“To improve is to change, so to be perfect is to have changed often.”
by Winston Churchill
Hi there!
It’s Kan from Exploratory.
We had another horrible week of hurricanes. One hit North Carolina of the U.S. (Florence) and another hit Philippine, Hong Kong and the southern regions of China. If you happen to live in the area, I’m hoping you and your family are fine and safe.
I have created this note to visualize the hurricanes that hit the U.S., which has been updated.
The green one is the hurricane Florence.
Also, I have put together another note about the ones that hit East Asia.
The pink one is the hurricane Mangkhut (or Typhoon No. 22 called in Japan) that hit Philippine and Hong Kong/China. This one was a monster. It was 156 mph when it hit Filippine!
Feels like the hurricanes are getting worse and worse. The impact of the climate change is one thing, but some researchers are claiming the moon also escalate the impact. (Link)
“the moon’s influence may not be obvious to most people, especially those who don’t live near coasts. But it shouldn’t be underestimated.”
Anyway, the two major countries who produce carbon oxidize the most are the ones getting hit by the monster hurricanes is ironic.
Anyway, before starting this week’s update, our next Data Science Booster training’s is in November. We have a student discount (50% off). If you are interested in learning Data Science without programming, sign up today!
Now, here’s this week’s update!
What We Are Reading
Decision-Making Should Be a Required Course in Every High School - Link
Working in Data Science field, we talk about ‘Decision Making’ often. The whole reason we analyze data is to make better decisions.
Decision making is probably one most important and most human skill especially in this age of AI and Big Data where AI has started replacing some of our decision making processes.
Knowing how to process information and make complicated decisions will help you have a better carrier and most importantly a better life!
But then, we don’t teach it at schools as a mandatory subject. Why?
Here is an essay by Steven Johnson, an author of this recently published book called “Farsighted: How We Make the Decisions That Matter the Most” about this topic.
Fake News Declined on Facebook, but Increased on Twitter - Link
A new study about fake news on social media has been published. They have studied the trend in the diffusion of misinformation on Facebook and Twitter and found that it declined significantly on Facebook, but it increased on Twitter.
As much as we all complain about Facebook but I think we should also appreciate their effort to address such unprecedented challenges they (and us) have. It’s a hard problem but they are serious and moving fast.
Since the last presidential election, Facebook has deployed more technological and human power to suppress fake news, hiring more content moderators, acquiring new companies, and deploying more AI software.
Fake News trend on Facebook
Fake News trend on Twitter
Modeling the impact of AI on the world economy - Link
McKinsey has built a model to analyze the impact of AI on the world economy. It’s hard to predict something like this so we want to take it as a grain of salt. Still, I like the way they have summarized the impact separated by Countries, Companies, and Individuals.
Countries are clustered into 4 groups.
G2 countries (the United States and China) dominates the most of the AI investments worldwide. 48% is from China and 38% from the U.S.
Companies, too, are categorized into 3 groups.
Here is one interesting take on the job, which we tend to overlook.
Because social and emotional skills cannot be easily replaced by AI applications, demand for nondigital and nonrepetitive tasks such as healthcare work could moderately increase, too.
The Seven Tools of Causal Inference with Reflections on Machine Learning - Link
This paper about the current limitation of Machine Learning from the causal inference perspective was published recently by Judea Pearl, whom I’ve introduced in this Weekly Update before. I’m finding this paper useful to understand what are the kinds of questions we can use Machine Learning to answer and not.
What We Are Writing
This week, we have written the following 4 posts.
What We Are Working On
We have been working hard at day and night to finish all the development for v5.0. We’re getting close to the last corner of the cycle, and I have to say, it’s coming along very good!
We are starting a beta test in a few days, please reach out to me if you are interested in trying it out and giving us your feedback.
We have redesigned the overall UI/UX especially around the data wrangling steps.
One thing about data wrangling is that you will end up having tons of the steps easily, which makes it harder to see what is going on. Now, you can click a button to collapse and expand the steps easily.
And while it is a critical part of data analysis, it’s not like you need it all the times. If so, you can hide it when you don’t need it.
With v4.x, one of the confusions our users had was that they had to click on the token in the command area (at the top) rather than the step itself (at the right hand side). So, we are changing it so that you can click the step to open the corresponding dialog directly.
There are many more cool features that come with this new UI/UX, which I will talk about more in the next Weekly Update, so stay tuned!
Data Science Booster Training
Our next online Data Science Booster training will be in this coming November. If you are interested in learning Data Science without programming, make sure to sign up soon!
Enroll November Booster Training!
If you are a current student, click here to get the student discount.
That’s it for this week.
Have a wonderful week!
Kan CEO/Exploratory
This is a weekly email update of what I have seen in Data Science / AI and thought were interesting, plus what Team Exploratory is working on.