Major League Baseball is entering uncharted waters with the start of its COVID-abridged season today. Nobody's really sure if the 60-game season will even be able to get through the World Series without disruption by the pandemic's spread. However, one crowd-sourced AI system already has a pretty good guess as to who will be taking home the Commissioner's Trophy. The folks at Unanimous AI have been making high profile predictions like these since 2016, when their UNU platform correctly figured 11 of 15 winners for that year's Academy Awards. In 2017, the company followed up by correctly guessing the Kentucky Derby's top four finishers -- in order, no less -- and in 2019, correctly figured that the Houston Astros would make it to the series (though nobody could have seen the Nat's miraculous postseason run coming). "The fundamental core of our system is a technology that captures input from groups of people by connecting them together in real time using AI algorithms modeled after swarms," Dr. Louis Rosenberg, Unanimous' founder and chief scientist, told Engadget.
Throughout most of recorded history buying and selling has been part of daily life. Whether it's agricultural products or the latest software, selling is a communication process whereby one side works to fulfill the needs of a second party via their product or service. The ability of the sales person to properly communicate why a prospect should buy or sell is the reason their role exists. Once an account relationship is in place less human interaction may be required (or desired) and automating the ordering process probably makes sense, except for new products or up-selling opportunities requiring human sales interaction. AI sales tools can augment the sales process both today and in the near future in so many time saving ways (if the right technology is applied, the reps are properly trained, buy into it and use it every day).
Hailey Dawson likes to be photographed with her 3D-printed hand front and center. Other times she keeps it flat as a pancake, elbow bent into a classic dab. However she holds it, the point is that it's there and she wants you to look at it. She's gotten baseball fans around the country to pay attention to it, too, by throwing the first pitch at every MLB stadium to raise awareness of the need for affordable prosthetics. After she pitches at Angel Stadium in Anaheim on Sunday afternoon, the 30th and last stadium on her list, she will complete what her family is calling her Journey to 30.
These venture bets on startups that "returned the fund," making firms and careers, were the result of research, strong convictions, and patient follow-through. Here are the stories behind the biggest VC home runs of all time. In venture capital, returns follow the Pareto principle -- 80% of the wins come from 20% of the deals. Great venture capitalists invest knowing they're going to take a lot of losses in order to hit those wins. Chris Dixon of top venture firm Andreessen Horowitz has referred to this as the "Babe Ruth effect," in reference to the legendary 1920s-era baseball player. Babe Ruth would strike out a lot, but also made slugging records. Likewise, VCs swing hard, and occasionally hit a home run. Those wins often make up for all the losses and then some -- they "return the fund." "If you do the math around our goal of returning the fund with our high impact companies, you will notice that we need these companies to exit at a billion dollars or more," he wrote.
The problem to accurately and parsimoniously characterize random series of events (RSEs) present in the Web, such as e-mail conversations or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, of which consecutive events follow each other at a relatively regular time and should not be correlated. On the other side, the self-exciting processes, which are able to generate bursts of correlated events and periods of inactivities. The existence of many and sometimes conflicting approaches to model RSEs is a consequence of the unpredictability of the aggregated dynamics of our individual and routine activities, which sometimes show simple patterns, but sometimes results in irregular rising and falling trends. In this paper we propose a highly parsimonious way to characterize general RSEs, namely the Burstiness Scale (BuSca) model. BuSca views each RSE as a mix of two independent process: a Poissonian and a self-exciting one. Here we describe a fast method to extract the two parameters of BuSca that, together, gives the burstyness scale, which represents how much of the RSE is due to bursty and viral effects. We validated our method in eight diverse and large datasets containing real random series of events seen in Twitter, Yelp, e-mail conversations, Digg, and online forums. Results showed that, even using only two parameters, BuSca is able to accurately describe RSEs seen in these diverse systems, what can leverage many applications.