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Main article: Transhumanism. Open Journal of Obstetrics and Gynecology; Volume 3,Pages , Scientific research Oocyte quality improvement using a herbal medicine comprising 7 crude drugs Client name has been kept confidential Economic Modelling; Volume 33, Pages , July Elsevier B. Differentiable programming Neural Turing machine Differentiable neural computer Automatic differentiation Neuromorphic engineering Cable theory Pattern recognition Computational learning theory Tensor calculus. Spadafora, Anthony 21 October

Artificial intelligence - Wikipedia

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Soft computing finds solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.

Soft computing approaches to AI include neural networks , fuzzy systems , Grey system theory , evolutionary computation and many tools drawn from statistics or mathematical optimization. Interest in neural networks and " connectionism " was revived by Geoffrey Hinton , David Rumelhart and others in the middle of the s. In the s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models HMM , information theory , and normative Bayesian decision theory to compare or to unify competing architectures.

The shared mathematical language permitted a high level of collaboration with more established fields like mathematics , economics or operations research.

The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific.

Nowadays results of experiments are often rigorously measurable, and are sometimes with difficulty reproducible. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

Bernard Goetz and others became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Statistical AI is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning.

They founded the subfield artificial general intelligence or "AGI" , which had several well-funded institutions by the s. AI is relevant to any intellectual task. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles such as drones and self-driving cars , medical diagnosis, creating art such as poetry , proving mathematical theorems, playing games such as Chess or Go , search engines such as Google Search , online assistants such as Siri , image recognition in photographs, spam filtering, predicting flight delays, [] prediction of judicial decisions, [] targeting online advertisements, [] [] [] and energy storage [].

With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, [] major publishers now use artificial intelligence AI technology to post stories more effectively and generate higher volumes of traffic. AI can also produce Deepfakes , a content-altering technology.

The boom of election year also opens public discourse to threats of videos of falsified politician media. There are three philosophical questions related to AI: []. Machines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. As such, there is a need for policy making to devise policies for and regulate artificial intelligence and robotics.

Joseph Weizenbaum in Computer Power and Human Reason wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy [l] was deeply misguided.

Weizenbaum was also bothered that AI researchers and some philosophers were willing to view the human mind as nothing more than a computer program a position now known as computationalism.

To Weizenbaum these points suggest that AI research devalues human life. The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. In all cases, only human beings have engaged in ethical reasoning.

The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this.

In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence.

Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics. Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.

One proposal to deal with this is to ensure that the first generally intelligent AI is ' Friendly AI ' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place.

Leading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI and the enormity and complexity of building sentient volitional intelligence.

Lethal autonomous weapons are of concern. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones. If an AI system replicates all key aspects of human intelligence, will it have a mind which has conscious experiences?

This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness.

David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain.

For example, consider what happens when a person is shown a color swatch and identifies it, saying "it's red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. Consider that a person born blind can know that something is red without knowing what red looks like. The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.

Computationalism is the position in the philosophy of mind that the human mind or the human brain or both is an information processing system and that thinking is a form of computing. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. The philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.

If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as " robot rights ", is currently being considered by, for example, California's Institute for the Future , although many critics believe that the discussion is premature. A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.

Superintelligence may also refer to the form or degree of intelligence possessed by such an agent. If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement.

Science fiction writer Vernor Vinge named this scenario " singularity ". Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Ray Kurzweil has used Moore's law which describes the relentless exponential improvement in digital technology to calculate that desktop computers will have the same processing power as human brains by the year and predicts that the singularity will occur in Robot designer Hans Moravec , cyberneticist Kevin Warwick , and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either.

Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler 's " Darwin among the Machines " as far back as , and expanded upon by George Dyson in his book of the same name in The long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment , but they generally agree that it could be a net benefit, if productivity gains are redistributed.

The relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects.

Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we're in uncharted territory" with AI. The potential negative effects of AI and automation were a major issue for Andrew Yang 's presidential campaign in the United States. And, of course, other risks come from things like job losses.

If we have massive numbers of people losing jobs and don't find a solution, it will be extremely dangerous. Things like lethal autonomous weapons systems should be properly governed — otherwise there's massive potential of misuse. Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute , among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks.

In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.

Some are concerned about algorithmic bias , that AI programs may unintentionally become biased after processing data that exhibits bias. Physicist Stephen Hawking , Microsoft founder Bill Gates , history professor Yuval Noah Harari , and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could " spell the end of the human race ". The development of full artificial intelligence could spell the end of the human race.

Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. In his book Superintelligence , philosopher Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down.

