AI Content Creator Course Review

AI Content Creator Course Review — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

WorkingPoint

WorkingPoint is a web-based application that provides a suite of small business management tools. It is designed to serve as a single point of access for various business operations, featuring a user-friendly interface. WorkingPoint's functionalities include double-entry bookkeeping, contact management, inventory management, invoicing, and bill and expense management. == Company == WorkingPoint, formerly Netbooks Inc, is a privately held corporation based in San Francisco, CA. The company is backed by CMEA Capital, also based in San Francisco. WorkingPoint has about ten employees and is led by CEO Tate Holt and Chairman Tom Proulx. Proulx is a co-founder of Intuit and an original author of that company’s Quicken personal finance software. The company was founded in 2007 under its original name Netbooks by co-creator Ridgely Evers. Evers set out to design a product that was more user-friendly than Intuit’s Quickbooks, which he also co-created. In mid-2009 the company officially rebranded itself and its flagship product “WorkingPoint”. The purpose of the re-branding was to disassociate the company from the product category of small laptops also known as netbooks. == Social Media Presence == WorkingPoint maintains a daily blog geared toward small business owners and managers. Each week the blog is updated with 3 WorkingPoint product feature or “how-to” posts, 2 subscriber company profiles, and 2 small business coaching posts. The company also maintains a Twitter page and a Facebook page. == Product Description (Free Version) == WorkingPoint allows businesses to invoice up to five customers (repeatedly) and provides account access for up to two individual users free of charge. Online Invoicing WorkingPoint allows users to create customized quotes and invoices online. The invoices can be used to bill customers via email or hardcopy post. WorkingPoint compiles the info from these invoices so users can track customer payments, inventory costs, shipping charges, accounts receivable and sales taxes. Users can also manage customer overpayments, provide customer loyalty discounts, and view a customer invoice history. Bill & Expense Management Users can track their bills and expenses by entering info into the WorkingPoint interface. WorkingPoint compiles this info so users can track categorized expenses, accounts paid, accounts payable, and vendor purchase history. The interface also allows users to add to their inventory while entering billing info. Double-Entry Bookeeping WorkingPoint automatically records entries under the double-entry bookkeeping system (also known as debits and credits) when the user completes invoicing and expense forms. Users can view transactions in general ledger format and perform closing entries if necessary. This functionality is designed for users who do not have an accounting background. Business Contact Management WorkingPoint provides an interface for users to manage their customer and vendor contact info. The software automatically tracks the user’s relationship with contacts, so users can track a contact’s sales and purchase history. Contacts can be imported and exported via numerous email clients including Microsoft Outlook, Yahoo! Mail, Google Gmail, and Mac Address Book. Inventory Management The software automatically adjusts inventory quantities after every purchase and sale. Users can track their current inventory quantity, average cost of inventory on-hand, cost of goods sold (COGS) and top-selling products. Users can also make manual adjustments to inventory when necessary. Financial Reporting Users can view a balance sheet, income statement, or cash flow statement pertaining to their business. The software automatically manages accruals to produce the balance sheet and income statement. Users can choose a data range from which to draw any of these reports. Financial reports can be converted to pdf format or exported (with formulas intact) to OpenOffice or Microsoft Excel. Cash Management WorkingPoint enables users to monitor cash balances on their bank accounts. The software automatically tracks cash inflows and outflows when users manage their accounts payable and accounts receivable. Business Dashboard The Business Dashboard visually and graphically displays key real-time business data. Users can customize the Dashboard to display data of their choosing. Online Company Profile Users can create an online company profile in order to have a presence on the Internet and as a basis for participation in WorkingPoint’s small business community features. Public profiles are featured in the WorkingPoint Company Directory and can be viewed externally using the URL format: https://businessname.workingpoint.com. == Product Description (Premium Version) == The premium version of WorkingPoint costs $10 per month. It includes all of the functionalities of the free version, allowing unlimited invoicing and account access. It also offers the following functions: 1099 Tax Reporting, invoice payment collection via PayPal, Email Marketing via VerticalResponse, and the Premium Reports & Accounting Package. 1099 Tax Reporting Users can identify qualifying companies and individuals for IRS Form 1099 or IRS Form 1096 reporting. WorkingPoint automatically tracks payments made to these companies and individuals. Users can then generate 1099 reports for distribution. Premium Reports & Accounting Package This includes: a Daily Operating Report providing users with sales and cash flow information, customizable accounts categorization, and cash flow statements using the indirect method of reporting. Invoice Payment Collection via PayPal Users can collect payment on their invoices via PayPal. Email Marketing via VerticalResponse The WorkingPoint premium package includes 500 email credits with the email marketing firm VerticalResponse.
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Degree of truth

In classical logic, propositions are typically unambiguously considered as being true or false. For instance, the proposition one is both equal and not equal to itself is regarded as simply false, being contrary to the Law of Noncontradiction; while the proposition one is equal to one is regarded as simply true, by the Law of Identity. However, some mathematicians, computer scientists, and philosophers have been attracted to the idea that a proposition might be more or less true, rather than wholly true or wholly false. Consider this pizza is hot. In mathematics, this idea can be developed in terms of fuzzy logic. In computer science, it has found application in artificial intelligence. In philosophy, the idea has proved particularly appealing in the case of vagueness. Degrees of truth is an important concept in law. The term is an older concept than conditional probability. Instead of determining the objective probability, only a subjective assessment is defined. In adjudicative processes, 'substantive truth' is distinct from 'formal legal truth' which comes in four degrees: hearsay, balance of probabilities, proven beyond reasonable doubt and absolute truth (knowledge reserved unto God).
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Argument mining

Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. Such argumentative structures include the premise, conclusions, the argument scheme and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse. The Argument Mining workshop series is the main research forum for argument mining related research. == Applications == Argument mining has been applied in many different genres including the qualitative assessment of social media content (e.g. Twitter, Facebook), where it provides a powerful tool for policy-makers and researchers in social and political sciences. Other domains include legal documents, product reviews, scientific articles, online debates, newspaper articles and dialogical domains. Transfer learning approaches have been successfully used to combine the different domains into a domain agnostic argumentation model. Argument mining has been used to provide students individual writing support by accessing and visualizing the argumentation discourse in their texts. The application of argument mining in a user-centered learning tool helped students to improve their argumentation skills significantly compared to traditional argumentation learning applications. == Challenges == Given the wide variety of text genres and the different research perspectives and approaches, it has been difficult to reach a common and objective evaluation scheme. Many annotated data sets have been proposed, with some gaining popularity, but a consensual data set is yet to be found. Annotating argumentative structures is a highly demanding task. There have been successful attempts to delegate such annotation tasks to the crowd but the process still requires a lot of effort and carries significant cost. Initial attempts to bypass this hurdle were made using the weak supervision approach.
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Vidby

Vidby AG (stylized in lower-case) is a start-up based in Rotkreuz, Switzerland specializing in AI language translation for videos. Founded by Alexander Konovalov (uk:Олександр Коновалов) and Eugen von Rubinberg in September 2021, the company has especially garnered attention for its use in translating speeches given by President Volodymyr Zelenskyy during the Russian invasion of Ukraine. == History == Vidby AG was founded by Alexander Konovalov and Eugen von Rubinberg. Konovalov is a native of Ukraine and retains Ukrainian citizenship; Rubinberg came to Switzerland from Germany and holds German citizenship. Both are residents of Switzerland. The latter founded his first business, a trading company, at age 16. In 2013, the business partners launched a consumer-oriented video-call translation service called DROTR (Droid Translator) AG, utilizing a Konovalov-created AI-powered language translation technology enabling simultaneous translation of messages, voice and video calls in 104 languages (written), with 44 available in spoken form. This was the world's first video calling app with translation. The technology was pronounced a competitor of Skype and Viber by Forbes and claimed first prize at the "Innovative Breakthrough 2013" Competition. In 2021, with a new business-oriented focus, DROTR became Vidby, with the former Google technology partners Konovalov and Rubinberg remaining at the helm, each with the title Co-CEO. While headquartered in Switzerland, Vidby's development team is, according to the company's founders, based in Ukraine. The technology behind Vidby has an accuracy level variously reported as up to 99 percent or 99 to 100 percent, equalling the highest level of human translation. Additionally, the technology is capable of removing the original language while maintaining ambient sounds. Currently, some 70 languages plus 60 dialects are possible with the algorithm-based technology. == Notable use == In addition to its use with speeches delivered by Pope Francis, the technology has been provided to Ukrainian authorities and embassies during the ongoing military conflict with Russia free of remuneration. By July, 2022, some 70 speeches given by President Zelenskyy totalling 650 minutes had been translated into 30 languages, for a total of over 10,000 minutes of video material. Of its use in translating Zelenskyy's wartime speeches, Konovalov has said, "Like any citizen, I want to help defend my country." Notable corporate clients of Vidby include Samsung, Siemens, Cisco, Kärcher, Generali and McDonald's Corporation; an academic client is Harvard University. Google Cloud Technology Partner status of Vidby was confirmed officially after a six-month audit in December 2022. Denys Krasnikov, a Vidby co-founder, is responsible for cooperation with Google, YouTube, Microsoft, and other key partners. After the launch of multilingual YouTube channels, Vidby started AI translating and dubbing creators' videos for this new type of channel at the end of February 2023. == Accolades == Vidby headed a list of the five best video translation services as named by TechRadar Deutschland in September, 2022. In the same month, Tech Times named Vidby #1 in their list of the five best such services. It similarly topped a list of the five best content translation technologies as judged by European Business Review in October, 2022. Prior to these lead-position rankings (August, 2022), it was featured as Business Insider's special start-up recommendation (German: "Unser Lesetipp auf Gründerszene"). In 2023, YouTube recognized Vidby as its recommended vendor.
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Randonautica

