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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.

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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:

  1. Tools and Techniques for Instructional Clarity and Accelerated Onboarding in Annotation Tasks.
  2. Interactive Tools and Techniques for Real-Time AI-Assisted Annotation.
  3. ML models for auto-labeling in low data regime that work across tasks and modalities, e.g. vision and language.
  4. 3D deep learning for autonomous driving for auto-labeling and data selection.

Timeline

  • Submission period: June 3 — July 15, 2022
  • Decision letters will be sent out December 2022

Award details

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.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page.

Proposal requirements

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.

Selection criteria

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.

Work with us

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US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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US, MA, Boston
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Applied Scientist, Artificial General Intelligence, AGI Data Services
US, MA, Boston
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