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.
I’m excited to announce that Emerj has “raised” a bit over a million dollars to fund our mission moving forward. The usual path of the 20-something who moves to Silicon Valley is to gain some bootstrapped traction, find some investors, and get a seed round raised. In our case, the funds came entirely from the sale of my eCommerce business, Science of Skill, which sold this February of this year.
Over the last four and half years, we went from zero to well over $2,000,000 in gross sales, with a 1100% three-year combined growth rate. This is a rare article where I'll be writing as myself, Dan Faggella, outside of my immediate role as founder and editor.
More data, less problems?
How AI is transforming the financial marketplace and operations from the inside out.
AI got a head-start in the information-rich financial industry over two decades ago, though the applications of today—robo-advisors and algorithmic traders, for example—are far more autonomous and omnipresent, accelerated in large by the increasing availability of data and advanced analytics technologies. The "prestige" associated with the use of AI and ML technologies in finance is reflected in initiatives like The AI Financial Summit, an invite-only conference that gathers C-level execs from across the financial industry sector and puts them in a room with AI experts and service providers. But a company doesn't necessarily need to be invited to a closed conference in order to apply or learn from emerging technologies in the industry.
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.
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.
Horse betting is harder than it looks. At the 142nd Kentucky Derby last week, only one of five experts from Churchill Downs Racetrack correctly predicted the winner. None of them correctly predicted the top four horses. Known as a superfecta, this latter bet came with 540 to 1 odds, meaning $100 down would return $540,000. And although the experts failed to predict the finishing order, an anonymous group of internet users did.