#POLYPLEX -2606

Fibonacci extension 6.857% 2937

#Probability

In 1937, a trucker got frustrated waiting for his cargo to be unloaded. He later sold his company for $1.1 billion.

Here’s the story of how Malcom McLean made a billion dollars and which startup could be following in his footsteps. (Inspired by our episode with @laurabehrenswu)

In the early 1950s, the shipping industry was a dying business.

New York’s docks were handling half as much domestic cargo in the early 1950s as they had been in the depressed 1930s.

In thirty years, no one had invested significant money in coastal shipping.

Why?

As McLean found out, loading ships was difficult and slow.

A truck or train would deliver items to the port. Each item was unloaded separately, recorded, and carried to storage.

When a ship was ready, each item was taken from storage, counted again, and hauled on-board.

The cost of this labor was significant. As one expert at the time explained:

“A four-thousand-mile voyage for a shipment might consume 50 percent of its costs in covering just the two ten-mile movements through two ports.”

Yet, despite the industry’s issues, it felt little pressure to change.

Foreigners were barred from operating domestically. Cartels ran international routes. And gov. subsidies eased the pain of labor costs.

Reshaping the industry needed an outsider.

Enter Malcolm McLean.

THE BATTLE OF PRATAPGARH (10 Nov, 1659)

Chhatrapati Shivaji Maharaj's impeccable military genius and matchless courage humbled the Adil Shahi Sultanate.

The beginning of what was to come later for the M0ghuls from one of the most indefatigable defenders of Dharma!

(Thread)

Shivaji’s growing influence in Maval region, made him a threat to the Adil Shahi ruler of Bijapur who sought to restrain him. The man who was given the responsibility of this mission, was their commander, Afzal Khan. Afzal was a formidable warrior known for his ruthlessness.(1)

His strategy was to bring Shivaji out into the plains, where he held an advantage, compared to the rocky Deccan terrain. Afzal attacked the holy town of Pandharpur, and later demolished the temple of Bhavani at Tuljapur to instigate Shivaji in a bid to bring him out.(2)

Shivaji was angry but remained patient. Knowing that he could not defeat Bijapuri Sultanate in an open battle, Shivaji moved to Pratapgarh fort, located on rocky heights, surrounded by thick forests of Jawali.(3)

This set the pattern for future Maratha attacks, using guerrilla warfare against larger invading armies rendering them ineffective in unfavorable terrain.(4)

💬 I often get Q’s like these:

How did you get your first customers for WIP? How did you grow BetaList’s traffic? Etc.

Makers are looking to reverse-engineer success. I see it everywhere.

I don’t think it works that way and the answers to those questions are mostly useless. 💥

I have built dozens of different products over the last couple of years. The vast majority failed. 😭

Surely if I know the answers to these questions, but still fail over and over again, these answers aren’t that useful. 🤷‍♀️

So what’s a better question to ask? 🤔

99.9% of the questions I receive are about the products that did well. In a way that makes sense, because we quickly forget about those that didn’t succeed.

🧠 This is known as survivorship bias.

Focusing on what survived, while ignoring what made it survive in the first place.

The real question, what you really want to know, is this:

What makes @WIP, @BetaList, and to some extent @AllStartupJobs succeed where my countless other attempts failed?

What separates a failed product 👎 from a successful product 👍?

Honestly, I don’t know. I wish I did.

It’s like Steve Jobs said “I’ll know it when I see it.” 👀

Same is true when we make products. We don’t know upfront what will work. But once we see an inkling of a product that does have potential, it’s not that hard to spot.

Can we control the output of neural generation systems?
Can we build a better chatbot using this control?
And what makes a good conversation anyway?

Check out the blog post for our NAACL paper:

https://t.co/UAAUxSeA0L

#AI #NLProc #DeepLearning #ConversationalAI @facebookai

This post got a little delayed since the paper's publication, but the intervening time has given me an opportunity to reflect.

So this post has some extra thoughts regarding more recent NLG developments - e.g., pretrained models like GPT(2), and the suitability of beam search.