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Building Data Science teams is hard. If you’d like to get some advice, discuss your challenges and generally come away feeling you are not alone. Join us for breakfast next Wednesday with Mike Hyde
(Director of Data & Insights Skype.) He’ll be sharing approaches and best practices. This is essential for anyone building their own data science team.
Researchers at DeepMind wrote in a paper published online Thursday that they had achieved a leap in the speed and performance of a machine learning system. It was accomplished by, among other things, imbuing technology with attributes that function in a way similar to how animals are thought to dream.
The paper explains how DeepMind’s new system — named Unsupervised Reinforcement and Auxiliary Learning agent, or Unreal — learned to master a three-dimensional maze game called Labyrinth 10 times faster than the existing best AI software. It can now play the game at 87 percent the performance of expert human players, the DeepMind researchers said.
Researchers at North Carolina State University have developed a combination of software and hardware that will allow them to use unmanned aerial vehicles (UAVs) and insect cyborgs, or biobots, to map large, unfamiliar areas – such as collapsed buildings after a disaster.
“The idea would be to release a swarm of sensor-equipped biobots – such as remotely controlled cockroaches – into a collapsed building or other dangerous, unmapped area,” says Edgar Lobaton, an assistant professor of electrical and computer engineering at NC State and co-author of two papers describing the work.
SearchInk, which has combined machine learning with “multi-writer handwriting recognition” and semantic labelling of handwritten documents, has now raised $4.5 million / €4.2 million in seed funding. The investment comes from Berlin-based investment bank IBB Berlin, as well as individual investors (including Michael Schmitt, former Engineering Director at Google Switzerland).
This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data. http://g.co/aiexperiments
Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google.
Facebook has launched a Kaggle competition to hire a data scientist
“This competition tests your text skills on a large dataset from the Stack Exchange sites. The task is to predict the tags (a.k.a. keywords, topics, summaries), given only the question text and its title. The dataset contains content from disparate stack exchange sites, containing a mix of both technical and non-technical questions.”
If you don’t win but give this a good go, come and discuss working with us 🙂