2 college students built a tool to fight fake news on Facebook using artificial intelligence

2 college students built a tool to fight fake news on Facebook using artificial intelligence

  • In late April, the two computer science majors built a Facebook Messenger bot that, when fed a link, will tell you whether the article in question is or isn’t “fake news.”
  • Bhat, who has built civic tech tools before in his spare time, thought that perhaps they could use machine learning to build a bot that would help put articles people find on Facebook in context.
  • To teach the algorithm to recognize right-leaning content they fed it thousands of articles from Breitbart, a hyperconservative news website.
  • NewsBot has also begun offering short news summaries of top articles.
  • Right now you can mark an article as fake news from a small drop down at the top, but if you’re a user just scrolling, the feed hasn’t really changed in any way.

This could change how you read the news.
Continue reading “2 college students built a tool to fight fake news on Facebook using artificial intelligence”

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.

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.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

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.

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.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

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.

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.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

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.

Read the full article, click here.


@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