IBM Journey to AI blog

JUNE 26, 2023

Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. in 2022 and it is expected to be hit around USD 118.06

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Marktechpost

NOVEMBER 30, 2023

Researchers from Lund University and Halmstad University conducted a review on explainable AI in poverty estimation through satellite imagery and deep machine learning. The review underscores the significance of explainability for wider dissemination and acceptance within the development community.

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Marktechpost

JULY 22, 2024

With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. This lack of explainability is a gap in academic interest and a practical concern. Analyzing the decision-making process of AI models is essential for building trust and reliability, particularly in identifying and addressing hidden biases.

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Marktechpost

JULY 6, 2023

This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent. On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods.

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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DataRobot Blog

FEBRUARY 14, 2022

With any AI solution , you want it to be accurate. But just as important, you want it to be explainable. Explainability requirements continue after the model has been deployed and is making predictions. DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle.

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Unite.AI

JUNE 18, 2024

Looking further ahead, one critical area of focus is Explainable AI , which aims to make AI decisions transparent and understandable. This transparency is necessary to build trust in AI systems and ensure they are used responsibly.

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Marktechpost

JUNE 9, 2024

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features.

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Pickl AI

OCTOBER 8, 2024

Summary: This blog discusses Explainable Artificial Intelligence (XAI) and its critical role in fostering trust in AI systems. One of the most effective ways to build this trust is through Explainable Artificial Intelligence (XAI). What is Explainable AI (XAI)? What is Explainable AI (XAI)?

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Unite.AI

SEPTEMBER 20, 2024

Its specialization makes it uniquely adept at powering AI workflows in an industry known for strict regulation and compliance standards. Palmyra-Fin integrates multiple advanced AI technologies, including machine learning, NLP, and deep learning algorithms.

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Marktechpost

MAY 8, 2024

Among the main advancements in AI, seven areas stand out for their potential to revolutionize different sectors: neuromorphic computing, quantum computing for AI, Explainable AI (XAI), AI-augmented design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity and AI for Environmental Sustainability.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Marktechpost

JANUARY 19, 2024

Researchers have proposed a deep learning prediction model named XAI-AGE (XAI stands for Explainable AI) that integrates previously identified biologically hierarchical information in a neural network model for predicting the biological age based on DNA methylation data.

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IBM Journey to AI blog

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

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Towards AI

FEBRUARY 22, 2024

If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Mh_aghajany is looking for fellow learners to explore Machine Learning, Deep Learning, and LLM. Our friends at Zoī are hiring their Chief AI Officer.

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IBM Journey to AI blog

JULY 17, 2023

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. Enterprise organizations (many of whom have already embarked on their AI journeys) are eager to harness the power of generative AI for customer service.

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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?

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How do we deal with this?

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NVIDIA

DECEMBER 13, 2023

Financial Services Firms Embrace AI for Identity Verification The financial services industry is developing AI for identity verification. Tackling Model Explainability and Bias GNNs also enable model explainability with a suite of tools.

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IBM Journey to AI blog

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

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Unite.AI

JANUARY 12, 2024

The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones. The rise of deep learning reignited interest in neural networks, while natural language processing surged with ChatGPT-level models.

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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. CorgiAI CorgiAI is a fraud detection and prevention tool designed to increase income and reduce losses due to fraud.

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ODSC - Open Data Science

FEBRUARY 13, 2023

Well according to our research, there is quite a lot going on in the world of machine learning safety and security. Explainable AI: As in the namesake, explainable AI’s purpose is to clearly and in a transparent manner, explain why a machine learning model came to a specific decision.

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AWS Machine Learning Blog

APRIL 12, 2023

Games24x7 employs an automated, data-driven, AI powered framework for the assessment of each player’s behavior through interactions on the platform and flags users with anomalous behavior. He is currently involved in research efforts in the area of explainable AI and deep learning.

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Marktechpost

JULY 19, 2023

Researchers have also shown that explainable AI, which is when an AI model explains at each step why it took a certain decision instead of just providing predictions, does not reduce this problem of AI overreliance. Check out the Paper and Stanford Article.

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Unite.AI

JUNE 24, 2024

Because mathematicians tend to favor elegant solutions over complex machinery, I’ve always tried to emphasize simplicity when applying machine learning to business problems. It’s been fascinating to see the shifting role of the data scientist and the software engineer in these last twenty years since machine learning became widespread.

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The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Truera Truera is a model intelligence platform designed to enable the trust and transparency of machine learning models. Learn more from the documentation. Monitor the performance of machine learning models.

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ODSC - Open Data Science

FEBRUARY 18, 2025

AI Data Curators: Given the critical importance of high-quality data for training AI models, AI data curators specialize in sourcing, cleaning, and organizing data to ensure its suitability for AI applications. Explainable AI (XAI) techniques are crucial for building trust and ensuring accountability.

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Pickl AI

SEPTEMBER 3, 2024

ReLU is widely used in Deep Learning due to its simplicity and effectiveness in mitigating the vanishing gradient problem. Tanh (Hyperbolic Tangent): This function maps input values to a range between -1 and 1, providing a smooth gradient for learning.

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Pickl AI

SEPTEMBER 17, 2024

What Is the Difference Between Artificial Intelligence, Machine Learning, And Deep Learning? Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

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ODSC - Open Data Science

DECEMBER 17, 2024

For example, AI-based lending tools could disproportionately deny loans to minority groups , even if unintentionally. Many AI models, especially deep learning systems, are black boxes with opaque decision-making processes. Transparency is another issue.

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Marktechpost

AUGUST 24, 2023

r/neuralnetworks The Subreddit is about Deep Learning, Artificial Neural Networks, and Machine Learning. members and is a great place to learn more about the latest AI. It has over 37.4k members and has active discussions on various ML topics. It features various posts and regular discussions on these topics.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

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Pickl AI

JANUARY 13, 2025

Key Features: Comprehensive coverage of AI fundamentals and advanced topics. Explains search algorithms and game theory. Machine Learning for Dummies By John Paul Mueller and Luca Massaron This book introduces the basics of Machine Learning with practical examples. Explains real-world applications like fraud detection.

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Pickl AI

AUGUST 28, 2024

Deep Learning Deep Learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are becoming increasingly popular for complex classification tasks like image and text classification. It is commonly used for binary classification tasks.

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