Welcome!
A BIG, FAT, WELCOME!
Hey there! We cannot believe you’re finally here.
Did you sign up voluntarily? Did someone sign up for you? Either way, it doesn’t matter as long as you’re here and ready to hop on this wonderful journey we call life together. Well, life is quite is broad, so we thought we’d limit ourselves a little bit.?
Every month, we share with you our take on the latest in Artificial Intelligence (AI), Data Science, Machine Learning, and Philosophy. As well some bits and pieces from around the world that we think are extremely relevant.
This is what we live for – democratizing knowledge. You will definitely feel smarter, faster, and ready to jump in on nerdy conversations any time your friends decide to talk about robots taking over the world.
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Let’s Roll.
There is no time to waste. So, to get you into the swing of things, we’ve catered an especially scrumptious collection of articles & content that will give you a feel for what’s to come over the coming months. Let’s do it.
Oh, and if you can’t wait till next month, feel free to check out the latest edition of this newsletter by clicking here .
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Forever Written, ‘techWhatnow’?
We’ve written quite a few articles over the past couple years as part of our in-house publication ‘techQualia ’ and we want to highlight those that we’re pretty sure you might like.
The Articles
Unethical AI and Algorithmic Biases in the MENA Region by Marco Rizk -
In this inquisitive piece, Marco Rizk takes us through the different forms of biases that are ‘coded by the data’ to reveal some shocking insights about how algorithms reproduce biases, and how it affects us in the MENA region. Can we debias AI if it’s fundamentally a mere tool that holds a mirror up to society? Read more >> https://www.synapse-analytics.io/post/unpacking-unethical-algorithms-biases-and-more
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领英推荐
Quality Over Quantity: Data's impact on machine learning by Nezar ElKady -
Redman once said, “If your data is bad, your machine learning tools are useless” and we agree. Machine Learning (ML) models are inherently sensitive to the data fed to them: any small error in the data could lead to large scale errors in the output of the model, leading to incorrect system output. So how can you assess the data quality to ensure its authenticity? Read more >> https://www.synapse-analytics.io/post/the-good-the-bad-and-the-machine-data-quality-s-impact-on-machine-learning
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From The Crew
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