Towards AI

NOVEMBER 1, 2023

Last Updated on November 2, 2023 by Editorial Team Author(s): Mirza Anandita Originally published on Towards AI. Consequently, in our case, the initial step in performing feature engineering is to group our features into three groups: categorical features, temporal features, and numerical features.

Categorization 115

Categorization Data Extraction AI AI 115

Flipboard

OCTOBER 25, 2023

By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data. After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. The solution integrates data in three tiers.

ML 167

ML Metadata Data Extraction AI 167

Unite.AI

JULY 26, 2024

As the current workforce ages, Gen Z and Millennials signal a high enthusiasm for AI according to a recent survey on sentiment towards AI in the workplace. This indicates an opportunity to use AI and digital transformation as a retention tool for the workforce of the future. If they don’t see it upfront, they may look elsewhere.

Automation 130

Automation AI AI Categorization 130

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Unite.AI

SEPTEMBER 16, 2024

Key Features: Real-time data replication and integration with major data warehouses. Cons: Confusing transformations, lack of pipeline categorization, view sync issues. It also offers EDI management features alongside data governance. Airbyte Airbyte is an open-source data movement platform with paid tiers.

Data Integration 173

Data Integration ETL Big Data Automation 173

Marktechpost

AUGUST 17, 2024

AI-related risks concern policymakers, researchers, and the general public. Although substantial research has identified and categorized these risks, a unified framework is needed to be consistent with terminology and clarity. This process led to the creating of an AI Risk Database containing 777 risks from 43 documents.

Categorization 64

Categorization AI AI Data Extraction 64

Unite.AI

SEPTEMBER 16, 2024

Key Features: Real-time data replication and integration with major data warehouses. Cons: Confusing transformations, lack of pipeline categorization, view sync issues. Visit Hevo Data → 7. It also offers EDI management features alongside data governance. Visit SAP Data Services → 10. Visit Boomi → 8.

Data Integration 173

Data Integration ETL Big Data Automation 173

Marktechpost

JULY 30, 2024

Traditional methods often flatten relational data into simpler formats, typically a single table. While simplifying data structure, this process leads to a substantial loss of predictive information and necessitates the creation of complex data extraction pipelines. If you like our work, you will love our newsletter.

Deep Learning 117

Deep Learning Neural Network Categorization Data Extraction 117

Marktechpost

AUGUST 4, 2023

uses AI to take care of the finances. The important information from an invoice may be extracted without resorting to templates or memorization, thanks to the hundreds of millions of invoices used to train the algorithms. They can easily implement AI to handle various facets of billing with their Autopilot technology.

AI Tools 98

AI Tools Artificial Intelligence Artificial Intelligence Automation 98

John Snow Labs

JANUARY 30, 2025

These AI tools can identify subtle patterns and risk factors that might be overlooked in traditional screening methods, potentially enabling earlier interventions and improving patient outcomes. In the second stage, the NER model identifies and extracts biomarker and biomarker result entities from clinical text.

NLP 52

NLP Large Language Models LLM Data Analysis 52

Marktechpost

DECEMBER 24, 2024

In the initialization phase, the system divides tasks into subtasks and assigns them to specialized agents, each with distinct roles like data extraction, retrieval, and analysis. Marked features like the monitor mechanism and memory categorization contributed significantly to this success.

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Categorization Large Language Models Data Extraction Artificial Intelligence 54

ODSC - Open Data Science

OCTOBER 27, 2023

With Intelligent Document Processing (IDP) leveraging artificial intelligence (AI), the task of extracting data from large amounts of documents with differing types and structures becomes efficient and accurate. The core idea behind this phase is automating the categorization or classification using AI.

IDP 98

IDP LLM Large Language Models Data Science 98

The MLOps Blog

MARCH 15, 2023

Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Data Consumption : You have reached a point where the data is ready for consumption for AI, BI & other analytics. Provides data security using AI & blockchain technologies.

ETL 59

ETL Categorization Data Integration Automation 59

Kavita Ganesan

JULY 28, 2022

One of the problems business leaders face in communicating with their technical counterparts is trying to describe their AI problem. To simplify some of the communication, here are some common AI problem types. Try to map AI opportunities at hand to these common problem types. Table Of Contents Common AI Problem Types 1.

AI 52

AI AI Categorization Data Extraction 52

John Snow Labs

SEPTEMBER 17, 2024

Named Entities in Clinical Data Abstraction based on NLP One of the most important tasks in NLP is named-entity recognition. Named entity recognition is a natural language processing technology that automatically scans full documents, extracts fundamental elements from the text, and categorizes them.

NLP 52

NLP Natural Language Processing Categorization Automation 52

AWS Machine Learning Blog

MAY 7, 2024

Large language models (LLMs) have unlocked new possibilities for extracting information from unstructured text data. Although much of the current excitement is around LLMs for generative AI tasks, many of the key use cases that you might want to solve have not fundamentally changed.

