Machine Learning (ML) is transforming mental health care by enabling early diagnosis, personalized treatment, and improved patient outcomes. Recent studies, such as the collaboration between IIT Madras and the Czech Academy of Sciences, have demonstrated that combining electroencephalography (EEG) data with ML algorithms can predict a patient’s response to antidepressant treatments within the first week…
Federated Learning (FL) is transforming the way machine learning models are trained by enabling decentralized data processing. FL allows models to learn from data across multiple devices without transferring the data to a central server, preserving privacy and reducing latency. This approach is particularly beneficial in industries where data privacy is paramount, such as healthcare…
Edge AI is revolutionizing machine learning by processing data directly on devices such as smartphones, IoT devices, and sensors. This approach reduces latency, enhances privacy, and enables real-time decision-making without relying on cloud connectivity. By deploying machine learning models on edge devices, organizations can achieve faster response times and improved user experiences. Edge AI is…
As AI systems take on more decision-making roles, determining accountability becomes increasingly complex. Who is responsible when an AI system causes harm or makes an erroneous decision? Defining Accountability in AI Establishing clear lines of accountability is essential to ensure that those who develop, deploy, and utilize AI systems are held responsible for their actions…
The increasing integration of AI into daily life raises concerns about human autonomy and the potential erosion of individual rights. Autonomy in Decision-Making AI systems can influence or even make decisions on behalf of individuals, potentially undermining personal autonomy and freedom of choice. Informed Consent and Transparency Ensuring that individuals are fully informed about how…
In 2025, corporate finance is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) into financial operations. AI is no longer a futuristic concept but a core component of financial strategy, enhancing efficiency, accuracy, and decision-making capabilities. One of the most impactful applications of AI in corporate finance is the automation of…
As machine learning models become more complex, understanding their decision-making processes is crucial. Explainable AI (XAI) aims to make AI systems transparent and interpretable, fostering trust and facilitating compliance with regulatory standards. XAI techniques, such as SHAP values and LIME, provide insights into how models arrive at specific predictions, enabling stakeholders to validate and trust…
Investment banking is experiencing a paradigm shift in 2025, with artificial intelligence (AI) playing a pivotal role in transforming operations, client services, and strategic decision-making. AI technologies are enabling investment banks to analyze complex datasets, identify market trends, and deliver personalized client experiences. One of the key applications of AI in investment banking is in…
Edge AI is revolutionizing machine learning by processing data directly on devices such as smartphones, IoT devices, and sensors. This approach reduces latency, enhances privacy, and enables real-time decision-making without relying on cloud connectivity. By deploying machine learning models on edge devices, organizations can achieve faster response times and improved user experiences. Edge AI is…
In 2025, artificial intelligence (AI) is playing a crucial role in promoting financial inclusion by expanding access to financial services for underserved populations. AI technologies are enabling financial institutions to offer tailored products, assess creditworthiness, and deliver services to individuals and businesses previously excluded from the formal financial system. One of the significant challenges in…
