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1. Artificial Intelligence in Video Games: A Survey
Written by: Igor Petković, Martin Hofer
Faculty of Mathematics
University of Belgrade
Serbia
E-mail: gliga023@gmail.com, hofye41@gmail.com
Web address: http://alas.matf.bg.ac.rs/~mi08233, http://alas.matf.bg.ac.rs/~mi08005

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Belgrade, December 2012
Abstract
Artificial Intelligence research has seen many uses and many dead ends. Video games are one of the
newest venues for artificial intelligence research, and many have found it an excellent way to model
human-level AI, machine learning, and scripted behavior. In this survey of the work that has been done in
the game industry, we examine the past, present, and future of AI research. We examine the primary uses
for AI in video games, the research s beginnings in board game systems, as well as modern techniques
used to model computer opponents. Included in these techniques are minimax/Alpha Beta algorithms,
decision trees, scripted behavior, genetic algorithms and learning agents. We also condense the future
outlook of the industry according to researchers and industry heads, outlining the impact of new
technologies such as multi core processors and online gaming.
I. Introduction
Video games have become big business; so large, in fact, that in yearly earnings the video game
industry has overtaken the movie industry (Laird & Lent 2001). As the industry grows, more
resources become allocated to the development of video games. In the growing field of video
game design, artificial intelligence has taken a lead role as topic of choice for researchers. End
users gauge a game’s value by the quality of the AI in the game. If the enemies and allies act
more human and more complex, then the game is more entertaining. This requires walking a line
that is rarely addressed in other fields of AI, but often seen in popular culture. AI agents in video
games must be smart enough to seem competent and even challenging opponents, but must also
operate within the confines of human senses and reaction times, to prevent the notion that the AI
opponent is somehow “cheating.”
Game AI has several applications and can be broken down into several roles an agent can fulfill
(Laird & Lent 2001). Enemies need to act at an individual tactical level, incorporating maneuvers
more complex than approaching and attacking the player. Groups of enemies need to work with
each other to overcome common goals, flanking the player or working together to trick them.
Partners and support characters must work with the player, anticipating their needs and supplying
relevant tips and assistance. In sports games commentators must react to uncertain actions, and in
role playing games Non-Player Characters (NPCs) must drive the story forward based on player
input. All of these problems and more may be addressed by using a host of AI techniques. The
resources that video game development has available allows for AI research that may have
implications outside the target field.

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Figure 1: Grand Theft Auto IV, featuring a town that runs on its own AI routine, had a budget of over $100million.
In this survey we will explore the history of AI research in games, its current uses, and its
possible future application. By examining the most prevalent tactics for game AI, we can see the
growing trends in creating the ultimate game AI goal, human-level AI. This will also
demonstrate why video games can be a driving force in AI research not only for video games,
but for the field at large.
II. Background
At the start of the interest in AI research, there were no real video games available, so there was
a strong push to test a computer’s intelligence. This was done using the popular games that had
been played for years and in some cases, even centuries. Trying to make a machine that could
play chess, checkers, bridge, poker and many other games that would seem to take intelligence to
play was too tempting to pass up.
The work on computer games has been one of the most successful and visible results of AI
research and has resulted in advances in numerous areas of computing (Schaeffer 2001). One of
the first games that grabbed the attention of AI researchers was chess, when, in 1950, Claude
Shannon published his work on how to program a computer to play chess (Schaeffer 2001). It
was another 50 years before the hardware and AI programming techniques had advanced far
enough for a computer to be able to win against a grandmaster in the game.
In 1997, Grandmaster Gary Kasparov lost a 6 game match to the chess computer DEEP BLUE.
DEEP BLUE was estimated to analyze 200,000,000 chess positions per second while a human
player only analyzes two per second (Schaeffer 2001). As great a victory for the AI community
as this was, it also proved how great the human mind is, and how far AI research has to go to
even come close to true intelligence. The score of the 6-game match was DEEP BLUE: 2 wins, 3
draws and 1 loss, showing the efficiency of the human cognitive power that could lose by such a
small margin while the margin of moves analyzed per second was astronomically huge. Arthur
Samuel worked on a checkers program. But unlike DEEP BLUE, which used tables for opening
moves, closing moves and an Alpha-Beta search pattern, it focused on the use of learning
algorithms that learned checkers by playing. It won a single game in an exhibition match against
checkers champion Robert Nealey in 1963. In 1970, a Duke University research team wrote a
program that beat Nealey in a short match (Schaeffer 2001). In 1989, a University of Alberta
research team, lead by Jonathan Schaeffer, wrote a checkers-playing program called CHINOOK.
In 1990 CHINOOK lost to World Champion Marion Tinsley, 4 games to 2. But in the 1994
rematch, CHINOOK played all6 games to a draw. CHINOOK uses alpha beta pruning with

