"Courage is not the absence of fear, but rather the judgment that something else is more important than fear. The brave may not live forever, but the cautious do not live at all" – Philippe Renaldi, The Princess Diaries (2001) Yes, it is always easier to apply advanced technologies such as Big Data, AI / ML, and Edge Analytics to optimize an existing process. But what if that process is out-of-date, or worse yet, wrong. Is your organization applying AI / ML to accelerate your organization's race to irrelevance? Here is a damning example of what can go wrong when an organization focuses on optimizing an existing flawed process.

They can only dream of what it's like to burst onto the field in The Big Show on Opening Day, but Purdue University outfielders Cam Thompson and Curtis Washington Jr. are among thousands of college baseball players with access to more data-juiced tech than ever to use in the hopes of getting to the majors. One of the tools their team has tested tracks and visualizes every joint in their bodies to measure and analyze their dynamic movements, helping them become a split-second faster on the base paths or gain an edge on runners when they throw home. "I was the slowest on the team," said Thompson in a video describing Purdue's use of 3D Athlete Tracking, or 3DAT, technology developed by Intel, which captures video footage and applies computer vision and deep learning to digitize an individual player's skeletal data and calculate biomechanics. The data and analytical insights gave Thompson and his coaches information revealing that he was bent over just slightly when launching himself from a base. "To the eye, you might not see this, but those first four or five steps were actually slowing him down," said John Madia, director of Baseball Player Development at Purdue.

Any baseball fan knows that analyzing data is a big part of the experience. But data analysis in sports is now taking teams far beyond old-school sabermetrics and game performance. The market for sports analytics is expected to reach almost $4 billion by 2022, as it helps a variety of sports organizations in a range of areas. Here's how analytics have (and will to) become more prevalent in the industry. No one knows the role of data analysis better than the Oakland Athletics, the baseball team that general manager Billy Beane helped lead to the playoffs on a shoestring budget by using in-game statistics to identify undervalued players.

Breakthroughs in AI happen with a team of people working behind the scenes to make it all possible. If AI development were a sport, it'd be closer to baseball than boxing. Headlines might make it seem like AI breakthroughs happen with a big knockout punch, but the reality is more akin to a baseball team grinding through a 162-game season. It's a process that involves having the right people in place over a long stretch, and fielding the best team is essential for success. The root cause for this can be found in the data.

About a month into Japan's professional baseball season, the Fukuoka SoftBank Hawks, the 2014 and 2015 national champions, are doing no worse. Many say the team's strong lineup is underpinned by the cash-rich SoftBank Group, a big telecom and technology group. The reality is quite the reverse. Unlike their rivals, the Hawks are a stand-alone club, though one with the financial leeway to allocate profits to areas where the front office sees fit, such as player development and information technology. With the help of its tech-savvy parent, the club may be about to rewrite "Moneyball," the 2003 bestseller about how a Major League Baseball team in the U.S. used statistical analysis to beat high-spending opponents.

Entity search is to retrieve a ranked list of named entities of target types to a given query. In this paper, we propose an approach of entity search by formalizing both context matching and category matching. In addition, we propose a result re-ranking strategy that can be easily adapted to achieve a hybrid of two context matching strategies. Experiments on the INEX 2009 entity ranking task show that the proposed approach achieves a significant improvement of the entity search performance (xinfAP from 0.27 to 0.39) over the existing solutions.