In the second of our round-ups of the invited talks at the International Conference on Learning Representations (ICLR) we focus on the presentation by Been Kim. Been Kim's research focusses on interpretability and explanability of AI models. In this presentation she talked about work towards developing a language to communicate with AI systems. The ultimate goal is that we would be able to query an algorithm as to why a particular decision was made, and it would be able to provide us with an explanation. To illustrate this point, Been used the example of AlphaGo, and the famous match against world champion Lee Sedol. At move 37 in one of the games, AlphaGo produced what commentators described as a "very strange move" that turned the course of the game.

MUMBAI: Federal Bank is transforming its traditional HR practices by shifting almost the entire hiring process to a tool driven on artificial intelligence-arguably a first in the domestic banking space. FedRecruit, the new HR tool of the Kochi-based private sector lender, employs technology to drive the HR function to the fullest, under which the only human intervention of its multistage hiring process is the final round where top HR executives meet up with the new recruits. Federal Bank is arguably the first in the domestic banking space to almost fully switch to technology for hiring as its large peers like HDFC Bank, which uses a lot of tech for banking, employs AI only at the primary screening level. FedRecruit relies on a series of connected events or data points and goes beyond the conventional one-sided resume to construct a 360-degree narrative of the candidate. These data points are gathered through multiple stages--robotic interviews, psychometric and game-based assessments processes etc., Federal Bank HR chief Ajith Kumar KK told PTI.

Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What is flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms; they should apply machine learning just as liberally in the early cleansing stages instead of relying on people to grapple with gargantuan data sets, according to Andy Palmer, co-founder and chief executive officer of Tamr Inc., which helps organizations use machine learning to unify their data silos.