• • • Artificial intelligence ( AI, also machine intelligence, MI) is displayed by, in contrast with the natural intelligence ( NI) displayed by humans and other animals. In AI research is defined as the study of ': any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term 'artificial intelligence' is applied when a machine mimics 'cognitive' functions that humans associate with other, such as 'learning' and 'problem solving'.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring 'intelligence' are often removed from the definition, a phenomenon known as the, leading to the quip 'AI is whatever hasn't been done yet.' For instance, is frequently excluded from 'artificial intelligence', having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully, competing at a high level in systems (such as and ),, intelligent routing in,, and interpreting complex data, including images and videos. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an '), followed by new approaches, success and renewed funding. [ ] For most of its history, AI research has been divided into subfields that often fail to communicate with each other. The traditional problems (or goals) of AI research include,,,,, and the ability to move and manipulate objects.

Because this book is now out of print, this Portable Document File (PDF) is formatted for two-sided printing to. Premises, based on solid expertise, have been used to construct a logically valid forecast—with virtually. Work of Richards J. On the psychology of intelligence analysis available to a new generation of.

Is among the field's long-term goals. Approaches include,, and. Many tools are used in AI, including versions of, and. The AI field draws upon,,,,,, and many others. The field was founded on the claim that 'can be so precisely described that a machine can be made to simulate it'. This raises philosophical arguments about the nature of the and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by, and since. Some people also consider AI if it progresses unabatedly.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in, large amounts of, and theoretical understanding; and AI techniques have become an essential part of the, helping to solve many challenging problems in computer science. Main articles: and While thought-capable appeared as in antiquity, the idea of actually trying to build a machine to perform useful reasoning may have begun with (c. With his, extended the concept of the ( engineered the first one around 1623), intending to perform operations on concepts rather than numbers. Since the 19th century, artificial beings are common in fiction, as in 's or 's. The study of mechanical or began with and mathematicians in antiquity.

The study of mathematical logic led directly to 's, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the. [ ] Along with concurrent discoveries in, and, this led researchers to consider the possibility of building an electronic brain. The first work that is now generally recognized as AI was and ' 1943 formal design for 'artificial neurons'. The field of AI research was born at at in 1956. Attendees (), (), (), () and () became the founders and leaders of AI research.

They and their students produced programs that the press described as 'astonishing': computers were winning at the game checkers, solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. Was heavily funded by the and laboratories had been established around the world. AI's founders were optimistic about the future: predicted, 'machines will be capable, within twenty years, of doing any work a man can do'.

Agreed, writing, 'within a generation. The problem of creating 'artificial intelligence' will substantially be solved'. They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of and ongoing pressure from the US Congress to fund more productive projects, both the U.S.

And British governments cut off exploratory research in AI. The next few years would later be called an ', a period when obtaining funding for AI projects was difficult. In the early 1980s, AI research was revived by the commercial success of, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.

In the late 1990s and early 21st century, AI began to be used for logistics,, and other areas. The success was due to increasing computational power (see ), greater emphasis on solving specific problems, new ties between AI and other fields and a commitment by researchers to mathematical methods and scientific standards. Became the first computer chess-playing system to beat a reigning world chess champion, on 11 May 1997. Advanced statistical techniques (loosely known as ), access to and enabled advances in and perception.

[ ] By the mid 2010s, machine learning applications were used throughout the world. [ ] In a exhibition match, 's,, defeated the two greatest Jeopardy champions, and, by a significant margin. The, which provides a 3D body–motion interface for the and the Xbox One use algorithms that emerged from lengthy AI research as do in.

In March 2016, won 4 out of 5 games of in a match with Go champion, becoming the first to beat a professional Go player without. In the 2017, won a with, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a 'sporadic usage' in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011. He attributes this to an increase in affordable, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people. Goals [ ] The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems.

These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention. Reasoning, problem solving [ ] Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with or incomplete information, employing concepts from and. For difficult problems, algorithms can require enormous computational resources—most experience a ': the amount of memory or computer time required becomes astronomical for problems of a certain size. The search for more efficient problem-solving algorithms is a high priority.

Human beings ordinarily use fast, intuitive judgments rather than step-by-step deduction that early AI research was able to model. AI has progressed using 'sub-symbolic' problem solving: approaches emphasize the importance of skills to higher reasoning; research attempts to simulate the structures inside the brain that give rise to this skill; mimic the human ability to guess.

Knowledge representation [ ]. Main articles: and and are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains.

A representation of 'what exists' is an: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The of these are captured as concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the. The most general ontologies are called, which attempt to provide a foundation for all other knowledge by acting as mediators between that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations are suitable for content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery via automated reasoning (inferring new statements based on explicitly stated knowledge), etc. Video events are often represented as rules, which can be used, among others, to automatically generate subtitles for constrained videos. Among the most difficult problems in knowledge representation are: and the Many of the things people know take the form of 'working assumptions'. For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies.

None of these things are true about all birds. Identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem. The breadth of commonsense knowledge The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base of (e.g., ) require enormous amounts of laborious —they must be built, by hand, one complicated concept at a time. A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the Internet, and thus be able to add to its own ontology.

[ ] The subsymbolic form of some commonsense knowledge Much of what people know is not represented as 'facts' or 'statements' that they could express verbally. For example, a chess master will avoid a particular chess position because it 'feels too exposed' or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge.

As with the related problem of sub-symbolic reasoning, it is hoped that,, or will provide ways to represent this kind of knowledge. Planning [ ].

Main article: Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the (or 'value') of available choices.

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment. Uses the and competition of many agents to achieve a given goal.

Such as this is used by and. Learning [ ]. Main article: Machine learning, a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience. Is the ability to find patterns in a stream of input. Includes both and numerical. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories.

Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

These three types of learning can be analyzed in terms of, using concepts like. The mathematical analysis of machine learning algorithms and their performance is a branch of known as. [ ] Within, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.). Natural language processing [ ]. Main article: gives machines the ability to read and human language. A sufficiently powerful natural language processing system would enable and the acquisition of knowledge directly from human-written sources, such as newswire texts.

Some straightforward applications of natural language processing include,, and. A common method of processing and extracting meaning from natural language is through. Although these indexes require a large volume of user input, it is expected that increases in processor speeds and decreases in data storage costs will result in greater efficiency.

Perception [ ]. Main article: The field of is closely related to AI. Intelligence is required for robots to handle tasks such as object manipulation and, with sub-problems such as,, and. These systems require that an agent is able to: Be spatially cognizant of its surroundings, learn from and build a map of its environment, figure out how to get from one point in space to another, and execute that movement (which often involves compliant motion, a process where movement requires maintaining physical contact with an object). Social intelligence [ ]. A robot with rudimentary social skills Affective computing is the study and development of systems that can recognize, interpret, process, and simulate human.

It is an interdisciplinary field spanning,, and. While the origins of the field may be traced as far back as the early philosophical inquiries into, the more modern branch of computer science originated with 's 1995 paper on 'affective computing'. A motivation for the research is the ability to simulate, where the machine would be able to interpret human emotions and adapts its behavior to give an appropriate response to those emotions.

Emotion and social skills are important to an intelligent agent for two reasons. First, being able to predict the actions of others by understanding their motives and emotional states allow an agent to make better decisions. Concepts such as,, necessitate that an agent be able to detect and model human emotions. Second, in an effort to facilitate, an intelligent machine may want to display emotions (even if it does not experience those emotions itself) to appear more sensitive to the emotional dynamics of human interaction.

Creativity [ ]. Main articles: and Many researchers think that their work will eventually be incorporated into a machine with, combining all the skills mentioned above and even exceeding human ability in most or all these areas. A few believe that features like or an may be required for such a project. Many of the problems above also require that general intelligence be solved. For example, even specific straightforward tasks, like, require that a machine read and write in both languages (), follow the author's argument (), know what is being talked about (), and faithfully reproduce the author's original intent ().

A problem like machine translation is considered ', but all of these problems need to be solved simultaneously in order to reach human-level machine performance. Approaches [ ] There is no established unifying theory or that guides AI research.

Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying? Or is as irrelevant to AI research as bird biology is to? Can intelligent behavior be described using simple, elegant principles (such as or )?

Or does it necessarily require solving a large number of completely unrelated problems? Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require 'sub-symbolic' processing? John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as, a term which has since been adopted by some non-GOFAI researchers. Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Walt Disney Pc Games Free Downloads. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy, is used to develop an adaptive, free-flowing computer mind.

Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish. Together, the humanesque behavior, mind, and actions make up artificial intelligence. Cybernetics and brain simulation [ ]. Main article: When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions:, and, and each one developed its own style of research. Named these approaches to AI 'good old fashioned AI' or '.

During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on or were abandoned or pushed into the background.

Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with and considered this the goal of their field. Cognitive simulation [ ] Economist and studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as, and. Their research team used the results of experiments to develop programs that simulated the techniques that people used to solve problems.

This tradition, centered at would eventually culminate in the development of the architecture in the middle 1980s. Logic-based [ ] Unlike and, felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. His laboratory at () focused on using formal to solve a wide variety of problems, including, and.

Logic was also the focus of the work at the and elsewhere in Europe which led to the development of the programming language and the science of. Anti-logic or scruffy [ ] Researchers at (such as and ) found that solving difficult problems in and required ad-hoc solutions – they argued that there was no simple and general principle (like ) that would capture all the aspects of intelligent behavior.

Described their 'anti-logic' approaches as ' (as opposed to the ' paradigms at and ). (such as 's ) are an example of 'scruffy' AI, since they must be built by hand, one complicated concept at a time.

