IBM Journey to AI blog

MAY 9, 2023

Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. But we’ve faced a paradoxical challenge: automation is labor intensive. ” These large models have lowered the cost and labor involved in automation.

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Unite.AI

JUNE 17, 2024

Pascal Bornet is a pioneer in Intelligent Automation (IA) and the author of the best-seller book “ Intelligent Automation.” He is regularly ranked as one of the top 10 global experts in Artificial Intelligence and Automation. It's true that the specter of job losses due to AI automation is a real fear for many.

Automation 147

Automation Artificial Intelligence Artificial Intelligence Generative AI 147

Unite.AI

SEPTEMBER 22, 2023

Jay Mishra is the Chief Operating Officer (COO) at Astera Software , a rapidly-growing provider of enterprise-ready data solutions. And then I found certain areas in computer science very attractive such as the way algorithms work, advanced algorithms. Data warehousing has evolved quite a bit in the past 20-25 years.

Large Language Models 278

Large Language Models Automation Artificial Intelligence Artificial Intelligence 278

Unite.AI

SEPTEMBER 18, 2024

AI's integration into sales processes can significantly enhance efficiency, streamline workflows, and drive business success through insights derived from complex data. Automating Routine Tasks Sales professionals often spend a significant amount of time on repetitive tasks such as data entry, email management, and scheduling.

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Automation AI AI Algorithm 200

Unite.AI

DECEMBER 27, 2024

The future of AI demands both, but it starts with the data. Why Data Quality Matters More Than Ever According to one survey, 48% of businesses use big data , but a much lower number manage to use it successfully. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential.

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Data Quality Data Drift Explainability Big Data 130

IBM Journey to AI blog

JANUARY 25, 2024

Consider these questions: Do you have a platform that combines statistical analyses, prescriptive analytics and optimization algorithms? Do you have purpose-built algorithms to improve intermittent and variable demand forecasting? Master data enrichment to enhance categorization and materials attributes.

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Algorithm Categorization Data Quality Artificial Intelligence 233

IBM Journey to AI blog

SEPTEMBER 3, 2024

This improvement will lead to the automation of low-level tasks and the augmentation of human abilities, enabling workers to accomplish more with greater proficiency. ” For example, synthetic data represents a promising way to address the data crisis. In this context, data quality often outweighs quantity.

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Generative AI LLM Large Language Models AI 308

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Unite.AI

MAY 23, 2024

Taking stock of which data the company has available and identifying any blind spots can help build out data-gathering initiatives. From there, a brand will need to set data governance rules and implement frameworks for data quality assurance, privacy compliance, and security.

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Machine Learning AI AI AI Engineer 269

Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Unite.AI

OCTOBER 12, 2023

Could you discuss the types of machine learning algorithms that you work on at LXT? Artificial intelligence solutions are transforming businesses across all industries, and we at LXT are honored to provide the high-quality data to train the machine learning algorithms that power them.

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Machine Learning Deep Learning Conversational AI Data Quality 278

IBM Journey to AI blog

APRIL 25, 2024

.” Shantha Farris, Global Digital Commerce Strategy and Offering Leader at IBM iX The vast amounts of data businesses collect, combined with external data sources, can be used to present cross-selling and upselling opportunities that genuinely appeal to customers.

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Generative AI Categorization Automation Data Quality 263

Aiiot Talk

APRIL 30, 2024

Using AI to Enhance Pattern Recognition Advanced AI algorithms trained on large enough datasets can find various patterns and provide detailed insights into the condition of materials. Automated Defect Detection AI provides a viable framework for automatically detecting specific defects like corrosion and deposits by analyzing test images.

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IBM Journey to AI blog

DECEMBER 20, 2023

The automation of tasks that traditionally relied on human intelligence has far-reaching implications, creating new opportunities for innovation and enabling businesses to reinvent their operations. Establish a data governance framework to manage data effectively. Artificial intelligence (AI) is a transformative force.

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AI Strategy Artificial Intelligence Artificial Intelligence Machine Learning 224

Unite.AI

OCTOBER 17, 2023

By collecting extensive data (including purchase history, farm size, types of crops grown, irrigation methods used, technology adoption, automation rate, and more), and letting AI algorithms analyze it, the firm detected that farm size is one of the most critical factors that influence a farmer’s purchasing decision.

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AI AI Machine Learning Algorithm 277

Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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Data Quality ETL Machine Learning Data Ingestion 52

DataRobot Blog

MAY 10, 2022

Since SR 11-7 was initially published in 2011, many groundbreaking algorithmic advances have made adopting sophisticated machine learning models not only more accessible, but also more pervasive within the financial services industry. Developing Robust Machine Learning Models within a MRM Framework. To reference SR 11-7: .

