Marktechpost

JULY 7, 2024

Researchers from the University of Toronto present an insightful examination of the advanced algorithms used in modern ad and content recommendation systems. This survey examines these systems’ most effective retrieval algorithms, highlighting their underlying mechanisms and challenges.

Algorithm 97

Algorithm Neural Network Metadata Large Language Models 97

Unite.AI

AUGUST 13, 2024

From virtual assistants like Siri and Alexa to advanced data analysis tools in finance and healthcare, AI's potential is vast. However, the effectiveness of these AI systems heavily relies on their ability to retrieve and generate accurate and relevant information. This is where BM42 comes into play.

Algorithm 255

Algorithm Chatbots AI AI 255

Unite.AI

JULY 19, 2024

That's an AI hallucination, where the AI fabricates incorrect information. The consequences of relying on inaccurate information can be severe for these industries. These tools help identify when AI makes up information or gives incorrect answers, even if they sound believable. This reduces the likelihood of hallucinations.

LLM 279

LLM AI AI Data Quality 279

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. That is still in flux and being worked out.

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. That is still in flux and being worked out.

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. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

AI News

OCTOBER 17, 2024

Data integrity and security emerged as the biggest deterrents to implementing new AI solutions. Executives also reported encountering various AI performance issues, including: Data quality issues (e.g.,

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Big Data AI AI Data Quality 251

Unite.AI

NOVEMBER 15, 2024

Algorithms, which are the foundation for AI, were first developed in the 1940s, laying the groundwork for machine learning and data analysis. Most consumers trust Google to deliver accurate answers to countless questions, they rarely consider the complex processes and algorithms behind how those results appear on their computer screen.

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Automation AI AI ChatGPT 255

AI News

NOVEMBER 21, 2023

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. Companies have struggled with data quality and data hygiene.

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Big Data Data Quality Generative AI Explainability 269

Marktechpost

NOVEMBER 16, 2024

Synthetic data creation uses sophisticated algorithms like GANs, VAEs, or diffusion models to generate imitation datasets that mimic the statistical characteristics of real-world data. The study also showed a trade-off between privacy and data quality. Check out the Paper.

Deep Learning 105

Deep Learning Data Quality Categorization Machine Learning 105

IBM Journey to AI blog

MAY 8, 2023

Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality. Technological risk—security AI algorithms are the parameters that optimizes the training data that gives the AI its ability to give insights. Above all, put in a robust AI governance program.

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AI AI Algorithm Generative AI 243

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

Marktechpost

OCTOBER 11, 2024

Information Retrieval (IR) systems for search and recommendations often utilize Learning-to-Rank (LTR) solutions to prioritize relevant items for user queries. These models heavily depend on user interaction features, such as clicks and engagement data, which are highly effective for ranking.

Neural Network 114

Neural Network Algorithm Data Quality ML 114

Unite.AI

OCTOBER 12, 2023

You're talking about signals, whether it's audio, images or video; understanding how we communicate and what our senses perceive, and how to mathematically represent that information in a way that allows us to leverage that knowledge to create and improve technology. The vehicle for my PhD was the bandwidth extension of narrowband speech.

Machine Learning 278

Machine Learning Deep Learning Conversational AI Data Quality 278

Towards AI

OCTOBER 3, 2024

Yet, despite these advancements, AI still faces significant limitations — particularly in adaptability, energy consumption, and the ability to learn from new situations without forgetting old information. Mimicking the brain’s neuron firing mechanism, SNNs process information only when spikes occur, leading to energy-efficient computations.

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Neural Network Algorithm Continuous Learning AI 124

IBM Journey to AI blog

NOVEMBER 29, 2023

Structured synthetic data types are quantitative and includes tabular data, such as numbers or values, while unstructured synthetic data types are qualitative and includes text, images, and video. Exploring “what-if” scenarios or new business events using synthetic data synthesized from agent-based simulations.

Data Scientist 295

Data Scientist Machine Learning Neural Network AI Modeling 295

How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. By the end, you’ll see why investing in quality data is not just a good idea, but a necessity. Why Does Data Quality Matter? Machine learning algorithms rely heavily on the data they are trained on.

Machine Learning 52

Machine Learning Data Quality Algorithm Chatbots 52

IBM Journey to AI blog

JANUARY 9, 2024

Challenges in rectifying biased data: If the data is biased from the beginning, “ the only way to retroactively remove a portion of that data is by retraining the algorithm from scratch.” This may also entail working with new data through methods like web scraping or uploading.

Data Ingestion 230

Data Ingestion Data Integration Data Quality LLM 230

Aiiot Talk

APRIL 30, 2024

AI and ML are augmenting human capabilities and advanced data analysis, paving the way for safer and more reliable NDT processes in the following ways. 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.

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Automation Algorithm AI AI 130

Unite.AI

MARCH 4, 2024

Knowledge workers use their specialized skills, expertise, and creativity to generate, process, and communicate information. Knowledge workers are confused regarding AI due to exposure to conflicting and contradictory information and uncertainty about its impact on their professional lives. Why Are Knowledge Workers Confused About AI?

