The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

The Theorem Every Data Scientist Should Know (Part 2)  #MachineLearning #DataScience

  • The standard deviation of the population distribution is tied with the standard deviation of the sampling distribution.
  • With the standard deviation of the sampling distribution and the sample size, we are able to calculate the standard deviation of the population distribution.
  • The standard deviation of the sampling distribution is called the standard error.
  • The mean of the sampling distribution will cluster around the population mean.
  • If we collect a large number of different samples mean, the distribution of those samples mean should take the shape of a normal distribution no matter what the population distribution is.

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@MikeTamir: “The Theorem Every Data Scientist Should Know (Part 2) #MachineLearning #DataScience”


Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.


The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

Governments have much to gain from applying algorithms to public policy, but controversies loom

If used with discretion, governments and societies stand to gain from machine learning

  • Many areas of policy, he suggests, could do with a dose of machine learning.
  • By helping to allocate scarce public funds more accurately, machine learning could save governments significant sums.
  • Machines are trained to find patterns that predict future criminality from past data.
  • Many police chiefs already have a simple system to flag “at risk” officers.
  • Mr Mullainathan and his colleagues show that machine learning can help predict the risk of death.

Read the full article, click here.


@TheEconomist: “If used with discretion, governments and societies stand to gain from machine learning”


Governments have much to gain from applying algorithms to public policy, but controversies loom


Governments have much to gain from applying algorithms to public policy, but controversies loom

FirstMark Capital’s Matt Turck on the big data landscape

FirstMark Capital’s Matt Turck on the big data landscape | #BigData #Artificialintelligence…

  • Whilst suggesting that there must also be willingness from larger companies “to begin playing with big data.”
  • The recent obsession and progression of AI would not have been possible without big data.
  • Many big data technologies have been embraced by a broader range of companies; we still have a long way to go.
  • The evolution of big data can be viewed in three distinct timeframes.
  • Turck even suggests that “AI is the child of big data.”

Read the full article, click here.


@Ronald_vanLoon: “FirstMark Capital’s Matt Turck on the big data landscape | #BigData #Artificialintelligence…”


Today, the big data sector amounts to more than 7.5 percent of total venture investments. So where are we in the world of big data, and is the recent..


FirstMark Capital’s Matt Turck on the big data landscape

IT-Trends 2016 for the insurance industry

.@MunichRe's (insurance) industry #IT-trends 2016
(src: ):
#BigData #IoT #IoE #AI #ML #SmartX

  • The publication is available exclusively to Munich Re clients.
  • The objective of the IT Trend Radar 2016 is to identify relevant new technologies for ERGO, Munich Re and MEAG and evaluate them from a group perspective.
  • Those who identify the potential of new technologies, trends and technological progress at an early stage have a competitive advantage and build the foundation for innovation.
  • Topics Online RSS feed Recommend page by e-mail Contact
  • We focus on the potential for innovation offered by individual trends, and review their suitability in practice for the reinsurance and primary insurance sector.

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@DiegoKuonen: “.@MunichRe’s (insurance) industry #IT-trends 2016
(src: ):
#BigData #IoT #IoE #AI #ML #SmartX”


Those who identify the potential of new technologies, trends and technological progress at an early stage have a competitive advantage and build the foundation for innovation. Constantly increasing technological complexity is leading to a growing number of areas that are relevant for our company to keep an eye on.


IT-Trends 2016 for the insurance industry

Elon Musk’s OpenAI is Using Reddit to Teach An Artificial Intelligence How to Speak

Elon Musk's OpenAI is Using #Reddit to Teach An #ArtificialIntelligence How to Speak  #AI

  • The supercomputer has a whopping 170 teraflops of computing power-equivalent to 250 conventional servers.
  • Elon Musk’s OpenAI is Using Reddit to Teach An Artificial Intelligence How to Speak
  • “I thought it was incredibly appropriate that the world’s first supercomputer dedicated to artificial intelligence would go to the laboratory that was dedicated to open artificial intelligence,” Huang added.
  • The supercomputer will tackle the most difficult challenges facing the artificial intelligence industy…by reading through Reddit forums .
  • The “AI supercomputer in a box” is packed with 170 teraflops of computing power-that’s equivalent to 250 conventional servers.

Read the full article, click here.


@nigewillson: “Elon Musk’s OpenAI is Using #Reddit to Teach An #ArtificialIntelligence How to Speak #AI”


NVIDIA has delivered the first DGX-1 supercomputer to non-profit artificial intelligence research company OpenAI. The supercomputer has a whopping 170 teraflops of computing power—equivalent to 250 conventional servers.


Elon Musk’s OpenAI is Using Reddit to Teach An Artificial Intelligence How to Speak

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an #AI to write Python code with Python code  #MachineLearning #DeepLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

Read the full article, click here.


@MikeTamir: “Teaching an #AI to write Python code with Python code #MachineLearning #DeepLearning”


OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.


Teaching an AI to write Python code with Python code • Will cars dream?