Randonautica (a portmanteau of "random" + "nautica") is an app launched on February 22, 2020 founded by Auburn Salcedo and Joshua Lengfelder. It randomly generates coordinates that encourages the user to explore their local area and report what is found. According to its creators, the app is "an attractor of strange things," letting one choose specific coordinates based on a specific theme. It gained controversy after a report of two teenagers coincidentally finding a corpse while using the application. == Overview == The app, which creators claim to be inspired by chaos theory and Guy Debord's Theory of the Dérive, offers its users three types of coordinates to choose from: an attractor, a void, or an anomaly. The app has a cult following on YouTube and TikTok and there is a subreddit made by the creators for users of the app. == History == 29-year-old circus performer Joshua Lengfelder discovered a bot called Fatum Project in a fringe science chat group on Telegram in January 2019. According to The New York Times, "He absorbed the project’s theories about how random exploration could break people out of their predetermined realities, and how people could influence random outcomes with their minds." Lengfelder then created a Telegram bot using Fatum Project's code, generating coordinates. He then created the subreddit r/randonauts in March. In October, developer Simon Nishi McCorkindale made the bot's webpage. With the help of Auburn Salcedo, chief executive of a TV agency, both created Randonauts LLC. Salcedo became the chief operating officer while Lengfelder was the CEO. The app, called Randonautica, was launched on February 22, 2020. Later the same year the app and back-end got completely overhauled by a new team of developers and got a more visual and friendlier design and logo. In April 2022 Lengfelder exited Randonauts LLC and Auburn Salcedo became CEO. == Reception == The app has as many as 10.8 million users as of July 2020, gaining popularity amid the COVID-19 pandemic in the United States as restrictions have been lightened. Emma Chamberlain made a YouTube video about the app that helped increase its following. i-D reported that the hashtag #randonautica has gained 176.5 million views on TikTok, although it has not marketed itself yet. === Controversy === With the app's popularity, users started reporting coincidences which many find unsettling. The majority of reports were from TikTok and Reddit, as well as Telegram. The most notable controversy involved a group of people heading to a beach in Duwamish Head, Puget Sound, West Seattle per the app, where they found a bag with two dead bodies, a 27-year-old male and a 36-year-old female, as reported by the Seattle Police homicide detectives. In August 2020, police arrested and charged their landlord, Michael Lee Dudley, in connection with the murders. In March 2021, Dudley was denied bail while other people were under suspicion of aiding Dudley in the dismemberment and disposal of the bodies, but no one else had been charged. This has caused speculation that the app has an intended, puzzle-like theme. However, Lengfelder stated that it is "a shocking coincidence." Salcedo called the videos fake, and that "It’s so hard to manage, because people are really taking creative liberties after seeing how much traction the app is getting in that fear factor." In 2022, Michael Dudley was convicted of second degree murder for killing both victims, who were identified as Jessica Lewis and Austin Wenner. He was sentenced to 46 years in prison the following year. In their questions page, Randonautica's creators have said that if the app generates coordinates inside a private property, it is a violation of their terms and conditions to trespass. In addition, Randonautica has also received allegations that the app is used for human trafficking, which its creators have denied, saying that data collected by the app are anonymous. It also ensured that the app is not designed to violate religious customs, saying that "the app is simply a tool. Just as a knife can be used either to prepare dinner or to cut somebody."
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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 ==
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International Conference on Automated Planning and Scheduling

The International Conference on Automated Planning and Scheduling (ICAPS) is a leading international academic conference in automated planning and scheduling held annually for researchers and practitioners in planning and scheduling. ICAPS is supported by the National Science Foundation, the journal Artificial Intelligence, and other supporters. == The IPC and PDDL == ICAPS conducts the International Planning Competition (IPC), a competition scheduled every few years that empirically evaluates state-of-the-art planning systems on a collection of benchmark problems. The Planning Domain Definition Language (PDDL) was developed mainly to make the 1998/2000 International Planning Competition possible, and then evolved with each competition. PDDL is an attempt to standardize Artificial Intelligence (AI) planning languages. PDDL was first developed by Drew McDermott and his colleagues in 1998, inspired by STRIPS, ADL, and other sources. == History == The ICAPS conferences began in 2003 as a merge of two bi-annual conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). == List of events ==
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Shadowrun

