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The three information management trends strengthening resilience in 2022

#artificialintelligence

With recent business uncertainty, resilience has become one of the most important organisational values leaders can adopt. From the highs and lows of business performance to embracing a remote workforce, leaders who can respond appropriately to change will help their businesses thrive in an evolving digital landscape. Disruption is likely to be a way of life for businesses in the future, so it's important to put technologies and systems in place to help manage that disruption. Being agile and flexible is essential so that organisations can adapt quickly. In addition, the information management systems that businesses rely on are instrumental in positioning them for ongoing success.


Unification of Information Maximization and Minimization

Neural Information Processing Systems

In the present paper, we propose a method to unify information maximization and minimization in hidden units. The information maximization and minimization are performed on two different levels: collective and individual level. Thus, two kinds of information: collective and individual information are defined. By maximizing collective information and by minimizing individual information, simple networks can be generated in terms of the number of connections and the number of hidden units. Obtained networks are expected to give better generalization and improved interpretation of internal representations.


Unification of Information Maximization and Minimization

Neural Information Processing Systems

In the present paper, we propose a method to unify information maximization and minimization in hidden units. The information maximization and minimization are performed on two different levels: collective and individual level. Thus, two kinds of information: collective and individual information are defined. By maximizing collective information and by minimizing individual information, simple networks can be generated in terms of the number of connections and the number of hidden units. Obtained networks are expected to give better generalization and improved interpretation of internal representations.