Every number released in conjunction with Fortnite is staggering, even within the context of a $137 billion industry. On the same day as its Fortnite Pro-Am tournament at E3, the video-game industry’s largest convention, the game was released for the Nintendo Switch, and within 24 hours it had been downloaded more than 2 million times. Analysts estimate that Fortnite is currently raking in more than $300 million a month, and has made its maker, Epic Games, more than $1.2 billion since its battle royale mode launched in late September.
Fortnite is virtually identical on every platform, and players can move from their PlayStation to their phone and back without missing a beat. Milligan first heard about the game back in September. “It was the next new game, like when Minecraft came out, but way more popular.”
The cadence of a Fortnite game is that nothing is happening and then, very suddenly, everything is happening. The game has three main modes: solo (every player for themselves), duos (teams of two), and squads (teams of three or four), but there are consistently around 100 players in every session.
Even when kids aren’t playing Fortnite, they’re talking about Fortnite or finding ways to profit from it.
Video games pioneered the dopamine-rush cycle. Using bright graphics and sound effects to make players feel continual accomplishment, arcade games were honed to make players feel like they needed to feed in just one more quarter over and over again — slot machines that kept people entranced without ever having to pay out. The addictive core of video-gaming never went away, even as games became more complicated: Every win, every high score, every 100 percent completion, every secret and Easter egg was a chance for a little rush of accomplishment and satisfaction.
And then mobile products learned to do the same thing. Give people goals, reward them with flashes of color, and you could entrance them into something resembling addiction. This was called, tellingly and unsurprisingly, “gamification”: Treat every app and every activity as a video game, with scores, prizes, and leaderboards. Snapchat rewarded users who talked every day with “streaks”; the exercise app Strava allowed you to compete with other joggers and earn badges; Foursquare turned the entire world into a game of king of the hill.
The process has come full circle. Fortnite is a gamified video game.
An interesting discussion on the use of blockchain for academic libraries on the LITA listserv
in response to a request from the Library Association in Pakistan for an hour long session on “block chain and its applications for Academic Libraries”.
While Nathan Schwartz, MSIS Systems & Reference Librarian find blockchain only related to cryptocurrencies, Jason Griffey offers a MOOC focused on Blockchain for the Information Professions: https://ischoolblogs.sjsu.edu/blockchains/
According to Jason, “Blockchain, as a data storage technology, can be separated from the idea of cryptocurrencies and expressions of value and coin.”
With the incorporation of cloud-based tools, K–12 schools are starting to consider more effective privacy and security measures, such as identity access management and managed cloud services from third-party vendors that can take responsibility for overseeing security.
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