Shadowrun is a science fantasy tabletop role-playing game set in an alternate future in which cybernetics, magic and fantasy creatures co-exist. It combines genres of cyberpunk, urban fantasy, and crime, with occasional elements of conspiracy, horror, and detective fiction. From its inception in 1989, it has spawned a franchise that includes a series of novels, a collectible card game, two miniature-based tabletop wargames, and multiple video games. The title is taken from the game's main premise – a near-future world damaged by a massive magical event, where industrial espionage and corporate warfare runs rampant. A shadowrun – a successful data theft or physical break-in at a rival corporation or organization – is one of the main tools employed by both corporate rivals and underworld figures. Deckers (futuristic hackers) can tap into an immersive, three-dimensional cyberspace on such missions as they seek access, physical or remote, to the power structures of rival groups. They are opposed by rival deckers and lethal, potentially brain-destroying artificial intelligences called "Intrusion Countermeasures" (IC), while they are protected by street fighters and/or mercenaries, often with cyborg implants (called cyberware), magicians, and other exotic figures. Magic has also returned to the world after a series of plagues; dragons who can take human form have returned as well, and are commonly found in high positions of corporate power. == Publication history == Shadowrun was developed and published by FASA from 1989 until early 2001, when the company closed and Shadowrun was transferred to WizKids, a company founded by former FASA employees. Two years before its closure, FASA sold its videogame branch, FASA Interactive, to Microsoft corporation, keeping rights to publishing novels and pen and paper RPGs. Since then, digital rights to Shadowrun IP have belonged to Microsoft. WizKids licensed the RPG rights to Fantasy Productions, who were already publishing a German version, until WizKids was acquired by Topps in 2003. Catalyst Game Labs, a publishing imprint of InMediaRes Productions, licensed the rights from Topps to publish new products. WizKids itself produced an unsuccessful collectible action figure game based on the property, called Shadowrun Duels. A fifth edition of Shadowrun was announced in December 2012. A limited-edition softcover was sold at the Origins Game Fair in June 2013, and the PDF in July 2013. A hardcover was published in August 2013. Shadowrun Anarchy was published in October 2016 It is a simplified version of the ruleset which allows focus more on the narration than on the rules. The sixth edition, called Shadowrun, Sixth World, was announced on May 1, 2019 to coincide with the game's 30th anniversary, along with a new website at shadowrunsixthworld.com. The game was published on August 26, 2019. The mechanics for this new version are generally similar to those of fifth edition, with some rules reworked for what line developer Jason Hardy describes as streamlining. This new version also progressed the in-game year to 2080. Since 2004, Shadowrun Missions (SRM) has offered fans "living campaigns" that allow for persistent character advancement. SRM is broken down into seasons which are made up of up to 24 individual missions that can be played at home, with special missions available to play exclusively at conventions. Each SRM season develops an overarching plot focused on a specific city from the Shadowrun setting. Missions settings have included the divided city of Denver, the corporate city-state of Manhattan, the Seattle Metroplex city-state, the formerly walled-off wastelands of Chicago, and Neo-Tokyo. For Shadowrun, Sixth World missions returned to Seattle, with twenty-four missions set in 2081, right after Seattle declared independence from the UCAS. The current Shadowrun Missions setting is 2083 New Orleans. The Shadowrun role-playing game has spawned several properties, including Shadowrun: The Trading Card Game, eight video games, an action figure game (Shadowrun Duels), two magazines, an art book and more than 50 novels, starting with the Secrets of Power series which introduces some of the original characters of Shadowrun and provides an introduction to this fictional universe. In addition to the main rule book there have been over 100 published supplements including adventures and expansions to both the rules and the game settings. Catalyst Game Labs announced that 2013 would be "The Year of Shadowrun," and in addition to the release of Shadowrun fifth edition that it has collaborated with publishers on the following properties: Shadowrun: Crossfire, The Adventure Deck-building Game; Shadowrun: Sprawl Gangers, a tactical miniatures wargame; and Shadowrun: Hostile Takeover, a board game designed by Bryan C.P. Steele was planned for release in late 2014/early 2015. Catalyst had been in collaboration with Nordic Games and Cliffhanger Studios to create Shadowrun Chronicles: Boston Lockdown online RPG, however it was shuttered November 30, 2018, with the producers citing lack of funding and the end of the license terms for use of the IP. == Fictional universe == Shadowrun takes place several decades in the future (2050 in the first edition, currently 2088). The end of the Mesoamerican Long Count calendar ushered in the "Sixth World", with once-mythological beings (e.g. dragons) appearing and forms of magic suddenly emerging. Large numbers of humans have "Goblinized" into orks and trolls, while many human children are born as elves, dwarves, and even more exotic creatures. In North America, indigenous peoples discovered that their traditional ceremonies allow them to command powerful spirits, and rituals associated with a new Ghost Dance movement let them take control of much of the western U.S. and Canada, where they formed a federation of Native American Nations. Seattle remains under U.S. control by treaty as a city-state enclave, and most game materials are set there and assume campaigns will use it as their setting. In parallel with these magical developments, the setting's 21st century features technological and social developments associated with cyberpunk science fiction. Megacorporations control the lives of their employees and command their own armies; many of the largest have extraterritoriality, such as currently enjoyed by foreign heads of state. Technological advances make cyberware (mechanical replacement body parts) and bioware (augmented vat-grown body parts implanted in place of or in tandem with natural organs) common. The Computer Crash of 2029 led to the creation of the Matrix, a worldwide computer network that users interact with via direct neural interface. When conflicts arise, corporations, governments, organized crime syndicates, and even wealthy individuals subcontract their dirty work to specialists, who then perform "shadowruns" or missions undertaken by deniable assets without identities or those that wish to remain unknown. The most skilled of these specialists, called shadowrunners, have earned a reputation for getting the job done. They have developed a knack for staying alive, and prospering, in the world of Shadowrun. The Shadowrun world is cross-genre, incorporating elements of both cyberpunk and urban fantasy. Unlike in a purely cyberpunk game, in the Shadowrun world, magic exists and has "worked" since 2011. Among other things, this split humankind into subtypes, also known as metatypes/metahumans. Some of these metatypes take the form of common fantasy races. Likewise, some animals have turned into familiar monsters of past fantasy and lore and both monsters and human magicians have regained magical powers. By the second half of the 21st century, in the time the game is set, these events are accepted as commonplace. Man, machine, and magic exist in a world where the amazing is among the most common and technology has entered into every facet of human (and metahuman) life. === Races === Characters in Shadowrun can be humans, orks, trolls, elves, dwarves, as well as certain diverging subspecies (known as metavariants) such as gnomes, giants, dryads, etc. In the early days, when magic returned to the world, humans began to either change into, or give birth to, elf and dwarf infants, a phenomenon called Unexplained Genetic Expression (UGE). Later, some juvenile and adult humans "goblinized" into other races (mostly orks, but also some trolls). The term "metahuman" is used either to refer to humanity as a whole, including all races, or to refer specifically to non-human races, depending on context. The return of Halley's Comet brought even further variation in the form of changelings, who have variation atypical to their metatype or even species, such as electroreception. Two of the metahuman races, elves and orks, have fictional languages. Additionally, a virus known as the Human Meta-Human Vampiric Virus (HMHVV), with many variant strains, has been known to cause f
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Vulnerabilities Equities Process

