IBM Journey to AI blog

FEBRUARY 8, 2023

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic.

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Responsible AI Explainability Automation Explainable AI 130

Unite.AI

APRIL 9, 2024

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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Composite AI Explainability Black Box AI Explainable AI 182

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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Explainable AI Explainability Deep Learning Neural Network 52

IBM Journey to AI blog

OCTOBER 12, 2023

The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Data Scientist Metadata Responsible AI Explainability 211

Unite.AI

JANUARY 12, 2024

These innovations signal a shifting priority towards multimodal, versatile generative models. Competitions also continue heating up between companies like Google, Meta, Anthropic and Cohere vying to push boundaries in responsible AI development. Enhancing user trust via explainable AI also remains vital.

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Snorkel AI

MARCH 19, 2024

In an ideal world, every company could easily and securely leverage its own proprietary data sets and assets in the cloud to train its own industry/sector/category-specific AI models. There are multiple approaches to responsibly provide a model with access to proprietary data, but pointing a model at raw data isn’t enough.

Data Scientist 52

Data Scientist AI AI LLM 52

Snorkel AI

MARCH 19, 2024

In an ideal world, every company could easily and securely leverage its own proprietary data sets and assets in the cloud to train its own industry/sector/category-specific AI models. There are multiple approaches to responsibly provide a model with access to proprietary data, but pointing a model at raw data isn’t enough.

Data Scientist 52

Data Scientist AI AI LLM 52

The MLOps Blog

JUNE 27, 2023

Some model observability tools in the MLOps landscape in 2023 WhyLabs WhyLabs is an AI observability platform that helps data scientists and machine learning engineers monitor the health of their AI models and the data pipelines that fuel them. Evidently AI Evidently AI is an open-source ML model monitoring system.

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IBM Journey to AI blog

FEBRUARY 28, 2024

More specifically, the recent launch of IBM watsonx.governance helps public sector teams automate and address these areas, enabling them to direct, manage and monitor their organization’s AI activities.

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