Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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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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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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Towards AI

OCTOBER 3, 2024

Neuroplasticity in AI Promising Research: a. Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. By adjusting their parameters in real-time, liquid neural networks handle dynamic and time-varying data efficiently.

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IBM Journey to AI blog

AUGUST 9, 2024

Algorithms and architectures Most generative AI models rely on these architectures: Diffusion models work by first adding noise to the training data until it’s random and unrecognizable, and then training the algorithm to iteratively diffuse the noise to reveal a desired output.

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Marktechpost

JUNE 4, 2024

Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how DNNs make decisions in a way that humans can comprehend.

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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

JULY 6, 2023

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. This is why when Explainable AI models can give a clear idea of why a decision was made but not how it arrived at that decision.

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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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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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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.

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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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Unite.AI

NOVEMBER 22, 2024

It helps explain how AI models, especially LLMs, process information and make decisions. By using a specific type of neural network called sparse autoencoders (SAEs) , Gemma Scope breaks down these complex processes into simpler, more understandable parts. How Does Gemma Scope Work?

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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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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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Dlabs.ai

AUGUST 16, 2022

This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.

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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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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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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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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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Towards AI

NOVEMBER 15, 2024

With policymakers and civil society demanding reliable identification of AI content, SynthID represents an important development in addressing issues around AI-driven misinformation and authenticity. Community workshop on explainable AI (XAI) in education. The second network then scans for this pattern in […]

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). Well-trained computer vision models produce consistent quantitative data instantly.”

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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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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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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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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data. Hyperparameter Tuning: Adjusting model parameters to improve performance and accuracy.

Deep Learning 52

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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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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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ODSC - Open Data Science

DECEMBER 17, 2024

However, debugging complex systems like neural networks remains challenging, especially when decisions hinge on non-linear patterns that are difficult to interpret even with advancedtools. The Role of Explainability inAI Explainable AI (XAI) is becoming a priority for lenders and regulators alike.

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Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Explain The Concept of Supervised and Unsupervised Learning. What Is the Purpose of The Activation Function in Artificial Neural Networks?

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Viso.ai

JULY 9, 2024

On the other hand, the generative AI task is to create new data points that look like the existing ones. Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests.

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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:

Computer Vision 52

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Pickl AI

JULY 1, 2024

Neural Networks Inspired by the human brain, artificial neural networks learn complex relationships within data for highly accurate demand forecasting, especially with vast datasets. They are particularly effective when dealing with high-dimensional data. Ensemble Learning Combine multiple forecasting models (e.g.,

Data Science 52

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Ofemwire

MARCH 1, 2024

D – Deep Learning : A subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning networks can automatically learn to represent patterns in the data with multiple levels of abstraction.

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Pickl AI

AUGUST 28, 2024

Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. Lazy Learners These algorithms do not build a model immediately from the training data. Instead, they memorise the training data and make predictions by finding the nearest neighbour.

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How to Learn Machine Learning

JUNE 9, 2024

It is quite beneficial for organizations looking to capitalize on the potential of AI without making significant investments. 2) Explainable AI Explainability AI and interpretable machine learning are the different names of the same things. Explainability AI addresses these challenges of AI/ML solutions.

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