How desperate are you to get to your destination? Very? Then be ready to pay a higher price for your Uber ride. Uber is using Machine Learning Algorithms to estimate how much a customer is willing to shell out for a ride. You could be a young woman waiting to get back home from a late night party in New Delhi, or you could be someone rushing to a hospital to visit your close ones or someone getting late to an official meeting- Uber could easily know that using Machine Learning and the data that you have willingly handed over. It can understand your level of desperation and prices the ride just below the level you are willing to pay. While you will take the ride, be sure have been taken for a ride!
The much overused term, ‘sharing economy’ has become a rage and platforms want to be under its big tent because of the positive symbolic value of sharing. While it has been using a socially-progressive feel-good rhetoric of sharing an underutilized asset, the sharing economy is also generating enormous amounts of wealth (for a few). What are we “sharing” in the sharing economy and who is really gaining?
We all remember the Aesop’s fable “A Monkey and Two Cats” and know happened to the two cats who wanted to share the cheese and went to a monkey to complete the transaction. Think of monkey as the sharing economy platform here. We should not expect the outcome to be any different.
We come across problems every day that forces us to make difficult choices. Our inherent biases hardwired in our brains, makes our decisions less than optimal. What if we find solutions to everyday problems from algorithms/computer science? Thinking algorithmically about the world, understanding the fundamental structures of the problems we face can help us see how good we actually are, and better understand the errors that we make.
Optimal stopping tells us when to look and when to leap. The Explore-Exploit trade-off tells us how to find the balance between trying new things and enjoying our favourites. The Sort-Search trade-off tells us when our effort in arranging stuff makes sense.
It is important that we use and understand these and similar algorithms so that we are better prepared for the age of the machines.
As a society, we are now comfortable trusting aggregated reviews of unknown individuals, forming the foundation of the Trust Economy. Blockchain brings in greater transparency and is set to improve the trust economy. Blockchain powered trust currency will give a great boost to consumer purchase decisions which are currently driven by the wisdom of crowds. From trusting strangers we could graduate to trusting the views of people who have earned the trust and can establish the same in the public ledger. Welcome the informed Blockchain powered user, who now knows good from the bad, separate wheat from chaff, and the baby from the bath water…..
Ads have well-deserved reputation for annoying users and I bet all of us would approve of an ad-free internet. Ad-Blocking feature is already available Safari and Firefox browsers. Is Google Chrome coming up with such a feature too? If yes, will it impact its advertising revenues?
When you allow ads to be shown next to the free content that we consume, you are in-effect “paying” to access the content. So is Ad-Blocking equivalent to stealing? We instinctively believe that information is meant to be free. Read two sides to this Ad-Blocking argument in this blog post…
The next generation attribution algorithms to be based on Neural Networks and Deep Learning are all Black Boxes and these Black Boxes are going to take over the WORLD. The current set of algorithms can be understood, comprehended and explained as to how the results where arrived at if you put in the EFFORT. Believe me, with the arrival of Deep Learning (Neural Networks) we can forget what’s under the hood. read more..