Unite.AI

JULY 7, 2023

And if these applications are not expressive enough to meet explainability requirements, they may be rendered useless regardless of their overall efficacy. Based on our findings, we have determined that Explainable AI using expressive Boolean formulas is both appropriate and desirable for those use cases that mandate further explainability.

Explainable AI 260

Explainable AI Explainability Machine Learning Computer Scientist 260

Marktechpost

JANUARY 14, 2025

These challenges highlight the need for systems that can adapt and learnproblems that Machine Learning (ML) is designed to address. ML has become integral to many industries, supporting data-driven decision-making and innovations in fields like healthcare, finance, and transportation. The benefits of ML are wide-ranging.

Machine Learning 79

Machine Learning ML Algorithm Artificial Intelligence 79

Unite.AI

SEPTEMBER 13, 2023

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

Explainability 273

Explainability Generative AI AI Tools Algorithm 273

Analytics Vidhya

JUNE 12, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Agglomerative Clustering using Single Linkage (Source) As we all know, The post Single-Link Hierarchical Clustering Clearly Explained! appeared first on Analytics Vidhya.

Explainability 271

Explainability Data Science Algorithm ML 271

Machine Learning Mastery

NOVEMBER 22, 2024

This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task. Unity makes strength.

Explainability 114

Explainability Neural Network Machine Learning ML 114

Marktechpost

OCTOBER 31, 2024

However, explainability is an issue as they are ‘black boxes,’ so to say, hiding their inner working. XElemNet, the proposed solution, employs explainable AI techniques, particularly layer-wise relevance propagation (LRP), and integrates them into ElemNet. Check out the Paper.

Neural Network 85

Neural Network Explainable AI Explainability Machine Learning 85

AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

ML 86

ML Generative AI AI AI 86

Analytics Vidhya

JANUARY 17, 2022

Introduction As a part of writing a blog on the ML or DS topic, I selected a problem statement from Kaggle which is Microsoft malware detection. Here this blog explains how to solve the problem from scratch. In this blog I will explain to […]. This article was published as a part of the Data Science Blogathon.

Explainability 349

Explainability Data Science ML Algorithm 349

AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. 8B model using the new ModelTrainer class. amazonaws.com/pytorch-training:2.2.0-gpu-py310"

ML 80

ML Python Data Scientist Natural Language Processing 80

ODSC - Open Data Science

MARCH 12, 2025

As a machine learning (ML) practitioner, youve probably encountered the inevitable request: Can we do something with AI? Stephanie Kirmer, Senior Machine Learning Engineer at DataGrail, addresses this challenge in her talk, Just Do Something with AI: Bridging the Business Communication Gap for ML Practitioners. The key takeaway?

ML 52

ML Machine Learning AI AI 52

Unite.AI

APRIL 18, 2024

Tackling Bias with Data Large Volumes of High-Quality Data Among many important data management practices, a key component to overcoming and minimizing bias in AI/ML models is to acquire large volumes of high-quality, diverse data. When it comes to AI/ML models, they can also protect the IP of the model being run–”two birds, one stone.”

ML 184

ML AI AI Explainability 184

Unite.AI

AUGUST 28, 2024

However, while many cyber vendors claim to bring AI to the fight, machine learning (ML) – a less sophisticated form of AI – remains a core part of their products. ML is unfit for the task. Deep learning (DL), the most advanced form of AI, is the only technology capable of preventing and explaining known and unknown zero-day threats.

Deep Learning 274

Deep Learning Explainability Neural Network Metadata 274

AI News

DECEMBER 15, 2023

And that’s a big struggle,” explains Grande. Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. . “We are seeing a lot of companies struggle with the dataset.

Big Data 339

Big Data Neural Network Machine Learning Explainability 339

Flipboard

OCTOBER 25, 2023

However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data. We explain this in detail later in this post. The solution integrates data in three tiers.

ML 167

ML Metadata Data Extraction AI 167

Towards AI

JULY 22, 2024

I have been studying machine learning for the past 6 years, in which I worked as an ML student researcher for over 2 years, and have even written my first 3 papers. My journey started studying computer engineering, not knowing what ML was or even that it existed, to where I am now, soon joining my favorite AI startup as a research scientist!

ML 119

ML Robotics Machine Learning Explainability 119

Unite.AI

MARCH 3, 2025

Can you explain what neurosymbolic AI is and how it differs from traditional AI approaches? Our ultimate goal is to bring actionable transparency, where the AI systems can explain their reasoning in a way thats independently logically verifiable. Can you explain how it works and its significance in solving complex problems?

Automation 162

Automation Algorithm Explainability Large Language Models 162

AWS Machine Learning Blog

FEBRUARY 19, 2025

More importantly, Automated Reasoning checks can explain why a statement is accurate using mathematically verifiable, deterministic formal logic. Automated Reasoning cant predict future events or handle ambiguous situations, nor can it learn from new data such as ML models. However, its important to understand its limitations.

Automation 75

Automation Explainability Auto-complete Generative AI 75

Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. Enter Explainable AI (XAI), a field dedicated to making AI’s decision-making process more transparent and understandable.

Explainability 105

Explainability Explainable AI Natural Language Processing Prompt Engineer 105

Analytics Vidhya

JULY 1, 2022

Introduction Leading biopharmaceutical industries, start-ups, and scientists are integrating Machine Learning (ML) and Artificial Intelligence Learning (AIL) into R&D to analyze extensive large data & data sets, identify patterns, and generate algorithms to explain them.

