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Recently I was going thru an agricultural AI named Kissan AI. It had 7 billion parameters. Gemini AI models work on about 5-7 trillion parameters.

 I wanted to understand the basic diff and application of these parameters in AI models and how apply to the output from the model.

Just like more pixels in a pic make it more detailed and clear, more parameters in an AI model make the AI output more reliable, relevant and accurate. So simple .. 

George..

Starting of Containerisation 1955 ..

Year 1955. The Board of Citibank was staring at a loan application that seemed almost unbelievable. A man named Malcolm McLean had applied for a loan of $42 million—almost the maximum amount the bank could lend at that time. The problem?

McLean had no significant experience in the shipping industry and very little collateral. He had started his career as a truck driver and, in his early days, had only one truck.

Board reaction was: "A truck driver is asking for $42 million? This application should have been rejected by a junior officer within five minutes. Why has it reached the Board?"

Every one pointed at young banker named Walter Wriston. He was the one who had first examined McLean's proposal. He had convinced people at every level to take the proposal seriously upto board. His only request was: "Listen to his idea first. Then reject it if you want."

So McLean began explaining. "I used to drive trucks near the port. Whenever a ship arrived, hundreds of workers would line up. Cargo had to be unloaded piece by piece, passed from one worker to another, and finally loaded onto trucks. It could take days—or even weeks. Ships were sitting idle in ports. Thousands of workers and sailors had to be paid. Food, wages, handling costs… everything added up."

Then he had an idea. "Why not put the cargo into large standardized containers, like giant boxes? A crane could simply lift the entire container from the truck onto the ship—and from the ship onto the truck. Instead of spending days loading and unloading a ship, we could do it in a fraction of the time."

The idea was revolutionary. But there was a problem. McLean had already suggested the idea to the company he worked for. They rejected it. "Containers? This will never work." So McLean decided: "Then I'll buy a shipping company myself and make it work."

A shipping company called Waterman Steamship Corporation was available for purchase. His proposal was simple: "Give me the loan. I'll buy the company, transform its operations using containers, make it profitable, and repay the loan."

The Board was stunned. "You don't have enough shipping experience. You don't have enough personal assets. How can we trust you with $42 million?" Then they turned to the young banker, Walter Wriston.

"You are recommending that we lend $42 million based on your judgment. What happens if he fails to repay?" Wriston had an answer. "The money isn't completely unsecured." He explained that the company McLean wanted to buy already had ships, assets and cash. The company's assets were roughly equal to the amount being borrowed. So even if McLean failed, the bank could take control of the company's assets.

The risk was not as large as it initially appeared. The Board thought about it. The business plan made sense. If McLean failed, the bank could recover its money, If he succeeded, the bank would gain a major new customer.

Then came the decision: "We will give him the loan." That decision didn't just change Citibank's history. It helped change the history of global transportation.

McLean went on to build Sea-Land company, and containerization transformed shipping. Loading and unloading became dramatically faster. Ships spent far less time sitting in ports. Transportation costs fell. Global supply chains became far more efficient. The humble shipping container eventually became one of the most important inventions in modern global trade.

And Walter Wriston? That young banker later became one of Citibank's most influential leaders and played a major role in its global expansion. But nobody sitting in that room in 1955 knew they were making a decision that would influence the future of shipping, banking and global trade. They were simply deciding whether to approve a loan.

And there is a beautiful lesson here: Most people look at a person's assets, experience and past. Great decision-makers sometimes look at the opportunity in front of them. A truck driver saw a problem.

A young banker saw an opportunity. And a bank was willing to bet on both.

The question is not always, "What do you have?" Sometimes the more important question is, "What do you see?" ....