What is MAI-1: Microsoft’s New Proprietary AI Model

AI Machine Learning

If, like me, you’ve been following Microsoft’s Generative AI investment strategy ever since it first announced its partnership with ChatGPT creator, OpenAI, you might be wondering about MAI-1.

For some time now, Microsoft generative AI apps and tools, including its range of Copilot generative AI assistants, have been developed with support from external AI experts.

In fact, Microsoft has invested more than $11 billion into its partnership with OpenAI alone. However, similar to its competitor Google, the technology giant is reluctant to rely exclusively on partner support. That’s why, as of 2024, it’s working on its own AI model: MAI-1.

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Machine Learning for Computer Vision: Foundations and Use Cases

Articles Machine Learning

With advances in camera quality, image fidelity, and neural network research focused on solving image- and video-based challenges, computer vision continues to capture the attention and imaginations of machine learning researchers and practitioners.

But computer vision is an incredibly broad umbrella term that encompasses an array of specific tasks and challenges, and the field continues to expand.

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Detecting Pneumonia in an iOS App with Create ML

Articles Machine Learning

Pneumonia is an inflammatory condition of the lung affecting the alveoli. Typically, symptoms include some combination of productive or dry cough, chest pain, fever, and trouble breathing. According to UNICEF pneumonia remains the leading infectious cause of death among children under five, killing approximately 2,400 children a day.

Early diagnosis of this treatable condition is very important, as it could facilitate earlier treatment and hopefully result in improved clinical outcomes. There are several tests used to confirm diagnoses, including analysis of chest X-ray images.

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Implementing K-Nearest Neighbors in Your Machine Learning Model

Articles Machine Learning

Imagine we have a set of labeled and unlabeled data, and we want to build a classifier which takes the unlabeled data as input and labels that data as output.

With this kind of situation, we’ll need to build a classification model that will learn from already-labeled data (training data). Later we’ll use that model to predict our unlabeled data (test data).

This type of machine learning is called supervised learning, which we can define as feeding data into a machine learning algorithm.

In doing so, we’re actually showing that groups exist, and which data belong to which groups.

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