Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
Data science is the big draw in business schools
959 days ago
7 Effective Methods for Fitting a Liner
969 days ago
3 Thoughts on Why Deep Learning Works So Well
969 days ago
3 million at risk from the rise of robots
969 days ago
Top 10 Hot Artificial Intelligence (AI) Technologies
Artificial intelligence in the workplace changes roles of employees
ORLANDO, Fla. - Machine learning and data analytics are on the rise, leaving some employees to fear that computers will take over their jobs, but that is not the case.
Machine learning will simply change the role that humans play in the workforce, said Malcolm Gladwell, journalist and best-selling author, in a keynote at Citrix Synergy 2017.
In the past, the job of professionals was to gather data and information as if they were solving a puzzle, but that changes with today‚??s data analytics and artificial intelligence in the workplace. Employees today aren‚??t puzzle solvers who go out and gather information, but mystery solvers who must make sense of complex information that machines gather, Gladwell said.
"What we want our experts to do in this modern world [is] ... occupy critical points along the decision-making chain that a machine could never touch," he said.
One example is the role of an NBA general manager who has to predict whether or not a basketball player will perform well and then pay him accordingly.
Machine learning technology has uncovered a pattern showing that NBA players typically have their best seasons at the ages of 24 or 25 years old, but drop off for at least one year around 26 or 27. The reason for the drop-off is typically because players receive their first big contract around this time, and they get complacent, Gladwell said.
But there are exceptions to patterns that machines find, and that‚??s where the human touch comes in. Gordon Hayward of the Utah Jazz, for instance, was a better player each year following his new deal in 2014.
"How do you find these workers?" Gladwell said. "Does data tell you this? No."
Finding these workers requires sitting down with people and getting to know them, he said. No amount of automation or staring at an Excel spreadsheet can glean that information. Computers can gather a great amount of data, but it is up to humans to draw conclusions from this data correctly.
"In the future, we are not getting rid of human judgment," Gladwell said. "We are much more in need of human judgment than ever before." Read More