Marktechpost

MARCH 11, 2024

However, understanding their information-flow dynamics, learning mechanisms, and interoperability remains challenging, limiting their applicability in sensitive domains requiring explainability. These matrices are leveraged to develop class-agnostic and class-specific tools for explainable AI of Mamba models.

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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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Towards AI

JULY 22, 2023

So, don’t worry, this is where Explainable AI, also known as XAI, comes in. HEALTHCARE WITH AI: SOURCE: [link] Let’s go through some instances to help you understand why Explainable AI is so important: Imagine a healthcare system in which, instead of speaking with a doctor, you interact with an AI system to assist you with diagnosis.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

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Marktechpost

MAY 5, 2024

Existing surveys detail a range of techniques utilized in Explainable AI analyses and their applications within NLP. They explore methods to decode information in neural network models, especially in natural language processing. Recent approaches automate circuit discovery, enhancing interpretability.

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Marktechpost

MAY 12, 2024

It is based on adjustable and explainable AI technology. Fraud.net Fraud.net’s AI and Machine Learning Models use deep learning, neural networks, and data science methodologies to improve insights for various industries, including financial services, e-commerce, travel and hospitality, insurance, etc.

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Heartbeat

NOVEMBER 27, 2023

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. Deep learning automates and improves medical picture analysis.

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Unite.AI

APRIL 9, 2024

As organizations strive for responsible and effective AI, Composite AI stands at the forefront, bridging the gap between complexity and clarity. 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.

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ODSC - Open Data Science

OCTOBER 24, 2023

Generating synthetic data NoGAN is the first algorithm in a series of high-performance, fast synthesizers not based on neural networks such as GAN. Indeed, the whole technique epitomizes explainable AI. It is easy to fine-tune, allowing for auto-tuning.

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Mlearning.ai

FEBRUARY 6, 2023

Classifiers based on neural networks are known to be poorly calibrated outside of their training data [3]. Additionally, multiple different models could be trained to identify AI-Generated Text in different subject matters, reducing the need for generalization. This is why we need Explainable AI (XAI). Serrano, N.

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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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Viso.ai

MAY 10, 2024

Training procedure The high-level training procedure for building an AI model is pretty much the same regardless of the type of model. For neural networks, gradient descent is accomplished through a technique called backpropagation. text vs images) and (2) the desired output (e.g.

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Defined.ai blog

AUGUST 10, 2023

Emerging Trends in AI and Financial Data Analysis In the fast-evolving field of AI, new trends are constantly emerging, and financial data analysis is no exception. Let’s look at a few trends shaping the future of data analysis using AI.

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IBM Journey to AI blog

JULY 6, 2023

Deep learning algorithms are neural networks modeled after the human brain. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes. Deep learning teaches computers to process data the way the human brain does.

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial.

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Dlabs.ai

MAY 22, 2022

The instructors are very good at explaining complex topics in an easy-to-understand way. Machine Learning Author: Andrew Ng Everyone interested in machine learning has heard of Andrew Ng : one of the most respected people in the AI world.

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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 is a place for beginners to ask stupid questions and for experts to help them!

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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 can we explain it in simple terms?

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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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NVIDIA

DECEMBER 13, 2023

Financial Services Firms Embrace AI for Identity Verification The financial services industry is developing AI for identity verification. Harnessing Graph Neural Networks and NVIDIA GPUs GNNs have been embraced for their ability to reveal suspicious activity.

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Ofemwire

MARCH 1, 2024

At its core, AI is designed to replicate or even surpass human cognitive functions, employing algorithms and machine learning to interpret complex data, make decisions, and execute tasks with unprecedented speed and accuracy. If you dont get that, let me explain what AI is, like I would do to a fifth grader.

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Viso.ai

MARCH 26, 2024

With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 1: Fraud Detection and Prevention No.2:

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Viso.ai

DECEMBER 6, 2023

State of Computer Vision Tasks in 2024 The field of computer vision today involves advanced AI algorithms and architectures, such as convolutional neural networks (CNNs) and vision transformers ( ViTs ), to process, analyze, and extract relevant patterns from visual data. Get a demo here.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

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Mlearning.ai

FEBRUARY 2, 2023

In this article, I show how a Convolutional Neural Network can be used to predict a person's age based on the person's ECG Attia et al 2019 [1], showed that a person's age could be predicted from an ECG using convolutional neural networks (CNN). Ismail Fawaz et al., Data Min Knowl Disc 34 , 1936–1962 (2020).

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The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Truera offers capabilities such as model debugging, explainability, and fairness assessment to gain insights into model behavior and identify potential issues or biases. Learn more from the documentation.

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The MLOps Blog

AUGUST 11, 2023

But some of these queries are still recurrent and haven’t been explained well. More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition.

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Pickl AI

APRIL 2, 2024

It explains key concepts, explores applications for business growth, and outlines steps to prepare your organization for data-driven success. Deep Learning: Neural networks with multiple layers used for complex pattern recognition tasks. Summary This blog post demystifies data science for business leaders.

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IBM Journey to AI blog

FEBRUARY 9, 2024

In December of 2023, Mistral released “Mixtral,” a mixture of experts (MoE) model integrating 8 neural networks, each with 7 billion parameters. They make AI more explainable: the larger the model, the more difficult it is to pinpoint how and where it makes important decisions. on most standard benchmarks.

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