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iterative deepening, a transposition table, move ordering, search extensions, and search
reductions (Schaeffer 2001). CHINOOK has the honor of being the first program to win a human
world championship for any game. In 2007, CHINOOK was declared by the scientists at the
Web site of Journal Science to be unbeatable. The best that an opponent can ever hope to achieve
would be a draw. Checkers is nowjust a larger version of tic-tac-toe. Players desiring to
challenge CHINOOK can do so at:http://www.cs.ualberta.ca/~chinook/
Other games where computers have come to dominate the human competition are Othello,
Scrabble, Nine Men‟s Morris, Connect-4, Qubic, Go Moku, and 8x8 Domineering. After
witnessing the Scrabble program MAVEN beat World Champion Adam Logan, Brian Sheppard
wrote “…MAVEN should be moved from the championship caliber class to the abandon hope
class” (Schaeffer 2001). Even though there are many disadvantages for a computer simulating a
game, some of the games have distinct and even unfair advantages. For instance, the Scrabble
program has the entire Scrabble dictionary programmed into it as data.
Although great strides have been taken for artificial intelligence for every area of game design,
there are some games that still are not able to play at the master level, let alone the grandmaster
level. A couple of the reasons for this are that some of the games have a very high branching
factor or they have too much unknown information. A few of the titles that still defy computer
AI mastery are Bridge, Go, and Poker (especially Texas Hold‟em) (Schaefer 2001).A notable
phenomenon that arises when a game joins the solved category is that researchers seem to lose
interest in it. A been-there-done-that attitude appears, and attention is redirected to the next
challenge. This attitude keeps driving AI research ahead and keeps the field fresh and new. As
new games are created, they too benefit from all the research that has been done and in many
cases, set the bar even higher. Video game AI is in its infancy, but great advances are
already being made, and a great majority of the recent advances in AI have their roots in game
design. As Arthur Samuel, creator of the original checkers program, wrote, “Programming
computers to play games is but one stage in the development of understanding … it seems
reasonable to assume that these newer techniques will be applied to real-life situations with
increasing frequency…” (Schaeffer 2001)
III. Learning Agents
Learning Agents are a focal point of creating human-level AI in video games. If an intelligent
agent can observe and modify behavior based on what it sees the player doing, the world
becomes more immersive, more challenging, and hopefully more entertaining for the player. The
idea of evolving NPC AI has been broached for several topics, but none more prevalently than
evolving enemy AI. The idea of evolving enemies has persisted for decades, owing to the fact
that many games increase difficulty simply
by making enemies more resilient or increasing their number. This makes the idea of tactically
increasing difficulty immediately more appealing. The foremost room for study in this kind of
learning is in First Person Shooters (Overholtzer & Levi 2005). As shooting games can mimic
real life combat tactics in use today, there are many places we can get a baseline of behavior.
However, scripting this directly can only go so far. While the enemies may react like trained war
veterans, the player may act more erratically and disrupt established strategies. This is where an
element of learning comes in. Overholtzer and Levi demonstrated that real-time learning and
generational variations can mark a steady increase in agent capabilities and challenge. This was
accomplished by implementing a relatively simple genetic learning algorithm into an open

