IBM Journey to AI blog

JANUARY 8, 2024

The Ministry of Justice in Baden-Württemberg recommended using AI with natural language understanding (NLU) and other capabilities to help categorize each case into the different case groups they were handling. Explainability will play a key role. The courts needed a transparent, traceable system that protected data.

Categorization 264

Categorization Automation Explainability Generative AI 264

Unite.AI

SEPTEMBER 19, 2023

Bookkeeping involves the meticulous scanning of receipts, methodically tracking all income and expenses, and categorizing expenditures. However, now, you need to categorize it for tax purposes. Imagine this scenario: you’ve purchased a printer for your home office, and it turns out to be of great help.

Categorization 288

Categorization AI AI Automation 288

Unite.AI

DECEMBER 3, 2024

This article will explain what a semantic layer is, why businesses need one, and how it enables self-service business intelligence. Semantic layers can save this valuable time by categorizing the data and ensuring all the necessary means to access data. The demand for self-service BI is growing quickly. billion by 2032.

Business Intelligence 162

Business Intelligence Data Quality Categorization Explainability 162

Marktechpost

MARCH 22, 2024

Additionally, by displaying the potential transformations between several tables, DATALORE’s LLM-based data transformation generation can substantially enhance the return results’ explainability, particularly useful for users interested in any connected table. Check out the Paper. Also, don’t forget to follow us on Twitter.

Machine Learning 137

Machine Learning Explainability Categorization ETL 137

Pickl AI

MARCH 6, 2025

Summary: This blog explains the differences between one-way ANOVA vs two-way ANOVA, their definitions, assumptions, and applications. Step 3: Organise Your Data Structure your data with: One categorical independent variable (e.g., Step 3: Organise Your Data Set up your dataset with: Two categorical independent variables (e.g.,

Explainability 52

Explainability Categorization Data Analysis 52

IBM Journey to AI blog

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

ESG 238

ESG Categorization Large Language Models NLP 238

Unite.AI

JUNE 5, 2023

Artificial intelligence (AI) technologies, particularly Vision Transformers (ViTs), have shown immense promise in their ability to identify and categorize objects in images. Now, a group of researchers has developed a breakthrough solution: a novel methodology known as “Patch-to-Cluster attention” (PaCa). .

Computer Vision 246

Computer Vision Explainability Categorization Artificial Intelligence 246

AI Weekly

OCTOBER 12, 2023

techspot.com Applied use cases Study employs deep learning to explain extreme events Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations. [Get your FREE REPORT.]

Robotics 264

Robotics Large Language Models Deep Learning Artificial Intelligence 264

Marktechpost

AUGUST 31, 2024

” The SLM performs initial hallucination detection, while the LLM module explains the detected hallucinations. The reasoner is “constrained” in the sense that it focuses solely on explaining the SLM’s decision, rather than performing an open-ended analysis. The methodology focuses on three primary approaches: 1.

Large Language Models 130

Large Language Models Categorization LLM Explainability 130

Machine Learning Mastery

MAY 22, 2024

Introduction Classification algorithms are at the heart of data science, helping us categorize and organize data into pre-defined classes. It is for this reason that those new to data science must know about […] The post 5 Essential Classification Algorithms Explained for Beginners appeared first on MachineLearningMastery.com.

Explainability 67

Explainability Algorithm Categorization Data Science 67

Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

Neural Network 128

Neural Network Natural Language Processing Categorization Explainability 128

AWS Machine Learning Blog

APRIL 7, 2025

How to use prompt caching When evaluating a use case to use prompt caching, its crucial to categorize the components of a given prompt into two distinct groups: the static and repetitive portion, and the dynamic portion. n - Use clear and simple language, avoiding jargon unless it's necessary and explained." "nn4. Specifically, it:nn1.

Generative AI 132

Generative AI Explainability IDP LLM 132

Unite.AI

JANUARY 23, 2025

Bias in AI typically can be categorized into algorithmic bias and data-driven bias. Explainable AI tools make spotting and correcting biases in real time easier. Consequently, the foundational design of AI systems often fails to include the diversity of global cultures and languages, leaving vast regions underrepresented.

