• Exploring Gender Imbalance in AI: Numbers, Trends, and Discussions
  The AI Now Institute warned in a 2019 report that the AI industry needs to “acknowledge the gravity of its diversity problem and admit that existing methods have failed to contend with the uneven distribution of power.” It’s argued that the lack of gender diversity also creates the risk...that AI systems may perpetuate existing forms of structural inequality and cause harm to underrepresented groups...As part of this month’s Women in AI special project, Synced takes a look at some key numbers (and trends) on gender gaps in the AI industry and discusses possible ways to address the issue...

• How to Make Remote Work Effective for Data Science Teams
  Putting the systems in place to make remote teams effective and productive is not trivial. Simple things like collaboration and communication become challenging. Employees are more prone to feel lonely, distracted, or to feel like the workday never ends. In data science teams, where industry best practices are still very much taking shape, these issues can be even more pronounced...

• I’m a researcher who’s helped change how we tackle pandemics like coronavirus forever – this is what we’ve learned
  If you’ve been paying close attention to what scientists have been saying about coronavirus, you might have come across HealthMap, an online map that is tracking the outbreak in real time. Beneath the seemingly simple face of the Covid-19 map is a deep and meticulous dataset that is freely accessible to anyone involved in coronavirus research; something that represents a completely new approach to how we collect data and make it readily available during an outbreak. The data changes the game in terms of how we respond to new global threats such as these...

Data scientists are in demand on Vettery

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• AI for 3D Generative Design
  Using semi-supervised learning to encode the prior knowledge in a way that can be interacted with intuitively would allow designers to iterate faster, make fewer mistakes, and more deeply explore the design space...To make progress towards this lofty goal, my project aims to make the initial step in this process faster and more efficient with ML by generating simple, everyday 3D objects from natural language descriptions...

• Microsoft can filter out the sound of you eating potato chips on a conference call
  The software giant's trained artificial intelligence software to identify background noise that isn't your voice. Snackers, rejoice...

• Beating Atari Pong on a Raspberry Pi without Backpropagation
  In our previous post, we showed that we can now play Atari games from pixels on low-power hardware such as the Raspberry Pi. We can do so in an online, continually-learning fashion...We have now completely removed backpropagation from our algorithm, and the resulting algorithm performs better than before (and runs faster)!...The new algorithm relies entirely on the bidirectional temporal nature of the hierarchy to perform credit assignment...

• The End of Starsky Robotics
  In 2015, I got obsessed with the idea of driverless trucks and started Starsky Robotics. In 2016, we became the first street-legal vehicle to be paid to do real work without a person behind the wheel. In 2018, we became the first street-legal truck to do a fully unmanned run, albeit on a closed road. In 2019, our truck became the first fully-unmanned truck to drive on a live highway...And in 2020, we’re shutting down...

• A Neural Weather Model for Eight-Hour Precipitation Forecasting
  Predicting weather from minutes to weeks ahead with high accuracy is a fundamental scientific challenge that can have a wide ranging impact on many aspects of society...Building on our previous research into precipitation nowcasting, we present “MetNet: A Neural Weather Model for Precipitation Forecasting,” a DNN capable of predicting future precipitation at 1 km resolution over 2 minute intervals at timescales up to 8 hours into the future...

• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches?
  Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models. In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques...

• Generating music in the waveform domain
  In November last year, I co-presented a tutorial on waveform-based music processing with deep learning with Jordi Pons and Jongpil Lee at ISMIR 2019. Jongpil and Jordi talked about music classification and source separation respectively, and I presented the last part of the tutorial, on music generation in the waveform domain. It was very well received, so I’ve decided to write it up in the form of a blog post...

• Implementation of Generative Teaching Networks for PyTorch
  This is a partial implementation of Generative Teaching Networks by Such et al. (Uber AI Labs, 2019)...An MNIST Teacher/Learner was implemented using PyTorch and higher with the aim to confirm and further investigate curriculum generation properties of GTN...We were able to (approximately) reproduce the curriculum generation results; see below for more details. We did not attempt to reproduce the architecture search results, although given a working implementation of the base algorithm, this should now be a straightforward endeavour...

• Evaluating Visualization Authoring Systems through Critical Reflections
  A new generation of visualization authoring systems has emerged in the past few years. Designed to support a common goal, these systems vary in terms of their visualization models, system architectures, and user interfaces. What are the strengths and weaknesses of these systems? How do we choose the right tools to build our visualization? We propose to use critical reflections as a method to compare these systems...

State of Data Science 2020

It's time for the annual State of Data Science survey! We want to know about the languages and tools you’re using, what skills you’ve learned, and what concerns you most about the future of data science and machine learning. You can see last year's results here
. Complete the 2020 survey
for a chance to win one of four $250 Amazon gift cards! Start the Survey

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• Head of Data Science - Tessian - London, United Kingdom
  
  Our mission is to secure the Human Layer. This involves deploying near real-time machine learning models at massive scale to some of the world’s largest organisations to keep their most sensitive data private and secure. To do this, we're looking for an inspiring Head of Data Science ready to lead and grow our Data Science team, who is excited about the opportunities and challenges that come with building and deploying real-time production models.
  Find out more about life as a Tessian Engineer...

        Want to post a job here? Email us for details >> team@datascienceweekly.org

• Introduction to KNN: K-nearest neighbour algorithm using Examples
  KNN also known as K-nearest neighbour is a supervised and pattern classification learning algorithm which helps us find which class the new input(test value) belongs to when k nearest neighbours are chosen and distance is calculated between them...

• Which open source machine learning projects best exemplify good software engineering and design principles?
  As more and more engineers and scientists are creating production machine learning code I thought it'd be awesome to compile a list of examples to take inspiration from!...

• How do you know if the data you are working with is just straight up garbage?
  What sort of preliminary checks are useful to conduct on a given dataset to make sure its not all noisy garbage...

• Data Science in Production: Building Scalable Model Pipelines with Python
  This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production....
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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  P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian