Business Description
Isn’t it funny how misleading these machines can be? First we teach them how to play Chess. Then they beat our champions. We program them to play Jeopardy and Go. The ungrateful machines defeat our champions. Already they are threatening to remove drivers from cars. Six of the top eight hedge funds have earned $8 billion thanks to AI algorithms. They are picking stocks better than Wall Street experts. Healthcare and Law are being impacted. Should we be worried? The experts have left us confused. They cannot give us one coherent response. Will machines render us jobless? If so, just what can we do.
What To Do When Machines Do Everything by Malcolm Frank his two colleagues from Cognizant’s Center for the Future of Work are answering these questions. Technology is creating new jobs. Social media consultants, full stack engineers, search engine optimizers and content curators have all been created by recent developments in technology. Drones will need drone engineers. Virtual Reality and Augmented Reality hardware will need storytellers who will tell stories using the new medium. Whether we like it or not, algorithms, AI, bots and Big Data will impact careers whether we like it or not.
In 2014, the authors wrote Code Halo that told us how companies like Amazon, Pandora, Netflix, Spotify and Google are using our digital footprints to understand us with frightening accuracy. Netflix can predict which movies you will like with greater accuracy than your loved ones. This time they go beyond data to add more variables in the mix. The new business models will get created based on new hardware (sensors, connected devices with massive processing power); new software (AI and algorithms are creeping into everything) and human ingenuity.
If you are thinking of smashing the machines like the Luddites did when they saw machines replacing people in mills. Technology is already embedded into everything we do. How can we stay ahead? The book suggests five approaches that form the acronym AHEAD:
1. Automate: Pass on the rote work and computation to machines. PayPal, is using machine learning to compare millions of transactions and identify fraudulent transactions. Facebook’s researchers used four million photos of faces to train their machine to recognize faces.
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