The Vulnerabilities Equities Process (VEP) is a process used by the U.S. federal government to determine on a case-by-case basis how it should treat zero-day computer security vulnerabilities: whether to disclose them to the public to help improve general computer security, or to keep them secret for offensive use against the government's adversaries. The VEP was first developed during the period 2008–2009, but only became public in 2016, when the government released a redacted version of the VEP in response to a FOIA request by the Electronic Frontier Foundation. Following public pressure for greater transparency in the wake of the Shadow Brokers affair, the U.S. government made a more public disclosure of the VEP process in November 2017. == Participants == According to the VEP plan published in 2017, the Equities Review Board (ERB) is the primary forum for interagency deliberation and determinations concerning the VEP. The ERB meets monthly, but may also be convened sooner if an immediate need arises. The ERB consists of representatives from the following agencies: Office of Management and Budget Office of the Director of National Intelligence (including the Intelligence Community-Security Coordination Center) United States Department of the Treasury United States Department of State United States Department of Justice (including the Federal Bureau of Investigation and the National Cyber Investigative Joint Task Force) Department of Homeland Security (including the National Cybersecurity and Communications Integration Center and the United States Secret Service) United States Department of Energy United States Department of Defense (to include the National Security Agency, including Information Assurance and Signals Intelligence elements), United States Cyber Command, and DoD Cyber Crime Center) United States Department of Commerce Central Intelligence Agency The National Security Agency serves as the executive secretariat for the VEP. == Process == According to the November 2017 version of the VEP, the process is as follows: === Submission and notification === When an agency finds a vulnerability, it will notify the VEP secretariat as soon as is possible. The notification will include a description of the vulnerability and the vulnerable products or systems, together with the agency's recommendation to either disseminate or restrict the vulnerability information. The secretariat will then notify all participants of the submission within one business day, requesting them to respond if they have an relevant interest. === Equity and discussions === An agency expressing an interest must indicate whether it concurs with the original recommendation to disseminate or restrict within five business days. If it does not, it will hold discussions with the submitting agency and the VEP secretariat within seven business days to attempt to reach consensus. If no consensus is reached, the participants will suggest options for the Equities Review Board. === Determination to disseminate or restrict === Decisions whether to disclose or restrict a vulnerability should be made quickly, in full consultation with all concerned agencies, and in the overall best interest of the competing interests of the missions of the U.S. government. As far as possible, determinations should be based on rational, objective methodologies, taking into account factors such as prevalence, reliance, and severity. If the review board members cannot reach consensus, they will vote on a preliminary determination. If an agency with an equity disputes that decision, they may, by providing notice to the VEP secretariat, elect to contest the preliminary determination. If no agency contests a preliminary determination, it will be treated as a final decision. === Handling and follow-on actions === If vulnerability information is released, this will be done as quickly as possible, preferably within seven business days. Disclosure of vulnerabilities will be conducted according to guidelines agreed on by all members. The submitting agency is presumed to be most knowledgeable about the vulnerability and, as such, will be responsible for disseminating vulnerability information to the vendor. The submitting agency may elect to delegate dissemination responsibility to another agency on its behalf. The releasing agency will promptly provide a copy of the disclosed information to the VEP secretariat for record keeping. Additionally, the releasing agency is expected to follow up so the ERB can determine whether the vendor's action meets government requirements. If the vendor chooses not to address a vulnerability, or is not acting with urgency consistent with the risk of the vulnerability, the releasing agency will notify the secretariat, and the government may take other mitigation steps. == Criticism == The VEP process has been criticized for a number of deficiencies, including restriction by non-disclosure agreements, lack of risk ratings, special treatment for the NSA, and less than whole-hearted commitment to disclosure as the default option. == UK equivalent == British intelligence agencies—GCHQ in particular—follow a similar approach, also known as the Equities Process, to determine whether to disclose or retain security vulnerabilities. The Investigatory Powers Act 2016 was amended in 2022 to bring oversight of the operation of the process within the remit of the Investigatory Powers Commissioner. Details of the process were made public in 2018.
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Your AI Slop Bores Me

Your AI Slop Bores Me (stylized in all lowercase) is a website and social experiment created by programmer Mihir Maroju. Serving as a parody of large language models (LLMs) like ChatGPT and Claude, all questions and image prompts posed by users are answered by other, randomly-selected human users of the site. As of March 2026, the site has reached 50 million hits and sits at 16,000 concurrent users. == Background == In an interview with Fast Company, Maroju said he was inspired to create the site by his frustration with AI proliferating the internet with AI generated content, saying the site came from "a frustration for AI art and its proliferation, making artists' lives worse and also just filling the internet with low-effort generic slop". == Overview == The site has a credit system, in which a first-time user will be given 1 credit for free. Every 10 minutes, if a user has 0 credits, they will receive 2 credits. Once the credits are used up, the user can no longer do prompts unless the user earns them. The user can earn credits by responding to other user's prompts by "larping as AI" while given a 75-second time limit. Prompts can either be for a written response, or a drawing for the other user to fulfill the prompt. The maximum amount of credits a user can have is 6 credits, and cannot exceed the maximum limit. If the prompting user activates "thinking mode", the countdown is extended to 150 seconds for the cost of 2 credits. == Reception == The site has garnered attention and praise from X users, and across many online communities. The Daily Dot's Rachel Kiley wrote that "the best part about the game is that there's really no right or wrong way to do it. Humans aren't LLMs trained on copyrighted material and the whole of the free internet, but we do retain a certain amount of the information we've learned from those things over the course of our lives, while also being capable of creativity". Chris Taylor of Mashable called the site "amateurish and charming". Aftermath's Nicole Carpenter wrote that the site reminded her of "the human touch of chaos".
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Pippit

