IBM Big Data Hub

SEPTEMBER 6, 2023

Generative AI is powering a new world of creative, customized communications, allowing marketing teams to deliver greater personalization at scale and meet today’s high customer expectations. Enterprise marketing teams stand to benefit greatly from generative AI, yet introduction of this capability will require new skills and processes.

Marketing 112

Marketing Strategy B2B Cost-Benefit 112

Ontotext

FEBRUARY 16, 2023

Assessing the abilities of Artificial Intelligence (AI) systems is a critical aspect of development that has become increasingly important as AI has expanded into various areas of application. We can evaluate their performance through comparisons to human benchmarks, to other AI systems or a combination of both.

Metadata 59

Metadata Measurement Metrics Modeling 59

Laminar Security

DECEMBER 13, 2023

In the ever-evolving digital landscape, the importance of data discovery and classification can’t be overstated. As we generate and interact with unprecedented volumes of data, the task of accurately identifying, categorizing, and utilizing this information becomes increasingly difficult.

Data-driven 119

Data-driven Machine Learning Deep Learning Risk 119

Ontotext

MAY 26, 2021

But, as Ontotext’s CEO Atanas Kiryakov often says, we forget that nobody nurtures the Artificial Intelligence (AI) systems for 7 years to learn how to walk, talk, count, read, write and, even more important, not touch hot stoves and avoid tilting full glasses and asking: Can I get 3 more cookies? What do AI systems need to learn?

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

IBM Big Data Hub

JUNE 14, 2023

Sometimes the problem with artificial intelligence (AI) and automation is that they are too labor intensive. Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. You need to collect, curate, and annotate data for any specific task you want to perform.

Modeling 84

Modeling Data Lake Enterprise Deep Learning 84

Sisense

SEPTEMBER 25, 2020

As datasets become bigger and more complex, only AI, materialized views, and more sophisticated coding languages will be able to glean insights from them. A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistical techniques to handle nontrivial data.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

IBM Big Data Hub

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications.

Modeling 83

Modeling Enterprise Software Data Science 83

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning? the target or outcome variable is known).

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Ontotext

FEBRUARY 1, 2024

We as a taxonomy community are facing rapid technological change and grappling with where our discipline fits into a world captivated by ChatGPT and other generative models. The challenge of complexity Knowledge models for taxonomy have responded to an increasing need for sophistication and expressiveness.

Enterprise 59

Enterprise Management Metadata Modeling 59

Ontotext

MARCH 13, 2024

What is OTKG? OTKG models information about Ontotext, combined with content produced by different teams inside the organization. We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management. Where does AI fit into this?

Marketing 72

Marketing Knowledge Discovery Metadata Data-driven 72

IBM Big Data Hub

JUNE 28, 2023

Today, the rate of data volume increase is similar to the rate of decrease in sequencing cost. Processing terabytes or even petabytes of increasing complex omics data generated by NGS platforms has necessitated development of omics informatics. What is Amazon Omics?

Informatics 62

Informatics Consulting Cost-Benefit Data Architecture 62

Ontotext

MARCH 7, 2024

This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. We have experimented with different models that enable linking to Wikidata. Since they meet our requirements, we’ve chosen them as our primary target knowledge base.

Modeling 59

Modeling Metrics Business Intelligence Visualization 59

Ontotext

AUGUST 31, 2023

Have you ever been in a conversation where someone mentioned a “knowledge graph,” only to realize that their description was completely different from what you had in mind? What is a knowledge graph? An ontology enriches data within a knowledge graph with context and meaning that humans and computers can interpret.

Modeling 64

Modeling Knowledge Discovery Optimization Enterprise 64

Ontotext

MARCH 15, 2024

This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. Have you ever tried to find out what your medical records say after a doctor’s visit? Even for a human, this task is difficult to solve.

Modeling 59

Modeling Metadata Testing Optimization 59

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

Risk 64

Risk Modeling Machine Learning Data Quality 64

Ontotext

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. Next, I will explain how knowledge graphs help them to get a unified view to data derived from multiple sources and get richer insights in less time.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Cloudera

JULY 18, 2018

As such, we are witnessing a revolution in the healthcare industry, in which there is now an opportunity to employ a new model of improved, personalized, evidence and data-driven clinical care. Additionally, organizations are increasingly restrained due to budgetary constraints and having limited data sciences resources.

