AI's Bridge Across Divides: Deliberating Its Impact

The multifaceted applications of technology and AI have sparked extensive debates and discussions. Leveraging AI-based tools, stakeholders can gain access to data concerning stock consumption, carbon emissions, waste generation, and more throughout the supply chain. The supply chain is a broad network, serving as the nexus where manufacturers, suppliers, shippers, and government entities intersect. The expanded utilisation of AI has played a pivotal role in optimising these processes. Beyond these advantages, it presents an unparalleled opportunity to bridge existing gaps and foster more connections among various stakeholders. As this growth accelerates, it also underscores the importance of effective risk management.

Potential Risks and Solutions?

• The integrity of AI systems is influenced by the quality and fairness of their training data. When training data contains biases, AI systems may inherit those biases, leading to biased decision-making. These biases can result in discriminatory outcomes, which not only raise ethical concerns but also carry legal implications.
• Terms like "Neuro Symbolic'' and "Responsible AI" are gaining prominence in the AI landscape. These are part of a growing trend toward composite AI approaches that aim to develop more resilient and dependable AI models. Neuro Symbolic AI combines symbolic reasoning with neural network-based learning, aiming to create AI systems that possess both symbolic, human-like reasoning capabilities and the adaptability of machine learning.

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