Technologists in Sync: General and Narrow AI

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Artificial Intelligence - What Are Non-Player Characters And Emergent Gameplay?

Emergent gameplay occurs when a player in a video game encounters complicated scenarios as a result of their interactions with other players in the game.

Players may fully immerse themselves in an intricate and realistic game environment and feel the consequences of their choices in today's video games.

Players may personalize and build their character and tale.

Players take on the role of a cyborg in a dystopian metropolis in the Deus Ex series (2000), for example, one of the first emergent game play systems.

They may change the physical appearance of their character as well as their skill sets, missions, and affiliations.

Players may choose between militarized adaptations that allow for more aggressive play and stealthier options.

The plot and experience are altered by the choices made on how to customize and play, resulting in unique challenges and results for each player.

When players interact with other characters or items, emergent gameplay guarantees that the game environment reacts.

Because of many options, the tale unfolds in surprising ways as the gaming world changes.

Specific outcomes are not predetermined by the designer, and emergent gameplay can even take advantage of game flaws to generate actions in the game world, which some consider to be a form of emergence.

Artificial intelligence has become more popular among game creators in order to have the game environment respond to player actions in a timely manner.

Artificial intelligence aids the behavior of video characters and their interactions via the use of algorithms, basic rule-based forms that help in generating the game environment in sophisticated ways.

"Game AI" refers to the usage of artificial intelligence in games.

The most common use of AI algorithms is to construct the form of a non-player character (NPC), which are characters in the game world with whom the player interacts but does not control.

In its most basic form, AI will use pre-scripted actions for the characters, who will then concentrate on reacting to certain events.

Pre-scripted character behaviors performed by AI are fairly rudimentary, and NPCs are meant to respond to certain "case" events.

The NPC will evaluate its current situation before responding in a range determined by the AI algorithm.

Pac-Man is a good early and basic illustration of this (1980).

Pac-Man is controlled by the player through a labyrinth while being pursued by a variety of ghosts, who are the game's non-player characters.

Players could only interact with ghosts (NPCs) by moving about; ghosts had limited replies and their own AI-programmed pre-scripted movement.

The AI planned reaction would occur if the ghost ran into a wall.

It would then roll an AI-created die that would determine whether or not the NPC would turn toward or away from the direction of the player.

If the NPC decided to go after the player, the AI pre-scripted pro gram would then detect the player’s location and turn the ghost toward them.

If the NPC decided not to go after the player, it would turn in an opposite or a random direction.

This NPC interaction is very simple and limited; however, this was an early step in AI providing emergent gameplay.

Contemporary games provide a variety of options available and a much larger set of possible interactions for the player.

Players in contemporary role-playing games (RPGs) are given an incredibly high number of potential options, as exemplified by Fallout 3 (2008) and its sequels.

Fallout is a role-playing game, where the player takes on the role of a survivor in a post-apocalyptic America.

The story narrative gives the player a goal with no direction; as a result, the player is given the freedom to play as they see fit.

The player can punch every NPC, or they can talk to them instead.

In addition to this variety of actions by the player, there are also a variety of NPCs controlled through AI.

Some of the NPCs are key NPCs, which means they have their own unique scripted dialogue and responses.

This provides them with a personality and provides a complexity through the use of AI that makes the game environment feel more real.

When talking to key NPCs, the player is given options for what to say, and the Key NPCs will have their own unique responses.

This differs from the background character NPCs, as the key NPCs are supposed to respond in such a way that it would emulate interaction with a real personality.

These are still pre-scripted responses to the player, but the NPC responses are emergent based on the possible combination of the interaction.

As the player makes decisions, the NPC will examine this decision and decide how to respond in accordance to its script.

The NPCs that the players help or hurt and the resulting interactions shape the game world.

Game AI can emulate personalities and present emergent gameplay in a narrative setting; however, AI is also involved in challenging the player in difficulty settings.

A variety of pre-scripted AI can still be used to create difficulty.

Pre scripted AI are often made to make suboptimal decisions for enemy NPCs in games where players fight.

This helps make the game easier and also makes the NPCs seem more human.

