Current GenAI scaling law is losing its charm! That is saying, models with ever more parameters trained on ever larger data using ever larger compute, do not necessarily scale as anticipated! More recently though, the focus has shifted to enhancing prediction accuracies through computation (and additional costs) at inference time. This technique modifies the scale law while helping LLMs march towards higher intelligence. Noteworthy is that the training data to support inference benefits from being closed domain, for improved accuracy and rapid convergence. On the other hand, domain specific knowledge graphs deployed today, effectively guardrail LLMs to accurate answers in an explainable way, and cost efficiently. Inference time compute is an interesting technique, yet by hinting ready comparisons may further boost interest in knowledge graphs! -- #knowledgegraph #ontology #genai #llm #nlp #semantictechnology #graph #semanticgraph #graphrag #w3c #rdf #sparql #owl #dl #deeplearning #textdistil #explainableai #explainability #governance
“Gartner predicts that by 2025, 50% of all GenAI initiatives will include Knowledge Graphs.” Philip Rathle Knowledge Graphs are the hot topic at GenAI Summit! #answerquality #explainability #governance #GraphRAG #RDF
Co-Founder & Managing Partner @ StrtupBoost
1wInsightful take, Prasad! Balancing scale with precision is key, and your perspective on knowledge graphs offers a refreshing angle on enhancing AI accuracy. Keep sharing these thought-provoking insights!