Knowledge-based [ ] When computers with large memories became available around 1970, researchers from all three traditions began to build into AI applications. This 'knowledge revolution' led to the development and deployment of (introduced by ), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic [ ] By the 1980s progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially,, and. A number of researchers began to look into 'sub-symbolic' approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge. Embodied intelligence [ ] This includes,,, and. Researchers from the related field of, such as, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early researchers of the 1950s and reintroduced the use of in AI. This coincided with the development of the in the related field of: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Computational intelligence and soft computing [ ] Interest in and ' was revived by and others in the middle of the 1980s. Neural networks are an example of --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.

Other approaches to AI include, and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of. Statistical [ ] In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like, economics or ). And describe this movement as nothing less than a 'revolution' and 'the victory of the '. Critics argue that these techniques (with few exceptions ) are too focused on particular problems and have failed to address the long-term goal of general intelligence.

There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between and. Integrating the approaches [ ] Intelligent agent paradigm An is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as ). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic and others may use new approaches.

The paradigm also gives researchers a common language to communicate with other fields—such as and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s. And Researchers have designed systems to build intelligent systems out of interacting in a. A system with both symbolic and sub-symbolic components is a, and the study of such systems is.

A provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. ' was an early proposal for such a hierarchical system. [ ] Tools [ ] In the course of 60 or so years of research, AI has developed a large number of tools to solve the most difficult problems in.

A few of the most general of these methods are discussed below. Search and optimization [ ]. Main articles:,, and Many problems in AI can be solved in theory by intelligently searching through many possible solutions: can be reduced to performing a search.

For example, logical proof can be viewed as searching for a path that leads from to, where each step is the application of an. Algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called. Algorithms for moving limbs and grasping objects use in. Many algorithms use search algorithms based on. Simple exhaustive searches are rarely sufficient for most real world problems: the (the number of places to search) quickly grows to.

The result is a search that is or never completes. The solution, for many problems, is to use ' or 'rules of thumb' that prioritize choices in favor of those that are more likely to reach a goal, and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called ' the ').

Supply the program with a 'best guess' for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size. A very different kind of search came to prominence in the 1990s, based on the mathematical theory of. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top.

Other optimization algorithms are, and. Uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, only the fittest to survive each generation (refining the guesses). Forms of include algorithms (such as or ) and (such as,, and ).

Main articles: and is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the algorithm uses logic for and is a method for.

Several different forms of logic are used in AI research. Or is the logic of statements which can be true or false. Also allows the use of and, and can express facts about objects, their properties, and their relations with each other., is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0). Can be used for uncertain reasoning and have been widely used in modern industrial and consumer. [ ] models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence., and are forms of logic designed to help with default reasoning and the.

Several extensions of logic have been designed to handle specific domains of, such as:;, and (for representing events and time);; belief calculus; and. Probabilistic methods for uncertain reasoning [ ]. Main articles:,,,,, and Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from theory and economics.

Are a very general tool that can be used for a large number of problems: reasoning (using the algorithm), (using the ), (using ) and (using ). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping systems to analyze processes that occur over time (e.g., or ). A key concept from the science of economics is ': a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using,, and. These tools include models such as, dynamic, and. Classifiers and statistical learning methods [ ].

Main articles:,, and The simplest AI applications can be divided into two types: classifiers ('if shiny then diamond') and controllers ('if shiny then pick up'). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Are functions that use to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made.

All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.

A classifier can be trained in various ways; there are many statistical and approaches. The most widely used classifiers are the, such as the,,,, and.

The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the ' theorem. Determining a suitable classifier for a given problem is still more an art than science. Neural networks [ ].

A neural network is an interconnected group of nodes, akin to the vast network of in the. Neural networks are modeled after the neurons in the human brain, where a trained algorithm determines an output response for input signals. The study of non-learning began in the decade before the field of AI research was founded, in the work of and. Invented the, a learning network with a single layer, similar to the old concept of. Early pioneers also include,,,, Christoph von der Malsburg, David Willshaw,,,,, and others. The main categories of networks are acyclic or (where the signal passes in only one direction) and (which allow feedback and short-term memories of previous input events).

Among the most popular feedforward networks are, and. Neural networks can be applied to the problem of (for robotics) or, using such techniques as,. Today, neural networks are often trained by the algorithm, which had been around since 1970 as the reverse mode of published by, and was introduced to neural networks. Is an approach that models some of the structural and algorithmic properties of the.

Deep feedforward neural networks [ ]. Main article: in with many layers has transformed many important subfields of artificial intelligence, including,, and others. According to a survey, the expression 'Deep Learning' was introduced to the community by in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to in 2000. The first functional Deep Learning networks were published by and V. Lapa in 1965. [ ] These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks.

In 2006, a publication by and Ruslan Salakhutdinov introduced another way of pre-training many-layered (FNNs) one layer at a time, treating each layer in turn as an, then using for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses (CNNs), whose origins can be traced back to the introduced by in 1980. In 1989, and colleagues applied to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US. Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions.

Deep feedforward neural networks were used in conjunction with by, Google Deepmind's program that was the first to beat a professional human player. Deep recurrent neural networks [ ]. Main article: Early on, was also applied to sequence learning with (RNNs) which are general computers and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence. RNNs can be trained by but suffer from the. In 1992, it was shown that unsupervised pre-training of a stack of can speed up subsequent supervised learning of deep sequential problems.

Numerous researchers now use variants of a deep learning recurrent NN called the (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC). At Google, Microsoft and Baidu this approach has revolutionised. For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through to billions of smartphone users. Google also used LSTM to improve machine translation, Language Modeling and Multilingual Language Processing. LSTM combined with CNNs also improved automatic image captioning and a plethora of other applications. Control theory [ ].

Main article: In 1950, proposed a general procedure to test the intelligence of an agent now known as the. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results. [ ] For example, performance at (i.e. Checkers) is optimal, [ ] performance at chess is high-human and nearing super-human (see ) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.

A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from and. Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human.

In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Applications [ ]. Main article: AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the. High-profile examples of AI include autonomous vehicles (such as and ), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as ), online assistants (such as ), image recognition in photographs, spam filtering, prediction of judicial decisions and targeting online advertisements. With social media sites overtaking TV as a source for news for young people and news organisations 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. Competitions and prizes [ ].

A patient side surgical arm of. Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer.

In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called 'Hanover'. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.

Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. According to, there was a recent study by surgeons at the Children's National Medical Center in Washington which successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. IBM has created its own artificial intelligence computer, the, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy!

Against former champions, but, was declared a hero after successfully diagnosing a women who was suffering from leukemia. Automotive [ ] Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016, there are over 30 companies utilizing AI into the creation of. A few companies involved with AI include,, and. Many components contribute to the functioning of self-driving cars.

These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle. Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway. One main factor that influences the ability for a driver-less automobile to function is mapping.

In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings.

However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brakes, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions. Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high risk situations. These situations could include a head on collision with pedestrians.

The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programing of the car in these situations is crucial to a successful driver-less automobile.

Finance and Economics [ ] have long used systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in can be traced back to 1987 when in USA set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services. Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated competition. AI has also reduced fraud and financial crimes by monitoring of users for any abnormal changes or anomalies.

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI based buying and selling platforms have changed the law of in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient.

Other theories where AI has had impact include in,,,, and. Video games [ ]. Main article: Artificial intelligence is used to generate intelligent behaviors primarily in (NPCs), often simulating human-like intelligence. Platforms [ ] A (or ') is defined as 'some sort of hardware architecture or software framework (including application frameworks), that allows software to run'. As Rodney Brooks pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation. A wide variety of platforms has allowed different aspects of AI to develop, ranging from such as to to robot platforms such as the with open interface.

Recent advances in deep and distributed computing have led to a proliferation of software libraries, including,, and. Collective AI is a platform architecture that combines individual AI into a collective entity, in order to achieve global results from individual behaviors. With its collective structure, developers can crowdsource information and extend the functionality of existing AI domains on the platform for their own use, as well as continue to create and share new domains and capabilities for the wider community and greater good. As developers continue to contribute, the overall platform grows more intelligent and is able to perform more requests, providing a scalable model for greater communal benefit. Organizations like Inc.

And the have used this collaborative AI model. Education in AI [ ] A study found a shortage of 1.5 million highly trained data and AI professionals and managers and a number of private bootcamps have developed programs to meet that demand, including free programs like or paid programs like. Partnership on AI [ ] Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence. They stated: 'This partnership on AI will conduct research, organize discussions, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning.' Apple joined other tech companies as a founding member of the Partnership on AI in January 2017. The corporate members will make financial and research contributions to the group, while engaging with the scientific community to bring academics onto the board.

Philosophy and ethics [ ]. Main articles: and There are three philosophical questions related to AI: • Is possible?

Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish? • Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically? • Can a machine have a, and in exactly the same sense that human beings do?

Can a machine be, and thus deserve certain rights? Can a machine cause harm? The limits of artificial general intelligence [ ].

— A common concern about the development of artificial intelligence is the potential threat it could pose to humanity. This concern has recently gained attention after mentions by celebrities including,, and.

A group of prominent tech titans including, Amazon Web Services and Musk have committed $1billion to a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. In his book, provides an argument that artificial intelligence will pose a threat to mankind.

He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not 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. For this danger 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. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence. Concern over risk from artificial intelligence has led to some high-profile donations and investments. In January 2015, donated ten million dollars to the to fund research on understanding AI decision making. 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 and to 'just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there.' Development of militarized artificial intelligence is a related concern. Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers. Devaluation of humanity [ ]. Main article: wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as or 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 ). To Weizenbaum these points suggest that AI research devalues human life. Decrease in demand for human labor [ ] Martin Ford, author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, and others argue that specialized artificial intelligence applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers to perform most routine and repetitive jobs.