Suboptimal pre-scripted decisions make the enemy NPCs easier to handle.

Optimal decisions however make the opponents far more difficult to handle.

This can be seen in contemporary games like Tom Clancy’s The Division (2016), where players fight multiple NPCs.

The enemy NPCs range from angry rioters to fully trained paramilitary units.

The rioter NPCs offer an easier challenge as they are not trained in combat and make suboptimal decisions while fighting the player.

The military trained NPCs are designed to have more optimal decision-making AI capabilities in order to increase the difficulty for the player fighting them.

Emergent gameplay has evolved to its full potential through use of adaptive AI.

Similar to prescript AI, the character examines a variety of variables and plans about an action.

However, unlike the prescript AI that follows direct decisions, the adaptive AI character will make their own decisions.

This can be done through computer-controlled learning.

AI-created NPCs follow rules of interactions with the players.

As players go through the game, the player interactions are analyzed, and some AI judgments become more weighted than others.

This is done in order to provide distinct player experiences.

Various player behaviors are actively examined, and modifications are made by the AI when designing future challenges.

The purpose of the adaptive AI is to challenge the players to a degree that the game is fun while not being too easy or too challenging.

Difficulty may still be changed if players seek a different challenge.

This may be observed in the Left 4 Dead game (2008) series’ AI Director.

Players navigate through a level, killing zombies and gathering resources in order to live.

The AI Director chooses which zombies to spawn, where they will spawn, and what supplies will be spawned.

The choice to spawn them is not made at random; rather, it is based on how well the players performed throughout the level.

The AI Director makes its own decisions about how to respond; as a result, the AI Director adapts to the level's player success.

The AI Director gives less resources and spawns more adversaries as the difficulty level rises.

Changes in emergent gameplay are influenced by advancements in simulation and game world design.

As virtual reality technology develops, new technologies will continue to help in this progress.

Virtual reality games provide an even more immersive gaming experience.

Players may use their own hands and eyes to interact with the environment.

Computers are growing more powerful, allowing for more realistic pictures and animations to be rendered.

Adaptive AI demonstrates the capability of really autonomous decision-making, resulting in a truly participatory gaming experience.

Game makers are continuing to build more immersive environments as AI improves to provide more lifelike behavior.

These cutting-edge technology and new AI will elevate emergent gameplay to new heights.

The importance of artificial intelligence in videogames has emerged as a crucial part of the industry for developing realistic and engrossing gaming.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is AI Embodiment Or Embodied Artificial Intelligence?

Embodied Artificial Intelligence is a method for developing AI that is both theoretical and practical.

It is difficult to fully trace its his tory due to its beginnings in different fields.

Rodney Brooks' Intelligence Without Representation, written in 1987 and published in 1991, is one claimed for the genesis of this concept.

Embodied AI is still a very new area, with some of the first references to it dating back to the early 2000s.

Rather than focusing on either modeling the brain (connectionism/neural net works) or linguistic-level conceptual encoding (GOFAI, or the Physical Symbol System Hypothesis), the embodied approach to AI considers the mind (or intelligent behavior) to emerge from interaction between the body and the world.

There are hundreds of different and sometimes contradictory approaches to interpret the role of the body in cognition, the majority of which utilize the term "embodied."

The idea that the physical body's shape is related to the structure and content of the mind is shared by all of these viewpoints.

Despite the success of neural network or GOFAI (Good Old-Fashioned Artificial Intelligence or classic symbolic artificial intelligence) techniques in building row expert systems, the embodied approach contends that general artificial intelligence cannot be accomplished in code alone.

For example, in a tiny robot with four motors, each driving a separate wheel, and programming that directs the robot to avoid obstacles, the same code might create dramatically different observable behaviors if the wheels were relocated to various areas of the body or replaced with articulated legs.

This is a basic explanation of why the shape of a body must be taken into account when designing robotic systems, and why embodied AI (rather than merely robotics) considers the dynamic interaction between the body and the surroundings to be the source of sometimes surprising emergent behaviors.

The instance of passive dynamic walkers is an excellent illustration of this method.

The passive dynamic walker is a bipedal walking model that depends on the dynamic interaction of the leg design and the environment's structure.

