About

And sow fields and plant vineyards, 
And produce an abundant harvest. 
Psalms 107:37(AMP)

Vineyards AI Lab was founded with the vision of democratising artificial intelligence and expanding access to high-quality AI education and research. We focus on advancing AI understanding through applied research, real-world problem solving, and structured youth education programs.

We operate at the intersection of technology, education, and society, aiming to empower young people to explore, create, and responsibly shape the future of AI. By bridging research, practical applications, and student-centred learning, we seek to generate meaningful social impact while nurturing the next generation of AI innovators and leaders.

For more details about the lab, please visit our website, www.vineyardsailab.com. Thanks.  



Popular posts from this blog

Research from the Lab's Little AI Scholars: Learn the life language using artificial intelligence

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1. Background 1.1 project/research background This project explores the applications of Artificial intelligence (AI) techniques for classifying Deoxyribonucleic Acid (DNA) sequences by three high school students under teaching and supervision in the Lab's Little AI Scholars program. To explain AI information and concepts understandably, a couple of analogies were introduced during the research. They were displayed using interesting images to give the high school students a better understanding, and we have successfully achieved our goal of Auto Recognition of DNA Sequences. We first transformed the DNA sequences into human-like language. Then we employed Natural Language Processing (NLP) and Multi-layer perceptron (MLP) to complete sequence classification into 7 gene families from 3 organisms (humans, dogs, and chimpanzees). During this exciting research, the high school students deeply understood the biological and mathematical knowledge they learned in class and adapted them to t...
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Lab's weekly topic - week 50 2023 - Detailed explanation for encoder of U-Net

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In U-Net the encoder block comprises two components: 2D convolution and max-pooling operations. The 2D convolution is a technique in image processing that generates a feature map. While max-pooling can reduce the size/shape of the feature map. The 2D convolution equation is as follows: \begin{equation} \mathbf{F}(m, n) = \sum_j\sum_k \mathbf{K}(i, j)\mathbf{I}(m-i)(n-j) \end{equation} Where 𝑲 is the kernel or filter, pass it over the image 𝑰. The indexes of rows and columns of the results feature map are marked with m and n respectively. In Figure 1, we show what are the operations in the encoder block, including 2D convolution, max pooling and related technologies.     Figure 1.  Operations in the encoder block In Figure 1, we add a layer with the number “0” called padding, which is used to maintain the size of the original input. Then the filter K shaped as a 3 by 3 matrix performs an element-wise multiplication with the input image 𝑰. The values are summed to ...
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Lab's weekly topic - week 49, 2023 - What's limitations and possible solutions for a new AI tools to detect blood poisoning?

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  A New, Transparent AI Tool May Help Detect Blood Poisoning It is a promising application using AI for medical services. It is a hot topic using AI to help people’s health issues. This novel algorithm can scan electric records and may reduce sepsis death, but the wide application still needs more time. Summary A solution based on machine learning was developed to help physicians detect sepsis earlier, which could save lives. Johns Hopkins Hospital developed the solution and has been deployed at three different hospitals for a try. Electronic records are used directly for an automation alert.  Limitations User side: Physicians could not have a high willingness to try a new tool they are not familiar with. Solution side: The solution depends on the electronic records. It has missing fields and values in the records, which could block the solution. Model side: It is better that we can explain the results predicted by the model detecting sepsis. Deep learning usually has a bette...
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