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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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs.

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Aiiot Talk

JULY 7, 2023

When developers and users can’t see how AI connects data points, it is more challenging to notice flawed conclusions. Black-box AI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap.

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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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Towards AI

OCTOBER 7, 2023

Last Updated on October 9, 2023 by Editorial Team Author(s): Lye Jia Jun Originally published on Towards AI. Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. The other safeguards personal data but lacks speed.

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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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Pickl AI

APRIL 8, 2024

This experiment highlighted the importance of developing robust security measures for AI systems. Lack of Explainability Many AI systems, particularly deep learning models, known for their “black box” nature. How Can We Ensure the Transparency of AI Systems?

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Pickl AI

JANUARY 29, 2024

Emerging Trends Emerging trends in Data Science include integrating AI technologies and the rise of Explainable AI for transparent decision-making. AI trends involve increased focus on ethical AI, AI-powered automation, and the development of more sophisticated Natural Language Processing.

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Towards AI

APRIL 2, 2024

On the other hand, new developments in techniques such as model merging (see story below from Sakana) can provide a new avenue for affordable development and improvement of open-source models. Hence, we are focused on making AI more accessible and releasing AI learning materials and courses! Why should you care?

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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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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. These are the mathematical formulas written to simulate functions of the brain, which underlie the AI programming.

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IBM Journey to AI blog

FEBRUARY 28, 2024

IBM watsonx™ , an integrated AI, data and governance platform, embodies five fundamental pillars to help ensure trustworthy AI: fairness, privacy, explainability, transparency and robustness. This platform offers a seamless, efficient and responsible approach to AI development across various environments.

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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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