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source first person shooter called Cube. While the data set used is fairly rudimentary, the
advancement of gaming hardware over time shows that this kind of learning is promising and
can be applied. In fact, these ideas are used in AI today under the blanket term of ALife (Baker
2002). ALife is a blanket term used to describe AI that simulates biology. This means agents
may choose from multiple actions instead of a set script, an idea called „fuzzy logic”. This may
also include multiple agents coordinating together, or genetic algorithms wherein the agents
evolve based upon a heuristic showing which agents have performed best. These genetic
algorithms are an important part of the evolution of video game AI.
Learning for learning’s sake does come with a price. While the end goal of a learning agent
within a game may be to create a more believable character or a stiffer challenge, many games
focus so much on the learning aspect that they become little more than virtual fish tanks.
Attempts to incorporate this level of learning into an entertaining, playable game have been slow,
but progressing. NERO is an open source strategy game where learning takes a key role in game
play (Stanley, Bryant, Karpov, & Miikkulainen 2006). In NERO, players must spend a period of
time training their soldier agents using a given toolkit before sending them against each other.
The idea is that the better army will win over the inferior one. The system uses a kind of
algorithm called a Neural Network (rtNEAT, specifically) to determine which routines are
completing the training optimally and propagate those traits as best possible. This displays the
possibility of a real time learning agent, but NERO does not show the graphical fidelity nor the
depth of a modern blockbuster game. Until processers can handle both a wealth of content and
real time learning, alternative forms of learning are being investigated.
Figure 2: NERO shows impressive technology, but low graphical quality
Some researchers are finding that behavior may be learned dynamically, then transformed into a
script and implemented statically at runtime (Kelly, Botea & Koenig 2008). In an experiment
with the open-ended role playing game The Elder Scrolls 4: Oblivion, researchers were able to
use large scale planners outside of the resource-heavy implementation required to plan as the
game is playing. Instead, basic information is fed into a planning algorithm and the work of
creating a script is automated. Creating a unique set of behavior in this way takes, at its longest,
thirty seconds. A human doing the scripting manually, however, would take several hours or

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more depending on the complexity of the behavior desired. This could be widely applied to
games currently using scripted behavior and allow for scripts more complex than those we
currently have. While this does not allow for learning as the game is played, it allows for agents
that have already learned before the game starts, creating a more entertaining experience for the
user. This may be preferable to many of the existing techniques used for gaming AI today.
IV. Simple AI Technologies
Video game AI has not always been a complicated ordeal. For years, programmers have relied
on simple mechanisms to allow enemies to react to a player. For a long time, “artificial
intelligence” for an enemy meant very simple behavior, such as approaching an enemy in a
straight or semi-straight pattern.
The classic arcade game Space Invaders featured constructs that could only advance toward the
player in a short, repeated pattern, and Pong featured a paddle that would simply detect the
trajectory of a ball and attempt to intercept (Wexler 2002).
Until the past decade or so, game AI made few attempts to progress past the charging enemy.
Other forms of games, however, had been setting the stage for more complex interactions.
Perhaps the most famous example is DEEP BLUE. By calculating hundreds of millions of moves
per second to a human‟s two, DEEP BLUE managed to outwit the champion by sheer brute
force (Schaeffer 2001).The most common form of this algorithm seen in game playing is
minimax. In a minimax algorithm, an AI agent recursively calls an optimal opponent to see what
their best move would be, whom calls the original agent to check their move, until a decision is
reached. This creates a tree structure that allows the agent to decide the best move available in its
current state. This is recalculated each move, allowing the agent to make the best possible move
at all times. This works well in a situation where the agent can observe the entire world and all
possible actions, such as in a board game. While this approach has been used in othervideo
games before, the size of the tree created can grow exponentially and the computational time can
swiftly get out of hand. As most video games must perform in real time, this algorithm is
increasingly out of place in a gaming
landscape populated by fast-paced action games and swiftly tactical first person shooters.
As technology has only recently found itself up to the task of dynamically computing any
behavior in real-time, the most common form of video game AI for the past two
decades has been scripted behavior (Baker 2002). Scripted behavior involves giving a computer
opponent a set of possible player inputs and a set of reactions to that input, so that the opponent
may know how to react to any given player action. This allows the opponent to react swiftly to
stimuli, as it only needs to look up its action in some sort of table.

