Sarah Luger
Sarah Luger host of the AI Artifacts podcast (www.aiartifacts.net) and the Co-Chair of the Data Sets Working Group for AI benchmarking organization, MLCommons. Data Sets Working Group continues the research initiated with the Rigorous Evaluation of AI Systems workshop series at AAAI Human Computation and AAAI conferences. The goal is to develop robust schemas and infrastructure supporting the Open Source hosting of benchmark evaluation data sets. The group aims to provide free storage for researchers who have human-generated data (spoken word data is the current focus) of generally high quality.
Sarah is a Contributing Member of the MLCommons AI Safety Stakeholder Engagement, Benchmarks and Tests, and Platform Technology working groups. This nonprofit engineering consortium guides the ML industry by developing benchmarks, public datasets, and best practice.
Her current AI Safety work focuses on building LLM Safety Test Sets, Creating Scoring System, and Running Benchmarks. Sarah is leading the subsequent work automating the translation of safety test prompts into in low-resource languages.
Jonathan Bennion
Sergey Davidovich
Sergey is an entrepreneur, technological visionary and machine intelligence enthusiast, who continually strives to bridge the gap between human and machine reasoning and interaction. He’s passionate about computational knowledge representation, acquisition, storage, reasoning, and processing.
Sergey has served in a range of executive technological positions in disruptive startup companies. Prior to co-founding SparkBeyond, Sergey served as GM and SVP of R&D for NewBrandAnalytics, a social business intelligence pioneer. He’s also served as VP R&D of SemantiNet, a semantic reasoning engine, and co-founded Delver, a social search engine that was acquired by Sears, where he served as CTO. Prior to founding Delver, Sergey was the architect of a large-scale award-winning predictive maintenance system.
Vipul Raheja
Vipul Raheja is an Applied Research Scientist at Grammarly. He works on developing robust and scalable approaches centered around improving the quality of written communication, leveraging Natural Language Processing and Deep Learning. His research interests lie at the intersection of large language models and controllable text generation. He has published several papers at top-tier Machine Learning and Natural Language Processing conferences and is also an organizer of the workshops on Intelligent and Interactive Writing Assistants held at ACL and CHI conferences. He obtained an MS in Computer Science from Columbia University.
SparkBeyond
Website: https://www.sparkbeyond.com/
SparkBeyond Ltd is a pioneering global artificial intelligence company established in 2013, dedicated to leveraging AI-driven insights for advanced problem-solving.
We have spent the last decade building a proprietary platform which has been used by our partners (e.g. McKinsey) in hundreds of enterprise engagements, with a focus on systematically deriving knowledge from structured data.
Unlike traditional methods, SparkBeyond's innovative platform addresses a significant challenge in Enterprise large language models (LLMs): the blind spot preventing access to crucial insights within high velocity, operational data. While many companies integrate LLMs like GPT into core functions, they often overlook the vast reservoir of valuable information concealed within structured data from telemetry and digital footprint —the real-world performance metrics essential for informed decision-making. SparkBeyond revolutionizes this landscape by extracting deep insights from complex data ecosystems, uncovering hidden patterns, and transforming statistical truths into accessible language. By bridging the gap between data and LLMs, SparkBeyond empowers decision-makers with real-time, actionable intelligence, leading to measurable enhancements in company KPIs.
