How A.I. is used in the Gaming industry

We seem to be living in the age of A.I. Everywhere you look, companies are touting their most recent AI and machine learning (ML) breakthroughs, even when they are far short of anything that could be called a “breakthrough.” “A.I.” has probably superseded “Blockchain”, “Crypto”, and/or “ICO” as the buzzwords of today. Indeed, one of the best ways to raise VC funding is to stick ‘A.I.’ or ‘ML’ at the front of your prospectus and a “.ai” at the end of your website. Separating A.I. fact from fiction is one of the main goals of this article; the other is to help gaming and esports executives utilize A.I. in ways that are simplistic, complex and, hopefully, rather ingenious.  

 

Once a mostly academic area of study, twenty-first century A.I. enables a plethora of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and A.I. planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood.

As shown above, machine learning is a subset of A.I., and deep learning is a subset of machine learning. According to Wikipedia, ML is the sub-field of computer science that “explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.”

ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence.” It “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.”

As per Wikipedia, ML can be broken down into the following three categories:

1.       Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.

2.       Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

3.       Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

Gaming companies can tap into five prominent segments of A.I.—sound, time series, text, image, and video. Areas such as CRM, customer loyalty, marketing automation, social marketing and social listening should all be radically affected by A.I. and ML.

There are so many use cases for ML and deep learning for marketing departments that it is impossible to create an exhaustive list here, but it is particularly useful for marketing personalization, customer recommendations, spam filtering, network security, optical character recognition (OCR), voice recognition, computer vision, fraud detection, predictive asset maintenance, optimization, language translations, sentiment analysis, and online search.

The table below shows the general use cases for AI broken down by industry. This is a generalized list and many of these use cases can be utilized by the gaming industry as well.

GENERAL USE CASE

INDUSTRY

Sound

Voice recognition

UX/UI, Automotive, Security, IoT

Voice search

Handset maker, Telecoms

Sentiment analysis

CRM for most industries

Flaw detection

Automotive, Aviation

Fraud detection

Finance, Credit cards

Time Series

Log analysis/Risk detection

Data centers, Security, Finance

Enterprise resource planning

Manufacturing, Auto, Supply Chain

Predictive analytics using sensor data

IoT, Smart home, Hardware manufacturing

Business and Economic analytics

Finance, Accounting, Government

Recommendation engine

E-Commerce, Media, Social Networks

Text

Sentiment analysis

CRM, Social Media, Reputation mgmt.

Augmented search, Theme detection

Finance

Threat detection

Social Media, Government

Fraud detection

Insurance, Finance

Image

Facial recognition

Multiple industries

Image search

Social Media

Machine vision

Automotive, Aviation

Photo clustering

Telecom, Handset makers

Video

Motion detection

Gaming, UX, UI

Real-time threat detection

Security, Airports

Fraud

ML can also be used to spot credit card or transaction fraud while it is happening. ML can build predictive models of credit card transactions based on their likelihood of being fraudulent and the system can compare real-time transactions against these models. When the system spots potential fraud it can alert either the bank or retail outlet where the transaction occurred. This is exceptionally important for business with online retail presences because online fraud is on the rise and this could be an additional security layer that ensures purchases made are purchases paid. 

Voice

Deep learning has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition,” the Artificial Intelligence and Life in 2030 study adds.

Google Duplex has shown that AI bots can do things like make reservations at hair dressing salons and restaurants and this is one of the deep learning futures. Businesses need to develop voice and speech understanding technology or risk being left behind by their competition. Voice, in particular, is a technology waiting for mass use. We communicate through voice as much as any other sense and the companies that win the battle in voice will win the battle for the 21st Century consumer. 

Business who want to stay ahead of the SEO curve need to look at voice as it can give them a big leg-up on the competition. Tomorrow's leaders in voice are shaping the landscape today and this could be a technology that leaves secondary players in the dust. 

 

NLP

Natural language refers to language that is spoken and written by people, and natural language processing (NLP) attempts to extract information from the spoken and written word using algorithms. NLP can be used to help the gaming company's call center, as well as be the basis for creating chatbots. NLP can be used for sentiment analysis and social media listening, as per below:

Chatbots

A chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner. Although chatbots are cheaper than handling customer service inquiries over the phone, there is a catch as chatbots can only deliver highly personalized and contextual assistance if they have access to universal consumer profiles that are populated by real-time data. This means, done correctly, developing chatbots is an expensive upfront investment, it is an investment that should be done company-wide, not siloed by just the marketing or customer service department as information that chatbots tap into are useful throughout the organization.

Why Choose A.I.?

So why choose to go down the complex A.I. road? Well, in the article Artificial intelligence Unlocks the True Power of Analytics, Adobe explains the vast difference between doing things in a rules-based analytics way and an A.I.-powered way, including:

·       Provide warnings whenever a company activity is falls outside the norm. The difference:

o   Rules-based analytics: You set a threshold for activity (e.g., “200–275 orders per hour”) and then manually investigate whether each alert is important.

o   A.I.-powered analytics: The A.I. analytics tool automatically and determines that the event is worthy of an alert, then fires it off automatically. 

·       Conduct a root cause analysis and recommend action. The difference:

o   Rules-based analytics: You manually investigate why an event may have happened and consider possible actions.

o   A.I.-powered analytics: Your tool automatically evaluates what factors contributed to the event and suggests a cause and action. 

·       Evaluate campaign effectiveness:

o   Rules-based analytics: The business manually sets rules and weights to attribute the value of each touch that led to a conversion.

o   A.I.-powered analytics: The AI analytics tool automatically weights and reports the factors that led to each successful outcome and attributes credit to each campaign element or step accordingly. 

·       Identify customers who are at risk of defecting:

o   Rules-based analytics: You manually study reports on groups of customers that have defected and try to see patterns.

o   A.I.-powered analytics: Your tool automatically Identifies which segments are at greatest risk of defection.

·       Select segments that will be the most responsive to an upcoming campaign:

o   Rules-based analytics: You manually consider and hypothesise about the attributes of customers that might prove to be predictive of their response.

o   A.I.-powered analytics: Your tool automatically creates segments based on attributes that currently drive the desired response. 

·       Find your best customers:

o   Rules-based analytics: You manually analyse segments in order to understand what makes high-quality customers different.

o   A.I.-powered analytics: Your tool automatically identifies statistically significant attributes that high-performing customers have in common and creates segments with these customers for you to take action on.”

 

Knowing all this, why wouldn't you choose A.I.?