Ford predicts that many knowledge-based occupations—and in particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible to. [ ] Artificial moral agents [ ] This raises the issue of how ethically the machine should behave towards both humans and other AI agents. This issue was addressed by Wendell Wallach in his book titled Moral Machines in which he introduced the concept of (AMA). For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as 'Does Humanity Want Computers Making Moral Decisions' and 'Can (Ro)bots Really Be Moral'.

For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs. Machine ethics [ ]. Main article: 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. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines.

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.'

Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition 'Machine Ethics' that stems from the AAAI Fall 2005 Symposium on Machine Ethics. Malevolent and friendly AI [ ]. Main article: Political scientist believes that AI can be neither designed nor guaranteed to be benevolent.

He argues that 'any sufficiently advanced benevolence may be indistinguishable from malevolence.' Humans should not assume machines or robots would treat us favorably, because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). 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. Physicist, founder, and founder 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 '. One proposal to deal with this is to ensure that the first generally intelligent AI is ', and will then be able to control subsequently developed AIs. Some question whether this kind of check could really remain in place.

Leading AI researcher 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.' Machine consciousness, sentience and mind [ ]. Main articles: and Computationalism is the position in the that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.

Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers and.

Strong AI hypothesis [ ]. Main article: 's considers a key issue in the: if a machine can be created that has intelligence, could it also? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, such as the film, in which humanoid machines have the ability to feel emotions.

This issue, now known as ', is currently being considered by, for example, California's, although many critics believe that the discussion is premature. Some critics of argue that any hypothetical robot rights would lie on a spectrum with and human rights. The subject is profoundly discussed in the 2010 documentary film. Superintelligence [ ]. Main articles: and If research into 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.

The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer named this scenario '. Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. 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. Has used (which describes the relentless exponential improvement in digital technology) to calculate that will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.

Transhumanism [ ]. —, April 1985 Robot designer, cyberneticist and inventor have predicted that humans and machines will merge in the future into that are more capable and powerful than either. This idea, called, which has roots in and, has been illustrated in fiction as well, for example in the and the science-fiction series. In the 1980s artist '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 and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Argues that 'artificial intelligence is the next stage in evolution', an idea first proposed by 's ' (1863), and expanded upon by in his book of the same name in 1998.

In fiction [ ]. Main article: Thought-capable artificial beings have appeared as storytelling devices since antiquity. The implications of a constructed machine exhibiting artificial intelligence have been a persistent theme in since the twentieth century. Early stories typically revolved around intelligent robots. The word 'robot' itself was coined by in his 1921 play, the title standing for '.

Later, the SF writer developed the. He subsequently explored these in his many books, most notably the 'Multivac' series about a super-intelligent computer of the same name. Asimov's laws are often brought up during layman 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. The novel, by, tells a science fiction story about Androids and humans clashing in a futuristic world. Elements of artificial intelligence include the empathy box, mood organ, and the androids themselves. Throughout the novel, Dick portrays the idea that human subjectivity is altered by technology created with artificial intelligence.

Nowadays AI is firmly rooted in popular culture; intelligent robots appear in innumerable works., the murderous computer in charge of the spaceship in (1968), is an example of the common 'robotic rampage' archetype in science fiction movies. (1984) and (1999) provide additional widely familiar examples. In contrast, the rare loyal robots such as Gort from (1951) and Bishop from (1986) are less prominent in popular culture. See also [ ] • • • • • • • • • • Notes [ ]. Berlin: Springer.. Introduction to Artificial Intelligence (2nd ed.).

• Neapolitan, Richard; Jiang, Xia (2012).. Chapman & Hall/CRC.. Artificial Intelligence: A New Synthesis. Morgan Kaufmann.. •; (2003), (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall,. Upper Saddle River, New Jersey: Prentice Hall... New York: Oxford University Press..

Artificial Intelligence. Reading, MA: Addison-Wesley.. Artificial Intelligence. Artificial Intelligence: An Introductory Course (2nd ed.).

Edinburgh University Press.. Cambridge University Press.. History of AI [ ].

Typo in the article. The second sentence in the second item in the list at the beginning of the Conclusion looks like it should be after the list.

That is: “the complex problem of pay-to-publish journals with lax standards that cash in on the ultra-competitive publish-or-perish academic environment. At least one of these sicknesses led to “The Conceptual Penis as a Social Construct” being published as a legitimate piece of academic scholarship, and we can expect proponents of each to lay primary blame upon the other. ” Should be: “the complex problem of pay-to-publish journals with lax standards that cash in on the ultra-competitive publish-or-perish academic environment. At least one of these sicknesses led to “The Conceptual Penis as a Social Construct” being published as a legitimate piece of academic scholarship, and we can expect proponents of each to lay primary blame upon the other.

I’m certain you are trying to make a point Clayton Cramer but somehow I’m missing it. The submission of a hoax and having it published is using the work of someone else without paying for it?

Either way, in response to your question of “theft” being a progressive vs. Conservative trait (a ridiculous premise to ponder), perhaps you should look to the fact that more than 98% of all prison inhabitants in the United States are staunch supporters of progressives, when surveyed. Many residing in prison because of theft convictions (and murder, and rape, and various other unfathomable actions). Extrapolate the facts surrounding “theft” and “conservatives” and “progressives” yourselfwhich group of political supporters are truly deplorable? Anyone wondering if One wonders who is trolling whom really here is actually trolling too.

Anyone wondering if Anyone wondering if One wonders who is trolling whom really here is actually trolling too. Is actually trolling too. Anyone wondering if Anyone wondering if Anyone wondering if One wonders who is trolling whom really here is actually trolling too. Is actually trolling too. Is actually trolling too. ***************************************** this is known as a Mad Magazine level three cascade anyone actually typing such a thing needs to figure out how to actually ( split infinitive ) use other ways!!!!

Also note that the previous sentence is a candidate for a cascade. I think that the record over 10 or so issues was several pages Dr.Sidethink Hp.

Even Sokol explicitly said that the actions of a couple of reviewers and editors should not lead anyone to condemn an entire field. (www.physics.nyu.edu/sokal/noretta.html) Why so much more strident here? By the authors’ logic, physics should be considered worthless nonsense because Jan Hendrik Schon had papers published in Science and Nature. (www.americanscientist.org/bookshelf/pub/physics-and-pixie-dust) And yet they consider that peer review system to be beyond reproach. I wonder why the different standards? Why does making up the contents of an article that is accepted for publication only reflect badly on non-scientific disciplines?

To be sure, that the authors’ spoof paper was accepted is a problem for many reasons. But the leap of logic to dismiss an entire field as being useless, something Sokol specifically warned against, is an equally big problem. The authors worry that gender studies folk will believe that, “men do often suffer from machismo braggadocio, and that there is an isomorphism between these concepts via some personal toxic hypermasculine conception of their penises.” But I don’t really see why a gender studies academic wouldn’t believe this This is NOT a case of cognitive dissonance. As much as the authors like to pretend like they have “no idea” what they are talking about, they clearly do. They are taking existing gender study ideas and just turning up the volume and adding more jargon. As if this proves a point against the field. The author’s biases are on their sleeve.

Their arguments are about as effective as a Men’s Rights Activist on Reddit. By using a backhanded approach in an attempt to give a coup de grace to gender studies academaniacs, all they’ve done is blow $625 and “exposed” the already well known issue of pay-to-play. If they wanted to make an actual case against the “feminazis” writ large, I suggest they “man” up and actually make a real argument rather than show a bunch of fancy words can fool some people.

Hard science has a longstanding,rigorous peer review process borne out of hundreds of years of testing hypotheses,using appropriate methodology yielding empirically derived evidence to support or disprove a hypothesis. Gender studies has its ideological narrative aggressively promulgated by those with a political agenda,and fails to even conceal its biases. Consequently any resultant research resembles a horse before cart approach. Flood the publishing houses with faux articles like this one to prove the point. And there’s also rubbish journals with fucked up reviewing – if reviewing takes place at all – in the hard sciences.

The question that this single experiment cannot answer is whether this is an endemic problem particularly in the field or just a bad journal. If you ask 10 renowned gender studies profs about the 10 best journal in their field and this one comes up repeatedly, then this indicates a problem. If you get similar papers accepted at multiple journals named as top-journals of the field, then there is a serious problem. If a majority of renowned gender studies academics defends this paper, then we have a problem. Congrats; you proved that a tiny journal meant to published C-grade papers for profit, in a completely broken publishing system, will have errors. You proved they’re also probably politically inclined, and you did so in such a way to betray your own inclination. You went all this length to attack the field of gender studies in a tortured way and now we need gender studies to explain why you were motivated to do it.

Absolutely super congrats to you both, The Big Boys of Rational Thinking, a thousand standing ovations to everybody involved. A field that’s been gutted from funding but which young people want to study in large numbers has been proven to have a political agenda. You did this by publishing in a journal that isn’t in the field, but a profit mechanism for a publishing house. Meanwhile, all the big fans of your hoax are – oh, surprise – on the anti-feminist alt-right and the diminished-reputation rationalists of the Dawkins/Harris brand of hectoring. Big boy high fives to you and your fans. I just want to point out that the term “penis” IS a social construct in a very limited sense — in that it can be used to refer to a variety of sperm-transferring organs that evolved independently of each other and are structurally very different from one another. So biologists will describe male ostriches and ducks as having a “penis,” but the female spotted hyena has a “pseudo-phallus” or a “greatly enlarged clitoris.” In some ways, the lady hyena’s junk is much more “penis like” than a gentleman ostrich’s junk, but in the case of the ostrich, the organ’s main function is to channel sperm, which is not the case for the hyena.

