Unite.AI

JULY 7, 2023

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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Explainable AI Explainability Machine Learning Computer Scientist 261

Ehud Reiter

MARCH 25, 2024

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

Explainability 129

Explainability ChatGPT Computer Scientist NLP 129

Marktechpost

SEPTEMBER 14, 2023

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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Machine Learning Explainability Computer Scientist Artificial Intelligence 124

Marktechpost

JULY 2, 2023

It is known that, similar to the human brain, AI systems employ strategies for analyzing and categorizing images. However, the precise mechanisms behind these processes remain elusive, resulting in a black-box model. The researchers also emphasized the importance of understanding how computers perceive images.

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Explainability Neural Network AI Tools Computer Vision 98

IBM Journey to AI blog

APRIL 18, 2024

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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IBM Journey to AI blog

AUGUST 11, 2023

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

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

IBM Journey to AI blog

JULY 6, 2023

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.

Data Science 175

Data Science Machine Learning Big Data Data Mining 175