The Microsoft Support Diagnostic Tool (MSDT) is a legacy service in Microsoft Windows that allows Microsoft technical support agents to analyze diagnostic data remotely for troubleshooting purposes. In April 2022 it was observed to have a security vulnerability that allowed remote code execution which was being exploited to attack computers in Russia and Belarus, and later against the Tibetan government in exile. Microsoft advised a temporary workaround of disabling the MSDT by editing the Windows registry. == Use == When contacting support the user is told to run MSDT and given a unique "passkey" which they enter. They are also given an "incident number" to uniquely identify their case. The MSDT can also be run offline which will generate a .CAB file which can be uploaded from a computer with an internet connection. == Security vulnerabilities == === Follina === Follina is the name given to a remote code execution (RCE) vulnerability, a type of arbitrary code execution (ACE) exploit, in the Microsoft Support Diagnostic Tool (MSDT) which was first widely publicized on May 27, 2022, by a security research group called Nao Sec. This exploit allows a remote attacker to use a Microsoft Office document template to execute code via MSDT. This works by exploiting the ability of Microsoft Office document templates to download additional content from a remote server. If the size of the downloaded content is large enough it causes a buffer overflow allowing a payload of Powershell code to be executed without explicit notification to the user. On May 30 Microsoft issued CVE-2022-30190 with guidance that users should disable MSDT. Malicious actors have been observed exploiting the bug to attack computers in Russia and Belarus since April, and it is believed Chinese state actors had been exploiting it to attack the Tibetan government in exile based in India. Microsoft patched this vulnerability in its June 2022 patches. === DogWalk === The DogWalk vulnerability is a remote code execution (RCE) vulnerability in the Microsoft Support Diagnostic Tool (MSDT). It was first reported in January 2020, but Microsoft initially did not consider it to be a security issue. However, the vulnerability was later exploited in the wild, and Microsoft released a patch for it in August 2022. The vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. Originally discovered by Mitja Kolsek, the DogWalk vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. The vulnerability is exploited by creating a malicious diagcab file that contains a specially crafted path. This path contains a sequence of characters that is designed to exploit the path traversal vulnerability in the sdiageng.dll library. When the diagcab file is opened, the MSDT tool will attempt to follow the path. However, the path will contain characters that are not valid for a Windows path. This will cause the MSDT tool to crash. When the MSDT tool crashes, it will generate a memory dump. This memory dump will contain the malicious code that was executed by the MSDT tool. The attacker can then use this memory dump to extract the malicious code and execute it on their own computer. == Retirement == Microsoft will no longer be supporting the Windows legacy inbox Troubleshooters. In 2025, Microsoft will remove the MSDT platform entirely. Get Help is the replacement tool. == Windows versions == Windows 7 Windows 8.1 Windows 10 Windows 11 (up to 22H2) Future versions and feature upgrades will deprecate the MSDT after May 23, 2023.
Developmental robotics
Developmental robotics (DevRob), sometimes called epigenetic robotics, is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines. As in human children, learning is expected to be cumulative and of progressively increasing complexity, and to result from self-exploration of the world in combination with social interaction. The typical methodological approach consists in starting from theories of human and animal development elaborated in fields such as developmental psychology, neuroscience, developmental and evolutionary biology, and linguistics, then to formalize and implement them in robots, sometimes exploring extensions or variants of them. The experimentation of those models in robots allows researchers to confront them with reality, and as a consequence, developmental robotics also provides feedback and novel hypotheses on theories of human and animal development. Developmental robotics is related to but differs from evolutionary robotics (ER). ER uses populations of robots that evolve over time, whereas DevRob is interested in how the organization of a single robot's control system develops through experience, over time. DevRob is also related to work done in the domains of robotics and artificial life. == Background == Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop without the intervention of an engineer once it is "out of the factory"? What can it learn through natural social interactions with humans? These are the questions at the center of developmental robotics. Alan Turing, as well as a number of other pioneers of cybernetics, already formulated those questions and the general approach in 1950, but it is only since the end of the 20th century that they began to be investigated systematically. Because the concept of adaptive intelligent machines is central to developmental robotics, it has relationships with fields such as artificial intelligence, machine learning, cognitive robotics or computational neuroscience. Yet, while it may reuse some of the techniques elaborated in these fields, it differs from them from many perspectives. It differs from classical artificial intelligence because it does not assume the capability of advanced symbolic reasoning and focuses on embodied and situated sensorimotor and social skills rather than on abstract symbolic problems. It differs from cognitive robotics because it focuses on the processes that allow the formation of cognitive