Tamil Karaoke Songs For Male Singers Free Download. Whoever criticizes the left is now labeled “alt-right”. The great bogeyman of our age. Wait, strike that. I meant to say the great “bogeyperson” nearly offended 8 or 9 people there. Crisis averted. Anyway, the alt-right, sure yeah.

Makes a lot of sense too. I mean, think about it. Who else would question the deranged dogma coming from the safe space cult? It could only be those wacky caricatures known as the alt-right! Because as you know, no one disagrees with the left except the right.

No such thing. Only (alt)left and (alt)right. I said alt-right more than three times in this post.

Doesn’t that summon evil spirits or something? I think I read an article that said it does, so it’s pretty much a fact now. As is typically the case, the answer lies somewhere in the middle.

What these authors MAY have stumbled upon is a smoking gun that is problematic in the field. That one (frankly terrible) journal published it isn’t all that surprising.

As the journaling world goes, authors know to submit their work to subsequently less-prestigious journals to be published in. This is a crap journal but A meta-analysis of multiple papers of it’s ilk (and some way of “rating” a journal’s prestige with a blind study on the side among academics) could be a potentially amazing story if it hasn’t already been done.

This feels strangely like something John Oliver covered •. This paper should clearly have never been published. However, there is a key difference between what you have done here, and what Sokal did.

Sokal’s paper on quantum gravity was pure, unadulterated nonsense. What you have done is take a concept that is fundamentally sound – the conceptual penis – and dressed it up in nonsense. We often refer to male sexual organs in a conceptual, figurative sense. When a man is tough and daring, he has huge balls. When a middle-aged man buys a sports car, we describe it as a penis substitute.

A swaggering, confident male is described as a big swinging dick. We do conceptualise the penis; contrary to your assertion that your paper “didn’t say anything meaningful”, I think that there is a small kernel of meaning in amongst the thousands of words of jibberish. I’ll pull out the ‘no true Scotsman’ here. ‘No true skeptic’ would actually think this hoax achieved anything like what it claims, if they were being truely skeptical. When a result such as this conforms to your already held position, then you should, at the very least, apply some small amount skepticism, if not to the same level as you would if a result was the opposite of what you already believe.

If gender studies is BS, just like say homeopathy is BS, then argue about its specific claims. Getting a paper like this published demonstrates nothing other than it’s possible to get a paper like this published.

I imagine what I’d think if this happened in real science. If it was a one off, I think I’d dismiss it as BS and not an indictment of a whole field. But then again •.

I don’t think drawing the conclusions that you do about postmodern scholarship and gender studies are justified based on your experiences publishing something in an open-access, pay-to-play journal. Get this published in a more respectable place and then you might have something to talk about. It is also interesting to note that both authors work in fields that are very masculine and they happen to be fields where men often feel most threatened when gender inequality is raised. Perhaps we are dealing with some fragile masculinities here? These “mighty oaks” should check out Connell’s “Masculintities.” •.

Using politics to critique the academic spaces of the marginalized is disgusting, but this is much worse – it’s lying through omission and obfuscation of context, through the suggestion of impropriety on the behalf of an entire field of legitimate study. That’s not moral or ethical, it’s shameful. Smoke, but no fire. The rot you’re revealing is not the rot you think it is, and any good this article could do will be drowned out by the insane harm it will assuredly do.

Maybe your real penises don’t match your conceptual ones? Journals in on other fields and traditional publishers have also published non-sense papers, e.g. A computer-generated nonsense paper [1] was published in the Elsevier journal Applied Mathematics and Computation. Should we now raise questions about the fundamental integrity of fields such as mathematics? Also considering the fake Elsevier journals, the STAP scandal, etc. It is obvious that the failure to detect basic issues with peer review is not limited to open-access pay-to-publish models.

Ah, such rigorous skepticism! Your main point was disproven when NORMA rejected you out of hand, but you went ahead and wrote the same article you would have otherwise.

Apparently getting published by an obscure, pay-for-publication, vanity journal with no connection to gender studies somehow proves that “the echo-chamber of morally driven fashionable nonsense coming out of the postmodernist social ‘sciences’ in general, and gender studies departments in particular.” It’s almost as if this blog was morally driven and speaking into an echo chamber. An idea for your next article: yell something into a paper bag and declare victory over critical race studies. These “hoaxers” are simply doing terrible science. An experimentalist can not simply run their experiment under progressively less stringent conditions until they get their desired result. This hoax typifies the inversion of science, where people have unchanging conclusions and seek only to find experimental support for them. They had a hypothesis, they did an experiment, the experiment did not support the hypothesis.

Science and scientific skepticism is, at its core, having the courage to let evidence change your view. From that, we can conclude these hoaxers are not doing science. I’ll pull out the ‘no true Scotsman’ here. ‘No true skeptic’ would actually think this hoax achieved anything like what it claims, if they were being truely skeptical. When a result such as this conforms to your already held position, then you should, at the very least, apply some small amount skepticism, if not to the same level as you would if a result was the opposite of what you already believe. If gender studies is BS, just like say homeopathy is BS, then argue about its specific claims. Getting a paper like this published demonstrates nothing other than it’s possible to get a paper like this published.

I imagine what I’d think if this happened in real science. If it was a one off, I think I’d dismiss it as BS and not an indictment of a whole field. But then again, maybe if the authors had just googled: – Publishers withdraw more than 120 gibberish papers – Nonsense paper written by iOS autocomplete accepted for conference •.

TC “I imagine what I’d think if this happened in real science. If it was a one off, I think I’d dismiss it as BS and not an indictment of a whole field.” It’s not a one-off.

Your point about the limited conclusions you can draw from this is worth considering, but you can find other papers like this one that have not been retracted. For instance: Feminist Glaciology, which was not a joke and is in a real geography journal. We know why this kind of junk gets published and where these kinds of ideas came from.

Its a reflection of the influence of postmodernism that some academics believe good scholarship is inscrutable and useless. I read this hoax more as an expose than a scientific experiment – the point was to call attention to a problem rather than prove it exists. Maybe Boghossian and Lindsay are planning a follow up similar to Fashionable Nonsense in the aftermath. In a discipline based on politics rather than scholarship, not what IS but what OUGHT, there is no standard of truth. There’s just what you can convince people of. Not that this at all excuses how they were able to just MAKE UP references and things.

It’s easier to get a paper published in a political journal than to get a decent grade on a term paper. But, as people have pointed out here, these sorts of hoaxes have been played on scholarly journals too. The biggest takeaway, to me, is that you can apparently get anything published as long as it plays to the prejudices of the editorial board.

That is a good thing to keep in mind for anyone in any discipline, even ‘zines like Skeptic. Just tell people what they want to hear. And in case any STEM majors out there are wondering why this journal was recommended in the rejection letter from the other one it was a polite way of saying “why don’t you send this article to the dump, where it belongs”. In the humanities, the onus is on the prospective researcher to understand the aptness of any journal that might get recommended in a rejection letter, and if they are unsure, they talk it out with their advisor.

This week, I was even at a talk for both STEM+humanities grads on how to get papers published, and the presenter actually discussed this. So who on earth are these Ph.D.s who don’t know about this? What’s more, I have no idea how the Skeptic editors missed how clueless these authors are. As I said, incredibly embarrassing. Based on my encounters with the social studies literature and my attempted discussions with individuals who place high value on its implications, I legitimately wonder if responses of the form: “While the Reviewer suggested that we provide additional background information to justify the assertions made in the text, we would like to emphasize that it is not the Authors’ job to educate the Reviewer. Furthermore, responding to their criticisms is very emotionally draining. Therefore, we suggest that the Reviewer properly educate themselves and then re-read our manuscript until they understand it or simply accept it.

The Reviewer should understand that to reject our manuscript would be to wield their position of privilege so as to erase our already-marginalized viewpoint, exclude us from the academic discourse, and enact violence against our collective sense of self-worth.” would be considered valid and/or convincing in the social studies. As the authors of this piece note, much of the “work” in these fields appears to simply consist of unjustified assertions that are presented from a presumed enlightened moral authority and cloaked in incoherent jargon. I often wonder how seemingly intelligent individuals buy into this sort of trash masquerading as scholarship. I am happy to see that there are others who recognize the intellectual emptiness that is at the heart of these fields. I have to say, this article got quite a few chuckles out of this feminist.

I enjoy making a little fun of anyone who takes themselves too seriously. I am reminded of the small ads I use to see in my teen girl magazines, offering to publish your best poem in a book.all you had to do was buy the book for 20 bucks! Even this 15 year old could spot a scam when I saw one. It is unfortunate that such things still occur, but I doubt many academics can’t see through the ruse. The only readers of Cogent Social Sciences are likely only the people published in it, and a few unfortunate people around them that they are trying to impress, as being “published” authors. It is telling that the target chosen was Gender Studies, as opposed to many other possibilities from the liberal arts and “soft science” fields.

I know we are finally making real progress because the males are getting uncomfortable. First, they laugh at you; then they denounce you; then they accept it as truth. We have progressed to step 2!

