Unite.AI

FEBRUARY 17, 2025

They build upon the foundations of predictive and generative AI but take a significant leap forward in terms of autonomy and adaptability. AI agents are not just tools for analysis or content generationthey are intelligent systems capable of independent decision-making, problem-solving, and continuous learning.

Automation 146

Automation Responsible AI Generative AI AI Modeling 146

Pickl AI

FEBRUARY 17, 2025

Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data. However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases.

Machine Learning 52

Machine Learning Algorithm Explainability Data Quality 52

Pickl AI

JUNE 23, 2023

These models learn from the patterns and relationships present in the data to make predictions, classify objects, or perform other desired tasks. Continuous Learning and Iteration Data-centric AI systems often incorporate mechanisms for continuous learning and adaptation.

Continuous Learning 52

Continuous Learning Data Quality AI AI 52

Unite.AI

JUNE 17, 2024

Rather than imposing AI solutions from the top down, organizations should engage workers in identifying areas where AI can assist them and designing the human-machine collaboration. This not only helps ensure that AI is augmenting in a way that benefits employees, but also fosters a culture of continuous learning and adaptability.

Automation 146

Automation Artificial Intelligence Artificial Intelligence Generative AI 146

Pickl AI

DECEMBER 16, 2024

GPUs, TPUs, and AI frameworks like TensorFlow drive computational efficiency and scalability. Technical expertise and domain knowledge enable effective AI system design and deployment. Transparency, fairness, and adherence to privacy laws ensure responsible AI use. Why is Data Quality Important in AI Implementation?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Algorithm 52

Unite.AI

NOVEMBER 21, 2024

This shift is also leading to new types of work in IT services, such as developing custom models, data engineering for AI needs and implementing responsible AI. The evolution of AI is promising but also brings many corporate challenges, especially around ethical considerations in how we implement it.

DevOps 146

DevOps Automation Responsible AI Software Development 146