The gait is not generated by an active control system.

The walker is propelled forward by gravity, inertia, and the forms of the feet, legs, and inclination.

This strategy is based on the biological concept of stigmergy.

Stigmergy is based on the idea that signs or marks left by actions in the environment inspire future actions.

AN APPROACH INFORMED BY ENGINEERING.

Embodied AI is influenced by a variety of domains. Engineering and philosophy are two frequent methods.

Rodney Brooks proposed the "subsumption architecture" in 1986, which is a method of generating complex behaviors by arranging lower-level layers of the system to interact with the environment in prioritized ways, tightly coupling perception and action, and attempting to eliminate the higher-level processing of other models.

For example, the Smithsonian's robot Genghis was created to traverse rugged terrain, a talent that made the design and engineering of other robots very challenging at the time.

The success of this approach was primarily due to the design choice to divide the processing of various motors and sensors throughout the network rather than trying higher-level system integration to create a full representational model of the robot and its surroundings.

To put it another way, there was no central processing region where all of the robot's parts sought to integrate data for the system.

Cog, a humanoid torso built by the MIT Humanoid Robotics Group in the 1990s, was an early effort at embodied AI.

Cog was created to learn about the world by interacting with it physically.

Cog, for example, may be shown learning how to apply force and weight to a drum while holding drumsticks for the first time, or learning how to gauge the weight of a ball once it was put in Cog's hand.

These early notions of letting the body conduct the learning are still at the heart of the embodied AI initiative.

The Swiss Robots, created and constructed in the AI Lab at Zurich University, are perhaps one of the most prominent instances of embodied emergent intelligence.

Simple small robots with two motors (one on each side) and two infrared sensors, the Swiss Robots (one on each side).

The only high-level instructions in their programming were that if a sensor detected an item on one side, it should move in the other direction.

However, when combined with a certain body form and sensor location, this resulted in what seemed to be high-level cleaning or clustering behavior in certain situations.

A similar strategy is used in many other robotics projects.

Shakey the Robot, developed by SRI International in the 1960s, is frequently credited as being the first mobile robot with thinking ability.

Shakey was clumsy and sluggish, and he's often portrayed as the polar antithesis of what embodied AI is attempting to achieve by moving away from higher-level thinking and processing.

However, even in 1968, SRI's approach to embodiment was a clear forerunner of Brooks', since they were the first to assert that the finest reservoir of knowledge about the actual world is the real world itself.

The greatest model of the world is the world itself, according to this notion, which has become a rallying cry against higher-level representation in embodied AI.

Earlier robots, in contrast to the embodied AI software, were mostly preprogrammed and did not actively interface with their environs in the manner that this method does.

Honda's ASIMO robot, for example, isn't an excellent illustration of embodied AI; instead, it's representative of other and older approaches to robotics.

Work in embodied AI is exploding right now, with Boston Dynamics' robots serving as excellent examples (particularly the non-humanoid forms).

Embodied AI is influenced by a number of philosophical ideas.

Rodney Brooks, a roboticist, particularly rejects philosophical influence on his technical concerns in a 1991 discussion of his subsumption architecture, while admitting that his arguments mirror Heidegger's.

In several essential design aspects, his ideas match those of phenom enologist Merleau-Ponty, demonstrating how earlier philosophical issues at least reflect, and likely shape, much of the design work in contemplating embodied AI.

Because of its methodology in experimenting toward an understanding of how awareness and intelligent behavior originate, which are highly philosophical activities, this study in embodied robotics is deeply philosophical.

Other clearly philosophical themes may be found in a few embodied AI projects as well.

Rolf Pfeifer and Josh Bongard, for example, often draw to philosophical (and psychological) literature in their work, examining how these ideas intersect with their own methods to developing intelligent machines.

They discuss how these ideas may (and frequently do not) guide the development of embodied AI.

This covers a broad spectrum of philosophical inspirations, such as George Lakoff and Mark Johnson's conceptual metaphor work, Shaun Gallagher's (2005) body image and phenomenology work, and even John Dewey's early American pragmatism.