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AI AI Explainability Algorithm 162

Towards AI

SEPTEMBER 25, 2024

A Comprehensive Data Science Guide to Preprocessing for Success: From Missing Data to Imbalanced Datasets This member-only story is on us. In just about any organization, the state of information quality is at the same low level – Olson, Data Quality Data is everywhere!

Machine Learning 95

Machine Learning Data Scientist Categorization Data Science 95

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 90

Unite.AI

SEPTEMBER 18, 2024

AI can analyse vast amounts of data to identify high-potential leads, assess their readiness to buy, and prioritise them accordingly – an approach known as lead scoring. AI-driven lead scoring systems use algorithms to evaluate the likelihood that a lead will convert based on behaviour, demographics, and interactions.

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Automation AI AI Algorithm 147

IBM Journey to AI blog

SEPTEMBER 3, 2024

” For example, synthetic data represents a promising way to address the data crisis. This data is created algorithmically to mimic the characteristics of real-world data and can serve as an alternative or supplement to it. In this context, data quality often outweighs quantity.

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Generative AI LLM AI AI 329

Unite.AI

MAY 23, 2024

For example, leveraging AI to create a more robust and effective product recommendation and personalization engine requires connecting user data from a CRM and sourcing product data from a Product Information Management (PIM) system.

AI 269

AI AI Machine Learning AI Engineer 269

Pickl AI

OCTOBER 9, 2023

However, with the emergence of Machine Learning algorithms, the retail industry has seen a revolutionary shift in demand forecasting capabilities. This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming.

Machine Learning 98

Machine Learning Algorithm ML Data Quality 98

Pickl AI

OCTOBER 2, 2024

Summary: Artificial Intelligence (AI) is revolutionising Genomic Analysis by enhancing accuracy, efficiency, and data integration. Despite challenges like data quality and ethical concerns, AI’s potential in genomics continues to grow, shaping the future of healthcare.

Neural Network 52

Neural Network Artificial Intelligence Artificial Intelligence Deep Learning 52

Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Algorithm Data Quality Neural Network 52

Marktechpost

AUGUST 7, 2024

The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs. When applying this to AI algorithms, the results must be explainable rather than simply produced by an opaque “black box” model. This includes considering patient population, disease conditions, and scanning quality.

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Explainable AI Explainability Neural Network Algorithm 109

Marktechpost

SEPTEMBER 11, 2024

Existing AI models rely on short-term memory, which fails to retain information across conversations. This system improves the ability of AI to store and retrieve information from past interactions. To address this problem, researchers proposed a Chrome extension, Claude Memory , a memory-enhancing system integrated with Claude AI.

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Natural Language Processing Data Extraction Data Quality Chatbots 64

Unite.AI

NOVEMBER 11, 2024

Jumio’s industry-leading AI-powered platform has evolved to integrate continually advanced AI and machine learning algorithms to analyze biometric data more effectively. We anticipate the increasing use of synthetic data generation, which offers greater controllability, data privacy and a focus on data quality rather than quantity.

AI 162

AI AI Machine Learning Natural Language Processing 162

Pickl AI

OCTOBER 17, 2024

This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Efficient management of ETL Data is essential for businesses seeking to leverage their information for strategic decision-making.

ETL 52

ETL Data Quality Data Integration Automation 52

Unite.AI

OCTOBER 17, 2023

Because its segmentation process is run only by data, we can then learn about customer segments that we hadn’t thought about, and this uncovers unique information about our customers. In those cases, a traditional approach run by humans can work better, especially if you mainly have qualitative data.

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AI AI Machine Learning Algorithm 277

TransOrg Analytics

AUGUST 2, 2024

The Enduring Significance of Classical Machine Learning Classical machine learning (ML) encompasses a range of algorithms and techniques that have been the backbone of AI advancements for decades. Unlike generative AI, which focuses on creating new data, classical ML specializes in analyzing and predicting based on existing data.

Machine Learning 52

Machine Learning Generative AI Algorithm ML 52

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. This approach enables a more granular and accurate assessment of the data’s quality.

Large Language Models 100

Large Language Models AI AI Data Quality 100

Unite.AI

FEBRUARY 28, 2024

Gathering this information – much less mastering it – is a difficult, time-consuming, and tedious process, especially for sales teams at smaller pharma firms, where resources are likely limited. Data abounds today, collected from a wide variety of sources, both inside and outside the organization.

Automation 100

Automation LLM Algorithm ChatGPT 100

Towards AI

NOVEMBER 6, 2024

This story explores CatBoost, a powerful machine-learning algorithm that handles both categorical and numerical data easily. CatBoost is a powerful, gradient-boosting algorithm designed to handle categorical data effectively. But what if we could predict a student’s engagement level before they begin?

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Categorization Algorithm Machine Learning Python 87