A Map of the 1973 Internet

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This is the whole map of the 1973 internet.

Reference: A Map of the 1973 Internet

Keys to the White House

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This is a very important article about election prediction. Of course, you have to be a historian to make a proper judgement with a deep understanding of the underlying process. It is evaluated by human, not machine.

His simple linear regression beats all modern machine learning algorithms today!

The Keys to the White House, developed by Allan Lichtman, is a system for predicting the popular-vote result of American presidential elections, based upon the theory of pragmatic voting. America’s electorate, according to this theory, chooses a president, not according to events of the campaign, but according to how well the party in control of the White House has governed the country. If the voters are content with the party in power, it gains four more years in the White House; if not, the challenging party prevails. Thus, the choice of a president does not turn on debates, advertising, speeches, endorsements, rallies, platforms, promises, or campaign tactics. Rather, presidential elections are primarily referenda on the performance of the party holding the White House.

V = 37.2 + 1.8 * True = 37.2 + 1.8 * 7 = 49.7

Reference: Keys to the White House

Donald Trump’s win was predicted by Allan Lichtman — the US election expert who has called every result since 1984

allan-lichtman

Political analyist concluded ‘Hillary doesn’t fit the bill’ partly because she lacked Barack Obama’s charisma. Allan Lichtman, a political analyst who has correctly predicted the results of every presidential election since 1984, correctly foresaw that Mr Trump would be the 45th US President.

Unlike many experts who fixated on Mr Trump’s controversial campaign when assessing the election outcome, Professor Lichtman’s calculations largely focused on the incumbent party’s potential for another victory based on 13 key assessments. The system entails “mathematically and specifically” measuring the performance of the party in office. It is a historically based prediction system. He derived the system by looking at every American presidential election from 1860 to 1980.

One of his keys is whether or not the sitting president is running for re-election, and right away, [the Democrats] are down that key.

Another one of his keys is whether or not the candidate of the White House party is, like Obama was in 2008, charismatic. Hillary Clinton doesn’t fit the bill.

Check out the articles below for details of his calculation:

 

Who got it right? These 3 unusual, unlikely things predicted Trump’s win

An employee holds up masks depicting Democratic presidential nominee Hillary Clinton and Republican presidential nominee Donald Trump at Hollywood Toys & Costumes in Los Angeles

Earlier this week, CBS News profiled five strange and unexpected things that have correctly predicted the results of the presidential election for decades. Now, it seems, three of those five predictors were right — forecasting Donald Trump would be the 45th president of the United States.

  • A mystic monkey in Changsha, China.
  • The “Halloween mask index” had Donald Trump ahead of Hillary Clinton, 55 to 45 percent.
  • The American University professor Allan Lichtman.

Reference: Who got it right? These 3 unusual, unlikely things predicted Trump’s win

Winner and Looser of 2016 US Presidential Election

2016-election

Well, this morning, everyone should already know the winner and looser of the 2016 US presidential election. So, based on the spirit of this blog, let’s look at the winner and looser in infographic and predictive analytics as a result of the 2016 US presidential election.

Winner: Google Election

Google made it so easy to understand election status and results in one simple page with tabs for overview, president, senate, house, .., etc. The bar between the two presidential candidates is so clear that we know who is winning and who is catching up, as well as how many electoral votes are needed to win.

google-dashboard

I especially like the semi-transparent red and blue to indicate the majority stakeholders by states and the status of swing states in the lower part of the dashboard. In contrast to what Google is doing, other media does not use the semi-transparent colors for the remaining states so it becomes less clear who would win the rest of the electoral votes and is difficult to see the trend.

nbc-dashboard

On the other hand, the bar between the candidates in the Google dashboard diffuses the bias introduced by the area chart of the US map (implying large areas having more electoral votes).

The only item I would add to the Google election dashboard is to apply the semi-transparent colors to the bar between candidates as well. This would make the dashboard perfect.

In summary, Google election dashboard does a excellent job for the US presidential election. It brings clarity to both the status and trend of the election results in a very precise manner. It deserves to be the winner of BI dashboard design for this election.

Looser: Predictive Analytics

Predictive analytics does a very poor job in this presidential election. All predictive models consistently say Mrs. Clinton would win the election over Mr. Trump. As we all know this morning, the prediction is a total failure.

538 is a pretty popular site about predictive analytics. The following image shows you its prediction during the night of the election results. The forecast had been in favor of Mrs. Clinton until 10 PM, when the curve started to switch to the favor of Mr. Trump. And, the switch did not become evident until 11:30 PM (at the big gap where red line above blue line in the lower part of the chart), when some people could already tell the trend before the forecast trend.

538-forecast

However, we probably should not blame predictive analytics for such big failure. It is because the strength of predictive analytics is to predict “major trend”, not a single outcome; and most of our predictive analytics today rely solely on “data” and nothing else.

As I pointed out in my recent blog on my site and KDnuggest site, if predictive analytics is purely based on data without understanding the underlying process, its forecast is subject to noise and bias in the data and could be very inaccurate. This becomes evident during this presidential election. Because all data were bias towards Mrs. Clinton, it predict Mrs. Clinton to win.

In addition, since the presidential election result is a single outcome and involves a lot of human factors which cannot be quantified analytically, predictive analytics may not be the right tool for the prediction at all! The totally failed prediction makes predictive analytics the looser of this election.

Predictive analytics still works well in a controlled context, but may not be the right tool for election prediction unless we are able to (1) quantify human factors and correlations accurately, (2) do not depend solely on data, and (3) fully disclose the prediction errors.

 

Elon Musk: Robots will take your jobs, government will have to pay your wage

nyt-robotic-manufacturing

Another article talks about robotics taking over future jobs, which I have been claiming for a long time. Governments need to be prepare for the trend by working on regulations for the future job market. I proposed social security contribution by companies employing robots, whereas people are talking about “universal income.” The only problem about the universal income is that it may make the government to big or powerful for some people to accept. Anyway, governments need to be ready for such job market trend.

Reference: Elon Musk: Robots will take your jobs, government will have to pay your wage

My previous posts:

Robots May Pay Taxes Under European Proposals

Why our recent technology advancement is not a revolution to economy?

A 19-year-old made a free robot lawyer that has appealed $3 million in parking tickets

The Bank of England has a chart that shows whether a robot will take your job

These adorable robots could someday put construction workers out of a job

Sustain Social Security under the Impact of Automation and a Shrinking Job Market

Study: Allowing guns on college campuses won’t reduce mass shootings

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Policies allowing civilians to bring guns on to college campuses are unlikely to reduce mass shootings on campus and are likely to lead to more shootings, homicides, and suicides on campus—especially among students—a new report concludes.

Reference: Allowing guns on college campuses won’t reduce mass shootings