AI/Machine Learning lacks the ability to compute cause and effect. It is at the first level in the ladder of causation where learning is based purely on association. Judea Pearl, a leading computer scientist/professor of computer science/statistics and director of the Cognitive Systems Laboratory at UCLA, in his important book “The Book of Why”, explains that AI/ML in the current state, lacks human like cognition and cannot reason why. Read more to find out…
Sundar Pichai unveiled the new AI-powered “Voice Assistant” at the recent Google IO conference to an ecstatic crowd. But, let us take a moment to think- is this to be unequivocally welcomed? Who is the real master of our friendly Google Assistant?
You or Google? Should we be excited or worried about the Google Assistant?
Read more to find out… Who will the Google Assistant Serve- When No Man Can Serve Two Masters!
From definitional issues of AI to the huge debate on AI leading to job losses, this article briefly addresses the AI paradox. Is AI any different from Machine Learning? What kinds of jobs at risk? People might imagine that AI driven automation will mainly replace manual labour which is not intellectually demanding, the reality is that the opposite is true: AI driven automation, is much more suited to intellectually demanding tasks. Put another way, if you wanted to beat chess grandmaster Magnus Carlsen, you would choose a computer. If you wanted to clean the chess pieces after the game, you would choose a human being. What is easy for humans is difficult for Robots and vice-versa, which is the Moravec’s paradox of AI. Read more to find out…
The “do no evil” company changed the slogan “to do the right thing” in October 2015. And they are doing the right thing- if you are a Google Shareholder- you are definitely happy 🙂 . Google dominates the search engine market globally. Recently, Google has been fined a record 2.4 Billion Euros by EU, for what they call, “abusing internet search monopoly”. This blog presents my view on- Did EU get it right? Can Google be called a natural monopoly? What will it take for a competitor like Bing to reach where Google is? Can Google search easily be construed as a public utility and is it time for it to be regulated? So, should you sell your Google stock now? Read more to find out…
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.
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..