It's difficult to say how often philosophical concerns drive engineering concerns, but it's clear that the philosophy of embodiment is probably the most robust of the various disciplines within cognitive science to have done embodiment work, owing to the fact that theorizing took place long before the tools and technologies were available to actually realize the machines being imagined.

This suggests that for roboticists interested in the strong AI project, that is, broad intellectual capacities and functions that mimic the human brain, there are likely still unexplored resources here.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - Who Is Daniel Dennett?

At Tufts University, Daniel Dennett(1942–) is the Austin B. Fletcher Professor of Philosophy and Co-Director of the Center for Cognitive Studies.

Philosophy of mind, free will, evolutionary biology, cognitive neuroscience, and artificial intelligence are his main areas of study and publishing.

He has written over a dozen books and hundreds of articles.

Much of this research has focused on the origins and nature of consciousness, as well as how naturalistically it may be described.

Dennett is also an ardent atheist, and one of the New Atheism's "Four Horsemen." Richard Dawkins, Sam Harris, and Christopher Hitchens are the others.

Dennett's worldview is naturalistic and materialistic throughout.

He opposes Cartesian dualism, which holds that the mind and body are two distinct things that merge.

Instead, he contends that the brain is a form of computer that has developed through time due to natural selection.

Dennett also opposes the homunculus theory of the mind, which holds that the brain has a central controller or "little man" who performs all of the thinking and emotion.

Dennett, on the other hand, argues for a viewpoint he refers to as the numerous drafts model.

According to his theory, which he lays out in his 1991 book Consciousness Explained, the brain is constantly sifting through, interpreting, and editing sensations and inputs, forming overlapping drafts of experience.

Dennett later used the metaphor of "fame in the brain" to describe how various aspects of ongoing neural processes are periodically emphasized at different times and under different circumstances.

Consciousness is a story made up of these varied interpretations of human events.

Dennett dismisses the assumption that these ideas coalesce or are structured in a central portion of the brain, which he mockingly refers to as "Cartesian theater." The brain's story is made up of a never-ending, un-centralized flow of bottom-up awareness that spans time and place.

Dennett denies the existence of qualia, which are subjective individual experiences such as how colors seem to the human eye or how food feels.

He does not deny that colors and tastes exist; rather, he claims that the sensation of color and taste does not exist as a separate thing in the human mind.

He claims that there is no difference between human and computer "sensation experiences." According to Dennett, just as some robots can discern between colors without people deciding that they have qualia, so can the human brain.

For Dennett, the color red is just the quality that brains sense and which is referred to as red in the English language.

It has no extra, indescribable quality.

This is a crucial consideration for artificial intelligence because the ability to experience qualia is frequently seen as a barrier to the development of Strong AI (AI that is functionally equivalent to that of a human) and as something that will invariably distinguish human and machine intelligence.

However, if qualia do not exist, as Dennett contends, it cannot constitute a stumbling block to the creation of machine intelligence comparable to that of humans.

Dennett compares our brains to termite colonies in another metaphor.

Termites do not join together and plot to form a mound, but their individual activities cause it to happen.

The mound is the consequence of natural selection producing uncomprehending expertise in cooperative mound-building rather than intellectual design by the termites.

To create a mound, termites don't need to comprehend what they're doing.

Likewise, comprehension is an emergent attribute of such abilities.

Brains, according to Dennett, are control centers that have evolved to respond swiftly and effectively to threats and opportunities in the environment.

As the demands of responding to the environment grow more complicated, understanding emerges as a tool for dealing with them.

On a sliding scale, comprehension is a question of degree.

Dennett, for example, considers bacteria's quasi-comprehension in response to diverse stimuli and computers' quasi-comprehension in response to coded instructions to be on the low end of the range.

On the other end of the spectrum, he placed Jane Austen's comprehension of human social processes and Albert Einstein's understanding of relativity.

However, they are just changes in degree, not in type.

Natural selection has shaped both extremes of the spectrum.

Comprehension is not a separate mental process arising from the brain's varied abilities.

Rather, understanding is a collection of these skills.

Consciousness is an illusion to the extent that we recognize it as an additional element of the mind in the shape of either qualia or cognition.