capabilities rather than these capabilities themselves. It differs from computational neuroscience because it focuses on functional modeling of integrated architectures of development and learning. More generally, developmental robotics is uniquely characterized by the following three features: It targets task-independent architectures and learning mechanisms, i.e. the machine/robot has to be able to learn new tasks that are unknown by the engineer; It emphasizes open-ended development and lifelong learning, i.e. the capacity of an organism to acquire continuously novel skills. This should not be understood as a capacity for learning "anything" or even “everything”, but just that the set of skills that is acquired can be infinitely extended at least in some (not all) directions; The complexity of acquired knowledge and skills shall increase (and the increase be controlled) progressively. Developmental robotics emerged at the crossroads of several research communities including embodied artificial intelligence, enactive and dynamical systems cognitive science, connectionism. Starting from the essential idea that learning and development happen as the self-organized result of the dynamical interactions among brains, bodies and their physical and social environment, and trying to understand how this self-organization can be harnessed to provide task-independent lifelong learning of skills of increasing complexity, developmental robotics strongly interacts with fields such as developmental psychology, developmental and cognitive neuroscience, developmental biology (embryology), evolutionary biology, and cognitive linguistics. As many of the theories coming from these sciences are verbal and/or descriptive, this implies a crucial formalization and computational modeling activity in developmental robotics. These computational models are then not only used as ways to explore how to build more versatile and adaptive machines but also as a way to evaluate their coherence and possibly explore alternative explanations for understanding biological development. == Research directions == === Skill domains === Due to the general approach and methodology, developmental robotics projects typically focus on having robots develop the same types of skills as human infants. A first category that is important being investigated is the acquisition of sensorimotor skills. These include the discovery of one's own body, including its structure and dynamics such as hand-eye coordination, locomotion, and interaction with objects as well as tool use, with a particular focus on the discovery and learning of affordances. A second category of skills targeted by developmental robots are social and linguistic skills: the acquisition of simple social behavioural games such as turn-taking, coordinated interaction, lexicons, syntax and grammar, and the grounding of these linguistic skills into sensorimotor skills (sometimes referred as symbol grounding). In parallel, the acquisition of associated cognitive skills are being investigated such as the emergence of the self/non-self distinction, the development of attentional capabilities, of categorization systems and higher-level representations of affordances or social constructs, of the emergence of values, empathy, or theories of mind. === Mechanisms and constraints === The sensorimotor and social spaces in which humans and robot live are so large and complex that only a small part of potentially learnable skills can actually be explored and learnt within a life-time. Thus, mechanisms and constraints are necessary to guide developmental organisms in their development and control of the growth of complexity. There are several important families of these guiding mechanisms and constraints which are studied in developmental robotics, all inspired by human development: Motivational systems, generating internal reward signals that drive exploration and learning, which can be of two main types: extrinsic motivations push robots/organisms to maintain basic specific internal properties such as food and water level, physical integrity, or light (e.g. in phototropic systems); intrinsic motivations push robot to search for novelty, challenge, compression or learning progress per se, thus generating what is sometimes called curiosity-driven learning and exploration, or alternatively active learning and exploration; Social guidance: as humans learn a lot by interacting with their peers, developmental robotics investigates mechanisms that can allow robots to participate to human-like social interaction. By perceiving and interpreting social cues, this may allow robots both to learn from humans (through diverse means such as imitation, emulation, stimulus enhancement, demonstration, etc. ...) and to trigger natural human pedagogy. Thus, social acceptance of developmental robots is also investigated; Statistical inference biases and cumulative knowledge/skill reuse: biases characterizing both representations/encodings and inference mechanisms can typically allow considerable improvement of the efficiency of learning and are thus studied. Related to this, mechanisms allowing to infer new knowledge and acquire new skills by reusing previously learnt structures is also an essential field of study; The properties of embodiment, including geometry, materials, or innate motor primitives/synergies often encoded as dynamical systems, can considerably simplify the acquisition of sensorimotor or social skills, and is sometimes referred as morphological computation. The interaction of these constraints with other constraints is an important axis of investigation; Maturational constraints: In human infants, both the body and the neural system grow progressively, rather than being full-fledged already at birth. This implies, for example, that new degrees of freedom, as well as increases of the volume and resolution of available sensorimotor signals, may appear as learning and development unfold. Transposing these mechanisms in developmental robots, and understanding how it may hinder or on the contrary ease the acquisition of novel complex skills is a central questi