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Figure 3: An example of scripted behavior
Scripted behavior is not without its detriments. As interactions within games get more complex,
it becomes more and more difficult to maintain a reaction for every possible player action. As a
player finds failings in a script, the player can exploit these reactions and make the game far too
easy. In addition, these failings often ruin immersion in a game world. A player who finds an
opponent running headfirst into a wall due to an overlooked scripting loophole becomes acutely
aware of the fact they are playing a game with non-human opponents.
This issue can be addressed reliably by incorporating decision trees into existing scripts (Wexler
2002). While scripts are necessary when true learning AI is too taxing on a
system, the behavior of an agent can be supplemented by building a tree of behaviors based on
the success of previous actions in similar situations. In this way, an opponent can run on scripted
behavior, but deem parts of the script a poor idea after it fails to deliver the desired results too
often. This allows the heavy computing to be done with a manual script, but incorporates the
leeway of a learning system to cover loopholes in logic and inhuman behavior. While these
decision trees may be incorporated statically, as with a script, the growth potential of using one
in concordance with an existing script has been found to yield greater benefits.

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Figure 4: A simple decision table for the game Black & White
Scripting and decision tables have carried the game industry for the better part of two decades.
With limited player inputs and relatively isolated gaming experiences,
developers could make easy assumptions about what the player would do and create opponent
behavior accordingly. However, games are getting more complex. A player in today’s gaming
landscape can interact with a game world almost as intimately as they can with the real one. In
addition, online play has opened up communication between players, allowing AI exploits to be
discovered significantly more easily than in years past. As players find ways to break the
intended systems, the need for agents that can learn has grown. One of the approaches at the
forefront of the movement for video game learning agents is the concept of genetic algorithms.
V. Genetic Algorithms
Genetic algorithms intend to copy the mechanism by which nature finds the species most likely
to survive, creating opponents that grow and learn organically. This allows a computer opponent
to replicate the learning undertaken by human players.
The concept for a genetic algorithm is simple, and to demonstrate it we will examine the typical
use of a genetic algorithm in a video game. Genetic algorithms involve making several agents
compete and allowing traits of the best performers to carry on to the next match. This weeds out
the worst agents and keeping the best. The algorithm starts out simply, with a sample of agents
having little instruction as to what it needs to do other than a heuristic on which to judge its
performance. In gaming, this may mean several agents with little other than finite state machines
mapping their possible actions (Geisler 2005). These agents are given random tweaks to their
possible behaviors (for example, how often an agent jumps when presented with a wall). The
agents are then set off to perform their tasks; within video games this often means the agents
play competitive games against each other. While there is no set method for keeping the best
performers, there is a notion of the top several staying and some of the worst performers being

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replaced by a cross-breed of the top performers. This “child” of the best performers of the last
round is often given a random and minor change, to allow for the continuing evolution of the
best performer.
Figure 5: A graph showing the evolution of a single behavior for a genetic agent over fifty generations.
Several studies have been conducted showing that, over time, a consensus is found on single
behaviors after a point. This usually shows roughly the optimal choice for a behavior. In figure 5,
a simple agent in a first person shooter is tasked with determining whether to jump over a wall
when approaching an enemy (Overholtzer & Levy 2005). After roughly 40 generations, the
algorithm finds that about thirteen in every fifteen situations, jumping is preferable. Even if this
behavior is not integrated directly into the behavior of an agent, this algorithm shows what
decision is optimal in a given situation and can be incorporated statically into a script in a
finished product.
VI. Neural Networks
In artificial intelligence research and programming, we constantly run into things that are either
incredibly difficult or near impossible to represent on a computer. Oddly enough, as
programmers and software designers, we take this for granted and are surprised when it comes
up. The thing that makes this odd is that the problems that arise the most often are usually things
that we do naturally every day without thinking about it. We recognize hundreds or even
thousands of faces, read writing in various scripts, even if part of the message is missing or
destroyed. We can talk and understand communication in verbal and even non-verbal modes.
The question that still needs an adequate answer is, “What is the difference between computers
and humans?” A neural network is modeled after the neurons in the human brain. It can be

