— Grace Hopper
— Teeka Tiwari
Here’s an idea of just how much value smart contracts can unleash for corporate America…
According to Forbes, credit card fraud costs U.S. merchants an estimated $190 billion per year. A smart contract tied to your identity on the blockchain could virtually eliminate credit card fraud.
A report from accounting firm Deloitte suggests smart contracts can save the mortgage, investment banking, and insurance industries as much as a combined $39 billion per year.
A report from global consulting firm McKinsey & Company says smart contracts could save businesses at least $50 billion in business-to-business (B2B) transactions.
Over the next few years, everything from managing property titles, clearing financial trades, and operating day-to-day commerce will happen via some form of smart contract.
And that’s not all.
Smart contracts will enable a new type of commerce called machine-to-machine (M2M) commerce. M2M refers to direct communication between electronic devices.
For example, a smart car will automatically communicate with smart parking meters to pay parking fees.
Computers will use smart contracts to automatically trade resources with other computers. Autonomous vehicles will use smart contracts to trade lane priority and traffic position with each other for money.
We’re not the only ones who think smart contracts are the future. Respected research firm Gartner Group projects 25% of global organizations will use smart contracts by 2022.
The broad use cases for smart contracts means we could see multiple billions more smart contracts than websites.
Here’s why that’s important. Only a fraction of websites are commercial sites that need protection.
On the other hand, virtually all smart contracts will need some form of security verification before they can safely be run. If we throw in M2M commerce, we could see as many as 20 billion smart contracts deployed. That’s because by 2020, it’s expected we’ll have over 20 billion connected devices.”
— Exponential Technology
Think back… Probably for as long as we might remember, we have always been using a keyboard (laptop, desktop, for some of us even a typewriter). During the last 30 years or so, we began using a mouse or trackpad to point and click. While this technology allows us to interface with machines, it is horribly inefficient and sometimes painful (think carpal tunnel syndrome or the crick in your neck).
The next wave of human machine interface technology is here. And it will be a combination of natural language processing technology (voice) and simple hand gestures (swipe, pinch, open, close, point, etc.).
In five years, we are going to look back and think… “What were we doing with those keyboards? That was terrible.” Parents and grandparents will tell their children and grandchildren how they spent their entire careers hunched over a keyboard hunting and pecking. The kids won’t believe it.
This inflection point in how we will interface with machines is exactly why the natural language processing market is starting to explode.
It was insignificant in 2016 – less than a billion dollars in total. By 2020, we’ll be looking at a $5 billion market… at least. And by 2025, this will be well in excess of $20 billion.
And the annual growth rate of the NLP market through 2024 is something to behold. For the next three years this market is growing at 25% or greater, and even as we get out to 2024, the market is still growing at 22% year-on-year.”
— Jim Rickards
… The computing power needed, the electricity consumed, and the wasted heat generated will all grow exponentially as bitcoin mining continues to solve more difficult problems (called “proof-of-work”) to validate transactions on the blockchain, a distributed record of all prior transactions.
The electricity usage in bitcoin mining is so extreme that many miners locate in China, where electricity is cheap due to government subsidies, or to Iceland, where the near-Arctic climate reduces the cost of cooling the computers.”
— Seth Stephens-Davidowitz From ‘Everybody Lies: Big data, New data, and What the internet can tell us about who we really are’ P. 16
… the microscope showed us there is more to a drop of pond water than we think we see. The telescope showed us there is more to the night sky than we think we see. And new, digital data now shows us there is more to human society than we think we see. It may be our era’s microscope or telescope — making possible important, even revolutionary insights.”
The New Future, June 2017
One clear trend that will develop rapidly are shared autonomous vehicles (SAVs). Companies are beginning to spring up with the intention of managing fleets of autonomous vehicles that are able to essentially run 24 hours a day.
These SAVs will be on demand and available through a simple smartphone application, just like how on-demand transportation services like Uber and Lyft are available today.
Uber and Lyft were so simple yet revolutionary to the traditional taxi industry. Passengers could receive much better service, newer and cleaner vehicles to ride in, and lower prices as compared to traditional taxis.
And when the driver is removed, on-demand transportation services will become dramatically cheaper.
Based on an analysis performed on taxis versus SAVs in a market like Manhattan, the average cost for a yellow taxi is $5 per mile driven versus a projected $0.50 per mile driven for an SAV. That is an incredible 90% cheaper from where they currently are today.
In fact, I envision a future where these kinds of services will be provided for free in exchange for opting in to advertising or commerce opportunities.
In 2016, Google generated more than $79 billion of revenue just from advertising on the internet. This is 88% of Google’s total revenues.
I believe it can do the same thing with SAVs.
And the impact on the world caused by SAVs will be profound. A recent study showed that one SAV could replace nine conventional vehicles. Think about that. Even with a growing population, the number of cars on the road will dramatically decrease.
What will this mean? In short, less traffic, less congestion, and fewer emissions.”
— The New Future, June 2017
A pilot of a Boeing 777 spends just about seven minutes actually flying the plane on a typical flight. The pilots of an Airbus airplane spend only about three and a half minutes due to even further automation. On a three-hour flight, that equates to less than 2% of the flight time being piloted by a human and 98% being fully autonomous.
There is a good reason for that, too.
In the case of commercial aircraft accidents, about 85% are caused by human performance problems. The current generation of autonomous aircraft have 0.17 fatal accidents per every 1 million flight cycles. This compares to the first generation of aircraft, which has an accident rate of 4.03 fatal accidents per million flight cycles.
The application of autonomous flight technology reduced the aircraft accident rate by more than 95%.”
— From The Master Algorithm – How the Quest for the ultimate learning machine will remake our world by Pedro Domingos P. 70
Sets of rules are popular with retailers who are deciding which goods to stock. Walmart was a pioneer in this area. One of their early findings was that if you buy diapers you are also likely to buy beer. Huh?
One interpretation of this is that Mom sends Dad to the supermarket to buy diapers, and as emotional compensation, Dad buys a case of beer to go with them. Knowing this, the supermarket can now sell more beer by putting it next to the diapers, which would have never occurred to it without rule mining.”
— From The Master Algorithm – How the Quest for the ultimate learning machine will remake our world by Pedro Domingos P. 63
In his Pensées, published in 1669, Pascal said we should believe in the Christian God because if he exists that gains us eternal life, and if he doesn’t we lose very little. This was a remarkably sophisticated argument for the time, but as Diderot pointed out, an imam could make the same argument for believing in Allah. And if you pick the wrong god, the price you pay is eternal hell. On balance, considering the wide variety of possible gods, you’re no better off picking a particular one to believe in than you are picking any other. For every god that says ‘do this,’ there’s another that says ‘no, do that.’
the practical consequence of the ‘no free lunch’ theorem is that there’s no such thing as learning without knowledge. Data alone is not enough. Starting from scratch will only get you to scratch. Machine learning is a kind of knowledge pump: we can use it to extract a lot of knowledge from data, but first we have to prime the pump.”
— From The Master Algorithm – How the Quest for the ultimate learning machine will remake our world by Pedro Domingos P. 59