• What does it mean to be a Senior Data Scientist?
  My job title is ‘Senior Data Scientist’ and I often joke I’ve no idea what that means. I’m trying to answer questions like ‘what do we expect from a Senior Data Scientist’...

• The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
  Many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases...

• The Gender Shades project
  This evaluation focuses on gender classification as a motivating example to show the need for increased transparency in the performance of any AI products and services that focused on human subjects. Bias in this context is defined as having practical differences in gender classification error rates between groups...

 
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• The Machine Learning Reproducibility Crisis
  It’s hard to explain to people who haven’t worked with machine learning, but we’re still back in the dark ages when it comes to tracking changes and rebuilding models from scratch. It’s so bad it sometimes feels like stepping back in time to when we coded without source control...

• When an AI finally kills someone, who will be responsible?
  Legal scholars are furiously debating which laws should apply to AI crime...

• Marketing for Data Science:
  A 7 Step ‘Go-to-Market’ Plan for Your Next Data Product
  As a product scientist at Indeed (product science is a team in data science — learn more here!), I think about launching both business products and internal data products. This has helped me see that marketing techniques for launching goods and services can also be applied to launching data products internally. With this perspective, I’ve helped the tools I developed become among the top 10% most used at Indeed...

• Evolution is the New Deep Learning
  Like Deep Learning (DL), EC was introduced decades ago, and it is currently experiencing a similar boost from the available big compute and big data. However, it addresses a distinctly different need: Whereas DL focuses on modeling what we already know, EC focuses on creating new knowledge. In that sense, it is the next step up from DL...

• You need 16 times the sample size to estimate an interaction than to estimate a main effect
  The most important point here, though, has nothing to do with statistical significance. It’s just this: Based on some reasonable assumptions regarding main effects and interactions, you need 16 times the sample size to estimate an interaction than to estimate a main effect. And this implies a major, major problem with the usual plan of designing a study with a focus on the main effect, maybe even preregistering, and then looking to see what shows up in the interactions...

• Network structure from rich but noisy data
  Here we present a general formalism for the optimal inference of network structure from rich but noisy data, and show how it can be applied to a range of data types...

• Adversarial Logit Pairing
  In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question...

• There’s No Such Thing as a Data Scientist
  The discipline has dramatically risen in popularity over the past few years. And while the number of data science jobs has increased, clarity around the role has declined. This post takes advantage of Indeed’s tremendous amounts of behavioral data to describe trends in the field and more specific definitions for data science roles...

• Examine MNIST Dataset from PyTorch Torchvision
  Learn how to examine the MNIST dataset from PyTorch Torchvision using Python and PIL, the Python Imaging Library, via a screencast video and full tutorial transcript...

• GAN with Keras: Application to Image Deblurring
  This article focuses on applying GAN to Image Deblurring with Keras. All the Keras code is available here...

• Deep Learning Framework Examples
  Demo of running NNs across different frameworks...

• Barking Up the Wrong Tree: The Surprising Science Behind Why Everything You Know About Success Is (Mostly) Wrong
  "The science of life-changing ideas told through memorable real-life stories..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...