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thought of as a mathematical function approximator where the input represents independent
variables and the output represents the dependent variable or variables (Bourg & Seemann 2004).
In the human brain, there are 10 billion neurons with 60 trillion connections, so the mathematical
function model does not do justice to the vastness of the human brain, but it is adequate for the
current capabilities of computers.
In the realm of video games, neural networks are one of the most exciting aspects on the horizon.
From a single player adventure game or first person shooter to a massively
multiplayer online role-playing game, the applications for artificial intelligence are almost
endless.
Learning techniques can increase both the longevity of video games as well as decrease their
production cost (Stanley, Bryant & Mikkulainen 2005). Neural networks allow a game designer
to set up scripts and game agents that can act in a realistic manner and even challenge the most
skilled of gamers. In first person shooters, neural networks can allow the computer controlled
opponents to learn as they go and get better. They will learn from mistakes that they make to
avoid repeating them, as well as learning strategies that are more effective against a particular
player.
One problem that can arise from having an agent actually learn while the game is being played is
that the game can become unpredictable. Another problem is that if the game agents are allowed
to learn too well or too much, it is likely that the game will become too hard or even
“unbeatable” and no one will want to play it. This is why the more common approach to neural
networks is to train the agents outside the game and then transfer the now static learned network
into the game.
The general algorithms for neural networks are normally not well suited for a real-time game
environment, because they have:
1. Large state/action space - making it difficult to check the many different possible actions and
checking the value of every possible action on every game tick.
2. Diverse behaviors - every agent should not settle on the same action as this would make for a
very boring game if every agent did the same thing.
3. Consistent individual behaviors - a player doesn’t want to see agents in the game doing
something totally random for no reason, because it destroys the illusion that the game is a real
word.
4. Fast adaptation - the game agents must be able to adapt quickly as players would
quicklybecome bored and/or frustrated if it took hours before an agent adapted.
5. Memory of past states - agents should be able to use past events to help to choose a better
action in the present. (Stanley, Bryant & Mikkulainen 2005)
In a real-time game environment, the typical neural network algorithms must be tweaked to
update at a regular interval, usually corresponding to a set number of game ticks.
One implementation of neural networks for real time video games is rtNEAT (real-time
NeuroEvolution of Augmenting Topologies). NERO (NeuroEvolving Robotic Operatives) is a
video game that was specifically written to explore the potential of rtNEAT.
The objective of NERO is to train a group of agents in military combat by exposing them to
experiences in a controlled environment. The agents begin with no skills at all and after the
agents are trained to the satisfaction of the player they can be pitted against other groups of

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agents that have been similarly trained. The training is performed in an interface that allows the
player to reward or punish different behaviors. (See figure 6).
Figure 6: NERO control pane used to set rewards and punishments for all the actions that an agent canmake.
The actions that can be trained are actions like approaching enemies, hitting targets, getting hit,
following friends, and dispersing. So one could train his or her agents to charge the enemy and
fire, avoid the enemy and shoot from a distance. One could even train half the agents for one and
half for the other. The control panel also allows the placement of objects like walls, turrets (a
stationary gun that shoots at the agents), and static enemies.
One of the experiments that was done with NERO shows the vast potential of Neural Networks.
In this experiment, the agents who spawn at one end were rewarded for going to the other end of
the map. During each training session, a wall was put in the path of the agents so they had to go
around it. Each time a wall was added, the path to go around the walls became more and more
complex until a virtual maze was created. The agents were able to traverse the maze and make it
to the other side without the aid of path-finding tables (Stanley, Bryant & Mikkulainen 2005).
VII. Future Outlook
While artificial intelligence in video games has started as a business venture, some look to video
games as an ideal topic for generalized AI research. As video games strive to make opponents
that react like humans, it is a natural fit for developing the kind of human-level AI dreamed of in
science fiction (Laird & Lent 2001).
There are several reasons some argue that video games are an optimal venue for future AI
research. First, funding is abundant. Video games are not just research reliant on grants;
they are a self sustaining and highly profitable industry. If only one idea every year of research
gets put into practice, this may still be enough for game companies to continue

