Remove AI Modeling Remove Computer Scientist Remove Explainability
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Explainable AI Using Expressive Boolean Formulas

Unite.AI

While AI exists to simplify and/or accelerate decision-making or workflows, the methodology for doing so is often extremely complex. Indeed, some “black box” machine learning algorithms are so intricate and multifaceted that they can defy simple explanation, even by the computer scientists who created them.

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Explaining complex information to patients

Ehud Reiter

years old), I’m trying to come back to this vision, collaborating with my students and colleagues in Aberdeen’s medical school in a variety of areas, including supporting cancer patients, helping people understand nutritional data, and explaining IVF predictions. Now that I’m in the last phase of my career (I’m 63.5

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UCI and Harvard Researchers Introduce TalkToModel that Explains Machine Learning Models to its Users

Marktechpost

Machine learning models have become indispensable tools in various professional fields, driving applications in smartphones, software packages, and online services. However, the complexity of these models has rendered their underlying processes and predictions increasingly opaque, even to seasoned computer scientists.

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Getting ready for artificial general intelligence with examples

IBM Journey to AI blog

Most experts categorize it as a powerful, but narrow AI model. Current AI advancements demonstrate impressive capabilities in specific areas. A key trend is the adoption of multiple models in production. This multi-model approach uses multiple AI models together to combine their strengths and improve the overall output.

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MLOps and the evolution of data science

IBM Journey to AI blog

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. MLOps and IBM Watsonx.ai

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

This led to the theory and development of AI. IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won. He wrote a checkers-playing program that same year.

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Tools for trustworthy AI

IBM Journey to AI blog

And using AI ethically isn’t just the right thing for businesses to do—it’s also something consumers want. In fact, 86% of businesses believe customers prefer companies that use ethical guidelines and are clear about how they use their data and AI models, according to the IBM Global AI Adoption Index.

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