Neuroshima
Neuroshima is a Polish tabletop roleplaying system inspired by such films and games as Mad Max, Fallout, The Matrix, Terminator and Deadlands: Hell on Earth. It is currently available only in Polish. The game's motto is "never trust the machines". Its designers include Michal Oracz and Ignacy Trzewiczek. == Setting == The game describes the United States in the mid-21st century, after a nuclear war started by a cybernetic revolt, which molded the continent into a barren wasteland. It seems that the reason for the war to break out was a sentient Artificial Intelligence commonly referred to as Moloch and made up of interconnected net of military computers: automated factories, military facilities, power plants and alike, that now cover the whole north of the U.S., from Oregon to the Great Lakes. On the south, there is another creation, called the Neojungle, that poses a threat to those who survived the war. It is a semi-intelligent carnivorous vegetation that grows very quickly, advancing north from Latin America. Right in the middle, there are humans. They are surrounded by mutant creatures, some bred by Moloch and hostile towards humans, and some simply animals and humans misshapen by nuclear fallout. On top of that there are Moloch's deadly machines lurking to complete the picture. But what is stressed in the book is that the worst enemy of humans is within them: hatred, indifference, greed. === Landscapes of Neuroshima === Car wrecks, ruined towns and villages, collapsed roofs on deserted houses, broken glass in the windows of abandoned gas stations fill the landscape of the United States of the middle of the 21st century. Technology is history - cars will not start, radios are jammed, no electricity whatsoever almost everywhere the characters go. Shops and malls are looted, prosperous villages are burned by gangers, and safe places are very sparse. === People in Neuroshima === No one knows how many people survived the war with machines, but it is estimated that their number oscillates around 2-3 million. Some people reverted to nomadic lifestyles and live in the deserts, some of them try to build the civilisation anew in devastated cities, some of them form gangs of highwaymen (called gangers), some of them just try to make a living by growing crops, and finally, there are those who just wander around the wasteland; the adventuring sort here is mostly represented by player characters. Each village they visit in this world is a discrete microcosm and nothing is certain as whether the inhabitants are welcoming or shoot strangers on sight. The continent is full of small, anonymous settlements, but there are places which aspire to become post-nuclear states. === Places in Neuroshima === In this world it is very important where you come from, and that is because people are prejudiced and afraid of strangers. Different places produce different kinds of people, and who you are is determined by where you are from. Examples: The Southern Hegemony - (commonly referred to as 'the Hegemony') - located in what was once Arizona, New Mexico and partially Texas. A place where brute force determines one's place in the society. Dominated by gangs and unhampered by Moloch, the Hegemony is a threat to neighbouring lands. Vegas - the only well-lit city in the post-apocalyptic world. Home to many playhouses and casinos, it attracts people from every part of the country. Mother Desert - if you were born in the desert, whenever you go away from civilisation, you feel at home. Many Native Americans still live out there and are doing fine - after all the warheads did not hit the deserts. Detroit - known for some of the best drivers and racers in the post-nuclear US. Home of many gangs, such as The Shultz (mafia styled), Hurons (punkers), The League (racers), Parker Lots (gothic assassins) and the Gas Drinkers (mutant barbarians). New York - a place which has established a strong government and would like to rebuild America. They maintain schools, factories and railways and send soldiers to fight Moloch. Surprisingly enough, they sometimes succeed. Texas - the healthiest place in America. Actually, the only place where one can find green vegetation. Modern Texans still grow crops, breed horses and herd cattle, like their ancestors in the 19th century did. The Appalachian Federation - a place ruled by feudal lords. They have a social class system, in which people are divided into nobility and peasantry. Thanks to its iron and coal deposits, it's one of the richest places in the post-nuclear U.S. The Outpost - A mobile settlement run by scientists who aim to destroy Moloch. In coalition with New York, they manage an army, which is yet to stop Moloch's advance south. They steal technology from the machines they destroy and apply it to their own advantage. == System == The game uses its own, custom system of rules. The dice you use is d20. This system does not have an official name, but it is unconnected to the d20 system, as it typically uses three twenty-sided dice. === Four colours === Neuroshima relies on the division of the gameplay into something the authors called Four Colours, namely steel, chrome, rust and mercury. The choice of a particular colour is made by the gamemaster (the decision can be consulted with the players in order to enhance the game experience) and determines the mood, atmosphere and the type of events/characters present in the story. The name of the colour itself implies the kind of gameplay it will symbolise. These colours are: Steel - this kind of gameplay is characterised by a slightly optimistic attitude towards the world. The aim is to raise the spirit of the characters by showing them that the war with the machines that is going on may be a difficult