I think this hoax is hilarious and confirms my view that there is a lot of complete nonsense written in some disciplines. That said, it occurs to me that there is no control i.e. Some sort of nonsense paper submitted to a physics or chemistry journal. If I were playing devil’s advocate, could I not claim that this hoax only proves that some journals will publish nonsense, rather than it is a problem with a specific discipline. I doubt that a serious science journal would publish nonsense, but it should be tested; and I wouldn’t be surprised if fake references could get past reviewers at a good science journal. The authors say that nobody is arguing, or has reason to argue, that reputable journals such as Nature have a fundamentally flawed or corrupt review process. That may be true, but there are people arguing that there is a fundamental problem with the way statistics are used in many serious journals and the conclusions which are drawn from those statistics.

The original post was spot on about gender studies. If there are students hoodwinked enough to pursue the field (and take out loans to do it!), I pity them. It is a bunch of pseudo intellectual nonsense. The lucky grads (the Brahmins) will find a teaching position and encourage more fools to major in a worthless subject.

How worthless is discovered by the majority of grads, those not lucky enough to get the cushy teaching position or NGO job. The have no real skills or critical faculties. Developing the latter would cause gender studies departments to impose since students with real skills of logic and inquiry would see through the BS. I’m enjoying the comments, particularly those trying to defend gender studies. Pretty difficult job to defend a field where radical politics is everything and any straying from the party line is condemned, no matter what the intellectual justification. I say close all those departments and give the positions to physics and math.

The University will improve measurably. News Flash: humanities departments are currently being underfunded compared to science departments and we’re all the worse off for it. Or would you rather have a system like the Chinese do that turns out legions of drone technicians who lack the writing skills to effectively convey their findings, or the critical thinking skills to form a rational hypothesis (or argument against a flawed one)? But we already have the best higher education system in the world, no need to dismantle it unless you want to pack it all up and concede to being second best (or worse). It’s not a zero sum game between the arts and the sciences. Thank you for bringing out two things I myself deal with in my own work: The insistence of the “echo-chamber” topics being INCLUDED in my work (even if they don’t FIT), and 2. The willingness of people to “get the word out” for those postmodern social topics in vogue presently without regard for accuracy.

The low information (or low-level of willingness to research on their own) folks will drink this like the Kool-Aid and no one will know any better. Thank you for doing this. It was absolutely necessary.

You essentially submitted a paper claiming that the phallus is a valid concept. Since there are hundreds of papers showing that it is, it was published. Sorry, where is the hoax? Is it that you think your paper is meaningless, like Sokal’s was? It’s a really uncontroversial claim that you appear to have no ability to assess because you have no background in gender studies.

For example, one of the claims you believe should have resulted in rejection is that your paper rejects simple notions of “biological male”, but these have been discredited in gender studies for so long that they have been discredited in endocrinology as well – the case that the term “male reproductive organ” or similar terms refers to a simple biological one-to-one relationship would have been difficult to make in the 19th century, and yet your paper only works as a rhetorical device if that simple penis=male statement is absolutely true. Ummm As scientists you should know better than to reach conclusions from a single data point.

You have a single gibberish article accepted into a marginal journal. To actually support your claim that it was accepted because of the catch-phrases it uses, you need to produce at least 20-30 gibberish articles with these catch-phrases, as well as another 20-30 without the catch-phrases, but which are otherwise no different.

If the 20-30 articles with the catch phrases are accepted at a significantly higher rate than those without the catch phrases, then, and only then, you can make a claim as to a positive relationship between presence of catch phrases and acceptance. At this point, all you have is an anecdote. I recommend that the authors take a refresher course in Experimental Design. I still can’t stop laughing at your “wonderful article.” Integrating it into climate change was priceless.

When I was in graduate school in the 1970s we were all studying Critical Theory from the Frankfurt School — Adorno, Horkheimer, Marcuse, etc. It was deep going because of the Hegelian/Marxist overlay best represented by journals such as TELOS. BUT IT FORCED USE TO READ THE GIANTS OF POLITICAL SCIENCE, SOCIOLOGY, AND ECONOMICS. Gender studies appears to be almost total nonsense with a vocabulary that is completely unintelligible.

Unfortunately, the entire field of gender and sexual studies appears to be a HOAX. This sort of thing needs to be done continually to expose the intellectually barren and preposterous stuff that goes on in the various grandchildren of sociology. Students are learning meaningless concepts and non-empirical “theories” in order to justify the appointment of closed-minded “scholars,” who argue that actually performing research would be enacting a paternalistic paradigm designed to oppress women and people of color. We worry about attacks on science from the right while often ignoring the crap that is accepted as science on the left. Why pick gender studies specifically to skewer with your really impressive conceptual penises? While your point that reputation and peer review is lacking in some quarters appears well grounded, I question your conclusion that the professionalism of the field of gender studies per se is called into question by your result.

I do not defend the field; I simply note you have failed to present convincing evidence to back up your claims about it. Your entire body of evidence relevant to this contention consists of your conclusion that the guilty journal is reputable. You base this conclusion upon the single fact that an individual from a supposedly reputable journal referred you to it. In effect, you have posited a transitive property of reputability of an absolute nature. There have to be better criteria for making a finding of reputability than that.

Further, you have reached a global conclusion based upon a single data point. Put it back in your pants, fellows. You’re embarrassing yourselves. I think that this hoax elucidates the problems with the open access journal publication process, rather than any overarching ideological problems concerning postmodernism. Simply claiming, over and over, that a subject is ‘nonsense’ does not make it nonsense.

It just means that the person shouting “this is nonsense” thinks it is so. For example, the authors of this article really just showed that they know surface level terms and ideas regarding the field of gender studies and postmodernism. The theory of the “conceptual penis” is not an original idea; it is essentially performativity theory with a specific focus on embodiment theory (applied to one aspect of the body). These authors are just construing the ideas of Judith Butler (in an extremely abridged and vague way that demonizes men– something that Butler does not aim to do in her revolutionary work). This could be why the peer-reviewers thought the conceptual penis was indeed enlightening- because it was based off of the ideas of Butler. The link to climate change is a satire of eco-feminism, a division of feminist thought that describes the intersectionality between patriarchy and environmental issues.

One could do essentially the same thing in reverse (just read any article written by The Onion). What I gleamed from this article is that one could use these absurd (non-academic) open access journals to publish a satire hidden in flowery academic lingo. I might be more concerned if this was a research funded project or masters thesis, but it’s really just laughable rather than anything ideologically concerning. You’re REALLLY easy on the vanity press angle here while displaying the same sort of hysterical “postmodernphoboa” that has existed and persisted since the 80s.

*Yawn.* Try being original next time if you want to try be clever. Pay for play is a problem, but it’s not linked to the conditions of a “publish or perish” environment. Rather, it smacks of the exploitative conditions of a system that values capital and property rights over the advancement of knowledge. And the apoligism for the corporate/predatory nature of the publishing racket here is reprehensible.

Mediocrity *should* be weeded out, and academics *should* produce legitimate research publications relative to their field, or else make room for more deserving individuals. We shouldn’t encorage the existence of a system that tolerates/rewards dead wood intellectuals to squat over tenured positions while more capable and ambitious individuals are forced into an exploitative adjunct system. And while I would agree that the “social sciences” are ersatz sciences, the phallogocentric elevation of science itself to the level of a master discourse smacks of uncritical religiosity. This type of “holier than thou” attitude is as childish as it is churlish. You realize that this paper not only got published, but will, in the future, be regarded as the most important social science paper of the early 21st century. Like Einstein’s 1905 paper on special relativity, it will launch entire new fields of study that fund the careers of thousands and change the way the human race thinks about reality for ever.

Statues of Peter Boyle and Jamie Lindsay will be erected (strike that) will be BUILT (yeah much better) on every major college campus in the country, and buildings, parks and safe spaces will be named after them. Thanks, guys. Not super impressive. The fact these guys try to put their work on par with Sokal’s hoax is unfortunate. It’s a little facile to even call this a hoax. I could write a junk poem and submit it to a predatory poetry journal that makes me pay to get it published and then say I’m a published poet. Look how easy it was!

Poetry is a scam. The authors worry that gender studies folk will believe that, “men do often suffer from machismo braggadocio, and that there is an isomorphism between these concepts via some personal toxic hypermasculine conception of their penises.” But I don’t really see why a gender studies academic wouldn’t believe this This is NOT a case of cognitive dissonance. Don’t get me wrong. I do enjoy pointing out the hypocrisy of the left as much as the next guy. But as much as the authors like to pretend like they have “no idea” what they are talking about, they clearly do. They are taking existing gender study ideas and just turning up the volume and adding more jargon.

I almost wish they were saying even less with more to prove a point. The author’s biases are on their sleeve. Their arguments are about as effective as a Men’s Rights Activist on Reddit. By using a backhanded approach in an attempt to give a coup de grace to gender studies academaniacs, all they’ve done is blow $625 and “exposed” the already well known issue of pay-to-play. If they wanted to make an actual case against the “feminazis” writ large, I suggest they “man” up and actually make a real argument rather than show a bunch of fancy words can fool some people. I just couldn’t stop laughing. I’m a “sharing” person.

I’ll “share” this WONDERFUL hoax with over 20 Facebook groups I hang with — with the URL to this article and full attribution, of course. Plus Twitter, my blog and a couple other bigger blogs. This might do more for REAL science than any of the usual complaints emanating from the “vast right wing conspiracy.” The number ONE characteristic one should bring to Internet browsing (and ingesting MSM offerings) is SKEPTICISM. It seems to be a trait in short supply regardless of one’s political leanings. BTW, for me it’s the most important characteristic to consider when voting for politicians.

Perhaps because real skepticism (non-tribal based skepticism) is all but nonexistent in this gullible group. The authors repeatedly claim that the article is nonsense or complete nonsense. As far as I can tell the only evidence in favor of this is their personal feeling that they deemed it so. Because my understanding of sensibility and meaning is one of readers’ interpretation or inability to do so, I’m skeptical than words on a page alone can technically be said to be meaningful or not on their own.

