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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 […]

Crafting a Secure Future: Integrating an AI-First Security Posture

It is now well understood that integrating AI into an organization’s digital infrastructure will unlock real-time insights for decision-makers, streamline internal workflows by automating repetitive tasks, and enhance customer service interactions with AI-powered assistants. This technology will accelerate everything from purchases to personalized service requests. These benefits have made AI an essential component of modern […]

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How to Make Generative AI Work for Natural Language Queries

As businesses increasingly rely on data-driven decision-making, the volume and complexity of data within enterprises have grown exponentially. However, processing this data and deriving actionable insights remains challenging due to the reliance on human analysts. The explosion in interest surrounding text-to-SQL (T2SQL) solutions and the capabilities of large language models (LLMs) like ChatGPT have presented […]

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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 […]

Generative AI Is Accelerating Data Pipeline Management

Data pipelines are like insurance. You only know they exist when something goes wrong. ETL processes are constantly toiling away behind the scenes, doing heavy lifting to connect the sources of data from the real world with the warehouses and lakes that make the data useful. Products like DBT and AirTran demonstrate the repeatability and […]

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Streamlining Your Data Needs for Generative AI

Companies are investing heavily in AI projects as they see huge potential in generative AI. Consultancies have predicted opportunities to reduce costs and improve revenues through deploying generative AI – for example, McKinsey predicts that generative AI could add $2.6 to $4.4 trillion to global productivity. Yet at the same time, AI and analytics projects have historically […]

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Why Effective Data Management is Key to Meeting Rising GenAI Demands

OpenAI’s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, it’s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized. While the possibilities are […]

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Through the Looking Glass: Metaphors, MUNCH, and Large Language Models

“What’s a metaphor?” Mr. Biergel posed the question one morning to my high school grammar class. Being typical teenagers, we looked at him with blank-eyed stares. We expected that if we waited long enough, he’d write a paragraph-long definition on the blackboard. “What’s a metaphor?” he repeated. “A place for cows to graze!” We groaned. […]

12 Key AI Patterns for Improving Data Quality (DQ)

AI is the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. A typical AI system has five key building blocks [1]. 1. Data: Data is number, characters, images, audio, video, symbols, or any digital repository on which operations can be performed by a computer. 2. Algorithm: An algorithm […]

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Maximizing AI’s Potential: High-Value Data Produces High-Quality Results

With the rapid development of artificial intelligence (AI) and large language models (LLMs), companies are rushing to incorporate automated technology into their networks and applications. However, as the age of automation persists, organizations must reassess the data on which their automated platforms are being trained. To maximize the potential of AI using sensitive data, we […]

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How a Neuro-Symbolic AI Approach Can Improve Trust in AI Apps

As a cognitive scientist, I’ve been immersed in AI for more than 30 years – specifically in speech and natural language understanding, as well as machine-based learning and rule-based decision-making. Progress in our field is always uneven, unfolding in fits and starts. Those of us in the AI field have witnessed multiple “AI winters” over the […]

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How AI Tools Can Help Organizations Maximize Their Enterprise Knowledge

AI and machine learning can be revelatory for organizations inundated with a sea of data and grappling to find ways to generate meaningful insights from it. AI tools help identify patterns and trends within large datasets that are often challenging for humans to discern. These tools can be trained to make predictions based on historical […]

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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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Now Is the Time for Executives to Deploy Ethical Rules Around AI

For better or worse, AI is causing disruption in almost every field imaginable. Corporations around the world are embracing its possibilities to make work more efficient. The success of ChatGPT and other generative AI tools has also caught the attention of nearly every industry in an effort to meet profitability, efficiency, and sustainability goals. Money […]

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Crossing the Data Divide: AI Data Assistants — A Data Leader’s Force Multiplier

The focus of my last column, titled Crossing the Data Divide: Data Catalogs and the Generative AI Wave, was on the impact of large language models (LLM) and generative artificial intelligence (AI) and how we disseminate knowledge throughout the enterprise and the future role of the data catalogs. Spoiler alert if you have not read […]

Maximizing Business Value with Generative AI

Have we ever seen something get adopted so quickly as generative AI (GenAI) compared to the past? Think about it: ChatGPT launched in 2022 and gained 100 million users in two months. In comparison, we have been hearing about AI for a few years, but the adoption rates of AI have varied from 25% to […]

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Getting Ahead of Shadow Generative AI

Like any new technology, a lot of people are keen to use generative AI to help them in their jobs. Accenture research found that 89% of businesses think that using generative AI to make services feel more human will open up more opportunities for them. This will force change – Accenture also found that 86% […]

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Creative Ways to Surf Your Data Using Virtual and Augmented Reality

Organizations often struggle with finding nuggets of information buried within their data to achieve their business goals. Technology sometimes comes along to offer some interesting solutions that can bridge that gap for teams that practice good data management hygiene. We’re going to take a look deep into the recesses of creativity and peek at two […]

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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How to Achieve Self-Service Data Transformation for AI and Analytics

Data transformation is the critical step that bridges the gap between raw data and actionable insights. It lays the foundation for strong decision-making and innovation, and helps organizations gain a competitive edge. Traditionally, data transformation was relegated to specialized engineering teams employing complex extract, transform, and load (ETL) processes using highly complex tooling and code. […]

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2024 Predictions in AI and Natural Language Processing (NLP)

While we were right at the dawn of generative AI this time last year, we didn’t predict quite the profound impact and seismic shift it would create around the world with the introduction of ChatGPT. In our set of 2023 predictions, we did note the potential effect of LLMs, with research showing their ability to self-improve, […]

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Author: Jeff Catlin and Paul Barba

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Jan 24 2024

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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