Tal saves Earth from an alien attack

Image public domain, modified from original.

Tal fell back from the flame,
Fireball missing its aim,
Turning to run, dropping his gun,
He made it to safety the same.

They continued to come,
Seeing, Tal became dumb,
Boarding the ship, gave them the slip,
They persist to destroy the slum.

Fortnight passed, sirens rail,
Tal busts his mate from jail,
Jumped off the wall, started to fall,
Captors in ’suit, they became pale.

Found the alien craft,
Then, he started to laugh,
Hold it steady, Tal is ready,
Canon split their vessel in half.

Tal saved the Earth,
Tho’ came a dearth,
He gave us mirth.

One was destined to greatness, the other to the gallows

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Once upon a time, there were two thieves — one destined to greatness, the other for the gallows. Marcus was the son of a great warrior who had earned the right to own land. Portman was a peasant, hungry his entire life. He lived on the land as a surf for Marcus’ family.

“Aha! Caught you,” Marcus grabbed Portman’s hand and held it up for everyone to see the loaf of bread he’d stolen from the main house.

“I’m sorry, my lord. My daughter is sick and hungry. I’m ashamed of myself.”

“Come with me, Portman.” Marcus led him into…

Image public domain, modified from original.

I was hoping for a kiss

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I hope it didn’t hurt

How to successfully implement a data science project

Photo by Chaitanya Tvs on Unsplash

These days everyone is on the data bandwagon. Companies scramble to collect more data, better talent, and promise useful insights. But when the rubber meets the road, useful insights are still challenging even with the latest technology and the brightest minds.

According to Gartner, 85% of data science projects fail. While that may seem shocking, this staggeringly large percentage of failed attempts accurately reflects what I see throughout the United States as a consultant. As business leaders, we need to understand why data science projects fail and circumnavigate the obstacles to a successful implementation. So, first, let’s take a look…

The six Vs of data, and why they matter for real-world use cases

WolvesOfTheTwilight derivative work: Conquistador, Public domain, via Wikimedia Commons

Data science, machine learning, and all forms of artificial intelligence have data at their core. We often focus the bulk of our attention on formulas or code when it comes to these disciplines and that makes sense for researchers in those areas of knowledge. But most professionals and hobbyists alike are practitioners of data science and machine learning instead of researchers.

For a practitioner, the formula, the code, and the necessary platforms are mostly borrowed. We can find the code on Github or some code-sharing space online. We can read and study the formulas in books or blogs. And we…

Artificial intelligent persons have rights too

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Objection, your honor. The question calls for speculation.”

“Overruled. Mr. Johnson, this is a civil trial and due to the unique circumstances, there isn’t even a jury. During your opening statements, no objections can be made. Now, please sit.”

Ralph Johnson sat at the counsel’s table on the defendant's side of the gallery. Opposite him was Sophie, the plaintiff in this civil trial — and her attorney.

“Now, Mr. Johnson,” the judge addressed Ralph. “The plaintiff is claiming emotional and physical abuse. Although the possible criminal implications are still being investigated, Sophie Johnson is proceeding with this civil suit. …

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Fear is a driving force for good and…

Rod Castor

Data Science | ML | AI | Tech Leadership and Strategy | Medium Top Writer in Leadership & Future | https://rodcastor.com

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