The world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this new-found automation?
Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the precarious nature of the future of work (long after the coronavirus pandemic ends), the continuing mixed attitudes of consumers about data privacy, and the possible resilience of this year’s investments in AI.
There’s plenty of consternation about artificial intelligence replacing many jobs and roles across the economic spectrum, — from salespeople to truck drivers to doctors. It’s worth mentioning, however, that market dominance will go to those innovators — human innovators, that is — who can assemble AI and machine learning-based components to move products and services more efficiently to customers. Successful entrepreneurs from this point forward will be maestros who can lead an AI symphony.
The COVID-19 crisis has put unprecedented strain on the global supply chain of every product, but most acutely on critical medical supplies.
Wash your hands, social distance, isolate! Humans are doing their part to fight COVID-19, but the technology side of the story that is currently underreported.
Blockchain is being used by leading organizations to aid in the adoption of digital ecosystems. While ecosystems have been in existence in some form or another for quite some time, digital changes all that. Digital ecosystems exceed the sum of their connections; they are complex, adaptive, learning and self-organizing.
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