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Prompt Engineering Prompt Engineer Large Language Models LLM 123

Marktechpost

DECEMBER 12, 2024

Large language models (LLMs) like askFDALabel offer promise by streamlining data extraction from FDA labels, achieving up to 78% agreement with human evaluations for cardiotoxicity. The model categorized toxicity using ternary (No, Less, Most) and binary (Yes, No) scales. Trending: LG AI Research Releases EXAONE 3.5:

Large Language Models 43

Large Language Models Categorization Data Extraction Machine Learning 43

John Snow Labs

MAY 24, 2023

Likewise, almost 80% of AI/ML projects stall at some stage before deployment. Developing a machine learning model requires a big amount of training data. Therefore, the data needs to be properly labeled/categorized for a particular use case. Also, ML and AI models need voluminous amounts of labeled data to learn from.

Natural Language Processing 52

Natural Language Processing NLP Machine Learning Auto-classification 52

Pickl AI

JULY 10, 2023

Packages like dplyr and tidyr offer a wide range of functions for filtering, sorting, aggregating, merging, and reshaping data. These tools enable users to clean and preprocess data, extract relevant information, and create derived variables. · Reproducible Research: R promotes reproducible research through literate programming.

Data Science 52

Data Science Data Scientist Machine Learning Data Analysis 52

John Snow Labs

JANUARY 30, 2025

With its ability to understand context and relationships between extracted information, Amazon Comprehend Medical offers a robust solution for healthcare professionals and researchers looking to automate data extraction, improve patient care, and streamline clinical workflows. not_matched : The entity was not detected at all.

NLP 52

NLP Natural Language Processing Large Language Models Machine Learning 52

John Snow Labs

JANUARY 30, 2025

With its ability to understand context and relationships between extracted information, Amazon Comprehend Medical offers a robust solution for healthcare professionals and researchers looking to automate data extraction, improve patient care, and streamline clinical workflows. While GPT-3.5 seconds.

OpenAI 52

OpenAI NLP Large Language Models Natural Language Processing 52

AWS Machine Learning Blog

AUGUST 1, 2024

In this post, we discuss how to use AWS generative artificial intelligence (AI) solutions like Amazon Bedrock to improve the underwriting process, including rule validation, underwriting guidelines adherence, and decision justification. This is a complex task when faced with unstructured data, varying document formats, and erroneous data.

Generative AI 123

Generative AI Natural Language Processing Machine Learning AI 123

AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Amazon Fraud Detector is called for a fraud prediction score using the data extracted from the mortgage documents.

Automation 96

Automation Computer Vision Data Extraction IDP 96

Dlabs.ai

AUGUST 21, 2023

Does Your Business Even Need AI? Before diving into the world of AI, first question if your business even needs it. From our experience, too many companies adopt AI without acknowledging more straightforward tools could do the job just fine. That’s why you should approach AI with a clear-eyed evaluation.

Large Language Models 98

Large Language Models Automation AI Modeling Categorization 98

Dlabs.ai

AUGUST 21, 2023

Does Your Business Even Need AI? Before diving into the world of AI, first question if your business even needs it. From our experience, too many companies adopt AI without acknowledging more straightforward tools could do the job just fine. That’s why you should approach AI with a clear-eyed evaluation.

Large Language Models 98

Large Language Models Automation Categorization Data Extraction 98

Viso.ai

AUGUST 20, 2024

Decentralized AI (a merger between AI and blockchain) combines two of the most revolutionary technological innovations in recent times: Artificial Intelligence (AI) allows machines and computers to imitate human thinking and decision-making processes. Convergence timeline of AI and blockchain – Source About us: viso.ai

AI Modeling 52

AI Modeling AI AI Machine Learning 52

Dlabs.ai

DECEMBER 6, 2022

After many long hours deliberating whether you need AI — having convinced your board, team, and every stakeholder under the sun that AI ‘just makes sense,’ you’ll feel like you’ve won the lottery. You need to find a partner capable of building the kind of AI you’ve promised your company. Best AI Companies in 2023 1.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Computer Vision Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 26, 2024

By harnessing the power of threat intelligence, machine learning (ML), and artificial intelligence (AI), Sophos delivers a comprehensive range of advanced products and services. The Sophos Artificial Intelligence (AI) group (SophosAI) oversees the development and maintenance of Sophos’s major ML security technology.

LLM 101

LLM Machine Learning ML Prompt Engineer 101

AWS Machine Learning Blog

JANUARY 7, 2025

The answer lay in using generative AI through Amazon Bedrock Flows, enabling them to build an automated, intelligent request handling system that would transform their client service operations. Experimentation framework The ability to test and compare different prompt variations while maintaining version control.

Generative AI 108

Generative AI Automation Data Extraction ETL 108

Viso.ai

MARCH 29, 2024

Normalization : Standardizing text by converting it to a uniform case, removing accents, and resolving abbreviations can reduce the complexity of ML and AI models. Entity Typing (ET): Categorizes entities into more fine-grained types (e.g., scientists, artists).

ML 40

ML Deep Learning NLP BERT 40