In general, Dennett advises mankind to avoid positing understanding when basic competence would suffice.

Humans, on the other hand, often adopt what Dennett refers to as a "intentional position" toward other humans and, in some cases, animals.

When individuals perceive acts as the outcome of mind-directed thoughts, emotions, wants, or other mental states, they adopt the intentional viewpoint.

This is in contrast to the "physical posture" and the "design stance," according to him.

The physical stance is when anything is seen as the outcome of simply physical forces or natural principles.

Gravity causes a stone to fall when it is dropped, not any conscious purpose to return to the ground.

An action is seen as the mindless outcome of a preprogrammed, or predetermined, purpose in the design stance.

An alarm clock, for example, beeps at a certain time because it was built to do so, not because it chose to do so on its own.

In contrast to both the physical and design stances, the intentional stance considers behaviors and acts as though they are the consequence of the agent's deliberate decision.

It might be difficult to decide whether to apply the purposeful or design perspective to computers.

A chess-playing computer has been created with the goal of winning.

However, its movements are often indistinguishable from those of a human chess player who wants or intends to win.

In fact, having a purposeful posture toward the computer's behavior, rather than a design stance, improves human interpretation of its behavior and capacity to respond to it.

Dennett claims that the purposeful perspective is the greatest strategy to adopt toward both humans and computers since it works best in describing both human and computer behavior.

Furthermore, there is no need to differentiate them in any way.

Though the intentional attitude considers behavior as agent-driven, it is not required to take a position on what is truly going on inside the human or machine's internal workings.

This posture provides a neutral starting point from which to investigate cognitive competency without presuming a certain explanation of what's going on behind the scenes.

Dennett sees no reason why AI should be impossible in theory since human mental abilities have developed organically.

Furthermore, by abandoning the concept of qualia and adopting an intentional posture that relieves people of the responsibility of speculating about what is going on in the background of cognition, two major impediments to solving the hard issue of consciousness have been eliminated.

Dennett argues that since the human brain and computers are both machines, there is no good theoretical reason why humans should be capable of acquiring competence-driven understanding while AI should be intrinsically unable.

Consciousness in the traditional sense is illusory, hence it is not a need for Strong AI.

Dennett does not believe that Strong AI is theoretically impossible.

He feels that society's technical sophistication is still at least fifty years away from producing it.

Strong AI development, according to Dennett, is not desirable.

Humans should strive to build AI tools, but Dennett believes that attempting to make computer pals or colleagues would be a mistake.

Such robots, he claims, would lack human moral intuitions and understanding, and hence would not be able to integrate into human society.

Humans do not need robots to provide friendship since they have each other.

Robots, even AI-enhanced machines, should be seen as tools to be utilized by humans alone.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is Cognitive Computing?

Self-learning hardware and software systems that use machine learning, natural language processing, pattern recognition, human-computer interaction, and data mining technologies to mimic the human brain are referred to as cognitive computing.

The term "cognitive computing" refers to the use of advances in cognitive science to create new and complex artificial intelligence systems.

Cognitive systems aren't designed to take the place of human thinking, reasoning, problem-solving, or decision-making; rather, they're meant to supplement or aid people.

A collection of strategies to promote the aims of affective computing, which entails narrowing the gap between computer technology and human emotions, is frequently referred to as cognitive computing.

Real-time adaptive learning approaches, interactive cloud services, interactive memo ries, and contextual understanding are some of these methodologies.

To conduct quantitative assessments of organized statistical data and aid in decision-making, cognitive analytical tools are used.

Other scientific and economic systems often include these tools.

Complex event processing systems utilize complex algorithms to assess real-time data regarding events for patterns and trends, offer choices, and make judgments.

These kinds of systems are widely used in algorithmic stock trading and credit card fraud detection.

Face recognition and complex image recognition are now possible with image recognition systems.

Machine learning algorithms build models from data sets and improve as new information is added.

Neural networks, Bayesian classifiers, and support vector machines may all be used in machine learning.

Natural language processing entails the use of software to extract meaning from enormous amounts of data generated by human conversation.