Algorithm 113

Algorithm AI Development AI Developer AI 113

Snorkel AI

JULY 2, 2024

Snorkel AI has thoroughly explained weak supervision elsewhere, but I will explain the concept briefly here. Expanding weak supervision to new frontiers Addressing non-categorical problems through weak supervision, particularly in ranking scenarios, underscores the versatility and power of this approach.

Categorization 52

Categorization Neural Network Explainability Large Language Models 52

Snorkel AI

JULY 2, 2024

Snorkel AI has thoroughly explained weak supervision elsewhere, but I will explain the concept briefly here. Expanding weak supervision to new frontiers Addressing non-categorical problems through weak supervision, particularly in ranking scenarios, underscores the versatility and power of this approach.

Categorization 52

Categorization Neural Network Explainability Large Language Models 52

Flipboard

MARCH 13, 2025

OpenAI engineer Wojciech Zaremba explained on a podcast that year that the company had determined there was not enough real-world data of how to move in the real world to keep making progress on the robot. A task, notably, is not the same as a job or occupation. But is it possible to be more systematic?

Robotics 178

Robotics Automation OpenAI AI 178

Towards AI

MAY 23, 2023

RL algorithms can be generally categorized into two groups i.e., value-based and policy-based methods. Policy Gradient Method As explained above, Policy Gradient (PG) methods are algorithms that aim to learn the optimal policy function directly in a Markov Decision Processes setting (S, A, P, R, γ).

Explainability 98

Explainability Neural Network Algorithm Categorization 98

Marktechpost

JULY 2, 2023

It is known that, similar to the human brain, AI systems employ strategies for analyzing and categorizing images. Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks.

Explainability 98

Explainability Neural Network AI Tools Computer Vision 98

Towards AI

FEBRUARY 10, 2025

We have categorized the posts into two main categories– those seeking help and those that do not. These misclassifications and errors explain that the models can sometimes misinterpret strong language when used along with the updates as urgent help-seeking posts.

Data Science 101

Data Science Machine Learning Natural Language Processing NLP 101

Marktechpost

JULY 7, 2023

It turns out that almost all of these LLMs (open-sourced) and projects deal with major security concerns, which the experts have categorized as follows: 1. A New Research Explains The Risk Factors Associated With Open-Source LLMs appeared first on MarkTechPost.

LLM 111

LLM Explainability Large Language Models Machine Learning 111

Towards AI

NOVEMBER 8, 2023

In this post, I will discuss the common problems with existing solutions, explain why I am no longer a fan of Kaggle, propose a better solution, and outline a personalized prediction approach. As shown in the profile of the dataset, there are both integer and categorical features. Product_Category_1 to _3: Category of the product.

Categorization 124

Categorization Explainability Machine Learning Data Analysis 124

AWS Machine Learning Blog

FEBRUARY 20, 2025

Although both RAG and fine-tuning have their pros and cons, RAG is the best approach for building a PAAS AI on the PAAS platform, given Verisks needs for real-time accuracy, explainability, and configurability. Issue categorization After an issue is identified, its categorized based on its nature.

Generative AI 114

Generative AI LLM Auto-classification Categorization 114

Marktechpost

APRIL 22, 2024

The author explains how to design meaningful visualizations by first understanding the data behind the same. It covers topics like plotting continuous and categorical variables, grouping, summarizing, and transforming data for plotting, creating maps, and refining plots to make them more understandable.

Explainability 137

Explainability Categorization Data Scientist Automation 137

AWS Machine Learning Blog

JUNE 3, 2024

To find the relationship between a numeric variable (like age or income) and a categorical variable (like gender or education level), we first assign numeric values to the categories in a way that allows them to best predict the numeric variable. Linear categorical to categorical correlation is not supported.

