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Dec 4 2024

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?

I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]

The Book Look: Enterprise Intelligence

Every once in a while, a book comes along that contains such innovative ideas that I find myself whispering “wow” and “interesting” as I read through the pages. “Enterprise Intelligence,” by Eugene Asahara, is one such book. Eugene takes three basic ingredients that are not so new (business intelligence, knowledge graphs, and large language models), […]

Through the Looking Glass: More Metaphors and LLMs

Part I When I finished my last column, “Through the Looking Glass: Metaphors, MUNCH, and Large Language Models,” I stated my intention to follow up with part II. I would cover whether a knowledge graph’s vocabulary of “triples” relates to metaphoric thinking. I even considered challenging an LLM on its ability to understand metaphors to […]

How to Regain Trust in Your Data: 5 Ways to Take the Fear Out of Data Management

“May you live in interesting times” is both a curse and a blessing. It’s a curse for those who fear what could go wrong, but it’s a blessing for those who look forward to changes with confidence. The same could be said of leveraging data. To be in that latter group, organizations need to be […]

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Ask a Data Ethicist: What Happens When Language Becomes Data?

At a recent presentation for a local post-secondary institution, I fielded a number of questions related to the use of language, primarily English language texts, as training data for generative AI. There were questions around cultural impacts and related ethical concerns. These queries were more nuanced than the usual ones I get around copyright or […]

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Generic LLMs vs. Domain-Specific LLMs: What’s the Difference?

Large language models (LLMs) are a special type of AI model that uses natural language processing (NLP) to understand and generate text similar to human language. They are a form of generative AI trained on textual data to produce textual content. ChatGPT stands out as a well-known example of generative AI. Trained on massive datasets, LLMs […]

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Beyond Generative AI and the Future of Innovation

The power of generative AI (GenAI) seems to have no limits. Every day, we see new barriers being broken and new use cases that no one thought possible. And yet, I can’t help but notice that most of these advances we’re hearing about revolve mostly around content creation. While remarkable in its own right, this begs the […]

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Why the Rise of LLMs and GenAI Requires a New Approach to Data Storage

The new wave of data-hungry machine learning (ML) and generative AI (GenAI)-driven operations and security solutions has increased the urgency for companies to adopt new approaches to data storage. These solutions need access to vast amounts of data for model training and observability. However, to be successful, ML pipelines must use data platforms that offer […]

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Generative AI Challenges and Opportunities for Modern Enterprises

Generative AI (GenAI), machine learning (ML), and large language models (LLMs) are all becoming increasingly important to modern enterprises, but achieving measurable value from AI is still a challenge. Part of the issue is that a well-trained AI model relies on a large amount of data, and for many companies, organizing and making use of […]

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Why Organizations Are Transitioning from OpenAI to Fine-Tuned Open-Source Models

In the rapidly evolving generative AI landscape, OpenAI has revolutionized the way developers build prototypes, create demos, and achieve remarkable results with large language models (LLMs). However, when it’s time to put LLMs into production, organizations are increasingly moving away from commercial LLMs like OpenAI in favor of fine-tuned open-source models. What’s driving this shift, and why are developers embracing it? The primary motivations are simple…

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The Rise of RAG-Based LLMs in 2024

As we step into 2024, one trend stands out prominently on the horizon: the rise of retrieval-augmented generation (RAG) models in the realm of large language models (LLMs). In the wake of challenges posed by hallucinations and training limitations, RAG-based LLMs are emerging as a promising solution that could reshape how enterprises handle data. The surge […]

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2024: Fewer Hallucinations, Private LLMs, and IP Challenges for GenAI Content

For those of us who have been in the AI field for a while, we’ve weathered at least two “AI winters,” interspersed with phases of rapid progress. However, 2023 stands out as a pivotal moment in the trajectory of AI. ChatGPT and other large language models (LLMs) have democratized AI for non-experts, offering immense utility, […]

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Blockchain-Based LLMs: A Game Changer for Data Privacy Protection

In today’s digital age, data privacy has become a major concern for individuals and organizations alike. With the increasing number of data breaches and unauthorized access to personal information, the need for robust data privacy protection measures has never been more pressing. That’s where blockchain-based large language models (LLMs) comes into play. Blockchain is a […]

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Five Trends Shaping Enterprise Data Labeling for LLM Development

In an era where large language models (LLMs) are redefining AI digital interactions, the criticality of accurate, high-quality, and pertinent data labeling emerges as paramount. That means data labelers and the vendors overseeing them must seamlessly blend data quality with human expertise and ethical work practices. Crafting data repositories for LLMs requires diverse and domain-specific […]

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🔴 Red Button Data

🔴 Red Button Data

Category Archives: LLMs