If this AI's goals do not fully reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. Bostrom also emphasizes the difficulty of fully conveying humanity's values to an advanced AI. He uses the hypothetical example of giving an AI the goal to make humans smile to illustrate a misguided attempt. If the AI in that scenario were to become superintelligent, Bostrom argues, it may resort to methods that most humans would find horrifying, such as inserting "electrodes into the facial muscles of humans to cause constant, beaming grins" because that would be an efficient way to achieve its goal of making humans smile.

Russell echoes some of Bostrom's concerns while also proposing an approach to developing provably beneficial machines focused on uncertainty and deference to humans, [] : possibly involving inverse reinforcement learning. Concern over risk from artificial intelligence has led to some high-profile donations and investments. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology.

Musk also funds companies developing artificial intelligence such as DeepMind and Vicarious to "just keep an eye on what's going on with artificial intelligence. For the danger of uncontrolled advanced AI to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence AI ; [] [] it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally, including in the European Union.

Given the concerns about data exploitation, the European Union also developed an artificial intelligence policy, with a working group studying ways to assure confidence in the use of artificial intelligence. These were issued in two white papers that seemed to have gone unnoticed in the midst of the COVID pandemic.

One of the policies on artificial intelligence is called A European Approach to Excellence and Trust. Thought-capable artificial beings appeared as storytelling devices since antiquity, [22] and have been a persistent theme in science fiction.

A common trope in these works began with Mary Shelley 's Frankenstein , where a human creation becomes a threat to its masters. This includes such works as Arthur C. In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still and Bishop from Aliens are less prominent in popular culture.

Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; [] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.

Transhumanism the merging of humans and machines is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the s, artist Hajime Sorayama 's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives.

Sorayama never considered these organic robots to be real part of nature but always an unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel , and thus to suffer. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.

Cite error: A list-defined reference named "Intelligent agents" is not used in the content see the help page. Cite error: A list-defined reference named "Agent architectures" is not used in the content see the help page. Cite error: A list-defined reference named "Hierarchical control system" is not used in the content see the help page.

See also: Logic machines in fiction and List of fictional computers. From Wikipedia, the free encyclopedia. For other uses, see AI disambiguation and Artificial intelligence disambiguation. Intelligence demonstrated by machines. Major goals. Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics. Symbolic Deep learning Bayesian networks Evolutionary algorithms. Timeline Progress AI winter. Applications Projects Programming languages.

Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural network. Reinforcement learning. Machine-learning venues. Related articles. Glossary of artificial intelligence List of datasets for machine-learning research Outline of machine learning. Main articles: History of artificial intelligence and Timeline of artificial intelligence.

Main articles: Knowledge representation and Commonsense knowledge. Main article: Automated planning and scheduling. Main article: Machine learning. Main article: Natural language processing. Main articles: Machine perception , Computer vision , and Speech recognition. Main article: Robotics. Main article: Affective computing. Main articles: Artificial general intelligence and AI-complete. Main articles: Cybernetics and Computational neuroscience. Main article: Symbolic AI. Main article: soft computing.

Main article: artificial general intelligence. Main article: Computational tools for artificial intelligence. Main article: Applications of artificial intelligence. This section should include only a brief summary of another article. See Wikipedia:Summary style for information on how to properly incorporate it into this article's main text.

August Main articles: Philosophy of artificial intelligence , Turing test , Physical symbol systems hypothesis , Dreyfus' critique of artificial intelligence , The Emperor's New Mind , and AI effect. Main article: Machine ethics. Main article: Friendly artificial intelligence.

Main articles: Hard problem of consciousness and Theory of mind. Main articles: Computationalism and Functionalism philosophy of mind. Main article: Chinese room. Main article: Robot rights. Main article: Superintelligence. Main articles: Technological singularity and Moore's law. Main article: Transhumanism. Main article: Workplace impact of artificial intelligence.

Main article: Existential risk from artificial general intelligence. Main articles: Regulation of artificial intelligence and Regulation of algorithms. Main article: Artificial intelligence in fiction. Computer programming portal. Rising AI control problem Artificial intelligence arms race Artificial general intelligence Behavior selection algorithm Business process automation Case-based reasoning Citizen Science Emergent algorithm Female gendering of AI technologies Glossary of artificial intelligence Regulation of artificial intelligence Robotic process automation Synthetic intelligence Universal basic income Weak AI.

These authors use the term "computational intelligence" as a synonym for artificial intelligence. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction.