one, but it is not unwinnable, and that humans, when strong and united, can build the world anew. Example of a story: a unit of soldiers dispatched from the Outpost is sent to build a bunker and establish a relay base far in the north in order to plan a counter-tactic against Moloch's advance south. Chromium - is characterised by a hedonistic attitude. The characters are supposed to enjoy anything that is left from the world after the war and the story is supposed to allow them to do that. Example: the characters are offered a well-paid job by a local ganger boss who extorts wares from local tradesmen. Their job is to drive around the county and pick up the extorted items and trade it for drugs. Rust - a depressing, pessimistic mood. The characters will encounter rust, dilapidation and ruin everywhere they go. All the elements and NPCs of a story played in this mood are supposed to put the characters down and destroy their spirit. Example: the characters, badly wounded after a gunfight and robbed of all their possession find refuge in a village which is constantly raided by gangers. The characters' quest is to repel those attacks, but the enemies outnumber them and are well equipped, whereas the characters have nothing to fight with. Mercury (Quicksilver) - the most depressing side of the game; usually stories played in this mood end with the death of all the characters. The aim of this mood is to show that any kind of action undertaken is futile and that the war is already over, hence all the people are already dead, which is a fact they just need to realise. Example: a group of soldiers stationed in a bunker is awaiting an attack by mutants. They are well-armed and trained, but there is a mistake in the intelligence they were given and they do not know yet that they are seriously outnumbered. The attack commences at dusk and it is already too late to retreat, so the characters decide to seal off the bunker, hopeful that the mutants will not be able to get inside and simply go away. The mutants attack the bunker with chemical weapons instead. The characters do not have enough gas masks to go around. As an effect, those strong enough will kill the weaker ones to get their masks, not knowing that the mutants will blow up the sealed entrance the following morning. == Official rulebooks and sourcebooks == The current edition is 1.5 [1]. Since the release of the game in 2003, sourcebooks have been appearing. The game keeps growing bigger with every add-on, as well as the storyline, which is updated in those sourcebooks and in Space Pirate (pl. Gwiezdny Pirat) magazine, also published by Portal. === List of released rulebooks and sourcebooks === Neuroshima 1.0 - the original edition of the core rulebook (out of print). Neuroshima 1.5 - enhanced and revised core rulebook, with new material added and some material cut out. Wyścig (The Race) - sourcebook dedicated to cars and racing; contains rules concerning building your own vehicle and new character classes connected with driving. Gladiator - sourcebook describing in detail the "Gladiator" character class. Supplement (Supplement) - sourcebook revising the core rulebook. Detroit - sourcebook describing the city of Detroit, its inhabi
Torment: Tides of Numenera
Torment: Tides of Numenera is a 2017 role-playing video game developed by inXile Entertainment and published by Techland Publishing for Microsoft Windows, macOS, Linux, PlayStation 4 and Xbox One. It is a spiritual successor to 1999's Planescape: Torment. The game takes place in The Ninth World, a science fantasy campaign setting written by Monte Cook for his tabletop RPG Numenera. Torment: Tides of Numenera, like its predecessor, is primarily story-driven while placing greater emphasis on interaction with the world and characters, with combat and item accumulation taking a secondary role. The game was crowd-funded through Kickstarter in March 2013. At the campaign's conclusion, Torment: Tides of Numenera had set the record for highest-funded video game on Kickstarter with over US$4 million pledged. The release date was initially set for December 2014, but was pushed back to February 2017. == Gameplay == Torment: Tides of Numenera uses the Unity engine to display the pre-rendered 2.5D isometric perspective environments. The tabletop ruleset of Monte Cook's Numenera has been adapted to serve as the game's rule mechanic, and its Ninth World setting is where the events of Torment: Tides of Numenera take place. The player experiences the game from the point of view of the Last Castoff, a human host that was once inhabited by a powerful being, but was suddenly abandoned without memory of prior events. As with its spiritual predecessor, Planescape: Torment, the gameplay of Torment: Tides of Numenera places a large emphasis on storytelling, which unfolds through a "rich, personal narrative", and complex character interaction through the familiar dialog tree system. The player is able to select the gender of the protagonist, who will otherwise start the game as a "blank slate", and may develop his or her skills and personality from their interactions with the world. The Numenera setting provides three base character classes: Glaive (warrior), Nano (wizard) and Jack (rogue). These classes can be further customized with a number of descriptors (such as "Tough" or "Mystical") and foci, which allow the character to excel in a certain role or combat style. Instead of a classic alignment system acting as a character's ethical and moral compass, Torment: Tides of Numenera uses "Tides" to represent the reactions a person inspires in their peers. Each Tide has a specific color and embodies a number of nuanced concepts that are associated with it. The composition of Tides a character has manipulated the most determines their Legacy, which roughly describes the way they have taken in life. Different Legacies may affect what bonuses and powers certain weapons and relics