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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

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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Ajay Kumar, CEO of SLK Software – Interview Series

Unite.AI

In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times. Fraud detection has become more robust with advanced AI algorithms that help identify and prevent fraudulent activities, thereby safeguarding assets and reducing risks.

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How Data Science and AI is Changing the Future

Pickl AI

This article explores the definitions of Data Science and AI, their current applications, how they are shaping the future, challenges they present, future trends, and the skills required for careers in these fields. AI automates processes, reducing human error and operational costs.

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Top AI Tools Enhancing Fraud Detection and Financial Forecasting

Marktechpost

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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2024 Tech breakdown: Understanding Data Science vs ML vs AI

Pickl AI

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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Top 10 Free Machine Learning And Artificial Intelligence Courses In 2023

Dlabs.ai

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. That’s why it helps to know the fundamentals of ML and the different learning algorithms before you do any data science work.