Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product. An efficient project manager will ensure that there is ample time from the concept to the final product so that a client's requirements are met without any delays and issues. As already established, efficient project management is of great importance in AI/ML/DL projects.

Social media firms claim they're just trying to build communities and connect the world and that they need ad revenues to remain free. But nothing is really free. For them, more views mean more money and so they've optimized their algorithms to maximize engagement. Views are the algorithms' "reward function" -- the more views the algorithms can attract to the platform the better. When an algorithm promotes a given post and sees an upsurge of views, it will double down on the strategy, selectively timing, targeting and pushing posts in ways that it has found will stimulate further sharing, a process called reinforcement learning.

Picture a reality where whether you are at work or picking up your child from school, someone is tracking exactly where you are and logging it away for analysis. On the way home, you stop at the grocery store and pick up a six-pack and a few snacks. Someone is tracking those activities, too. Then when you bring those snacks to a party that night with your friends, all your conversations are captured and logged as well. The next day you have a meeting with a loan officer at a bank where you're hoping to get the money you need to start a business you've been working toward creating for years but are denied.

Compared to previous revolutions involving processes like mechanization, mass production, and automation, the fourth industrial revolution is characterized by the convergence of new and emerging technologies in, complex socio-technical systems that permeate every aspect of human life. Convergence also implies the increasing interaction of multiple fields, such as AI, genomics and nanotechnology, which rapidly expands the range of possible impacts that need to be considered in any science policy exercise. Ten years ago, nanotechnology was celebrated largely for its impacts on chemistry and material sciences. But the ability to precision engineer matter at genetically relevant scale has resulted in significant advances in genomics and neurosciences, such as creating the ability to model networks of neurons. This example illustrates how the convergence of two emerging technologies --AI and genomics--leads to advances beyond the initial capabilities of either alone.

In the near-future, AI researchers involved in collaboration with policymakers should conduct additional in-depth studies to better understand and anticipate aspects of AI related to (for example) job automation at a more granular level, considering impact across time, sectors, wage levels, education degrees, job types and regions. For instance, rather than low-skills jobs that require advanced hand-dexterity, AI systems might more likely replace routine but high-level cognitive skills. Additional studies could investigate areas like national security.