The author chose to take the term “climate change”, remove it from the context of the sentence in which it is found, and then substitute the general cultural idea of climate change comma insert it back into the sentence and use that new and different meaning as an example of ridiculousness. This doesn’t strike me as a fair move. When I was reading it I interpreted climate change to reflect to general perception within an area of study by the relevant community. This seemed appropriate for the context, and I’m not sure that criticism based on these sorts of moves is entirely legit, but would be interested to see a defense of such a standard especially one that can completely adhere to its own guidelines. “Oppression theology” majors — women’s studies, black studies, gender studies, LGBT studies, etc. — prepare students for one occupation — TEACHING this stuff. No sane employer should hire these graduates for meaningful work, given their inculcated hyper-sensitivity to every nuance in everyday workplace interactions.

These grads are legal liabilities poised to flower into a lawsuit (or federal investigation) at the earliest opportunity. The irony is that the resulting hiring “discrimination” by the wiser employers will be viewed by these misguided graduates as proof that their “oppression” professors were right! The entire field of Oppression Theology is the REAL hoax in academia. To all those who say that all this hoax did is expose a weak “pay for play” journal, and not expose a problem in Gender Studies, is the Journal “Women’s Studies International Forum” a well-regarded journal? It seems to be from everything I see. Not pay for play, been around since 1978, middle of the pack in Impact Factor – a mainstream journal for the field.

Here is the abstract from a paper published in that journal: “Don’t be so feminist”: Exploring student resistance to feminist approaches in a Canadian university. Abstract: This paper explores student resistance to feminist course content in social science courses cross-listed with women’s studies as an example of social reproduction at work. Drawing on both interviews and anonymous student course evaluations, student resistance to feminism is examined from the layered perspectives of faculty, teaching assistants and students in these courses.

The author argues that a regime of rationality still operates in the academy and is made evident when feminist course content is met with continual dismissal or disavowal Three points: 1. Not a hoax, but could be given the last sentence of the abstract. The last sentence of the abstract is true.

The rot runs deep in Gender Studies. It is a disgrace that even a single student goes into non-discharable debt to pursue a degree in the field. Unless they are one of the lucky few who land a spot in the Gender Studies Perpetuation Machine, they are screwed since they have no skills.

Most of what they “know” is not true and they have weak (at best) critical thinking skills, because, as the cited paper makes clear, rational thinking causes the thinker to reject what passes for knowledge in Gender Studies. As noted above, I share the authors’ criticisms of gender (and other similar) studies. However, it is also true that no strong conclusions about anything can be drawn from this exercise. However, it seems that many people here do not understand what open access academic journals are. Open access journals make articles free to the public by charging the authors to publish, and there are many legitimate open access journals (often affiliated with traditional publishing outlets like Taylor and Francis, Elsevier, Wiley, Nature Publishing Group, etc.) that reject many papers based on the recommendations of independent reviewers.

The fact that the authors paid the journal to publish their article does not imply that the journal would publish anything given that they receive payment. Traditional journals tend to charge around $40.00 just to download an article, and most individuals that don’t work at an institution with a subscription will simply not read articles published in those journals for that very reason. Unfortunately, traditional journals still can and do sometimes publish garbage.

Open access journals provide a solution to the paywall problem, and they are frequently used by many high-profile academics who wish to make their work freely available. In summary, there are problems with both traditional and open-access publication models, and the simple fact that a journal is open access (and charges authors to publish) does not mean that it is of lower quality than traditional journals or a predatory outlet. Part of what makes most predatory outlets predatory is that they don’t actually have formal peer review, and literally will publish anything submitted to them. These outlets are also typically (but not always) unaffiliated with any major traditional publisher. While nearly all predatory journals are open access, not all open access journals are predatory. Please do not get the idea that open access journals = pay-to-play predatory journals.

This is one of the very view reasonable comments in this topic. I am not shure if that is the only intention of the authors of this “study” (aside skepsis for gender science or certain flavors of humanities) – nevertheless: Paper quality correlates with reviewer quality. This is a problem for many journals – not only open source. Reviewers that are really highly qualified (see also Kruger and Dunnig, 1999) AND willing to work for free (anonymous, profound and fast) are hard to find – no matter what kind of business model a paper has. Boghossian and Lindsay are the equivalent of upper middle class white boys in high school, writing naughty words on bathroom walls with a Sharpie.

They probably went home to listen to Green Day, or, no, wait! The Offspring (their first album was pretty darned good). Boghossian and Mr. Lindsay, what you have done is not even interesting, and your assumed conclusions make it clear that skepticism is not your forte.

Better luck next time. (Still, I heard a great interview with Mr. Boghossian on Cognitive Dissonance a few years ago. This present silliness makes me think of the “Repo Man” quote: “I remember when I used to like these guys.”) •. Never heard of this journal; and that it says that it is peer-reviewed doesn’t mean anything (even though open-source does say much, and that’s not good). Next, these authors would publish in some journal called Cosmic News–and claim that a natural-science journal has fallen for their hoax!

These authors are too stupid to try hoaxes. The Sokal hoax was real; this one is a third-rate imitation–and there have been so many good ones in-between, including outside the social sciences. Not impressed at all!

In fact, it exposes these authors as quite clueless. As an undergraduate I did a well-controlled experiment about 58 years ago which showed beyond any reasonable doubt that you can affect the growth rate of baby chicks by prayer, in either a positive or negative direction. When I told one of my professors (Experimental Psychology) about this experiment a few years later, he advised me that I should not publish the study for fear of never getting a career in academe. Is it too late to publish my results now? How about in the Journal of Experimental Studies of Prayer? Or maybe I should just publish my study as a book and call it: Prayer Can Change Your Chickens.

Even leaving aside questions about the authors’ experimental method (which some of the other commenters have already addressed), as someone who considers himself a postmodern thinker, I’m of the opinion that this article is, in some ways, missing the point. It points out, quite rightly, that postmodernism really has no business involving itself in science, and that to refer to any postmodern work as scientific is misleading. However, I would argue that the scientific mode of understanding is not the only applicable one, and that in some areas it is in fact quite useless. Scientific thought is an appropriate tool—indeed, the only appropriate tool—for approaching mechanistic and quantifiable problems, but outside of that sphere, it quickly becomes next to useless. For example, if I asked a scientist why I enjoy the ‘Pirates of the Caribbean’ films, they could tell me a great deal about the function of dopamine in my brain, my reaction to various stimuli, and how certain evolutionary factors condition my response to Kiera Knightley in a lacy shift. Moving beyond the purely phenomenological, however, into questions such as why, for example, I continue to love the franchise when many other well-educated people consider it pulpish garbage, the scientist is forced to rely on increasingly thin and untestable hypotheses and not, in fact, on science at all. That is the appropriate territory of postmodernism as a school of critical thought, where science simply cannot go.

At that point, concept and understanding become more important than fact, because there are no stable facts. Meaning arises not only from the text, but from the reader. The ideal postmodern text resembles a Zen Koan as much as it does a thesis, and tells a reader less about the subject of the criticism than it does about how the reader is actually engaging with that subject. Indeed, with meaning understood to arise in the relation of the text and the reader rather than the author and the text, it is no longer necessary that the author understand or even intend the text. It is no longer necessary that there be an author at all.

One could argue, of course, that such an exercise is meaningless and valueless. I cannot easily refute that charge, for, as I have written, the proper domain of the postmodern is that which cannot be quantified. All I can say is that I believe there is value in self-evaluation and improvement, and in encouraging others to those same ends, even if that value is not apparent in any quantifiable fashion. Ok a few things: 1) Yes, gender studies and other “soft” social sciences are rife with this postmodern bullshit. I know this from personal experience, since I was constantly engaging with it as a graduate student in anthropology. 2) This does not mean gender studies offers nothing of value.

I’ve read some very clear, very good work in the field–Allison Wiley’s work on gender biases in archaeological interpretation, and “Imagining Transgender: An Ethnography of a Category” are two examples. However, the former is committed to grounding her theorization of gender biases in empirical reality, and the latter is an ethnography based on reporting what real people (mostly poor black people) actually say about themselves, so they reveal a reality which is (predictably) nothing like what the postmodern theorists say. 3) Gender studies as an institution (as opposed to gender studies insights as applied to other fields) undermines itself by being dominated by a very vocal extreme-left anti-science strand. There are legitimate (even brilliant) arguments to be found in what Judith Butler writes, for example, but you have to cut through so much bullshit to find them that few people who aren’t committed to doing so are going to take the trouble. 4) Unfortunately, this particular study was done in bad faith, and the trolls got trolled. If you know anything about the academic publishing industry, it’s clear from what they write that when their original paper was rejected by the original journal, and referred to “Cogent Social Sciences,” this was basically a polite “fuck you” by the publishers.

The “Cogent” series is obviously a money-making scam which will take money from anyone who is willing to pay to get published. The authors do focus on this a bit, but unfortunately choose to blame gender studies and postmodernism as to why their paper got published. In this, they succumbed to their own confirmation bias. Had they chosen to focus more on the perverse incentives of pay-to-publish open source–and on the even more perverse fact that respected publishing houses are directing their rejected papers to such scam journals, lending those journals an aura of legitimacy they don’t deserve–it would have been a much better paper.

5) This study will, unfortunately, just create more unnecessary bad blood between feminists and skeptics. As someone who identifies as both, this pisses me off, as I know I will be alienating a portion of my friends and colleagues on either side of the barricades simply by insisting on being both. Yes, it IS possible to be a fan of both Sam Harris and Sam Bee, and I refuse to take seriously anyone who tells me otherwise.

