M.J. Rose

A photographer’s eye on the ruins of North Carolina’s Wizard of Oz theme park…

From The Creator’s Project,

North Carolina’s Abandoned ‘Wizard of Oz’ Theme Park Will Haunt You
by Luis Carreño

“Written as a novel by L. Frank Baum in 1900, the Wizard of Oz became an acclaimed Technicolor film in 1939. The success of that film led to the exploration of prequels and sequels desperately seeking the fame and recognition that the original musical film garnered. Perhaps the boldest iteration was the recreation of the Land of Oz as a theme park in North Carolina’s Beech Mountains.

The park was a tremendous attraction for Wizard of Oz lovers, but it closed just ten years after it opened, after project developers fell into bankruptcy. It has since become a grim artifact where nature is aggressively reclaiming Oz. Photographer Johnny Joo visited the site and photographed the ruins, full of wild roots and thick fog…”

To see all the photos, click here.

You’ve probably heard of streetlamp interference (if you’ve been reading this blog for a while, that is) — but have you heard of watch interference?

From Princetonwatches.com,

The Mystery of the Stopping Watch… Why do Watches Stop When Some People Wear Them?

“A mysterious and yet common occurrence; why do some watches stop working when people wear them? Why do some people seem to stop every watch they put on their wrist?

It seems there has not been a serious study regarding this phenomenon and much like something you may see on a popular television series, or read in an internet chat room, appears to be widely debated and has a cloud of skepticism around it.

Although it is true that some watches will not function properly when around some electronic or highly magnetic equipment, there doesn’t seem to be a clear answer on why, when some people put a watch on their wrist, it will inexplicably stop working immediately or within a few minutes…”

For the rest, click here.

Who knows, maybe the A.I. that eventually takes over humanity will turn out to be nicer than we are?

From FreeCodeCamp,

The mind-blowing AI announcement from Google that you probably missed.

“In the closing weeks of 2016, Google published an article that quietly sailed under most people’s radars. Which is a shame, because it may just be the most astonishing article about machine learning that I read last year.

Don’t feel bad if you missed it. Not only was the article competing with the pre-Christmas rush that most of us were navigating?—?it was also tucked away on Google’s Research Blog, beneath the geektastic headline Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System.

This doesn’t exactly scream must read, does it? Especially when you’ve got projects to wind up, gifts to buy, and family feuds to be resolved?—?all while the advent calendar relentlessly counts down the days until Christmas like some kind of chocolate-filled Yuletide doomsday clock.

Luckily, I’m here to bring you up to speed. Here’s the deal.

Up until September of last year, Google Translate used phrase-based translation. It basically did the same thing you and I do when we look up key words and phrases in our Lonely Planet language guides. It’s effective enough, and blisteringly fast compared to awkwardly thumbing your way through a bunch of pages looking for the French equivalent of “please bring me all of your cheese and don’t stop until I fall over.” But it lacks nuance.

Phrase-based translation is a blunt instrument. It does the job well enough to get by. But mapping roughly equivalent words and phrases without an understanding of linguistic structures can only produce crude results.

This approach is also limited by the extent of an available vocabulary. Phrase-based translation has no capacity to make educated guesses at words it doesn’t recognize, and can’t learn from new input.

All that changed in September, when Google gave their translation tool a new engine: the Google Neural Machine Translation system (GNMT). This new engine comes fully loaded with all the hot 2016 buzzwords, like neural network and machine learning.

The short version is that Google Translate got smart….”

For the rest, click here.