provide, as well as give a character special abilities and enhance certain skills. == Synopsis == === Setting === Tides of Numenera has a science fantasy setting. In the far future (one billion years), the rise and fall of countless civilizations have left Earth in a roughly medieval state, with most of humanity living in simple settlements, surrounded by technological relics of the mysterious past. The current age is called the "Ninth World" by its scholars, who believe that eight great ages existed and were destroyed, disappeared or left the Earth for unknown reasons before the present day, leaving ruins and various oddities and artifacts behind. These artifacts are known as the "numenera" and represent what is left of the science and technology of these past civilizations. Many of them are irreparably broken, but some are still able to function in ways that are beyond the level of understanding of most humans, who believe these objects to be magical in nature. === Characters === Character complexity and dialogue depth were identified among the primary elements of the Planescape: Torment legacy to be preserved and refined by the developers of Torment: Tides of Numenera. The tormented nature of the game's protagonist, the Last Castoff, attracts other, similarly affected people. They will play a significant role in his or her story as friends and companions, or as powerful enemies. The game contains seven companions in total: Aligern, Callistege, Erritis, Matkina, Oom, Tybir, and Rhin. === Plot === The protagonist of the story, known as the Last Castoff, is the final vessel for the consciousness of an ancient man, who managed to find a way to leave his physical body and be reborn in a new one, thus achieving a kind of immortality by means of the relics. The actions of this man, known as the Changing God to some, attracted the enmity of "The Sorrow" (renamed from "The Angel of Entropy" to reduce the potential to imply a religious role), who now seeks to destroy him and his creations. The Last Castoff, being one such "creation", is also targeted by the Sorrow, and must find their master before both are undone. To do so, the protagonist must explore the Ninth World, discovering other castoffs, making friends and enemies along the way. One means of such exploration are the "Meres" – artifacts that let their user gain control over the lives of other castoffs, and experience different worlds or dimensions through them. Through these travels the Last Castoff will leave their mark on the world – their Legacy – and will find an answer to the fundamental question of the story: What does one life matter? While the overall story varies wildly depending on personal preferences and specific interactions, the central storyline follows the Last Castoff as they search for a way to defeat or escape the Sorrow. They explore Sagus Cliffs after falling from a great height into a domed structure, destroying an artifact known as a resonance chamber that is believed to be capable saving the Last Castoff from the Sorrow. Finding another castoff, Matkina, The Last uses a Mere, a repository of memory to locate the entrance to Sanctuary. Using the Mere also alters the past, allowing Matkina to be healed of her mental damage. The Last finds Sanctuary, which the Changing God created as a hiding place from the Sorrow, where the Last finds a number of castoffs who represent both sides of the Eternal War: a conflict between followers of the Changing God, and followers of the First Castoff, who believe the God is selfish and malevolent. The Sorrow breaches Sanctuary after the Last is told that the resonance chamber will "defeat" the Sorrow by destroying every castoff in existence. After escaping the Sorrow through a portal to the Bloom, an apparition appears claiming to be the actual Changing God and attempts to possess the Last by force of will. == Development == In a 2007 interview, designers Chris Avellone and Colin McComb, who had worked on Planescape: Torment, stated that although a direct sequel was not considered because the game's story was over, they were open to the idea of a similar-themed Planescape game if they could gather most of the original development team and find an "understanding set of investors". This combination was deemed infeasible at the time. Talks about creating a sequel with the help of a crowd funding platform resumed in 2012, but attempts to acquire a Planescape license from Wizards of the Coast failed. Later that year, Colin McComb joined inXile, which was at the time working on its successfully crowd funded Wasteland 2 project. The studio gained the rights to the Torment title shortly thereafter. In January 2013, inXile's CEO Brian Fargo announced that the spiritual successor to Planescape: Torment was in pre-production and would be set in the Numenera RPG universe created by Monte Cook. Cook acted as one of the designers of the Planescape setting, and Fargo saw the Numenera setting as the natural place to continue the themes of the previous Torment title. Although the connections to its predecessor will not be relatively overt, due to licensing issues, it was noted that certain traditional RPG elements are relatively hard to copyright, and some elements of Planescape: Torment may make a reappearance. Development of the game began shortly after the acquisition of the Torment license, and various inXile staff will transition over to the Numenera team as production on Wasteland 2 winds down. In late January 2013, inXile confirmed the game's title as Torment: Tides of Numenera, and announced that Planescape: Torment composer Mark Morgan would create the soundtrack. The pre-production period was initially expected to continue until October 2013. During this phase, team composition for the project was to be finalised and development would focus on production planning, game design and dialog writing. With the Wasteland 2 project facing delays in 2014, full production of Torment: Tides of Numenera was rescheduled to a later date. A Kickstarter campaign to crowd fund Torment: Tides of Numenera was launched on March 6, 2013 with a US$900,000 goal. Project director Kevin Saunders explained this choice of a funding source by stating that the traditional publisher-based funding model is flawed