Machine Learning 51

Machine Learning Predictive Analytics Analytics Prescriptive Analytics 51

bridgei2i

AUGUST 12, 2021

Faster R-CNN is a single-stage model that is trained end-to-end. Here are some examples: In the above image, the green boxes are the Ground Truth, while the red boxes are what the first cut solution could detect. Several architecture firms work with community development companies to ensure safety and regulatory compliance.

Testing 52

Testing ROI Enterprise IT 52

Ontotext

MAY 22, 2023

What is the future of knowledge graphs in the era of ChatGPT and Large Language Models? To start with, Large Language Models (LLM) will not replace databases. They are good for compressing information, but one cannot retrieve from such a model the same information that it got trained on.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

Ontotext

AUGUST 4, 2023

Generating actionable insights across growing data volumes and disconnected data silos is becoming increasingly challenging for organizations. Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360. Data Fabric: Who and What?

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

DataRobot Blog

SEPTEMBER 11, 2019

Creating those plans require ingesting massive amounts of data resources, aggregating, cleansing, and standardizing that data, and then performing analysis on the finished product. That’s because Microsoft Excel is still the go-to tool for performing all of that data prep. AI, on the other hand, doesn’t miss a thing. .

Forecasting 52

Forecasting Visualization Sales Interactive 52

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

DataRobot Blog

MARCH 9, 2022

and discern what’s behind it. With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The first step in developing any model is gathering a suitable source of training data, and sentiment analysis is no exception.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Ontotext

AUGUST 22, 2024

This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. What’s the difference between a graph and a knowledge graph? You can play with this service interactively at [link].

Contextual Data 69

Contextual Data Metadata Modeling Interactive 69

IBM Big Data Hub

JULY 28, 2023

To prevent costly product recalls, excessive scrap, re-work and other costs associated with poor quality, companies look to automate inspections and identify weld defects early and consistently. The unsung heroes Welding is the fusion of two compounds with heat. The chair you’re sitting in while reading this likely has dozens of welds.

Machine Learning 52

Machine Learning Manufacturing Cost-Benefit Visualization 52

Ontotext

APRIL 19, 2024

Image by Gerd Altmann from Pixabay Assisted annotation and other automation methods that can be used for knowledge graph creation and natural language understanding should be more flexible and iterative than they often are. A well-designed knowledge graph describes the contexts or domains of the data it contains.

Metadata 52

Metadata Statistics Interactive Enterprise 52

Ontotext

DECEMBER 17, 2024

Introduction Whether you are in the position of Sherlock Holmes, a data analyst, or a business manager, its always useful to augment your vision of the available data to derive better insights. An intelligent assistant is a handy way to reveal the knowledge locked in the tangled hairball of data.

IT 59

IT Metadata Visualization Modeling 59

Ontotext

APRIL 15, 2020

Making Sense of Messy Data. One of the big challenges for the scientific community in the current situation is the vast volume of data that is constantly produced from various sources in several domains. What follows is a list of the COVID-19-related projects currently using GraphDB. medications: 16,406 instances.

Publishing 105

Publishing Metadata Data mining Data Architecture 105

Ontotext

MARCH 22, 2024

This is part of Ontotext’s AI-in-Action initiative aimed at enabling data scientists and engineers to benefit from the AI capabilities of our products. Disinformation is on the rise and fact-checkers struggle to keep up with the amount of data that is to be analyzed. Text2Event is a transformer model by Lu et al.²

Modeling 59

Modeling Metadata Structured Data Publishing 59

O'Reilly on Data

APRIL 15, 2025

Most AI teams focus on the wrong things. Heres a common scene from my consulting work: AI TEAM Heres our agent architectureweve got RAG here, a router there, and were using this new framework for ME [Holding up my hand to pause the enthusiastic tech lead] Can you show me how youre measuring if any of this actually works?

Experimentation 332

Experimentation Testing Metrics Measurement 332