Data Science World

In this episode of the SuperDataScience Podcast, I chat with Associate Professor at the University of California San Diego, Bradley Voytek, who was the first data scientist, and the person to kickstart data science at Uber. You will hear a lot about his past work in Uber and his current work in UCSD. You will learn four very valuable philosophical points about data science being a separate field and you will also learn what data science skills can help you resist automation.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/253

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 253: Solving Problems With Data Science & Uber

There’s been a lot of interest lately in the attention mechanism in neural nets—it’s got a colloquial name (who’s not familiar with the idea of “attention”?) but it’s more like a technical trick that’s been pivotal to some recent advances in computer vision and especially word embeddings. It’s an interesting example of trying out human-cognitive-ish ideas (like focusing consideration more on some inputs than others) in neural nets, and one of the more high-profile recent successes in playing around with neural net architectures for fun and profit.

Attention in Neural Nets

The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias around professions (as well as other forms of social bias getting reproduced in the algorithm’s embeddings). We covered the topic again a while later, covering methods for de-biasing embeddings to counteract this effect. And now we’re back, with a second pass on the original Word2Vec analogy task, but where the researchers deconstructed the “rules” of the analogies themselves and came to an interesting discovery: the bias seems to be, at least in part, an artifact of the analogy construction method. Intrigued? So were we…

Relevant link:
https://arxiv.org/abs/1905.09866

Revisiting Biased Word Embeddings

Sinan Ozdemir is back again, this time talking about his work since his company Kylie.ai was acquired by Directly. We discuss his work, the way he is creating human and AI synergy and the future of NLP as it continues to progress.

In this episode you will learn:
• Sinan’s company acquired [7:29]
• Explainable deep learning models [16:13]
• Airbnb case study [19:42]
• Microsoft case study [22:25]
• Sinan’s role at Directly [25:57]
• Work with Sinan [32:57]
• Preview of Sinan at DSGO [38:38]
• BERT [43:18]
• Sinan’s prediction for NLP in 2020 [53:17]

Additional materials: www.superdatascience.com/333

Data Science World

Fever Del Rey