Pushing the Boundaries of Applied AI since 1996
With over two decades at the forefront of artificial intelligence, Gregory's work spans space science, enterprise automation, and responsible AI systems. From NASA-backed research on semantic knowledge graphs to patented innovations in conversational AI and decision intelligence, each publication reflects a deep commitment to scalable, real-world impact. This page highlights key scientific contributions, patents, and peer-reviewed papers driving the future of AI.
Papers
Patents
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Session Handling Using Conversation Ranking and Augmented Agents (2018)
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Image representation of a conversation to self-supervised learning (2021)
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Natural transfer of knowledge between human and artificial intelligence (2021)
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Alignment of values and opinions between two distinct entities (2022)
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Systems and methods for linking a product to external content (2023)
AI Foresight Essays
NASA, SETI, FDL
NASA | SETI | Frontier Development Lab (FDL)
As one of the founding members of the FDL Technical Committee, I have played a long-standing role in shaping the AI research agenda at the intersection of science and space. Over the years, I’ve had the privilege to mentor multiple interdisciplinary teams composed of top-tier researchers, engineers, and domain experts, guiding them in the application of cutting-edge AI techniques—such as NLP, Knowledge Graphs, and LLMs—to some of the most complex challenges faced by NASA and the scientific community.
FDL is an AI research accelerator established in partnership with NASA, the SETI Institute, and other global institutions. Its goal: to apply AI to science for the benefit of humanity. I have been deeply involved in the program’s growth, direction, and delivery, with a focus on high-impact, real-world applications.
Below are a few of the key projects I have contributed to and supported in depth:
Energy Futures: H2 Discovery Engine (2022)
AI for Sustainable Energy Research: In this project, the team built the H2 Golden Retriever (H2GR)—an advanced AI system combining NLP pipelines and Knowledge Graphs to enable intelligent exploration of scientific literature on hydrogen energy. This system supports researchers by automatically linking concepts across tens of thousands of papers, identifying promising directions, and recommending novel research combinations.
SMD Knowledge Graph Discovery (2022)
Semantic Intelligence Across NASA Science Divisions. This initiative constructed a large-scale, cross-domain Knowledge Graph spanning the entirety of NASA’s Science Mission Directorate (SMD). The system helped scientists discover unexpected relationships across Earth science, heliophysics, astrophysics, and planetary science by semantically aligning publications, missions, and datasets. The project introduced powerful tools for interdisciplinary insight and mission planning.
Lightning and Extreme Weather (2020)
Predictive Modeling for Severe Weather Events: Using advanced machine learning and satellite data fusion, this project developed predictive models for high-impact lightning events. The AI system analyzed real-time lightning strike patterns to improve forecasting of severe storms and enhance the early-warning systems critical to climate resilience. This work laid the foundation for integrating AI in global meteorological pipelines.
Papers
Technology Readiness Levels for AI & ML
Abstract: The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment. Our Technology Readiness Levels for ML (TRL4ML) framework defines a principled process to ensure robust systems while being streamlined for ML research and product, including key distinctions from traditional software engineering. Even more, TRL4ML defines a common language for people across the organization to work collaboratively on ML technologies.
H2-Golden-Retriever: Methodology and Tool for an Evidence-Based Hydrogen Research Grantsmanship
Abstract: Hydrogen is poised to play a major role in decarbonizing the economy. The need to discover, develop, and understand low-cost, high-performance, durable materials that can help maximize the cost of electrolysis as well as the need for an intelligent tool to make evidence-based Hydrogen research funding decisions relatively easier warranted this study.In this work, we developed H2 Golden Retriever (H2GR) system for Hydrogen knowledge discovery and representation using Natural Language Processing (NLP), Knowledge Graph and Decision Intelligence. This system represents a novel methodology encapsulating state-of-the-art technique for evidence-based research grantmanship. Relevant Hydrogen papers were scraped and indexed from the web and preprocessing was done using noise and stop-words removal, language and spell check, stemming and lemmatization. The NLP tasks included Named Entity Recognition using Stanford and Spacy NER, topic modeling using Latent Dirichlet Allocation and TF-IDF. The Knowledge Graph module was used for the generation of meaningful entities and their relationships, trends and patterns in relevant H2 papers, thanks to an ontology of the hydrogen production domain. The Decision Intelligence component provides stakeholders with a simulation environment for cost and quantity dependencies. PageRank algorithm was used to rank papers of interest. Random searches were made on the proposed H2GR and the results included a list of papers ranked by relevancy score, entities, graphs of relationships between the entities, ontology of H2 production and Causal Decision Diagrams showing component interactivity. Qualitative assessment was done by the experts and H2GR is deemed to function to a satisfactory level.
NASA Science Mission Directorate Knowledge Graph Discovery
Abstract: The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.
Generative AI entails a credit–blame asymmetry
Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.
