Data is the lifeblood of modern applications, driving everything from application testing to machine learning (ML) model training and evaluation. As data demands continue to surge, the emergence of generative AI models presents an innovative solution. These large language models (LLMs), trained on expansive data corpora, possess the remarkable capability to generate new content across …
Picture this: you’re an Site Reliability Engineer (SRE) responsible for the systems that power your company’s machine learning (ML) services. What do you do to ensure you have a reliable ML service, how do you know you’re doing it well, and how can you build strong systems to support these services? As artificial intelligence (AI) …
Read more “An SRE’s guide to optimizing ML systems with MLOps pipelines”
American Sign Language is the third most prevalent language in the United States — but there are vastly fewer AI tools developed with ASL data than data representing the country’s most common languages, English and Spanish. NVIDIA, the American Society for Deaf Children and creative agency Hello Monday are helping close this gap with Signs, …
Approximately 500 NIST staffers, including at least three lab directors, are expected to lose their jobs at the standards agency as part of the ongoing DOGE purge, sources tell WIRED.
Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs integrate data inputs across modalities in a central hub that processes data in an input-type-agnostic fashion.
Large language models (LLMs), such as the model underpinning the functioning of the conversational agent ChatGPT, are becoming increasingly widespread worldwide. As many people are now turning to LLM-based platforms to source information and write context-specific texts, understanding their limitations and vulnerabilities is becoming increasingly vital.
In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and data, applying optimization processes with stochastic settings, and performing inference processes, to name a few.
This post is in six parts; they are: • The Complexity of NER Systems • The Evolution of NER Technology • BERT’s Revolutionary Approach to NER • Using DistilBERT with Hugging Face’s Pipeline • Using DistilBERT Explicitly with AutoModelForTokenClassification • Best Practices for NER Implementation The challenge of Named Entity Recognition extends far beyond simple …
Read more “How to Do Named Entity Recognition (NER) with a BERT Model”
Inference with transformer-based language models begins with a prompt processing step. In this step, the model generates the first output token and stores the KV cache needed for future generation steps. This prompt processing step can be computationally expensive, taking 10s of seconds or more for billion-parameter models on edge devices when prompt lengths or …
Foundational models (FMs) and generative AI are transforming how financial service institutions (FSIs) operate their core business functions. AWS FSI customers, including NASDAQ, State Bank of India, and Bridgewater, have used FMs to reimagine their business operations and deliver improved outcomes. FMs are probabilistic in nature and produce a range of outcomes. Though these models …