Pippit (Chinese: 小云雀; pinyin: Xiǎoyúnquè) is an artificial intelligence content creation platform developed by the Chinese technology company ByteDance. The platform, powered by CapCut leverages multimodal AI technology to streamline professional-grade video and image production, specifically targeting small and medium-sized enterprisesand social media creators. == History == In May 2025, ByteDance officially launched Pippit, which is positioned as an AI video and picture creation tool. In early 2026, Pippit underwent a major architectural overhaul with the integration of the Dreamina seedance 2.0. This technical milestone introduced the "Short Drama Agent" functionality, which enables the end-to-end conversion of scripts up to 100,000 words into fully rendered video productions.
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Conference on Neural Information Processing Systems

The Conference on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. The conference includes three days of invited talks along with oral and poster presentations of refereed papers, followed by two days of workshops and competitions. == History == The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in 1987 with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa as program chairman. Research presented in the early NeurIPS meetings included a wide range of topics from efforts to solve purely engineering problems to the use of computer models as a tool for understanding biological nervous systems. Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning, artificial intelligence and statistics. From 1987 until 2000 NeurIPS was held in Denver, United States. Since then, the conference was held in Vancouver, Canada (2001–2010), Granada, Spain (2011), and Lake Tahoe, United States (2012–2013). In 2014 and 2015, the conference was held in Montreal, Canada, in Barcelona, Spain in 2016, in Long Beach, United States in 2017, in Montreal, Canada in 2018 and Vancouver, Canada in 2019. Reflecting its origins at Snowbird, Utah, the meeting was accompanied by workshops organized at a nearby ski resort up until 2013, when it outgrew ski resorts. The first NeurIPS Conference was sponsored by the IEEE. The following NeurIPS Conferences have been organized by the NeurIPS Foundation, established by Ed Posner. Terrence Sejnowski has been the president of the NeurIPS Foundation since Posner's death in 1993. The board of trustees consists of previous general chairs of the NeurIPS Conference. The first proceedings was published in book form by the American Institute of Physics in 1987, and was entitled Neural Information Processing Systems, then the proceedings from the following conferences have been published by Morgan Kaufmann (1988–1993), MIT Press (1994–2004) and Curran Associates (2005–present) under the name Advances in Neural Information Processing Systems. The conference was originally abbreviated as "NIPS". By 2018 a few commentators were criticizing the abbreviation as encouraging sexism due to its association with the word nipples, and as being a slur against Japanese. The board changed the abbreviation to "NeurIPS" in November 2018. == Topics == Along with machine learning and neuroscience, other fields represented at NeurIPS include cognitive science, psychology, computer vision, statistical linguistics, and information theory. Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition, object recognition in images, image captioning, language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex (ConvNet) and reinforcement learning inspired by the basal ganglia (Temporal difference learning). Notable affinity groups have emerged from the NeurIPS conference and displayed diversity, including Black in AI (in 2017), Queer in AI (in 2016), and others. === Named lectures === In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. The NeurIPS Board introduced the Posner Lectureship in honor of NeurIPS founder Ed Posner; two Posner Lectures were given each year up to 2015. Past lecturers have included: 2010 – Josh Tenenbaum and Michael I. Jordan 2011 – Rich Sutton and Bernhard Schölkopf 2012 – Thomas Dietterich and Terry Sejnowski 2013 – Daphne Koller and Peter Dayan 2014 – Michael Kearns and John Hopfield 2015 – Zoubin Ghahramani and Vladimir Vapnik 2016 – Yann LeCun 2017 – John Platt 2018 – Joëlle Pineau 2019 – Yoshua Bengio 2020 – Christopher Bishop 2021 – Peter Bartlett In 2015, the NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman, who served on the NeurIPS Board from 1994 to 2005. Past lecturers have included: 2015 – Robert Tibshirani 2016 – Susan Holmes 2017 – Yee Whye Teh 2018 – David Spiegelhalter 2019 – Bin Yu 2020 – Marloes Maathuis 2021 – Gabor Lugosi 2022 – Emmanuel Candes 2023 – Susan Murphy 2024 – Arnaud Doucet == NeurIPS consistency experiment == In NIPS 2014, the program chairs duplicated 10% of all submissions and sent them through separate reviewers to evaluate randomness in the reviewing process. Several researchers interpreted the result. Regarding whether the decision in NIPS is completely random or not, John Langford writes: "Clearly not—a purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%." He concludes that the result of the reviewing process is mostly arbitrary. In NeurIPS 2021, the program chairs repeated the 2014 experiment and found similar levels of review inconsistency; 23% of duplicated submissions received different accept/reject decisions, and 50.6% of accepted papers would have been rejected under re-review. == Locations == 1987–2000: Denver, Colorado, United States 2001–2010: Vancouver, British Columbia, Canada 2011: Granada, Spain 2012 & 2013: Stateline, Nevada, United States 2014 & 2015: Montréal, Quebec, Canada 2016: Barcelona, Spain 2017: Long Beach, California, United States 2018: Montréal, Quebec, Canada 2019: Vancouver, British Columbia, Canada 2020: Vancouver, British Columbia, Canada (virtual conference) 2021: Virtual conference 2022 & 2023: New Orleans, Louisiana, United States 2024: Vancouver, British Columbia, Canada 2025: San Diego, California, United States and Mexico City, Mexico 2026: Sydney, New South Wales, Australia, with satellite events in Atlanta and Paris
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Structural risk minimization

Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data. This principle was first set out in a 1974 book by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension. In practical terms, Structural Risk Minimization is implemented by minimizing E t r a i n + β H ( W ) {\displaystyle E_{train}+\beta H(W)} , where E t r a i n {\displaystyle E_{train}} is the train error, the function H ( W ) {\displaystyle H(W)} is called a regularization function, and β {\displaystyle \beta } is a constant. H ( W ) {\displaystyle H(W)} is chosen such that it takes large values on parameters W {\displaystyle W} that belong to high-capacity subsets of the parameter space. Minimizing H ( W ) {\displaystyle H(W)} in effect limits the capacity of the accessible subsets of the parameter space, thereby controlling the trade-off between minimizing the training error and minimizing the expected gap between the training error and test error. The SRM problem can be formulated in terms of data. Given n data points consisting of data x and labels y, the objective J ( θ ) {\displaystyle J(\theta )} is often expressed in the following manner: J ( θ ) = 1 2 n ∑ i = 1 n ( h θ ( x i ) − y i ) 2 + λ 2 ∑ j = 1 d θ j 2 {\displaystyle J(\theta )={\frac {1}{2n}}\sum _{i=1}^{n}(h_{\theta }(x^{i})-y^{i})^{2}+{\frac {\lambda }{2}}\sum _{j=1}^{d}\theta _{j}^{2}} The first term is the mean squared error (MSE) term between the value of the learned model, h θ {\displaystyle h_{\theta }} , and the given labels y {\displaystyle y} . This term is the training error, E t r a i n {\displaystyle E_{train}} , that was discussed earlier. The second term, places a prior over the weights, to favor sparsity and penalize larger weights. The trade-off coefficient, λ {\displaystyle \lambda } , is a hyperparameter that places more or less importance on the regularization term. Larger λ {\displaystyle \lambda } encourages sparser weights at the expense of a more optimal MSE, and smaller λ {\displaystyle \lambda } relaxes regularization allowing the model to fit to data. Note that as λ → ∞ {\displaystyle \lambda \to \infty } the weights become zero, and as λ → 0 {\displaystyle \lambda \to 0} , the model typically suffers from overfitting.
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AI Snake Oil

AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference is a 2024 non-fiction book written by scholars Arvind Narayanan and Sayash Kapoor. It is a critique of the tech industry's overly inflated promises and capabilities of artificial intelligence (AI) as well as a debunking of the flawed science fueling AI hype while attempting to outline both the potential positives and negatives that come with different modes of the technology. == Contents == === Publication === The book was published in September 2024 by the Princeton University Press. AI Snake Oil consists of 360 pages and features eight chapters, and sections for acknowledgements, references, and an index. An updated edition with a new preface and epilogue by the authors was published in September 2025. The authors use the term "AI snake oil" derived from the U.S. idiom for a fraudulent remedy, to describe overhyped AI systems. === Chapter one: Introduction === Narayanan and Kapoor argue that many individuals do not yet have the literacy to detect functioning aspects of AI compared to potential snake oil, which they identify as "AI that does not and cannot work as advertised". Some of the major examples utilized by the authors include Allstate's 2013 use of predictive AI, as well as the concern surrounding actors and AI attempting to replicate or use their likeness. Important discussions regarding discrimination are brought up and explored in the first chapter, including the false arrests of six Black individuals due to errors with AI facial recognition tools. The chapter concludes with a comparison to the Industrial Revolution, where Narayanan and Kapoor highlight the extensive human labour that is necessary for artificial intelligence technologies to function. === Chapter two: How Predictive AI Goes Wrong === Chapter two focuses on predictive artificial intelligence, and criticizes the overestimation of the capabilities of the technology. === Chapter three: Why Can't AI Predict the Future? === Chapter three works to inform the reader about the history of early computational prediction attempts, with examples from companies like Simulatics. === Chapter four: The Long Road to Generative AI === The fourth chapter goes in more in-depth in explorations of generative AI. Generative AI software examples include ChatGPT, Midjourney, and DALL-E. The section begins with a positive example of generative AI. As the chapter progresses, the authors begin to provide examples of harm produced by generative AI, including the suicide of a Belgian man after connecting with Chai, a generative chatbot. Issues of deepfakes and preservation of artistic property are also discussed. The use of generative AI to create non-consensual pornographic deepfake content is discussed in relation to female celebrities. === Chapter five: Is Advanced AI an Existential Threat? === The fifth chapter draws attention the AGI, or Artificial General Intelligence. The authors describe AGI as "AI that can perform most or economically relevant tasks as effectively as any human". They summarize that many contributors to the field of artificial intelligence believe AGI to be an impending threat that demands attention. However, they argue that the perceived threat of AGI would only exist if the technology continually functioned reliably. In order to better illustrate the hype surrounding AGI, Narayanan and Kapoor use the Ladder of Generality, which is described as a visual tool in which "each rung represents a way of computing that is more flexible, and more general, than the previous one". They note that we are not yet aware of the next rungs on the ladder, or if the ladder will eventually result in a dead end. The rungs that have been identified so far are as follows: (0, or floor) special purpose hardware, (1) programmable computers, (2) stored program computers, (3) machine learning, (4) deep learning, (5) pretrained models, and, finally, (6) instruction-tuned models. The potential for future rungs and what those rungs might be are currently undetermined. The chapter also discusses the ELIZA effect, which Lawrence Switzky discusses in his article "ELIZA Effects". Switzky attributes the coined term ELIZA Effect to Sherry Turke, who defined it as "our more general tendency to treat responsive computer programs as more intelligent than they really are". === Chapter six: Why Can't AI Fix Social Media? === The sixth chapter focuses on content moderation, why it is important, and how it has been and could be affected by artificial automation. The first issue raised in regard to AI-driven content moderation is the inability for computers and machines to understand context and nuance, resulting in potential for discriminatory moderation and shadow banning. While they note that there are issues with automating content moderation, Narayanan and Kapoor also highlight the psychological impact on human content moderators and their labour. They indicate the hidden labour behind moderation, which is often outsourced to less developed countries, where labourers sort through potentially traumatizing content for pay. However, the discussion focuses more heavily on why automated moderation can be problematic, including discriminatory algorithms and lack of nuance. To balance their argument, issues of discrimination and bias are also discussed in relation the human content moderators. To automate moderation, there are two types of AI used, which are fingerprint matching and machine learning. === Chapter seven: Why Do Myths about AI Persist? === The seventh chapter outlines possible factors that contribute to hype surrounding AI. Narayanan and Kapoor explain how companies often promote their new AI models without properly disclosing how the model works, and what it is learning from. They attribute hype to several different groups, including journalists, researchers, and companies. They explain the impact of companies and the misplaced hype that they spread can be attributed to greed and a desire to grow corporate funds. For journalists, one of the stated sources of hype, they argue that news media has a tendency to prioritize financial incentives over validity and quality of writing. As well, Narayanan and Kapoor point out the emergence of company statement regurgitation in news media, leading to clickbait. Hype from researchers is potentially linked to lack of reproducibility in studies as well as leakage, which occurs when AI models are tested on their training data. === Chapter eight: Where do we go from here? === The final chapter, chapter eight, turns its attention to the future. The authors express their ideas and predictions for how the technology will evolve and be utilized in the upcoming years. == Authors == Author Narayanan is a computer science professor at Princeton University. Kapoor is a doctoral candidate at the same university, and both scholars are located at the Center for Information Technology at Princeton. In 2023, Narayanan and Kapoor appeared on the TIME100 Artificial Intelligence list, which features influential figures in the field. == Reception == Nature, a science and technology peer-reviewed journal, released an article highlighting the top "10 essential reads from the past year", listing Arvind Narayanan and Sayash Kapoor's AI Snake Oil. The article states the that text is "one of the best on this controversial subject". Elizabeth Quill, in her review of the text in Science News, writes that the authors "squarely achieve their stated goal: to empower people to distinguish AI that works well from AI snake oil". Joshua Rothman of The New Yorker writes that "compared with many technologists, Narayanan, Kapoor, and Vallor [Shannon Vallor, University of Edinburgh], are deeply skeptical about today's A.I. technology and what it can achieve. Perhaps they shouldn't be". Rothman argues, following an interview with prominent computer scientist Geoffrey Hinton of University of Toronto, that the potential for AI to replicate complexity is already here and continues to be heavily funded, enhancing the prospective capabilities of the technology. However, he does praise the author's ability to address questions regarding the existential human experience. Alexya Martinez discusses the text in a book review for Journalism and Mass Communication Quarterly, critiquing AI Snake Oil for its extensive focus on the West. Martinez writes that Narayanan and Kapoor "do not fully explore how AI impacts other countries", and suggests more focus on countries outside of the United States to enhance their argument.
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The Old Axolotl

The Old Axolotl (Polish: Starość aksolotla) is a 2015 digital-only novel by Polish science-fiction author Jacek Dukaj. The novel was released in Polish on March 10, 2015, and shortly afterward, on March 24 that year, in English (translated by Stanley Bill). It has been described as "an experiment in reading (and creating) the electronic literature of the future". It is Dukaj's first novel to be published in English, though several of his short stories (The Golden Galley, 1996, The Iron General, 2010, The Apocrypha of Lem, 2011) have been translated prior to this. The novel has inspired two Netflix original series: the 2020 Belgian Into the Night, and its 2022 Turkish language spin-off Yakamoz S-245. == Plot == The novel presents a post-apocalyptic, cyberpunk vision of Earth where biological life has been wiped out, inhabited by robots and mechs, many of which are humans whose consciousness has been digitized in the wake of an extinction event. == Significance and analysis == The novel is an example of electronic literature, available only in digital formats, and has no traditional paper version. It was designed from the beginning not only to incorporate more traditional elements such as illustrations, but also hypertext, and 3D-printable models of main robotic characters designed by Alex Jaeger, the art director of Transformers films. The novel composition is layered, with the narrative layer, an encyclopedic/hyperlinked footnote layer, and a multimedia layer, including illustrations and a short promotional video by the Oscar-nominated Platige Image studio. One of the novel's central questions is: "What does it mean to be human?" Other subjects include post humanism and other "staples of cyberpunk and related genres, such as the artificial intelligence". The novel is representative of Dukaj's prose, posing philosophical questions about the future of man and technology. The author explained that: "stories such as The Old Axolotl that model an ‘escape from the body’ are born out of a sense of progress as a process of ‘de-animalising’ human beings through science. This has its origin in the pre-Enlightenment intuition of ‘liberation from nature’. For one of the last shackles of nature is corporeality itself, the limitations of our physicality." The other major element of the novel is Dukaj's attempts to introduce the reader to the new style of electronic literature. The novel was nominated for the 2016 Janusz A. Zajdel Award.
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