP should be prepared according to the proposal template and are encouraged to be a maximum of 4 pages, not including Appendices.
ARA will make the funding decisions based on the creativity and quality of the scientific content, and potential impact to the research community and society at large.
Expectations from recipients
To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.