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One of the challenges with training an agent to play Snoopy Pop is the large volume of gameplay data to learn effective behaviors and strategies. Additionally, most games in development are constantly evolving, so training times need to be reasonably fast. This allows you to modify and implement ML-Agents according to your needs. The toolkit has everything you need to get started, including ready-to-use state-of-the-art algorithms and robust documentation and example projects.

You also get the support of a helpful gamedev community. You can create intelligent characters without a lot of coding. Whether your project is a 2D game, continuous control system, or large game space, several starter environments are available to help you get started. Access to C , communication protocol, and a low-level Python API that gives you the flexibility to try different algorithms and methods for training agents enriches your advanced AI and research use cases.

In Source of Madness , an action rogue-lite game created by Carry Castle, you traverse an ever-changing dynamic world, battling new procedurally generated monsters each playthrough, brought to life by a powerful machine-learning AI.

Using the ML-Agents toolkit — and, specifically, deep reinforcement learning — the team trained and created a neural network model that produced the right behavior. Then, using the Unity Inference Engine, they embedded the model into the game.

This project shows how reinforcement learning via Unity ML-Agents was used to teach planes to fly. The airplanes fly freely in space using raycast for vision. This project also uses imitation learning to train the agents. Two different artificial neural networks battle each other in a simple game of soccer using deep reinforcement learning to train neural networks. The AI consists of a deep neural network with three hidden layers of neurons each.

It is trained with the proximal policy optimization PPO algorithm, a reinforcement learning approach. Using the Unity ML-Agents framework, a metallic waste collection robot built with Unity taught itself to pick up a can and drop it in the waste container. See how a video game learned to play itself using a gamepad, the Unity platform and ML-Agents toolkit.

The experiment inserts hardware into the reinforcement learning scenario. In this project, a neural network is trained to land a rocket on a platform using Unity Physics.

Training took about 15 million steps. This project uses ML-Agents to stabilize a satellite rotating through one axis. AI controls two satellite engines, which can be on or off. The session starts with a rotating satellite. It took two hours of ML-Agents learning for the satellite to achieve the goal stabilizing, stopping its rotation and calibrating its position.

Inspired by the stealth game genre, this project was designed to train an agent to successfully run and hide from a traditional AI that patrols from room to room. A second variation was created to train against a faster AI using curriculum learning.

  Skills you will gain

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EC, No. Bioscience, Biotechnology, and Biochemistry; Vol. KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engi Springer. Journal of Nutritional Science and Vitaminology; Vol. Journal of the Japan Petroleum Institute; Vol. Journal of Biological Chemistry; Vol. Journal of Radiation Research; Vol. Cellular Microbiology; doi: Japan Agricultural Research Quarterly; Vol. Journal of Veterinary Diagnostic Investigation; September vol.

Molecular and Cellular Biology; September vol. Optics Letters; Vol. Applied Environmental Microbiology; September vol. Applied and Environmental Microbiology; April , P. ACS Publications. Journal of Surface Analysis; Vol. Science and Technology of Energetic Materials; Vol. Optics Express; Vol. Journal of Clinical Microbiology; November , vol. Journal of Physical Chemistry B; vol. Journal of Health Science; Vol. Elsevier B. Nihon Reoroji Gakkaishi; Vol.

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Simian immunodeficiency virus infection and flow cytometric characterization of Japanese macaque Macaca fuscata hematopoietic cells Tsukamoto Tetsuo BMC Neurology Springer Propionibacterium acnes-associated chronic hypertrophic pachymeningitis followed by refractory otitis media: a case report Amano Eiichiro Neuropathology Blackwell Publishing Inc.

Early development and occurrence patterns of the Pagrinae seabream Argyrops bleekeri Perciformes: Sparidae : Does early life history have implications for sparid relationships? Mucosal delivery of fish vaccines: Local and systemic immunity following mucosal immunisations Somamoto Tomonori Interventional Neuroradiology Centauro SRL Safety of carotid artery stenting for elderly patients with cervical carotid artery stenosis Oshita Jumpei Environmental Pollution Elsevier Ltd.

Part I. Kambe Kosuke Tetrahedron Letters Elsevier Organocatalytic asymmetric conjugate addition of substituted 5-benzylfurfurals to nitroalkenes based on stereocontrol of trienamine Miura Tsuyoshi Clinical Journal of Gastroenterology Springer Two cases of esophageal ulcer after surgical treatment for ulcerative colitis Kuroki Hirosuke Oncology S. Preeclamptic patient-derived circulating cell-free DNA activates the production of inflammatory cytokines via toll-like receptor 9 signalling in the human placenta Shirasuna Koumei Hirosaki Medical Journal Hirosaki Daigaku Hyaluronan synthesis inhibitor 4-methylumbelliferone affects extracellular matrix components in cultured human skin fibroblasts Endo Yasufumi Journal of Cancer Research and Clinical Oncology Springer Verlag The efficacy of immune checkpoint inhibitors in advanced non-small-cell lung cancer with liver metastases Okuma Yusuke Surgical Endoscopy Springer Verlag Linear or circular stapler?

