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Hitting the right notes in 2017: AI in a song for Data Scientists

Here’s a look at a Few of our Favorite things in AI, machine learning and data science

A lot, I mean lots and lots of great articles have been written already about AI’s epic journey in 2017. They all generally agree that 2017 sets the stage for AI in very real terms. 

We saw immense progress in academia, research and industry in terms of an explosion of new ideas (like capsNets), questioning of established ideas (like backprop, AI black boxes), new methods (Alpha Zero’s self-learning), tools (PyTorch, Gluon, AWS SageMaker), and hardware (quantum computers, AI chips). New and existing players gearing up to tap into this phenomena even as they struggled to tap into the limited talent pool at various conferences and other community hangouts. While we have accelerated the pace of testing and deploying some of those ideas in the real world with self-driving cars, in media & entertainment, among others, progress in building a supportive and sustainable ecosystem has been slow. We also saw conversations on AI ethics, transparency, interpretability, fairness, go mainstream alongside broader contexts such as national policies, corporate cultural reformation setting the tone of those conversations. While anxiety over losing jobs to robots keeps reaching new heights proportional to the cryptocurrency hype, we saw humanoids gain citizenship, residency and even talk about contesting in an election!

It has been nothing short of the stuff, legendary tales are made of: struggle, confusion, magic, awe, love, fear, disgust, inspiring heroes, powerful villains, misunderstood monsters, inner demons and guardian angels.

And stories worth telling must have songs written about them!

Here’s our ode to AI Highlights in 2017 while paying homage to an all-time favorite: ‘A few of my favorite things’ from Sound of Music. Next year, our AI friends will probably join us behind the scenes in the making of another homage to the extraordinary advances in data science, machine learning, and AI.

Stripes on horses and horsetails on zebras

Bright funny faces in bowls full of rameN

Brown furry bears rolled into pandAs

These are a few of my favorite thinGs

 

TensorFlow projects and crisp algo models

Libratus’ poker faces, AlphaGo Zero’s gaming caboodles

Cars that drive and drones that fly with the moon on their wings

These are a few of my favorite things

 

Interpreting AI black boxes, using Python hashes

Kaggle frenemies and the ones from ML MOOC classes

R white spaces that melt into strings

These are a few of my favorite things

 

When models don’t converge, and networks just forget

When I am sad

I simply remember my favorite things

And then I don’t feel so bad

 

PS: We had to leave out many other significant developments in the above cover as we are limited in our creative repertoire. We invite you to join in and help us write an extended version together!

The idea is to make learning about data science easy, accessible, fun and memorable!

 

 

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Aarthi Kumaraswamy

Managing Editor, Writer, Mom.

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