Unite.AI

DECEMBER 27, 2024

Its because the foundational principle of data-centric AI is straightforward: a model is only as good as the data it learns from. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential. Another promising development is the rise of explainable data pipelines.

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Unite.AI

OCTOBER 20, 2023

However, only around 20% have implemented comprehensive programs with frameworks, governance, and guardrails to oversee AI model development and proactively identify and mitigate risks. Given the fast pace of AI development, leaders should move forward now to implement frameworks and mature processes.

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Unite.AI

SEPTEMBER 13, 2023

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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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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Unite.AI

MARCH 18, 2024

This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications. While this expansion enriches the AI development landscape with varied datasets, it also introduces the risk of data contamination.

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Unite.AI

JULY 1, 2024

Transparency and Explainability Enhancing transparency and explainability is essential. Techniques such as model interpretability frameworks and Explainable AI (XAI) help auditors understand decision-making processes and identify potential issues. This involves human experts reviewing and validating AI outputs.

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Explainability BERT AI AI 182

Aiiot Talk

JULY 7, 2023

AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. One of the main risks associated with AI is its black-box nature.

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NVIDIA

MARCH 1, 2024

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

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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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Explainability Explainable AI AI Modeling Artificial Intelligence 52

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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Unite.AI

DECEMBER 1, 2023

Alex Ratner is the CEO & Co-Founder of Snorkel AI , a company born out of the Stanford AI lab. Snorkel AI makes AI development fast and practical by transforming manual AI development processes into programmatic solutions. This stands in contrast to—but works hand-in-hand with—model-centric AI.

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Data Scientist Auto-classification AI AI 147

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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Unite.AI

JANUARY 12, 2024

Competitions also continue heating up between companies like Google, Meta, Anthropic and Cohere vying to push boundaries in responsible AI development. The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. When you dissect AI’s supply chain, at the root, you will find algorithms. What factors does it weigh?

AI Modeling 144

AI Modeling Explainability Algorithm Machine Learning 144

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. comparable to much larger and more expensive models such as GPT-4.

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LLM OpenAI Explainable AI AI 103

AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Marktechpost

DECEMBER 11, 2024

Preference optimization was then employed using Direct Preference Optimization (DPO) and other algorithms to align the models with human preferences. Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 model scored 70.2.

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Towards AI

OCTOBER 7, 2023

Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. One algorithm could maximize efficiency but at the cost of privacy. If you were Sarah, which algorithm would you choose?

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ODSC - Open Data Science

FEBRUARY 25, 2025

Large Language Models & RAG TrackMaster LLMs & Retrieval-Augmented Generation Large language models (LLMs) and retrieval-augmented generation (RAG) have become foundational to AI development. AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering.

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Pickl AI

APRIL 8, 2024

Summary: AI’s immense potential is undeniable, but its journey riddle with roadblocks. This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. 13 AI Mistakes That Are Worth Your Attention 1.

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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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ODSC - Open Data Science

FEBRUARY 18, 2025

AI Ethicists: As AI systems become more integrated into society, ethical considerations are paramount. AI ethicists specialize in ensuring that AI development and deployment align with ethical guidelines and regulatory standards, preventing unintended harm andbias.

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Explainability Automation AI AI 59

Pickl AI

JANUARY 13, 2025

With clear and engaging writing, it covers a range of topics, from basic AI principles to advanced concepts. Readers will gain a solid foundation in search algorithms, game theory, multi-agent systems, and more. Key Features: Comprehensive coverage of AI fundamentals and advanced topics. Detailed algorithms and pseudo-codes.

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The MLOps Blog

FEBRUARY 27, 2025

Adhering to data protection laws is not as complex if we focus less on the internal structure of the algorithms and more on the practical contexts of use. The challenge for AI researchers and engineers lies in separating desirable biases from harmful algorithmic biases that perpetuate social biases or inequity. Lets get started!

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Artificial intelligence (AI) has enormous value but capturing the full benefits of AI means facing and handling its potential pitfalls. Here’s a closer look at 10 dangers of AI and actionable risk management strategies. Bias Humans are innately biased, and the AI we develop can reflect our biases.

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Pickl AI

DECEMBER 29, 2024

Understanding the consequences of AI misuse and the challenges of unregulated systems is essential to realising its benefits without harm. Examples of AI Misuse and Consequences AI misuse has led to notable failures with far-reaching impacts. Diverse perspectives help anticipate potential risks and biases in AI systems.

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Pickl AI

DECEMBER 10, 2024

Businesses face fines and reputational damage when AI decisions are deemed unethical or discriminatory. Socially, biased AI systems amplify inequalities, while data breaches erode trust in technology and institutions. Broader Ethical Implications Ethical AI development transcends individual failures.

Explainability 52

Explainability AI AI Artificial Intelligence 52

ODSC - Open Data Science

OCTOBER 3, 2024

AI Development Lifecycle: Learnings of What Changed with LLMs Noé Achache | Engineering Manager & Generative AI Lead | Sicara Using LLMs to build models and pipelines has made it incredibly easy to build proof of concepts, but much more challenging to evaluate the models.

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AI Weekly

AUGUST 22, 2024

bbc.com The unstoppable rise of Chubby: Why TikTok's AI-generated cat could be the future of the internet Tearjerker videos of AI-generated cats earned millions of views and a devoted following, blurring the line between spam and art. Is it the algorithm, or is this what the internet wants? pdf, Word, etc.)

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IBM Journey to AI blog

FEBRUARY 9, 2024

They make AI more explainable: the larger the model, the more difficult it is to pinpoint how and where it makes important decisions. Explainable AI is essential to understanding, improving and trusting the output of AI systems.

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