Watson from IBM and Siri from Apple are two examples.

Natural language comprehension is perhaps cognitive computing's Holy Grail or "killer app," and many people associate natural language processing with cognitive computing.

Heuristic programming and expert systems are two of the oldest branches of so-called cognitive computing.

Since the 1980s, there have been four reasonably "full" cognitive computing architectures: Cyc, Soar, Society of Mind, and Neurocognitive Networks.

Speech recognition, sentiment analysis, face identification, risk assessment, fraud detection, and behavioral suggestions are some of the applications of cognitive computing technology.

These applications are referred regarded as "cognitive analytics" systems when used together.

In the aerospace and defense industries, agriculture, travel and transportation, banking, health care and the life sciences, entertainment and media, natural resource development, utilities, real estate, retail, manufacturing and sales, marketing, customer service, hospitality, and leisure, these systems are in development or are being used.

Netflix's movie rental suggestion algorithm is an early example of predictive cognitive computing.

Computer vision algorithms are being used by General Electric to detect tired or distracted drivers.

Customers of Domino's Pizza can place orders online by speaking with a virtual assistant named Dom.

Elements of Google Now, a predictive search feature that debuted in Google applications in 2012, assist users in predicting road conditions and anticipated arrival times, locating hotels and restaurants, and remembering anniversaries and parking spots.

In IBM marketing materials, the term "cognitive" computing appears frequently.

Cognitive computing, according to the company, is a subset of "augmented intelligence," which is preferred over artificial intelligence.

The Watson machine from IBM is frequently referred to as a "cognitive computer" since it deviates from the traditional von Neumann design and instead draws influence from neural networks.

Neuroscientists are researching the inner workings of the human brain, seeking for connections between neuronal assemblies and mental aspects, and generating new mental ideas.

Hebbian theory is an example of a neuroscientific theory that underpins cognitive computer machine learning implementations.

The Hebbian theory is a proposed explanation for neural adaptation during the learning process.

Donald Hebb initially proposed the hypothesis in his 1949 book The Organization of Behavior.

Learning, according to Hebb, is a process in which the causal induction of recurrent or persistent neuronal firing or activity causes neural traces to become stable.

"Any two cells or systems of cells that are consistently active at the same time will likely to become'associated,' such that activity in one favors activity in the other," Hebb added (Hebb 1949, 70).

"Cells that fire together, wire together," is how the idea is frequently summarized.

According to this hypothesis, the connection of neuronal cells and tissues generates neurologically defined "engrams" that explain how memories are preserved in the brain as biophysical or biochemical changes.

Engrams' actual location, as well as the procedures by which they are formed, are currently unknown.

IBM machines are stated to learn by aggregating information into a computational convolution or neural network architecture made up of weights stored in a parallel memory system.

Intel introduced Loihi, a cognitive chip that replicates the functions of neurons and synapses, in 2017.

Loihi is touted to be 1,000 times more energy efficient than existing neurosynaptic devices, with 128 clusters of 1,024 simulated neurons on per chip, for a total of 131,072 simulated neurons.

Instead of relying on simulated neural networks and parallel processing with the overarching goal of developing artificial cognition, Loihi uses purpose-built neural pathways imprinted in silicon.

These neuromorphic processors are likely to play a significant role in future portable and wire-free electronics, as well as automobiles.

Roger Schank, a cognitive scientist and artificial intelligence pioneer, is a vocal opponent of cognitive computing technology.

"Watson isn't thinking. You can only reason if you have objectives, plans, and strategies to achieve them, as well as an understanding of other people's ideas and a knowledge of prior events to draw on.

"Having a point of view is also beneficial," he writes.

"How does Watson feel about ISIS, for example?" Is this a stupid question? ISIS is a topic on which actual thinking creatures have an opinion" (Schank 2017).

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is The Blue Brain Project (BBP)?

The brain, with its 100 billion neurons, is one of the most complicated physical systems known.

It is an organ that takes constant effort to comprehend and interpret.

Similarly, digital reconstruction models of the brain and its activity need huge and long-term processing resources.