Generative AI 122

Generative AI Categorization Auto-complete Auto-classification 122

Marktechpost

JULY 18, 2024

Get ranked applicant results, categorize candidates, and automate pre-screening Q&A collecting to deliver actionable insights. To summarize Serra, a candidate search engine powered by artificial intelligence, explains the challenges recruiters face and how it plans to address them.

UX Design 105

UX Design Categorization Artificial Intelligence Artificial Intelligence 105

ODSC - Open Data Science

MARCH 11, 2025

Lucena explained how random forests first introduced the power of ensembles, but gradient boosting takes it a step further by focusing on the residual errors from previous trees. CatBoost : Specialized in handling categorical variables efficiently. LightGBM : Optimized for speed and scalability, making it useful for large datasets.

Machine Learning 52

Machine Learning Deep Learning Categorization Data Scientist 52

Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Quanda differs from its contemporaries, like Captum, TransformerLens, Alibi Explain, etc.,

Explainability 59

Explainability Explainable AI Python Neural Network 59

AWS Machine Learning Blog

NOVEMBER 14, 2024

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. However, explaining why that decision was made requires next-level detailed reports from each affected model component of that AI system. Model risk : Risk categorization of the model version.

ML 101

ML Machine Learning Auto-complete Auto-classification 101

IBM Journey to AI blog

AUGUST 2, 2024

The AI Act takes a risk-based approach, meaning that it categorizes applications according to their potential risk to fundamental rights and safety. Depending on which role you have as a company, you will need to comply with different requirements,” Simons explains. Or are you actually fine-tuning the model quite a bit?

Categorization 122

Categorization Explainability AI AI 122

AWS Machine Learning Blog

JUNE 25, 2024

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Explainability Provides explanations for its predictions through generated text, offering insights into its decision-making process.

Automation 126

Automation Prompt Engineer Prompt Engineering Categorization 126

Towards AI

JANUARY 23, 2025

It explains how GNNs interpret nodes and edges, using examples like cities connected by roads. It defines AI agents, categorizing them by type and architectural topology, and outlines their characteristics and developmental stages within an enterprise context.

Neural Network 95

Neural Network Computer Vision LLM AI 95

Unite.AI

JULY 25, 2024

As Argonne postdoctoral researcher James (Jay) Horwath explains, “The way we understand how materials move and change over time is by collecting X-ray scattering data.” ” This ability to recognize and categorize patterns without human intervention allows for a more comprehensive and unbiased analysis of material behavior.

Neural Network 182

Neural Network Machine Learning Artificial Intelligence Artificial Intelligence 182

Marktechpost

AUGUST 13, 2024

However, the challenge lies in integrating and explaining multimodal data from various sources, such as sensors and images. AI models are often sensitive to small changes, necessitating a focus on trustworthy AI that emphasizes explainability and robustness.

Robotics 109

Robotics Explainability Explainable AI Categorization 109

Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

Explainability 52

Explainability Machine Learning Data Scientist Deep Learning 52

Marktechpost

MAY 5, 2024

Existing surveys detail a range of techniques utilized in Explainable AI analyses and their applications within NLP. The LM interpretability approaches discussed are categorized based on two dimensions: localizing inputs or model components for predictions and decoding information within learned representations.

Natural Language Processing 116

Natural Language Processing Categorization NLP Neural Network 116

Mlearning.ai

JULY 23, 2023

Text Classification : Categorizing text into predefined categories based on its content. It is used to automatically detect and categorize posts or comments into various groups such as ‘offensive’, ‘non-offensive’, ‘spam’, ‘promotional’, and others. It’s ‘trained’ on labeled data and then used to categorize new, unseen data.

Explainability 52

Explainability Categorization Algorithm NLP 52

AWS Machine Learning Blog

NOVEMBER 7, 2024

Through a runtime process that includes preprocessing and postprocessing steps, the agent categorizes the user’s input. Technical Info: Provide part specifications, features, and explain component functions. At this stage, the agent employs guardrails to make sure it stays within its defined scope and capabilities.

DevOps 129

DevOps Generative AI Python Automation 129