Vallée du silicium
Alain Damasio a passé le mois d’avril 2022 à rencontrer une trentaine de personnes, dont quelques cadres français de haut niveau en poste à Twitter, Meta, Google ou Apple, et aussi quelques personnalités du milieu de la Tech ou de la contre-culture de San Francisco,«peu oubliables»: Grégory Renard, Arnaud Augier, Jaron Lanier, Peter Maravelis ou encore Fred Turner. Il a rédigé sept chroniques qui forment le corps de Vallée du silicium, comme autant de récits de voyage mêlant souvenirs personnels ou intimes, observations, analyses politiques, servies par la langue riche en néologisme et trouvailles graphiques ou grammaticales, du style immédiatement identifiable de l’auteur de La Horde du Contrevent.
The Insight-Inference Loop: Efficient Text Classification via Natural Language Inference and Threshold-Tuning
Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets.
AI Foresight Essays
AI Visionary & Foresight Author [2010-2015]
Over a decade ago, Gregory began publishing tech foresight essays on gregoryrenard.wordpress.com, including “The Proactive Web Era” (2011), which predicted the rise of intelligent services and context-aware AI. These science-fiction-style writings consistently anticipated—with uncanny precision—the societal and technological impacts of AI. Today, nearly all of them have materialized within the forecasted timelines. This archive is a testament to my ability to see what’s next—and build for it.
Patents
Gregory Renard is a prolific inventor with a portfolio of over 15 patents spanning conversational AI, contextual computing, and enterprise automation. His innovations have been instrumental in advancing the fields of Natural Language Processing (NLP), Knowledge Graphs, and Large Language Models (LLMs), providing foundational technologies for next-generation AI systems.
Below are a few of the key patents or patents pending:
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Voice and connection platform (2015)
A system and method for providing a voice assistant includ ing receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech rec ognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding. -
Session Handling Using Conversation Ranking and Augmented Agents (2018)
A system and method to receive user input from a human user in a communication session between the human user and a first machine; autonomously determine a sentiment metric of the user input from the human user based on one or more sentiment criteria, wherein the sentiment metric represents an attitude of the human user; autonomously determine a quality metric associated with the communication session; autonomously rank the communication session between human user and first machine based on one or more of the sentiment metric and the quality metric; and determine based on the ranking, to recommend human agent intervention in the communication session between the human user and the first machine.
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Image representation of a conversation to self-supervised learning (2021)
A system and method for receiving, using one or more processors, a first conversation; identifying, using the one or more processors, a first set of utterances associated with a first conversation participant and a second set of utterances associated with a second conversation participant; and generating, using the one or more processors, a first image representation of the first conversation, the first image representation of the first conversation visually representing the first set of utterances and second set of utterances, wherein an utterance is visually represented by a first parameter associated with timing of the utterance, a second parameter associated with a number of tokens in the utter ance, and a third parameter associated with which conversation participant was a source of the utterance.
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Natural transfer of knowledge between human and artificial intelligence (2021)
A system and method for providing natural training to an Al/agent/bot. In one embodiment, the system and method include receiving a first input indicating a trainer's desire to provide knowledge or know-how to an artificially-intelligent agent; creating a first natural training session to capture the knowledge or know-how; receiving the knowledge or know how from the trainer; sending a first response to the trainer, the first response requesting a first prompt that, when received by the artificially-intelligent agent, prompts the artificially-intelligent agent to use the knowledge or know how captured in the first natural training session to respond; receiving a second input from the trainer including a first prompt; validating a capability of the artificially-intelligent agent to correctly respond to requests related to the knowledge or know-how captured by the first training session; and receiving additional.
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Alignment of values and opinions between two distinct entities (2022)
A method to determine alignment between first and second entities by collecting structured and unstructured data from sources including web search, social media, newspaper, and official sources of data; extracting entities and values; pro viding the entities and values text through multiple neural network text processing pipelines to an ensemblist density processing to generate the entities alignment values.
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Systems and methods for linking a product to external content (2023)
Systems and methods are disclosed to automatically associate a product or a service with external content by characterizing the product from unstructured data sources including a product text or text from similar products; generating a label for the product or service; applying the label as a search engine; extracting signals relating to the product or service; and providing business intelligence for the product or service.
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Systems and methods for providing machine learning of business operations and generating recommendations or actionable insights (2023)
An exemplary system that provides automated business intelligence from business data to improve operations of the business is disclosed. The system extracts signals from any unstructured data source. The system identifies anomalies in customer data and global trends for retail companies that present opportunities and crises to avoid and suggests optimal courses of action and estimated financial impact. The system also alerts individuals with opportunities and predicts customers' needs. The system extracts signals from any data source, structured or not, to alert the user of opportunities and anticipate customers' needs. The system determines the trends, what products are hits, the opportunities to pursue, and when to reach out to customers. This is done by collecting data from a multi-source data collection system. Systems and Methods for Analyzing Customer Reviews (2023)
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Systems and Methods for Analyzing Customer Reviews (2023)
Systems and methods are disclosed for analyzing a customer review of a product includes extracting product categories and predicates from the customer review; extracting product features from the customer review; extracting an activity with the product features from the customer review; performing sentiment analysis using a learning machine on the customer review; determining a life scene from the customer review; and analyzing a customer opinion from the customer review.
Connect with Greg
San Francisco - Silicon Valley | Paris | Bruxelles