April Fools is a good chance for some much-needed levity, but it's also an opportunity for tone-deaf companies to try and resonate with their younger clientele. You get some fantastic pranks and funny jokes, but you also get some cringe.
Google claimed to have a physical screen cleaner feature on their phones that would also create a long-lasting non-stick shield.
Spotify replaced Discover Weekly playlists with Discocover Weekly playlists, including actual disco cover songs from artists you listen to.
The Bad
Logitech said it was rebranding its wireless mice as "hamsters" since they don't have tails.
U.K. Based Libert Games created a "Trump V Kim Nuclear Foosball Table" which comes with latex masks of Donald Trump and Kim Jong-un so you can truly embody the roleplay.
Elon Musk released an auto-tuned rap song called "RIP Harambe" about the Cincinatti Zoo Gorilla who was unceremoniously killed in 2016
Yet, some of the best April Fools pranks came ages ago:
The Best of All Time
In 1957, the BBC ran a segment on the Swiss growing pasta on trees. The story was on television, and back then, the television wouldn't lie to you … right?!
In 1974, a local Sitka, Alaska prankster Oliver Bickar flew 70 old tires into Mt. Edgecumbe – a volcano that had been dormant for 400 years - and set them on fire. It was such a good prank that the Associated Press ran with it, Alaska Airlines used it in ads, and the admiral of the Coast Gaurd called the prank a "classic."
In 1996, Taco Bell announced it had purchased the Liberty Bell and named it the "Taco Liberty Bell" in an effort to help the national debt.
March Madness is in full swing and will have the world's attention for a few more days. As you could guess – most brackets have already busted. Louisville lost, UC Irvine won, and Duke barely weathered UCF's onslaught.
Lots of skill, lots of adrenaline and lots of natural talents (including amazing physical talents). Here is a picture of 7'6 Tacko Fall. Tacko still looks taller than his opponents even when kneeling.
The allure of March Madness is the same as gambling or trading. These are all fertile grounds for emotion, biases, and statistics.
The holy grail is mighty elusive in March Madness (as in most things) … For example, the odds of getting the perfect bracket is 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank). It's easier to win back-to-back lotteries than picking a perfect bracket. Nonetheless, I bet you felt pretty good when you filled out your bracket.
"Not only is there more to life than basketball, there's a lot more to basketball than basketball." – Phil Jackson
In 2017, I highlighted 3 people that were (semi) successful at predicting March Madness. A 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world. None of them had the same success even a year later.
Finding an edge is hard – maintaining an edge is even harder.
That's not to say there aren't edges to be found.
Bracket-choosing mimics the way investors pick trades or allocate assets. Some people use gut feel, some base their decisions current and historical performance, and some use predictive models. You've got different inputs, weights, and miscellaneous factors influencing your decision. That makes you feel powerful. But knowing the history, their ranks, etc. can be helpful in making an educated guess and they can also lead you astray.
The allure of March Madness is the same as gambling or trading. As sports fans, it's easy to believe we know something the layman doesn't. We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset.
You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or your musical loving co-worker.
In reality, several of the highest-ranked brackets every year are guesses.
The commonality in all decisions is that we are biased. Bias is inherent to the process because there isn't a clear cut answer. We don't have the answers to "who will win" and we don't have the answer to "what makes a perfect prediction".
Think about it from a market efficiency standpoint. People make decisions on many factors – sometimes irrational ones – and that creates inefficiencies & complexities. It can be hard to find those inefficiencies and capitalize on them – but they're there to be found.
In trading, that's where AI and advanced math come in – taking away our biases and identifying inefficiencies humans miss.
Can machine learning also help in March Madness?
“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting – that is, the more you should prefer simplicity” – Tom Griffiths
That being said, people have tried before with mediocre success. It's hard to overcome the intangibles of sports – hustle, the crowd, momentum – and it's hard to overcome 1 in 9.2 quintillion odds.
Two lessons can be learned from this:
People aren't as good at prediction as they predict they are.
Machine Learning isn't a one-size-fits-all answer to all your problems
The US grounded the Boeing 737 Max after similarities between two crashes. It's good they did, safety first, but it definitely caused some headaches coming back from Saskatchewan. There's an interesting thought experiment here to understand the complexities that the airlines had to solve.
Imagine a 737 holds 200 people and does 4 or 5 flights a day. American has 24 737s in its fleet. That means each day ~24,000 people are displaced on American Airlines alone. They're replacing those planes with smaller regional jets – meaning more flights, more small airports, and more delays. The logistical/supply chain dynamics are a nightmare.
Here are some of the posts that caught my eye recently. Hope you find something interesting.
Wesplitintogroups:Insane,MerelyCrazy,andtheTurtles. IfiguredIwasrelativelysafewiththeTurtles … I was wrong.
The machinesarecapableofglidingoverthesnow at speeds exceeding 120 milesperhour. I wasn't going nearly that fast … but the beasts were harder to tame than I expected.
Despite crashing numerous times, totaling a sled, and being sore for weeks … I had so much fun on year one that I brought my son with me on year two.
Last year, I recognized 2 things:
Humansaredeletioncreatures.Thatmeanstheycanholdseven things(plusorminustwo)intheirmemory. While they were focused on fun, I was focused on how to stay on the sled
You're supposed to stay on the sled, and leaning into the turn helps you do that … who'd've thunk it?
This year I learned a couple more:
People don't forget. Everyone remembered my less than skillful sledding.
Youth is wasted on the young. My son picked it up fast and was speeding as if he'd been on a sled his whole life (despite falls that would cause people with skulls that no longer have soft spots to proceed with caution).
When you find the right group of people, the fun gets more fun, and struggles seem less challenging.
While sledding is fun, it's the people that make the trip.
Likewise, it's the people you take with you through life that makes it so worth it.
Grammar isn't everyone's cup of tea … but it's something I spend a lot of time thinking about.
Should I use an ellipsis here or a dash? Is this an unnecessary parenthetical?
Because of that, the serial comma (commonly known as the Oxford comma) is surprisingly important to me.
If you don't know what an Oxford comma is, it is the comma before the word "and" at the end of a list.
I love it (and use it). I think it adds clarity in most situations, and while some lists make sense without it, it is helpful if not necessary in many lists.
Like below:
Who wants that?
Use the Oxford Comma … Save yourselves the imagery.
If you need a guide of when to use it, Check out this infographic … Click it to see the whole thing.
NASA's Mars Opportunity Rover has officially lost contact with earth after a fierce dust storm. Its last message was "My battery is getting low and it's getting dark." The rover's original mission was scheduled for 90 days … it lasted 14 years.
via 
via 