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Unintended Consequences of Machine Learning in Medicine


Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology).

Tips for Learning to Type With One Hand


Seven years ago, my left arm was permanently paralyzed from the elbow to the fingertips following a Vespa accident. Suddenly, relying on only one arm meant relearning many basic tasks, from buttoning my pants to tying my shoes. Since I was in college, typing became a priority on my list of what I needed to adapt. Professors were impatient when I needed more time to complete assignments that required a lot of time on a keyboard (most did). They wanted their work, and they wanted it now.

Transitioning Asia-Pacific to a new normal of work


The COVID-19 pandemic has forced a dramatic rethink in how people and organizations work. With social distancing becoming a leading strategy in combating COVID-19, travel to the office or to client sites is being discouraged, if not outright banned.

Importance Of Machine Learning Stressed


Visakhapatnam: The need of the hour is the pervasive application of machine learning and data science in business management. This area requires skill-building, renewal of public policy and innovation, said director, Indian Institute of Management-Visakhapatnam, Prof M Chandrasekhar. The two-day virtual symposium on'applications of machine learning and data science in interdisciplinary areas' concluded on Monday at the Inter-Disciplinary Decision Sciences and Analytics Lab (IDeAL) of IIM-V. Former director of the Bill and Melinda Gates Foundation in India, Dr Nachiket Mor, spoke about the application of artificial intelligence-machine learning in public health settings. Former head of the Data Analytics Cell at NITI Aayog and faculty, Indian School of Business, Hyderabad, Dr Avik Sarkar, spoke about the role of analytics and policy modelling in assessing the progress toward sustainable development goals.

Deep Learning in health industry


In the Covid-19 pandemic, the situation changed dramatically. During the crisis, we saw rapid digital transformation and the adoption of disruptive technologies across all industries. Healthcare organizations of all sizes, types, and specialists are increasingly interested in how artificial intelligence can support better patient care while reducing costs and improving efficiency.Deep learning is a good place to start. This branch of artificial intelligence has made rapid changes in health care, providing the ability to analyze data faster than ever before. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: example,Deep learning is a key technology behind driver less vehicles, enabling them to detect a stop sign, or to distinguish a pedestrian from a lamppost.