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

The following blog will emphasise on what the future of AI looks like in the next 5 years. Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications.

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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 also implements several algorithms for inspecting black-box models. Image Source 10.

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ODSC - Open Data Science

OCTOBER 24, 2023

Then, how to essentially eliminate training, thus speeding up algorithms by several orders of magnitude? My NoGAN algorithm, probably for the first time, comes with the full multivariate KS distance to evaluate results. Indeed, the whole technique epitomizes explainable AI. It is adjusted for dimension.

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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.

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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. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions.

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Defined.ai blog

AUGUST 10, 2023

AI refers to computer systems capable of executing tasks that typically require human intelligence. On the other hand, ML, a subset of AI, involves algorithms that improve through experience. These algorithms learn from data, making the software more efficient and accurate in predicting outcomes without explicit programming.

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Marktechpost

AUGUST 24, 2023

r/computervision Computer vision is the branch of AI science that focuses on creating algorithms to extract useful information from raw photos, videos, and sensor data. r/learnmachinelearning The subreddit is dedicated to learning the latest machine-learning algorithms. There are about 68k members. It has over 37.4k

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Viso.ai

MAY 10, 2024

Basic types of machine learning models Some common types of machine learning algorithms include: Regression models (e.g. Training procedure The high-level training procedure for building an AI model is pretty much the same regardless of the type of model. text vs images) and (2) the desired output (e.g.

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Dlabs.ai

MAY 22, 2022

It’ll help you get to grips with the fundamentals of ML and its respective algorithms, including linear regression and supervised and unsupervised learning, among others. Here, we’ll focus more on his AI courses, particularly the one on ML (one of the most popular and highly-rated Machine Learning online courses around).

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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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Heartbeat

SEPTEMBER 13, 2023

Through several " Explainable Models ," explainability enables users to achieve insights into the inner workings of the models and understand the factors that influence their output. Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems.

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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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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

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. 4: Algorithmic Trading and Market Analysis No.5:

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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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Ofemwire

MARCH 1, 2024

What is AI Artificial Intelligence, commonly referred to as AI, embodies the simulation of human intelligence processes by machines, especially computer systems. If you dont get that, let me explain what AI is, like I would do to a fifth grader. These processes include learning, reasoning, and self-correction.

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Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. The purpose is to give you an idea of modern computer vision algorithms and applications. 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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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. Scale AI combines human annotators and machine learning algorithms to deliver efficient and reliable annotations for your team.

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Pickl AI

APRIL 2, 2024

Predictive Analytics: Forecasting future outcomes based on historical data and statistical algorithms. Machine Learning: Subset of AI that enables systems to learn from data without being explicitly programmed. Deep Learning: Neural networks with multiple layers used for complex pattern recognition tasks.

Data Science 52

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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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