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Automation Machine Learning Data Quality Algorithm 64

Unite.AI

SEPTEMBER 12, 2024

Traditionally, AI research and development have focused on refining models, enhancing algorithms, optimizing architectures, and increasing computational power to advance the frontiers of machine learning. However, a noticeable shift is occurring in how experts approach AI development, centered around Data-Centric AI.

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Data Quality Data Scarcity AI AI 162

Unite.AI

FEBRUARY 17, 2025

A financial crime investigator who once received large volumes of suspicious activity alerts requiring tedious investigation work manually gathering data across systems in order to weed out false positives and draft Suspicious Activity Reports (SARs) on the others.

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Automation Responsible AI Generative AI AI Modeling 147

Unite.AI

OCTOBER 25, 2024

AI SDRs (Sales Development Representatives) have emerged as sophisticated systems that automate and enhance the traditional role of human SDRs, handling everything from initial prospecting and lead qualification to scheduling appointments and managing follow-ups.

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Automation Artificial Intelligence Artificial Intelligence AI 147

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

FEBRUARY 21, 2025

Akeneo's Supplier Data Manager (SDM) is designed to streamline the collection, management, and enrichment of supplier-provided product information and assets by offering a user-friendly portal where suppliers can upload product data and media files, which are then automatically mapped to the retailer's and/or distributors data structure.

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Marktechpost

NOVEMBER 20, 2024

Data labeling involves annotating raw data, such as images, text, audio, or video, with tags or labels that convey meaningful context. These labels act as a guide for machine learning algorithms to recognize patterns and make accurate predictions.

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Natural Language Processing Computer Vision Machine Learning NLP 104

ODSC - Open Data Science

APRIL 28, 2023

These are critical steps in ensuring businesses can access the data they need for fast and confident decision-making. As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation.

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Data Integration ML ESG Big Data 98

Marktechpost

JULY 7, 2024

In recent years, advancements in robotic technology have significantly impacted various fields, including industrial automation, logistics, and service sectors. Autonomous robot navigation and efficient data collection are crucial aspects that determine the effectiveness of these robotic systems.

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Robotics Neural Network Automation Data Quality 111

Marktechpost

MAY 8, 2024

From basic driver assistance to fully autonomous vehicles(AVs) capable of navigating without human intervention, the progression is evident through the SAE Levels of vehicle automation. Despite most scenarios being solvable with traditional methods, unresolved corner cases highlight the necessity for AI-driven solutions.

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Deep Learning Neural Network Data Scarcity Natural Language Processing 121

Unite.AI

FEBRUARY 27, 2024

In the future, this may evolve further into artificial general intelligence (AGI) where the data sets will map to even more complicated and sophisticated actions. You have years of experience working in search and personalization to help improve these algorithms.

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Large Language Models Generative AI Artificial Intelligence Artificial Intelligence 147

IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

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Unite.AI

JANUARY 20, 2025

Noah Nasser is the CEO of datma (formerly Omics Data Automation), a leading provider of federated Real-World Data platforms and related tools for analysis and visualization. By automating complex data queries, datma.FED accelerates access to high-quality, ready-to-use real-world data.

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Automation Data Platform Data Quality Explainability 130

Unite.AI

FEBRUARY 28, 2024

Automated LLM-based analysis of data sources using AI and machine learning -powered algorithms is not only the most effective way to extract these insights; in a world that gets more complicated and data-laden on a daily basis, it's really the only efficient option available.

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Towards AI

MAY 2, 2023

Still, we can prepare for those traditional threats that are already a growing challenge due to AI: Personalized Social Engineering Attacks: The development of generative language and image models, like GPT-4 & Midjourney v5, enables sophisticated and personalized social engineering attacks that are still automated. At what cost?

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Automation AI AI Algorithm 98

Marktechpost

JULY 23, 2024

Existing methods are primarily text-oriented, focusing on the textual content of instructions rather than the execution process, which limits their ability to comprehensively assess the quality and efficacy of instruction datasets. Check out the Paper and GitHub. All credit for this research goes to the researchers of this project.

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Large Language Models Data Quality AI AI 103

Pickl AI

FEBRUARY 17, 2025

Summary: Machine Learning’s key features include automation, which reduces human involvement, and scalability, which handles massive data. It uses predictive modelling to forecast future events and adaptiveness to improve with new data, plus generalization to analyse fresh data.

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Machine Learning Algorithm Explainability Data Quality 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. For many years, Philips has been pioneering the development of data-driven algorithms to fuel its innovative solutions across the healthcare continuum.

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Data Scientist ML Data Science Automation 127

Unite.AI

MAY 3, 2024

Additionally, human feedback allows AI researchers to detect more subtle forms of bias that might not be picked up by automated methods. This facilitates the opportunity to address biases through adjustments in the algorithms, underlying models, or data preprocessing techniques.

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