6) For a much better critique of the toxic consequences of hard-left moral panic in gender studies, see “This is what a Modern-Day Witch Hunt Looks Like” by Jesse Singal: 7) In conclusion, fuck binary thinking. Those who insist we must choose sides in this infantile flame war between the extreme left and the extreme reaction to it are both wrong. Each and every claim must be evaluated on its merits, and that includes qualitative data (such as recounted personal experiences and even traumas) which cannot be subject to controlled experiments.

8) And–here’s something of value I learned from gender studies–those of us doing the evaluations need to understand that sometimes, identity matters when doing research. As a Jew (an atheist Jew, but still a Jew), I understand anti-Semitism better than non-Jews do. Any non-Jew who tries “goysplain” to me why Hamas is not a bunch of antisemitic theocrats, but simply “an anticolonial resistance movement” (yes, I’ve actually had people say this to me) can go fuck themselves. They may well be an anticolonial resistance movement, but they are also antisemitic theocrats, and the latter cancels out any value I might see in the former. End of discussion. Likewise, the same principle implies that I as a man cannot truly understand sexism–not at the level of experience.

So when women talk to me about sexism, it is my responsibility to listen to what they have to say. This does not mean that I have a duty to let this principle infringe upon my other principles–intersectionality taken to its extremes results in the theater of the absurd that we call Twitter–but I don’t simply ignore it either. I do the best I can to be a feminist without also violating my other moral commitments (such as rationalism, atheism, and secular liberalism). This is called “making complex moral choices in a complicated world.” This is how most people behave most of the time. Unfortunately, it doesn’t fit very well into 240 characters. 9) My hunch is that much of what the authors, and many of the commenters, are characterizing as “the left,” “feminism,” and “gender studies” are a mental caricature they have encountered from the most ridiculous self-parodies which, unfortunately, are amplified by the system of incentives in both contemporary academia and on the vitriol of the internet and social media. Talk to these people in real life, and you will find the public persona drops and a complex human being emerges.

10) I would also urge feminists to consider doing the same for the authors of the article. It’s tempting to dismiss them as “science bros” and move on. Michael Shermer helped introduce me skepticism as a kid, and I’ll always appreciate that.

And as poor the generalizations these guys are making (and as poorly-done the experiment), had I taken a slightly different career path I could easily have made those same generalizations, and fallen for the same experiment. So it’s not like I don’t understand the place where they’re coming from. 11) Consider this very long comment a plea. A plea for dialogue and civility. On the internet.

In the age of Trump. “When they go low, you​ go high.” That may sound incredibly naive given that we lost the election, but in the long run, that’s the only way humanity is going to survive as a species. All Trump had to do was jump over the incredibly low bar of moral and ethical standards set by the Clinton Foundation, and endorsed by the people who picked her as a candidate and voted for her.

So climb down off your virtue broadcasting White Stallion. “9) My hunch is that much of what the authors, and many of the commenters, are characterizing as “the left,” “feminism,” and “gender studies” are a mental caricature they have encountered from the most ridiculous self-parodies which, unfortunately, are amplified by the system of incentives in both contemporary academia and on the vitriol of the internet and social media. Talk to these people in real life, and you will find the public persona drops and a complex human being emerges.” The same applies to the “deplorables”, friend. Jj2105: I actually agree with you about how many liberals preemptively dismiss Trump voters and their genuine pain–economic, cultural, whatever. You should understand that this liberal dismissal comes from a state of shock and humiliation after a period of hubris while we were winning the culture wars, as well as a very real and legitimate fear for our rights under a president who is highly authoritarian in his worldview. The “deplorables” comment by Clinton is indicative of a toxic attitude against those who don’t think like us that is far too common in the liberal bubble. There are liberals out there who are aware of this.

Many of them are often afraid to speak in their own social circles for fear of being publicly flogged. I happen to think that both Clinton and Sanders were problematic in many ways (she was too cynical, he was too naive, both were too politically correct). But from my perspective–that is, my economic interests, my moral convictions, my concerns about human rights and authoritarianism–Trump is just unacceptable.

Nobody’s perfect, but there’s no excuse for this guy. That said, liberals did a lot of things let him win, and you’re absolutely right about that. A few liberal pundits, intellectuals, and journalists are increasingly willing to admit it publicly: Lastly, please don’t call me a “white knight,” whatever the fuck that means. I’m not virtue signaling; I’m trying to see if it’s possible to have a non-toxic political discussion on deeply divisive issues.

The fact that you’re inclined to doubt that I could possibly be doing this in good faith is part of the problem. We have a broken political culture. It starts with individuals like like you and me trying to find out why we disagree, and assume good faith until proven otherwise. Also this one from Van Jones: There is SOME self-criticism on the left, if you’re willing to look. The point is that those of us who care about making life better for everyone, including skeptics, should encourage healthy self-criticism within our own factions and communities.

It’s not enough to have a self-correcting research method (although that’s certainly a good thing). It’s necessary to also have the ability to be self-critical as individuals and as groups–and to also listen to criticism from others, including members of groups we might consider to be “political enemies.” •.

This hilarious hoax highlights a problem both with current models of journal publishing (but not with Open Access as such) and with political and moral biases in gender studies. The authors far from dismiss the whole field; but they are spot on when they draw parallels to a cult. Cognitive dissonance might well explain why the reviewers accepted this paper. I did a little fun experiment of my own and asked my 8-year-old (curious about gender issues, as-yet relatively free of political biases, raised to be a thinker) to review the paper – or at least the gist of the paper. I didn’t tell her it was a hoax. I told her the paper was reviewed, accepted and published (she is familiar with the process).

You can see the results here •. “We have now sunk to a depth at which the restatement of the obvious is the first duty of intelligent men.” — G. Orwell It’s a shame (but but not a surprise) that Sokal’s warning went unheeded in the halls of academia.

I remember very well when the Sokal Hoax “scandal” broke, mostly because the permeation of society by increasingly specious theories (all fixated on matters of ethnicity, gender, and sexuality; especially as they relate to economics) was really starting to ramp up. Something needed to be done, before the idiocy got out of contro. I hoped (in vain) that Sokal’s attempt to wake up the larger academic community, would be successful. Alas, the “church” that masquerades as rigorous scholarship, within the cloister of Social Science, largely ignored Sokal. Now, two decades later, the “church” is more powerful than ever. With a substantial army of enablers and allies within the academic community, the entertainment community, the media, and even government. I use the word church, because I am not sure what else to call a body of fairly same-minded moral zealots who have an entirely faith-based and fanatical reliance on ideas and concepts which cannot be proven, nor verified, beyond the minds of the clergy and parishioners.

That this church happily masquerades beneath the banner of science, and co-opts the language and gravitas of science (while brow-beating skeptics who dare to criticize the nonsense) is an active threat to the edifices of law, and scholarship. Precisely because the zealots actively attack our (society’s) freedom to question, to examine, and to point out that gibberish is gibberish.

That we (via tax dollars) continue to fund and foster this anti-Enlightenment madness, is a stupendously sick joke. Defund, defund, defund. Faster, please. I’d say the most astonishing thing is that your comment appears to be serious. “Two white guys wrote about something see everyone! That proves it.” Hold on, let me translate my comment into gender-studies cult speak gibberish for you: You need to educate yourself!!

Your comment betrays a deep ignorance of bilateral, critical deconstructive, logical fiduciary theorems within the grounding of pre-neo, sub-human, post-colonial framework. It’s quite obvious you don’t understand the works already well established by Foucault and Deridda vis-a-vis the dichotomy of proof and non proof, that is to say that your aPriori assumptions made post hoc with regards to the non-binary structural and systemic delineations at play within this blog post are so far out of the realm of sensibility that they can only be described as, well – stupid. Most of the commenters here are clueless. They probably see themselves as “skeptics” but don’t even realize they are just feeding their own confirmation biases.

“skeptics, like their opponents, are less a conclave of ice-minded Bayesian ratiocinators than a sports team or political faction looking to win. This means that (like all of us) they are (a) inclined to clutch at anything that has a superficial resemblance to evidence supporting their beliefs, and (b) not to want to let go of it, even when it becomes clear that it isn’t evidence at all, both because of various forms of anchoring bias, and because they don’t want to hand an advantage to their opponents by admitting they were wrong.” •. Sascha Schuenemann says: “If you want to criticise social sciences or gender studies in particular, please look at the many real issues instead of focusing on such BS.” The problem is that the social sciences often operate as if they are beyond criticism. No theory is too loaded with junk, to be bounced. Rejecting a theory as garbage merely gets critics accused of moral crimes: racism, sexism, homophobia, transphobia, misogyny, cisnormativism, and on and on.

These accusations are academic death sentences, for careers. Nobody wants to put her job on the chopping block.

So the garbage theories get passed along as gospel, with precious little challenge. In the hard sciences, garbage theory eventually gets exposed and scuttled (over time) because the theory fails to match observable data. Too many people can discover (independently!) that the theory doesn’t work, fails to reflect reality, et cetera. In the gender/ethnicity/sexual wing of social science, EVERYTHING is a matter of assertion. Get enough people to go along with a theory, whether the theory is loaded with junk or not, and the theory becomes doctrine. As with the Sokal Hoax, Boghossian and Lindsay have succeeded in deliberately writing a piece of trash which was accepted by “peer review” and published for one and only one reason: the trash appeared to support and flatter the doctrine—as agreed upon by the cognoscenti (cough, church elders, cough) in the field. Everyone “knows” men are problematic, therefore male anatomy is problematic, therefore a garbage article that promoted and expounded upon this conventional wisdom—even though it was completely bogus—was readily accepted.

