AI Trends
AI is a red-hot topic, even overhyped, some say. We will show why the latter group may well be right–about general AI, anyway. One thing’s for sure: we’re not going to have personal AI assistants anytime soon. Narrow AI applications–that is, practical, focused ones–are a different matter altogether. We believe such applications in business will deliver on their promise and revolutionize the world as we know it.
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Machine learning techniques cannot magically make “signal” appear out of thin air, nor can they make unstable factors more stable. Data science typically succeeds where there are complex behaviors that can be described in data and where consistent inputs lead to consistent “predictable” outcomes.
Nevertheless, there are a number of useful best practices that set apart machine learning techniques from the traditional quantitative toolbox, and automated machine learning has a compelling proposition for modern quantitative traders and investors.
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The Insurance industry is risk-averse by its very nature. However, with a 10x increase in Insurance technology investment between 2010-2015, is the industry about to push through its own digital revolution? Hear from Tony Cassin-Scott, Insurance Data, Digital and IT Strategist, and Jerome Bugnet, Insurance API Specialist, MuleSoft, for a webinar as they discuss how the world’s most progressive insurers are embracing digital transformation and how this compares against their peers in other industries.
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Fueled by data, AI is transforming how businesses operate and deliver value, while improving efficiencies across the organization. However, to successfully scale AI throughout your organization, you must overcome data complexity, talent scarcity and a lack of trust in AI systems. It’s also important to remember that there’s no AI without IA (information architecture). To meet the demands of today and stay competitive tomorrow, these architectures need to be efficient and agile.
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