Web Picks (week of 30 September 2019)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • AllenNLP Interpret:
    A Framework for Explaining Predictions of NLP Models
  • OpenAI: Emergent Tool Use from Multi-Agent Interaction
    “We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.”
  • China’s new ‘super camera’ can instantly pinpoint specific targets among tens of thousands of people
    “Scientists have unveiled a 500 megapixel cloud camera system in China that they say is capable of capturing the facial details of each individual in a crowd of tens of thousands of people, raising fears facial recognition monitoring could soon reach a new level.”
  • You’re very easy to track down, even when your data has been anonymized
    A new study shows you can be easily re-identified from almost any database, even when your personal details have been stripped out.
  • AI competitions don’t produce useful models
    “They obviously aren’t to reliably find the best model. They don’t even really reveal useful techniques to build great models, because we don’t know which of the hundred plus models actually used a good, reliable method, and which method just happened to fit the under-powered test set.”
  • At Tech’s Leading Edge, Worry About a Concentration of Power
    “Computer scientists say A.I. research is becoming increasingly expensive, requiring complex calculations done by giant data centers, leaving fewer people with easy access to the computing firepower necessary to develop the technology behind futuristic products like self-driving cars or digital assistants that can see, talk and reason.”
  • Attention Mechanism
    “The introduction of the Attention Mechanism in deep learning has improved the success of various models in recent years, and continues to be an omnipresent component in state-of-the-art models. Therefore, it is vital that we pay Attention to Attention and how it goes about achieving its effectiveness.”
  • Deep Learning with Electronic Health Record (EHR) Systems
    “A comprehensive look at recent machine learning advancements in health.”
  • Google has released a giant database of deepfakes to help fight deepfakes
    It includes 3,000 AI-generated videos that were made using various publicly available algorithms.
  • Google: Tracking our progress on flood forecasting
    “With information obtained through our collaboration with Indian Central Water Commission, we create river flood forecasting models that can more accurately predict not only when and where a flood might occur, but the severity of the event as well.”
  • Artificial Intelligence Takes On Earthquake Prediction
    After successfully predicting laboratory earthquakes, a team of geophysicists has applied a machine learning algorithm to quakes in the Pacific Northwest.
  • Google: 2,602 uses of AI for social good, and what we learned from them
    “We received 2,602 applications from six continents and 119 countries, with projects addressing a wide range of issue areas, from education to the environment. Some of the applicants had experience with AI, but 55 percent of not-for-profit organizations and 40 percent of for-profit social enterprises reported no prior experience with AI.”
  • Write With Transformer
    Get a modern neural network to auto-complete your thoughts.
  • Keras 2.3.0 is out
    Keras 2.3.0 is the first release of multi-backend Keras that supports TensorFlow 2.0. It maintains compatibility with TensorFlow 1.14, 1.13, as well as Theano and CNTK.
  • Finally, Google has released an ML comic