Iron Man 2020 (event)
"Iron Man 2020" is a storyline published by Marvel Comics in 2020 which follows the character Arno Stark as he attempts to take over Stark Industries and the mantle of his estranged brother Tony Stark (Iron Man). The crossover characters of two different brands meeting up in one storyline received mixed reviews from critics. == Publication history == Marvel Comics released the teaser for the event at New York Comic Con in November 2019. It was also alluded to in December 2019's Incoming! In the original checklist released for the event, 2020 Force Works was originally titled Force Works 2020, while 2020 Machine Man was previously named Machine Man 2020, and so on. Additionally, 2020 Wolverine was going to be called Weapon.EXE 2020. The publication of this event was intended to span from January to June 2020, however, due to the COVID-19 pandemic, Diamond Comic Distributors suspended the distribution of new print titles between April 1 and May 27, which also caused digital releases by Marvel Entertainment to be postponed. The rescheduling of the postponed issues to new dates pushed the event's conclusion to August, and certain issues, namely 2020 Force Works #3 and 2020 Ironheart #1–2, were released exclusively in a digital format. == Main plot == Arno Stark wakes up from a nightmare involving the Extinction Entity, a monstrous amalgamation of alien and machine. He dreams that the Extinction Entity is going to come to Earth in a matter of weeks and create an artificial intelligence (A.I.) army to consume humanity. After eating breakfast with duplicates of Howard Stark and Maria Stark, Arno suits up as Iron Man and saves a construction worker from a hostage situation involving several Nick Fury Life Model Decoys, which represent the A.I. army trying to liberate construction robots. Over different news outlets, the media wonders about the whereabouts of Tony Stark, who declared himself as nothing more than a simulation of the real, late Tony Stark. At the A.I. army's base, Machine Man is commanding the robots' moves when Arno appears, having planned for the A.I. army's leader to show himself. Machine Man activates the bomb, forcing Arno to fly it away so it explodes somewhere safe while he escapes. Machine Man reaches the Thirteenth Floor, a dimensional-shunted plane of existence made of solid light, and a haven for robotkind that humans cannot access or comprehend. Aaron meets with the leader of the A.I. army and creator of Thirteenth Floor: Tony Stark -- who is now going by the name Mark One, having embraced his nature as artificial intelligence. Also in the A.I. army are Albert, Awesome Android, H.E.R.B.I.E., Machinesmith, and Quasimodo. The A.I. army continues its efforts to liberate artificial life forms by raiding places where robots are being subjugated. Iron Man intercepts an attack on a Futura Motors testing site by Quasimodo and H.E.R.B.I.E. and manages to recover an Un-Inhibitor allowing him to take control of all A.I.s. On the Thirteenth Floor, Mark One receives a transmission from a mole inside Baintronics -- codenamed Ghost in the Machine --revealing that Arno used the submission code on Jocasta, who received a new body, making her entirely compliant. Stark plans to upload the submission code to the internet to instantly infect robots. With only three hours before the code is transmitted to Stark Unlimited's satellite network, Mark One devises a heist on Bain Tower to tamper with the code before launch. Having discovered the secret behind the Thirteenth Floor, Arno shuts out the A.I. army, uses Jocasta to lure Machine Man away from the tower, infects Machinesmith with the submission code, and confronts Mark One. H.E.R.B.I.E., Awesome Android, and Machinesmith escape from Bain Tower and call for help to every robot in New York City. Mark One is left to fight Iron Man and is defeated. Meanwhile, Sunset Bain confronts and fires Andy Bhang under the accusation of working as a mole inside Stark Unlimited and feeding Bethany Cabe information to relay to the A.I. army. Arno takes Mark One inside Bain Tower to meet Howard and Maria Stark and asks Tony to join him, but he refuses and dismisses his rationale as lunacy. The robotic mob assembled by Machine Man reaches Bain Tower, giving Mark a distraction which allows him to fly off and disable the transmission dish from which Arno intends to broadcast the obedience O.S. to subjugate every robot. Tony manages to stop the upload and make the antenna unusable. In retaliation, Arno fires all of his armor's firepower at Tony as he falls to the ground. Tony Stark's remaining allies escape with his body as Arno attacks the robot protesters. Tony wakes up inside the Thirteenth Floor and is greeted by F.R.I.D.A.Y., who had plucked Tony's consciousness from his body during his fall. In the streets, Arno Stark tracks down Howard and Maria, who die from an illness inherited from Arno. When Sunset Bain objects to Arno creating new bodies for his parents and trying to control people, he reveals she is an A.I., a duplicate of the real Bain whom Arno replaced back when she solicited him to heal a scar on her face. He makes new bodies for Howard and Maria by recreating the Arsenal and Mistress bodies from the eScape. After learning of Arno's new plan, Dr. Shapiro (who is the actual mole) sneaks into a computer and warns F.R.I.D.A.Y. about it. When F.R.I.D.A.Y. relays that only Tony Stark can stop Arno, Tony insists that he is not the real Tony Stark, but is confronted by holographic manifestations of himself in different points of his life, until they all merge into him and he