ai future outlook Articles and Reports
Explore future perspectives on artificial intelligence applications and trends - including products and applications in marketing, finance, and other sectors.
The following article has been written by Luigi Congedo, principal at BootstrapLabs. BootstrapLabs is an AI-focused VC firm in San Francisco. Editing and quotes added by the Emerj team.For information about our contributed material and publishing arrangements with brands, please visit our partnerships page.
Over the past few years, we've sought out and spoken to some of the top researchers, entrepreneurs, and executives involved in the machine learning field across industries and domains. This week, we decided to collate our five most popular machine learning podcast episodes in one place (they're listed below, ranked in order from most popular by number of downloads).
At the recent KDD2016 (knowledge discovery and data mining) conference in San Francisco, Managing Director at Amazon Development Center Germany GmbH and Director of Amazon Machine Learning Ralf Herbrich discussed three lessons that he’s learned while working with sparse machine learning models at scale.
Bots are where the web was in 1994. The arena is still wide open, and we don’t know what’s going to work and what’s not, or areas where the overhype is most prevalent. The rise of the chat bots domain is still filled with unknowns, but there’s a tremendous amount of money to be invested and made in this industry, along with big wins and big losses, especially during this training-wheels period.
[This story has been revised and updated.]
Big data has turned out to be a key ingredient in turning machine learning from an abstract technology into a potentially invaluable tool of insight and foresight for businesses across industries. The burgeoning cognitive technologies of predictive analytics and data visualization are opening new windows of opportunity to companies trying to solve complex problems with multiple moving parts. From finding ways to retain new customers to more efficiently monitoring multiple performance metrics and easing performance volatility, more companies are gravitating towards machine learning-based data analysis tools in an effort to optimize operations and find innovative solutions and opportunities that were once too obscure for only the human eye.