Unite.AI

SEPTEMBER 13, 2023

Mystery and Skepticism In generative AI, the concept of understanding how an LLM gets from Point A – the input – to Point B – the output – is far more complex than with non-generative algorithms that run along more set patterns. Additionally, the continuously expanding datasets used by ML algorithms complicate explainability further.

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Aiiot Talk

JULY 7, 2023

AI can streamline and automate key safety processes such as design, monitoring, testing and more. AI-Powered Predictive Maintenance AI is a powerful tool for improving aircraft safety through predictive analytics. This information serves as a baseline for comparison so the algorithm can identify unusual activity.

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Black Box AI Algorithm Explainability Explainable AI 130

Unite.AI

SEPTEMBER 20, 2024

Its real-time trend analysis, investment evaluations, risk assessments, and automation features empower financial professionals to make informed choices efficiently. Key milestones in this evolution include the advent of algorithmic trading in the late 1980s and early 1990s, where simple algorithms automated trades based on set criteria.

Machine Learning 130

Machine Learning Large Language Models NLP Automation 130

Unite.AI

JANUARY 28, 2025

Both DeepSeek and OpenAI are playing key roles in developing more innovative and more efficient technologies that have the potential to transform industries and change the way AI is utilized in everyday life. The Rise of Open Reasoning Models in AI AI has transformed industries by automating tasks and analyzing data.

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OpenAI Natural Language Processing Explainable AI Explainability 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. Explainable AI — Explainable AI is achieved when an organization can confidently and clearly state what data an AI model used to perform its tasks.

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Data Scientist Metadata Responsible AI Explainability 234

IBM Journey to AI blog

FEBRUARY 8, 2023

For many of today’s organizations today, governing AI requires a lot of manual work that include the use of multiple tools, applications and platforms. Lack of automation can lead to lengthy model approval, validation and deployment cycles during which model drift and bias can happen.

Responsible AI 130

Responsible AI Explainability Explainable AI Automation 130

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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IBM Journey to AI blog

JULY 17, 2023

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. Watsonx.ai

Generative AI 171

Generative AI Auto-complete Automation AI 171

IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

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Large Language Models DevOps Data Science AI Modeling 238

IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. Deep learning algorithms are neural networks modeled after the human brain. Some people worry that AI and machine learning will eliminate jobs.

Data Science 175

Data Science Machine Learning Big Data Data Mining 175

Unite.AI

OCTOBER 25, 2024

SLK's AI-powered platforms and accelerators are designed to automate and streamline processes, helping businesses reach the market more quickly. In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times.

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Automation Explainability Responsible AI AI Automation 147

Unite.AI

APRIL 22, 2024

AI-driven telehealth platforms, employing tools like chatbots, autonomously handle patient interactions, schedule appointments, and deliver medical information. With more than 13 million global users, Ada Health exemplifies transparent, explainable AI in healthcare, providing clear insights into the diagnostic process.

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NYU Center for Data Science

JUNE 16, 2023

Home to both a thriving tech ecosystem and pioneering efforts on regulating algorithm decision-making systems, New York City provides a vibrant research environment and a plethora of interdisciplinary collaborators,” said Umang. For these reasons, I am excited to start my academic journey at NYU. By Meryl Phair

Machine Learning 113

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Unite.AI

DECEMBER 27, 2024

Its because the foundational principle of data-centric AI is straightforward: a model is only as good as the data it learns from. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential. Another promising development is the rise of explainable data pipelines. Why is this the case?

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Data Quality Data Drift Explainability Big Data 130

Marktechpost

MAY 12, 2024

It is based on adjustable and explainable AI technology. The technology provides automated, improved machine-learning techniques for fraud identification and proactive enforcement to reduce fraud and block rates. Its initial AI algorithm is designed to detect errors in data, calculations, and financial predictions.

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AI Tools Neural Network Artificial Intelligence Artificial Intelligence 103

Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. When you dissect AI’s supply chain, at the root, you will find algorithms. What factors does it weigh?

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Dlabs.ai

JULY 19, 2023

Example: Algorithmic Bias in the UK A-level Grading To illustrate, consider a real-world example that occurred during the COVID-19 pandemic in the UK. With the traditional A-level exams canceled due to health concerns, the UK government used an algorithm to determine student grades.

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IBM Journey to AI blog

JUNE 28, 2023

Most individual omics informatics tools and algorithms focus on solving a specific problem, which is usually part of a large project. This forces organizations to integrate multiple tools into a single pipeline to serve various goals. gene expression; microbiome data) and any tabular data (e.g.,

Data Analysis 186

Data Analysis Data Scientist Explainable AI AI Strategy 186

Pickl AI

OCTOBER 8, 2024

This blog will explore the concept of XAI, its importance in fostering trust in AI systems, its benefits, challenges, techniques, and real-world applications. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques that enable human users to comprehend and interpret the decisions made by AI systems.