acknowledges that he has always been Tony. As Arno Stark sets off to the Stark Space Station to install his mind-controlling device to enslave all of humanity, Tony Stark's allies assault the Stark Unlimited HQ, confronting Sunset Bain's duplicate and Arno's Iron Legion. Jocasta uploads a submission code to Bain and they place Tony's body inside a bio-pod that restores his body to normalcy, uploads his consciousness back into his body. Using the Thirteenth Floor's access mechanisms, Tony and his allies reach the Stark Space Station from one of the elevators within. Employing his new Virtual Armor, Tony defeats Arno in combat. When Arno prepares to activate his mind-controlling device, the Extinction Entity suddenly appears. Arno ultimately defeats the Extinction Entity by willingly assimilating with it, causing it to explode. The entity is revealed to be a delusion caused by Arno's terminal disease, of which he would die by the end of 2020. Unable to stop Arno, Tony placed him in a simulation where he successfully stopped the entity. Afterwards, Jocasta uses the submission code to force Sunset Bain's duplicate to confess all of Baintronics' crimes, also claiming responsibility for tricking Tony into thinking he was an artificial intelligence and pulling the strings of the A.I. Army, putting an end to the robot revolution. Tony gives up Stark Unlimited to Bhang Robotics and he flies off in a new armor, reasserting himself as Iron Man. == Issues involved == === Main issues === Iron Man 2020 (vol. 2) #1–6 === Tie-In issues === 2020 Force Works #1–3 2020 Iron Age #1 2020 Ironheart #1–2 2020 Machine Man #1–2 2020 Rescue #1–2 2020 iWolverine #1–2 == Critical reception == According to Comic Book Roundup, the entire crossover received an average score of 6.4 out of 10 based on 36 reviews. William Tucker from ButWhyTho Podcast stated "Iron Man 2020 #6 is an initially exciting end to a great event that eventually feels deflated. There is absolutely nothing wrong with the art, Woods has been incredible throughout, but the ending that Slott and Gage chose to round out an epic tale like this left me feeling cold. And while there were loads of enjoyable cameos, their involvement ultimately didn't seem important to the story as a whole. Which is disappointing, as the rest of the event really was a fun and exciting ride." Anthony Wendel from MonkeysFightingRobots wrote "The 2020 event seems like it is taking some big risk, and it doesn't inspire a lot of confidence from the start. Iron Man 2020 #1 has set the stakes and shown some very intense players on both sides of the board. Sadly, if it doesn't unfold just the right way, many may feel cheated about defending the path characters are taking." == Collected editions ==
Neurorobotics
Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural networks, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures. Neurorobotics is that branch of neuroscience with robotics, which deals with the study and application of science and technology of embodied autonomous neural systems like brain-inspired algorithms. It is based on the idea that the brain is embodied and the body is embedded in the environment. Therefore, most neurorobots are required to function in the real world, as opposed to a simulated environment. Beyond brain-inspired algorithms for robots neurorobotics may also involve the design of brain-controlled robot systems. == Major classes of models == Neurorobots can be divided into various major classes based on the robot's purpose. Each class is designed to implement a specific mechanism of interest for study. Common types of neurorobots are those used to study motor control, memory, action selection, and perception. === Locomotion and motor control === Neurorobots are often used to study motor feedback and control systems, and have proved their merit in developing controllers for robots. Locomotion is modeled by a number of neurologically inspired theories on the action of motor systems. Locomotion control has been mimicked using models or central pattern generators, clumps of neurons capable of driving repetitive behavior, to make four-legged walking robots. Other groups have expanded the idea of combining rudimentary control systems into a hierarchical set of simple autonomous systems. These systems can formulate complex movements from a combination of these rudimentary subsets. This theory of motor action is based on the organization of cortical columns, which progressively integrate from simple sensory input into a complex afferent signals, or from complex motor programs to simple controls for each muscle fiber in efferent signals, forming a similar hierarchical structure. Another method for motor control uses learned error correction and predictive controls to form a sort of simulated muscle memory. In this model, awkward, random, and error-prone movements are corrected for using error feedback to produce smooth and accurate movements over time. The controller learns to create the correct control signal by predicting the error. Using these ideas, robots have been designed which can learn to produce adaptive arm movements or to avoid obstacles in a course. === Learning and memory systems === Robots designed to test theories of animal memory systems. Many studies examine the memory system of rats, particularly the rat hippocampus, dealing with place cells, which fire for a specific location that has been learned. Systems modeled after the rat hippocampus are generally able to learn mental maps of the environment, including recognizing landmarks and associating behaviors with them, allowing them to predict the upcoming obstacles and landmarks. Another study has produced a robot based on the proposed learning paradigm of barn owls for orientation and localization based on primarily auditory, but also visual stimuli. The hypothesized method involves synaptic plasticity and neuromodulation, a mostly chemical effect in which