Understanding Causality in Machine Learning and Future of Hiring With AI – Weekly Guide

Along with the new applications and advancements that Artificial Intelligence is enabling across industries, it is constantly improving and developing itself. Now we are at a stage of innovation in the field of AI and Machine Learning, where we can talk about things like common sense, emotions, and IQ of the artificially intelligent systems. In this week’s AI guide, we will see how Causality is the next challenge for machine learning. 

Understanding Causality Is the Next Challenge for Machine Learning

Causality will play an important role in the next steps in the progress of machine learning. So far, deep learning has comprised learning from static datasets, which makes AI really good at tasks related to correlations and associations. But, neural networks are unable to interpret cause-and-effect. Also, they cannot decipher why these associations and correlations exist. Neural networks are also not good at tasks that involve imagination, reasoning, and planning. This, in turn, limits AI from being able to generalise their learning and transfer their skills to another related environment.

The paper on CausalWorld describes benchmarks in a simulated robotics manipulation environment using the open-source TriFinger robotics platform. Robotic agents can be given tasks that comprise pushing, stacking, placing, and so on, informed by how children have been observed to play with blocks and learn to build complex structures. This is the inception of introducing causality in machine learning.

Zoom Out | Here’s a peek at the future of hiring with artificial intelligence

Recruiting and hiring will look a lot different in a few years from now as they change shape by adopting AI solutions. AI is rapidly shaping functions across verticals in an organisation and HR and talent acquisition are no exception. The biggest challenge faced by the domain is the allegation of existing bias and AI is expected to remove this human bias. 

Here are some examples of how bias could be eliminated with AI:

  1. Masking candidate photos and gender
  2. Masking location
  3. Masking recruiter feedback
  4. Masking university/college attended (in some cases)
  5. Highlighting skills and certain keywords based on the job description

Head to the Great Learning Academy for free courses on Artificial Intelligence and Machine Learning. 

Source : https://www.mygreatlearning.com/blog/artificial-intelligence/