Towards AI

APRIL 2, 2024

However, the AI community has also been making a lot of progress in developing capable, smaller, and cheaper models. This can come from algorithmic improvements and more focus on pretraining data quality, such as the new open-source DBRX model from Databricks. As per the official blog, Grok-1.5

LLM 103

LLM OpenAI Explainable AI AI 103

AWS Machine Learning Blog

NOVEMBER 15, 2024

Robustness in AI systems makes sure model outputs are consistent and reliable under various conditions, including unexpected or adverse situations. A robust AI model maintains its functionality and delivers consistent and accurate outputs even when faced with incomplete or incorrect input data.

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Responsible AI Prompt Engineer Prompt Engineering AI 111

ODSC - Open Data Science

NOVEMBER 20, 2024

Understanding Prompt Engineering and the Evolution of Generative AI A particularly intriguing part of the conversation touched upon prompt engineering, a skill Yves believes will eventually phase out as generative AI models evolve. Yves Mulkers stressed the need for clean, reliable data as a foundation for AI success.

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Generative AI Prompt Engineer Prompt Engineering Data Quality 71

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

JUNE 24, 2024

And with synthetic data then you can avoid privacy issues, and fill in the gaps in training data that’s small or incomplete. This can be helpful for training a more domain-specific generative AI model, and can even be more effective than training a “larger” model, with a greater level of control.

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Data Scientist Large Language Models Machine Learning Software Engineer 130

Defined.ai blog

AUGUST 10, 2023

It can quickly process large amounts of data, precisely identifying patterns and insights humans might overlook. Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better.

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Business Intelligence Data Analysis Neural Network Data Quality 52

Bugra Akyildiz

MAY 25, 2024

Articles OpenAI has announced GPT-4o , their new flagship AI model that can reason across audio, vision, and text in real-time. The blog post acknowledges that while GPT-4o represents a significant step forward, all AI models including this one have limitations in terms of biases, hallucinations, and lack of true understanding.

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ChatGPT Machine Learning Data Analysis OpenAI 59

DataRobot Blog

DECEMBER 20, 2022

Datarobot enables users to easily combine multiple datasets into a single training dataset for AI modeling. City’s pulse (quality and density of the points of interest). The great thing about DataRobot Explainable AI is that it spans the entire platform. You can understand the data and model’s behavior at any time.

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Explainability Automation Machine Learning AI 59

Pickl AI

JANUARY 12, 2025

Data forms the backbone of AI systems, feeding into the core input for machine learning algorithms to generate their predictions and insights. For instance, in retail, AI models can be generated using customer data to offer real-time personalised experiences and drive higher customer engagement, consequently resulting in more sales.

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Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

The MLOps Blog

AUGUST 11, 2023

OpenAI, on the other hand, has been at the forefront of advancements in generative AI models, such as GPT-3, which heavily rely on embeddings. The concept of Explainable AI revolves around developing models that offer inference results and a form of explanation detailing the process behind the prediction.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

Unite.AI

JULY 5, 2024

Robust data management is another critical element. Establishing strong information governance frameworks ensures data quality, security and regulatory compliance. Lastly, predictive analytics powered by Gen AI have groundbreaking potential. Transparent, explainable AI models are necessary for informed decision-making.

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Data Integration Data Quality Algorithm AI Modeling 146

Pickl AI

DECEMBER 10, 2024

Risk Management Strategies Across Data, Models, and Deployment Risk management begins with ensuring data quality , as flawed or biased datasets can compromise the entire system. Model validation and stress testing are crucial steps to identify weaknesses before deployment.

Explainability 52

Explainability AI AI Artificial Intelligence 52