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Machine Learning and SLAs: Staying on Track by Predicting Usage Trends
Machine Learning (ML) algorithms spot patterns and trends from past data. Typically, the more data that can be fed into the algorithm, the better the prediction. One example application is how ML can be used to assist IT in more…
IT Resilience: An Insurance Policy against Future Disruptions
What happens when IT stumbles? Data center outages or damage from fire or weather-related incidents.Are businesses able to recover? Key to a strong response is being able to act before the incident in a preventative and proactive way. That approach…
Algor-ethics: Building Ethical Algorithms
Trust, ethics, governance. Technology needs to build these qualities into algorithms and their software implementations. A failure to do so is not only a reputational risk to technology companies but also a blow to fairness and decency in society. But…
Artificial Intelligence: AutoML: Self-Evolving Algorithms
Artificial Intelligence (AI) algorithms have been criticized because their developers purposely, but more likely, inadvertently, embed biases and prejudices into the design and workings of the AI. A report by AI Now finds that built-in biases “affect how AI companies…
Technological Sovereignty: Cultivating Technology Leadership in Europe
Technology is dominated by American and increasingly Chinese businesses. And that has caused European countries to worry that their countries are becoming technologically less competitive and at odds with the goals of non-EU based technology companies. The EU is trying…