Data Science 271

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 271

Unite.AI

AUGUST 30, 2024

OctoAI was spun out of the University of Washington by the original creators of Apache TVM, an open source stack for ML portability and performance. TVM enables ML models to run efficiently on any hardware backend, and has quickly become a key part of the architecture of popular consumer devices like Amazon Alexa.

ML 157

ML Prompt Engineer Prompt Engineering Generative AI 157

AI News

SEPTEMBER 27, 2024

“Upon release, DBRX outperformed all other leading open models on standard benchmarks and has up to 2x faster inference than models like Llama2-70B,” Everts explains. “It ” Genie: Everts explains this as “a conversational interface for addressing ad-hoc and follow-up questions through natural language.”

Large Language Models 308

Large Language Models Big Data Explainability ETL 308

Marktechpost

AUGUST 13, 2024

Georgia Tech and IBM Research researchers have introduced a novel tool called Transformer Explainer. Transformer Explainer is an open-source, web-based platform allowing users to interact directly with a live GPT-2 model in their web browsers. This tool is designed to make learning about Transformers more intuitive and accessible.

Explainability 132

Explainability AI Modeling Natural Language Processing Machine Learning 132

Marktechpost

MARCH 22, 2024

Data scientists and engineers frequently collaborate on machine learning ML tasks, making incremental improvements, iteratively refining ML pipelines, and checking the model’s generalizability and robustness. To build a well-documented ML pipeline, data traceability is crucial.

Machine Learning 135

Machine Learning Explainability Categorization ETL 135

AI News

AUGUST 29, 2023

Operationalisation needs good orchestration to make it work, as Basil Faruqui, director of solutions marketing at BMC , explains. “If CRMs and ERPs had been going the SaaS route for a while, but we started seeing more demands from the operations world for SaaS consumption models,” explains Faruqui. So that’s on the vendor side.

Data Ingestion 243

Data Ingestion Big Data Explainability ETL 243

Marktechpost

MARCH 8, 2025

Also,feel free to follow us on Twitter and dont forget to join our 80k+ ML SubReddit.

Explainability 52

Explainability Neural Network AI AI 52

Marktechpost

OCTOBER 6, 2024

To address these challenges, researchers are exploring Multimodal Large Language Models (M-LLMs) for more explainable IFDL, enabling clearer identification and localization of manipulated regions. Although these methods achieve satisfactory performance, they need more explainability and help to generalize across different datasets.

Large Language Models 113

Large Language Models Explainability Explainable AI AI 113

Flipboard

JANUARY 22, 2025

Make sure the role includes the permissions for using Flows, as explained in Prerequisites for Amazon Bedrock Flows , and the permissions for using Agents, as explained in Prerequisites for creating Amazon Bedrock Agents. Irene Arroyo Delgado is an AI/ML and GenAI Specialist Solutions Architect at AWS.

Generative AI 171

Generative AI ML Automation Explainability 171

Analytics Vidhya

JUNE 1, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In this article, I’m gonna explain about DBSCAN algorithm. The post Understand The DBSCAN Clustering Algorithm! appeared first on Analytics Vidhya.

Algorithm 306

Algorithm Data Science Explainability ML 306

Towards AI

JANUARY 31, 2025

Explaining a black box Deep learning model is an essential but difficult task for engineers in an AI project. Image by author When the first computer, Alan Turings machine, appeared in the 1940s, humans started to struggle in explaining how it encrypts and decrypts messages. Author(s): Chien Vu Originally published on Towards AI.

Deep Learning 95

Deep Learning Computer Vision Explainability NLP 95

Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

Neural Network 177

Neural Network Algorithm Machine Learning Explainability 177

Flipboard

FEBRUARY 14, 2025

Hemant Madaan, an expert in AI/ML and CEO of JumpGrowth, explores the ethical implications of advanced language models. Artificial intelligence (AI) has become a cornerstone of modern business operations, driving efficiencies and delivering insights across various sectors. However, as AI systems

Explainable AI 128

Explainable AI Explainability Algorithm Artificial Intelligence 128

AI News

AUGUST 16, 2024

The benchmark, previously known as Geekbench ML during its preview phase, has been rebranded to align with industry terminology and ensure clarity about its purpose. “Measuring performance is, put simply, really hard,” explained Primate Labs. The release of Geekbench AI 1.0

Big Data 329

Big Data AI AI Machine Learning 329

Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

Composite AI 176

Composite AI Explainability Neural Network Explainable AI 176

Unite.AI

DECEMBER 1, 2023

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. SHAP ( Source ) Both LIME and SHAP have emerged as essential tools in the realm of AI and ML, addressing the critical need for transparency and trustworthiness.

LLM 261

LLM Machine Learning Explainability Algorithm 261

Towards AI

DECEMBER 15, 2024

In this article, I have explained each of these key metrics in a short and concise way, using real-life examples to make them easy to understand. 👉 Easy to interpret and explain. This will help you apply these concepts in real-world scenarios and answer interview questions accurately, meeting the interviewers expectations.

ML 111

ML Explainability Data Scientist Machine Learning 111

AWS Machine Learning Blog

NOVEMBER 26, 2024

We use the following prompt to read this diagram: The steps in this diagram are explained using numbers 1 to 11. Can you explain the diagram using the numbers 1 to 11 and an explanation of what happens at each of those steps? Architects could also use this mechanism to explain the floor plan to customers.

Convolutional Neural Networks 96

Convolutional Neural Networks LLM Metadata Explainability 96

AI News

AUGUST 8, 2024

Law firms are seen as traditional, not as eager adopters of new technology, but most have used machine learning (ML) for years. Embedded in popular platforms like Westlaw, ML is often incorporated into core operations. Three points help explain those results. The gains are not incremental.

AI 351

AI AI Large Language Models Generative AI 351