Aneurysm resection interposed with a spiral saphenous vein graft in a patient with a popliteal venous aneurysm with thrombosis Yamamoto Yoshitaka Urology Case Reports Elsevier Saunders Duodenal and rectal obstructions due to urothelial cancer infiltration from recurrent renal pelvic cancer in the bladder wall: An autopsy case Ando Takashi American Journal of Infection Control Mosby Inc.

An isoform-selective inhibitor of tropomyosin receptor kinase A behaves as molecular glue Furuya Noritaka Clinical Journal of Gastroenterology Springer Verlag Long-term survival and renal dysfunction in a patient with recurrent colorectal cancer treated with Bevacizumab Kataoka Shigeki Pathology International Blackwell Publishing Inc.

Sweet proteins lysozyme and thaumatin are protein-type agonists for the calcium-sensing receptor Ito Keisuke Clinical Genitourinary Cancer Elsevier Inc. The effect of treatment sequence on overall survival for men with metastatic castration-resistant prostate cancer: A multicenter retrospective study Hatakeyama Shingo Analytica Chimica Acta Elsevier BV Effects of pendant-like hydrophilic monomers on the adsorption properties of reversed-phase-type sorbents for solid-phase extraction Teshima Norio Applied Mathematics Letters Elsevier Ltd.

Radiative properties of scattering media containing directionally controlled nanofibers Gonome Hiroki Biochemical and Biophysical Research Communications Elsevier Inc. Effect of hippotherapy on gait symmetry in children with cerebral palsy: A pilot study Mutoh Tatsushi Acta Haematologica S. The AI consists of a deep neural network with three hidden layers of neurons each. It is trained with the proximal policy optimization PPO algorithm, a reinforcement learning approach.

Using the Unity ML-Agents framework, a metallic waste collection robot built with Unity taught itself to pick up a can and drop it in the waste container. See how a video game learned to play itself using a gamepad, the Unity platform and ML-Agents toolkit.

The experiment inserts hardware into the reinforcement learning scenario. In this project, a neural network is trained to land a rocket on a platform using Unity Physics. Training took about 15 million steps. This project uses ML-Agents to stabilize a satellite rotating through one axis. AI controls two satellite engines, which can be on or off. The session starts with a rotating satellite. It took two hours of ML-Agents learning for the satellite to achieve the goal stabilizing, stopping its rotation and calibrating its position.

Inspired by the stealth game genre, this project was designed to train an agent to successfully run and hide from a traditional AI that patrols from room to room. A second variation was created to train against a faster AI using curriculum learning.

Using machine learning, an agent flips a pancake from a pan to a plate and a robot dodges obstacles to deliver the butter. Check out some recent highlights, or explore more posts on the blog. Ask questions, find answers, and connect with other Unity ML-Agents experts and experimenters, including Unity staff. Prototype, test, and train your robots in high-fidelity, realistic simulations before deploying them to the real world.

Harness the power of cloud to run millions of simulations to generate training data for machine learning, test and validate AI algorithms, or evaluate and optimize modeled systems. Use Unity ML-Agents and state-of-the-art deep learning technology to create complex AI environments and an intelligent game experience. We use cookies to ensure that we give you the best experience on our website. Visit our cookie policy page for more information. Train and embed intelligent agents by leveraging state-of-the-art deep learning technology.

Download from Github. How Unity ML-Agents works. Train Connect your integrated Unity project and start training the agents to learn the right behaviour. Embed When training is complete, embed the trained agent model back into your Unity project. Sign up now. Read more. Key benefits. See also Wikipedia:Comparison of file managers. For archiving and compression command-line tools, see Archiving and compression.

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See also Git server Advanced web applications. See also Wikipedia:List of tools for code review. See also Wikipedia:List of game engines. See Keyboard shortcuts Xorg. See the main article: Input method. See File systems Types of file systems.

See Disk cloning Disk cloning software. See S. GUI Applications. See File recovery List of utilities. See also Wikipedia:Font management software. See man page Installation. See Time synchronization. See Xrandr Graphical front-ends. See Backlight Backlight utilities. See Power management Userspace tools. See Systemd GUI configuration tools. See pacman tips Utilities.