reward neurotransmitters such as dopamine or serotonin affect the firing sensitivity of a neuron to be sharper. The robot used in the study adequately matched the behavior of barn owls. Furthermore, the close interaction between motor output and auditory feedback proved to be vital in the learning process, supporting active sensing theories that are involved in many of the learning models. Neurorobots in these studies are presented with simple mazes or patterns to learn. Some of the problems presented to the neurorobot include recognition of symbols, colors, or other patterns and execute simple actions based on the pattern. In the case of the barn owl simulation, the robot had to determine its location and direction to navigate in its environment. === Action selection and value systems === Action selection studies deal with negative or positive weighting to an action and its outcome. Neurorobots can and have been used to study simple ethical interactions, such as the classical thought experiment where there are more people than a life raft can hold, and someone must leave the boat to save the rest. However, more neurorobots used in the study of action selection contend with much simpler persuasions such as self-preservation or perpetuation of the population of robots in the study. These neurorobots are modeled after the neuromodulation of synapses to encourage circuits with positive results. In biological systems, neurotransmitters such as dopamine or acetylcholine positively reinforce neural signals that are beneficial. One study of such interaction involved the robot Darwin VII, which used visual, auditory, and a simulated taste input to "eat" conductive metal blocks. The arbitrarily chosen good blocks had a striped pattern on them while the bad blocks had a circular shape on them. The taste sense was simulated by conductivity of the blocks. The robot had positive and negative feedbacks to the taste based on its level of conductivity. The researchers observed the robot to see how it learned its action selection behaviors based on the inputs it had. Other studies have used herds of small robots which feed on batteries strewn about the room, and communicate its findings to other robots. === Sensory perception === Neurorobots have also been used to study sensory perception, particularly vision. These are primarily systems that result from embedding neural models of sensory pathways in automatas. This approach gives exposure to the sensory signals that occur during behavior and also enables a more realistic assessment of the degree of robustness of the neural model. It is well known that changes in the sensory signals produced by motor activity provide useful perceptual cues that are used extensively by organisms. For example, researchers have used the depth information that emerges during replication of human head and eye movements to establish robust representations of the visual scene. == Biological robots == Biological robots are not officially neurorobots in that they are not neurologically inspired AI systems, but actual neuron tissue wired to a robot. This employs the use of cultured neural networks to study brain development or neural interactions. These typically consist of a neural culture raised on a multielectrode array (MEA), which is capable of both recording the neural activity and stimulating the tissue. In some cases, the MEA is connected to a computer which presents a simulated environment to the brain tissue and translates brain activity into actions in the simulation, as well as providing sensory feedback The ability to record neural activity gives researchers a window into a brain, which they can use to learn about a number of the same issues neurorobots are used for. An area of concern with the biological robots is ethics. Many questions are raised about how to treat such experiments. The central question concerns consciousness and whether or not the rat brain experiences it. There are many theories about how to define consciousness. == Implications for neuroscience == Neuroscientists benefit from neurorobotics because it provides a blank slate to test various possible methods of brain function in a controlled and testable environment. While robots are more simplified versions of the systems they emulate, they are more specific, allowing more direct testing of the issue at hand. They also have the benefit of being accessible at all times, while it is more difficult to monitor large portions of a brain while the human or animal is active, especially individual neurons. The development of neuroscience has produced neural treatments. These include pharmaceuticals and neural rehabilitation. Progress is dependent on an intricate understanding of the brain and how exactly it functions. It is difficult to study the brain, especially in humans, due to the danger associated with cranial surgeries. Neurorobots can improved the range of tests and experiments that can be performed in the study of neural processes.
Fuzzy finite element
The fuzzy finite element method combines the well-established finite element method with the concept of fuzzy numbers, the latter being a special case of a fuzzy set. The advantage of using fuzzy numbers instead of real numbers lies in the incorporation of uncertainty (on material properties, parameters, geometry, initial conditions, etc.) in the finite element analysis. One way to establish a fuzzy finite element (FE) analysis is to use existing FE software (in-house or commercial) as an inner-level module to compute a deterministic result, and to add an outer-level loop to handle the fuzziness (uncertainty). This outer-level loop comes down to solving an optimization problem. If the inner-level deterministic module produces monotonic behavior with respect to the input variables, then the outer-level optimization problem is greatly simplified, since in this case the extrema will be located at the vertices of the domain.