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Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

Marktechpost

AUGUST 24, 2023

You will never miss any updates on ML/AI/CV/NLP fields because it is posted on a daily basis and highly moderated to avoid any spam. r/Automate The sub has more than 75k members participating in discussions and posts focused on automation. The subreddit has excellent computer vision and artificial intelligence content.

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Artificial Intelligence Artificial Intelligence Machine Learning Computer Vision 97

Pickl AI

FEBRUARY 17, 2025

Summary: Machine Learning’s key features include automation, which reduces human involvement, and scalability, which handles massive data. Algorithmic bias can result in unfair outcomes, necessitating careful management. Transparency in AI systems fosters trust and enhances human-AI collaboration.

Machine Learning 52

Machine Learning Algorithm Explainability Data Quality 52

How to Learn Machine Learning

JUNE 9, 2024

The brief yet convincing answer to these questions is the ability of ML solutions to automate routine tasks and facilitate decision-making. 5 Top Machine Learning Trends in 2024 Here are the top 5 machine learning trends that you must watch for in 2024: 1) AutoML Some people refer to Automated Machine Learning as AutoML.

Machine Learning 52

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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.

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ODSC - Open Data Science

FEBRUARY 18, 2025

Recent breakthroughs include OpenAIs GPT models, Google DeepMinds AlphaFold for protein folding, and AI-powered robotic assistants in industrial automation. These innovations enable AI to transition from tool-like applications to fully autonomous problem-solvers.

Explainability 59

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Pickl AI

NOVEMBER 5, 2024

Summary: Data Science and AI are transforming the future by enabling smarter decision-making, automating processes, and uncovering valuable insights from vast datasets. AI automates processes, reducing human error and operational costs. The impact is profound and far-reaching.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

SEPTEMBER 17, 2024

Summary : AI is transforming the cybersecurity landscape by enabling advanced threat detection, automating security processes, and adapting to new threats. Introduction In the rapidly evolving landscape of cybersecurity, Artificial Intelligence (AI) has emerged as a powerful tool in the fight against cyber threats.

IDP 52

IDP Machine Learning AI AI 52

Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. Opportunities abound in sectors like healthcare, finance, and automation. billion in 2022 to a remarkable USD 484.17 billion by 2029.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

Pickl AI

APRIL 8, 2024

Summary: AI’s immense potential is undeniable, but its journey riddle with roadblocks. This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. 13 AI Mistakes That Are Worth Your Attention 1.

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Viso.ai

MARCH 26, 2024

2: Automated Document Analysis and Processing No.3: 4: Algorithmic Trading and Market Analysis No.5: Viso Suite is the Computer Vision Enterprise Platform Computer Vision Algorithms for Finance Models like YOLO (You Only Look Once) models and Faster R-CNN have set benchmarks in real-time processing as well.

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Pickl AI

SEPTEMBER 3, 2024

Explainable AI As ANNs are increasingly used in critical applications, such as healthcare and finance, the need for transparency and interpretability has become paramount. ANNs are being deployed on edge devices to enable real-time decision-making in applications such as smart cities, autonomous vehicles, and industrial automation.

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Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

ODSC - Open Data Science

DECEMBER 17, 2024

But the growing role of AI is sparking debates about its fairness, transparency, and long-term implications. How AI Shapes Loan Decisions AI algorithms analyze vast amounts of data, including credit histories, employment records, and spending habits, to predict the likelihood of repayment. The efficiency of AI is evident.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Generative AI TrackBuild the Future with GenAI Generative AI has captured the worlds attention with tools like ChatGPT, DALL-E, and Stable Diffusion revolutionizing how we create content and automate tasks.

Data Scientist 52

Data Scientist Large Language Models ML Engineer Machine Learning 52

Mlearning.ai

SEPTEMBER 5, 2023

Automated Discovery No more waiting around for manual analysis. InsightsAct’s AI engine works continuously in the background to surface relevant insights. Contextual Recommendations InsightsAct doesn’t just identify issues; its algorithms go further by providing contextual recommendations to address uncovered opportunities.

Business Intelligence 52

Business Intelligence Automation Big Data Explainable AI 52

Pickl AI

AUGUST 1, 2024

Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms. Understanding this role is crucial for anyone interested in pursuing a career in AI and Machine Learning.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

If you are planning on using automated model evaluation for toxicity, start by defining what constitutes toxic content for your specific application. Automated evaluations come with curated datasets to choose from. Accuracy evaluation helps AI models produce reliable and correct outputs across various tasks and datasets.

Responsible AI 113

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AWS Machine Learning Blog

NOVEMBER 9, 2023

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. This will enable us to test the pattern to trigger automated retraining of the model. csv dataset.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed.

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