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giving money to AI research departments. Hardware is also an increasing factor. Real-time,
learning AI requires a large amount of processing power. Luckily, today’s video game consoles
and personal computers have multiple core systems, and have taken to using separate hardware
to take care of graphical work. This means that entire processers may be dedicated solely to the
artificial intelligence algorithms running in games. Some speculate that future consoles will
include dedicated AI logic units that can take care of basic AI functions for the programmer
(Snider 2007).
The fact that these agents are working out with a large sample set- many games today sell
millions of copies in America alone- allow for interactions that teach us more about how AI
agents work. Damian Isla of developer studio Bungie speaks of his experiences with complex AI
systems:
“The interaction of all those rules is absolutely unpredictable. There is simply no way that I as a
programmer can predict what is going to happen next. What we get is „emergence,‟ one of the
holy grails of AI: some really complex, interesting results out of very, very simple rules.” (Snider
2007)Emergence is one of the cornerstones of human level AI. A system that can act in ways that
the programmer did not intend shows some degree of independent thought, whether we think of
this as conscious or simply reactions. Players react positively to an unpredictable opponent,
provided that there is some logic that can be gleaned from that unpredictable behavior. Many
„god games,‟ wherein the player takes an omniscient role over subjects that the player has no
direct control over, strive for these kinds of reactionary yet independent actions from their agents
(Laird & Lent 2001).
One of the signs of a possible research slowdown within the industry, however, is the advent of
online gaming. In today’s gaming landscape, it is easy for players to use a network-connected
personal computer or video game console to play games with or against players at distance, at
any time (Schaeffer 2001). The easy access of real humans to the player lessens the desire to play
a multiplayer match against non-human opponents, no matter how realistically they react. While
this may slow research somewhat, single player campaigns remain a staple of any large-budget
game. In addition, modes wherein players can cooperate online against AI opponents have seen a
swell of popularity with recent games such as Epic Games introduction of a cooperative Horde
mode in Gears of War 2, wherein human players take on increasingly difficult swarms of
enemies and the fully cooperative online survival game Left 4 Dead by Valve.
These changes in the online landscape may offset the possible loss of AI resources as research
needs to be made for these cooperative games. The real future of video game artificial
intelligence is in continual growth. While some disciplines need a large breakthrough to continue
research, video games give a suitable environment for incremental growth of AI sophistication
(Laird & Lent 2001). Game companies may not be able to dedicate large amount of resources per
game to AI research and development, but the number of games released each year is steadily
increasing, as are the budgets of those games. The resources going into game development
should see some growth in the AI research and development portion of game development,
making it an increasingly attractive field for AI researchers.

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VIII. Conclusion
For decades of popular culture, humans have been interested in creating artificial humans, or
inanimate objects that are seemingly intelligent. Our mythology, writing,
movies and even thoughts are full of references to seemingly magically constructed automatons,
talking mirrors, and intelligent robots.
When computers came along, it was only a matter of time before people tried to project our own
humanity into it. There are so many areas that could be used as a testing ground for artificial
intelligence research, but games have quickly become the chosen medium.
Luckily, because new AI algorithms can be tested without risking safety or vital systems when
tested in a game space. This is much safer and arguably better than testing it in areas where a
malformed algorithm could be high in cost of life and property, as well as financial loss.
A great number of the major advances in artificial intelligence were born in game design before
they were used in real world applications. Alpha beta algorithms, minimax, and advances in
human level AI are just a few of the most important AI advances in the past several decades, and
all were borne of game design. As the industry and budgets increase, the chances of new
breakthroughs increase exponentially alongside them.
Many researchers are even calling for more research in the field. Michael Buro argues that real
time strategy games (RTS) have a wealth of opportunities for research that are yet untouched.
Current levels of RTS intelligence are low, but the number of agents and the number of actions is
high (Buro 2004). Laird and Lent insist that video games are the premiere showcase for human
level AI (Laird & Lent 2001). And nearly every algorithm shown in this paper is intended only to
be an early stage of their kind of research, and source code is made available for researchers
wishing to expand upon it. What could lie just over the horizon? What new algorithm is waiting
to change the world as we know it? Will the rapid advances in medicine, hardware engineering
and software technology open doors that we can only dream of? While nobody knows for sure,
the possibilities are invigorating researchers and the future will certainly be exciting to see.

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