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Which Database is Best?
What’s the best database for an analyst? | Mode
Benn Stancil, Chief Analyst at Mode, approached the “which database is best?” question from the analyst’s perspective. Specifically, he focused on determining which is the easiest database to query. He looked at millions of queries run in Mode’s editor and focused on the eight most popular databases before determining that Amazon Redshift was the clear winner. “For analysts looking for ease of use without sacrificing too much speed—driving to the grocery store is faster than walking, after all—Redshift is the clear winner. Add the collective vote of analysts using Mode to the growing pile of recommendations.”
Machine Intelligence 2.0
The current state of machine intelligence 2.0 | O’Reilly
Last year, Shivon Zillis, partner and founding member of Bloomberg Beta, attempted to map the machine intelligence landscape by learning about every artificial intelligence, machine learning, or data related startup she could find (at the time her list was at 2,529). Given the explosion of activity of the course of this past year, Zillis decided to focus on “highlighting areas of innovation,” and found that the two biggest changes have been “(1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.”
MyConnectome
The First Quantified Brain | Priceonomics
Rosie Cima tells the story of neuroscientist, Russell Poldrack, who recently published a paper on his year and a half long self-study where he collected data on metrics including: his mood alertness, stress levels, diet, alcohol consumption, sleep, blood pressure, pulse, severity of his psoriasis, muscle soreness, and combined all of this with a daily diary and a twice a week brain scan. Poldrack was most enthusiastic about tracking his connectome, which is the “wiring diagram” for an organism’s neurons. Like a genome, a connectome “could contain a lot of information about who we are, and why we feel what we feel, and do what we do.”
The New York Time’s Top Data Visualizations
The NYT’s best data visualizations of the year | Information is Beautiful
The New York Times Graphics team is widely considered one of the best when it comes to pushing new forms of effective data visualizations. The Kantar Information is Beautiful team posted their favorite NYT’s visualizations of the year.
Bad Data Guide
The Quartz guide to bad data | GitHub
The team at Quartz compiled an “exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.” The guide was created from the perspective of a reporter who works with data on a daily basis and provides “thorough descriptions and possible solutions to many of the kinds of problems” encountered when working with data.
RJMetrics #DataScience Roundup: The @qz guide to bad data https://goo.gl/SpU4Jz
Two on Artificial Intelligence
How Elon Musk and Y Combinator Plan to Stop Computers From Taking Over | Medium
Steven Levy interviewed Elon Musk and Sam Altman on the announcement of their non-profit OpenAI project. “Essentially, OpenAI is a research lab meant to counteract large corporations who may gain too much power by owning super-intelligence systems devoted to profits, as well as governments which may use AI to gain power and even oppress their citizenry.” Musk and Altman will serve as co-chairs of the non-profit, and Amazon Web Services will be donating a massive amount of infrastructure to be used for the research efforts. When asked about the risk that could come from marking artificial intelligence capabilities more available, Musk replied: “I think the best defense against the misuse of AI is to empower as many people as possible to have AI. If everyone has AI powers, then there’s not any one person or a small set of individuals who can have AI superpower.”
Inside Deep Dream: How Google Made Its Computers Go Crazy | Medium
Steven Levy also published an article that tells the fascinating story of how a trio of Google engineers located in Zurich, Seattle, and Mountain View, came together to develop the Deep Dream phenomenon. What started as an experiment tinkering with a vision-recognition neural net in the early morning hours in Zurich, led to deep exploration of the associations between artificial and biological neural nets, and ultimately about the potential of using AI to teach us about our own minds.
Each week we surface, summarize, and share the most interesting stories and biggest news from the world of data science. Have articles or podcasts that you think we should be covering in our Data Science Roundup? Send them to editor@rjmetrics.com.
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