The Blue Brain Project, a Swiss brain research program supported by the École Polytechnique Fédérale de Lausanne (EPFL), was founded in 2005. Henry Markram is the Blue Brain Project's founder and director.

The purpose of the Blue Brain Project is to simulate numerous mammalian brains in order to "ultimately, explore the stages involved in the formation of biological intelligence" (Markram 2006, 153).

These simulations were originally powered by IBM's BlueGene/L, the world's most powerful supercomputer system from November 2004 to November 2007.

In 2009, the BlueGene/L was superseded by the BlueGene/P.

BlueGene/P was superseded by BlueGene/Q in 2014 due to a need for even greater processing capability.

The BBP picked Hewlett-Packard to build a supercomputer (named Blue Brain 5) devoted only to neuroscience simulation in 2018.

The use of supercomputer-based simulations has pushed neuroscience research away from the physical lab and into the virtual realm.

The Blue Brain Project's development of digital brain reconstructions enables studies to be carried out in a "in silico" environment, a Latin pseudo-word referring to modeling of biological systems on computing equipment, using a regulated research flow and methodology.

The possibility for supercomputers to turn the analog brain into a digital replica suggests a paradigm change in brain research.

One fundamental assumption is that the digital or synthetic duplicate will act similarly to a real or analog brain.

Michael Hines, John W. Moore, and Ted Carnevale created the software that runs on Blue Gene hardware, a simulation environment called NEURON that mimics neurons.

The Blue Brain Project may be regarded a typical example of what was dubbed Big Science following World War II (1939–1945) because of the expanding budgets, pricey equipment, and numerous interdisciplinary scientists participating.

Furthermore, the scientific approach to the brain via simulation and digital imaging processes creates issues such as data management.

Blue Brain joined the Human Brain Project (HBP) consortium as an initial member and submitted a proposal to the European Commission's Future & Emerging Technologies (FET) Flagship Program.

The European Union approved the Blue Brain Project's proposal in 2013, and the Blue Brain Project is now a partner in a larger effort to investigate and undertake brain simulation.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also:

General and Narrow AI; Human Brain Project; SyNAPSE.

Further Reading

Djurfeldt, Mikael, Mikael Lundqvist, Christopher Johansson, Martin Rehn, Örjan Ekeberg, Anders Lansner. 2008. “Brain-Scale Simulation of the Neocortex on the IBM Blue Gene/L Supercomputer.” IBM Journal of Research and Development 52, no. 1–2: 31–41.

Markram, Henry. 2006. “The Blue Brain Project.” Nature Reviews Neuroscience 7, no. 2: 153–60.

Markram, Henry, et al. 2015. “Reconstruction and Simulation of Neocortical Microcircuitry.” Cell 63, no. 2: 456–92.

Artificial Intelligence - What Are Non-Player Characters And Emergent Gameplay?

In its most basic form, AI will use pre-scripted actions for the characters, who will then concentrate on reacting to certain events.

Contemporary games provide a variety of options available and a much larger set of possible interactions for the player.

Emergent gameplay has evolved to its full potential through use of adaptive AI.

Changes in emergent gameplay are influenced by advancements in simulation and game world design.

See also:

Further Reading:

Artificial Intelligence - What Is AI Embodiment Or Embodied Artificial Intelligence?

Embodied AI is still a very new area, with some of the first references to it dating back to the early 2000s.

AN APPROACH INFORMED BY ENGINEERING.

See also:

Further Reading:

Artificial Intelligence - Who Is Daniel Dennett?

See also:

Further Reading:

Artificial Intelligence - What Is Cognitive Computing?

The term "cognitive computing" refers to the use of advances in cognitive science to create new and complex artificial intelligence systems.

See also:

Further Reading

Artificial Intelligence - What Is The Blue Brain Project (BBP)?

The purpose of the Blue Brain Project is to simulate numerous mammalian brains in order to "ultimately, explore the stages involved in the formation of biological intelligence" (Markram 2006, 153).

The Blue Brain Project may be regarded a typical example of what was dubbed Big Science following World War II (1939–1945) because of the expanding budgets, pricey equipment, and numerous interdisciplinary scientists participating.

What Is Artificial General Intelligence?

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