“Real issues” are not helped by academics failing to do their due diligence, or (worse yet) actively supporting and promoting concepts which are anti-Enlightenment. Jj2105: I actually agree with you about how many liberals preemptively dismiss Trump voters and their genuine pain–economic, cultural, whatever. You should understand that this liberal dismissal comes from a state of shock and humiliation after a period of hubris while we were winning the culture wars, as well as a very real and legitimate fear for our rights under a president who is highly authoritarian in his worldview. The “deplorables” comment by Clinton is indicative of a toxic attitude against those who don’t think like us that is far too common in the liberal bubble. There are liberals out there who are aware of this. Many of them are often afraid to speak in their own social circles for fear of being publicly flogged. I happen to think that both Clinton and Sanders were problematic in many ways (she was too cynical, he was too naive, both were too politically correct).

But from my perspective–that is, my economic interests, my moral convictions, my concerns about human rights and authoritarianism–Trump is just unacceptable. Nobody’s perfect, but there’s no excuse for this guy. That said, liberals did a lot of things let him win, and you’re absolutely right about that.

A few liberal pundits, intellectuals, and journalists are increasingly willing to admit it publicly: Lastly, please don’t call me a “white knight,” whatever the fuck that means. I’m not virtue signaling; I’m trying to see if it’s possible to have a non-toxic political discussion on deeply divisive issues. The fact that you’re inclined to doubt that I could possibly be doing this in good faith is part of the problem. We have a broken political culture. It starts with individuals like like you and me trying to find out why we disagree, and assume good faith until proven otherwise. Also this one from Van Jones: There is SOME self-criticism on the left, if you’re willing to look. The point is that those of us who care about making life better for everyone, including skeptics, should encourage healthy self-criticism within our own factions and communities.

It’s not enough to have a self-correcting research method (although that’s certainly a good thing). It’s necessary to also have the ability to be self-critical as individuals and as groups–and to also listen to criticism from others, including members of groups we might consider to be “political enemies.” •.

When I look at my own thing as a social construct, I whip it out when talking to women. Just showing the thing inside my pants can cause the environment and climate to change.

For you see nobody can ignore my big proverbial enormous Trouser Snake hidden in the depths of my pants. The Earth as a whole reacts sending a tidal wave and hurricanes when the light shines from within. As a result, the entire ecosystem and structure of the food chain is changed forever. Instead of using a penis, society can use hot dog wieners or the ever popular Bratwurst so women are not offended.

I don’t want the Penis to become another social deconstruction of a nation. You show the stupidity of those who lay claim to a university education, and the perceived value of the peer review process. There is value there, but only if the work is done. Just wait till common core takes hold, and those children grow up to participate. It’s hard enough even now, to straighten out a grade school student, let alone those who pay to sit in front of professors that were taught by other professors with a weaker foundation of understanding, if you will. Case in point; An entire generation of geologists, who actually believed for many years, that oil is a fossil fuel. That dead animals and plant matter generates it, so it has a finite supply, and producers can therefore justify ever increasing prices for it, as it becomes harder to deliver to markets of increasing consumption.

It’s complete scientific nonsense of course, but once you gather a relatively young and uneducated group, and place them in front of a few who claim knowledge, you can spread this nonsense to an entire generation of youth, and hide the real science until a bright mind or improved technology emerges to challenge the status quo. Fact is, the PHD gains a mere 4%, of the total knowledge available in any field of study. The rest is by invitation only. Interference in education as far as I can determine, began about 1920, when for example we find quote “give me liberty or give me death”, was found in far fewer history books written after that year. This is one aspect of those in power, which were in the publishing business, for the express purpose of “dumbing down” education. This process continues of course, and has shown itself successful as a strategy.

Fewer students seek knowledge from the best books and sources. Not to let the philosophies and falsehoods of much of mankind throwing them off balance or off their course. Fewer seem to sift and discern error as they study and seek the truth. The purpose is to cause as many as they will to believe lies, that an illusion is complete. This then is combined by other forms of illusion creation, like advertising etc Lenin said” Give me a child for eight years, and it will be a Bolchevic forever”. He said the soundest strategy in war, is to postpone operations, until the moral disintegration of the enemy renders the mortal blow possible, and easy.

What we find today is a parallel in the west, to the time of the fall of Rome. Ignoring history, the west is already in decline. I believe this will continue, and few will emerge from the ashes. False educational ideas, are a serious threat. Fortunately I only had a limited exposure to higher learning to overcome, and to go back to the departure from truth to start again.

I had half my education in trades schools, and then military intelligence. I am a veteran of ten years.

Yes military and intelligence are definitely mutually exclusive words. I never had to un mess myself up too much. Since the only institutions of learning I attended after that were the few I was invited to speak. This is very disturbing that the “Skeptic” magazine is “proud” to publish this article.

Please read this article This article actual contains the following quote from Sokal “From the mere fact of publication of my parody I think that not much can be deduced. It doesn’t prove that the whole field of cultural studies, or cultural studies of science — much less sociology of science — is nonsense. Nor does it prove that the intellectual standards in these fields are generally lax. (This might be the case, but it would have to be established on other grounds.) It proves only that the editors of one rather marginal journal were derelict in their intellectual duty” But Lindsey, Shermer, and Boghossian try to claim that getting their hoax article in a pay-for-print journal after being turned down with by a journal with a ZERO influence score disproves the entire Gender Studies discipline.

Other links: I have lost all respect for Michael Shermer, Peter Boghossian and James Lindsey. Not to mention the Skeptic Society and Skeptic Magazine and any other skeptic/atheist leader that promoted this as nothing other than a publicity stunt by two “academics” spouting a political agenda as extremist as what they are failing to attack. I agree with other readers who would rather have them write an actual GS studies article disputing the claims of GS rather than clearly failing to produce a hoax article that could not get published in any truly respected journal. This reminds me of my final assignment in an MBA class I took called “Skills for Leadership”. (I didn’t take an MBA, I took another more niche program that has an overlap in courses with the MBA, but in hindsight, that program was BS too.) Not only did I get an A+ on my final assignment, but the lowest and highest scores were actually posted to us in email, and mine was the highest in a class of roughly 30.

From what I gathered through my peers, they all took this class (and the assignment) seriously. I, on the other hand, bullshitted like I never bullshitted before. I didn’t have the patience to learn the ad hoc and completely non-rigorous “methods” they taught us in the class as metaphorical means of “framing a problem” so I simply made up my own methods. I devised tools like the “ladder of opportunistic progress” or “social management cycle” that illustrated steps to be taken in a pseudo-psychological take of internal conflict. (I have no background in psychology.) I ended up with the best grade in the class. This is a respected class that is mandatory for MBA students. As the publisher of this hoax article, we wanted to let you and the readers of this post know about the steps we are taking following its publication.

You can read more about this here: Although the original intention was to question the field of gender studies, the fact that it was published in one of our journals is disappointing and has led us to conduct a thorough investigation. The authors, Peter Boghossian and James Lindsay, have agreed to speak to us and we are currently waiting to hear when they are available to do so. We also appreciate James’ comments on Twitter regarding those who have leapt to label us a “sham vanity journal”, something we categorically are not. As you’ll see more fully explained when you follow the link above, we will be using this incident to review our peer review processes, and academic editor and peer reviewer education programs.

We hope the many researchers, reviewers, and journal editors we have worked with since our inception will continue to support us as we do this. “We are reviewing our academic editor and peer reviewer education program to ensure editors and peer reviewers are fully equipped with the skills they need to assess whether a paper is fit for publication.” This itself indicates that Cogent is not a real journal. Real journals don’t have “education programs” to equip their reviewers and editors with “the skills they need to assess whether a paper is fit for publication” as they draw on established academics in the appropriate fields. Indeed, that this is needed shows that Cogent’s editors and referees are NOT equipped to assess papers. As others have already noted, this hoax paper draws more legitimate attention to important problems involving modern peer-review mechanisms than it does the field of gender studies.

If the goal was to explore the validity of the field, it may have been more useful to let their hoax paper sit published for a year or so, then examine the extent to which their paper was referenced by subsequent gender studies papers. One other point in the following section the authors have misused the word ‘significant’, which in the research world has well defined statistical implications.

“ wherever Cogent Social Sciences belongs on the spectrum just noted, there are significant reasons to believe that much of the problem lies within the very concept of any journal”. In this context, use of the word ‘important’ in place of ‘significant’ would more accurately convey the intended meaning without adding any potential, and in this case unwarranted, statistical gravitas. The fact that this article was published in a Skeptical magazine is the *real* disgrace. As others have already eloquently noted, the authors proved absolutely nothing about social studies and in fact went into the enterprise with an extremely *unskeptical* approach. I know it’s all the rage these days for skeptics to be Status Quo Warriors bravely fighting for the poor, downtrodden, White Male, but seriouslythis is just such an embarrassment. This is truly the Skeptic version of believing with intense certainty that the Earth is flat. I am really disappointed in the skeptic magazine for this piece it makes me really question a publication I have had so much respect and love for.

This is useful piece in exposing the dangers and problems of “author friendly” pay for publication journals. As another Sokolov study it shows the flaws in this system: auto generated responses, lack of any in-depth expert peer review and a willingness to publish anything that pays them. However similar to privious iterations of this approach this cannot be extrapolated to the validity of a whole field. This would be like saying computer science is all pseudoscience because the “get me off your fucking email list” paper was published.

There is no logical inference that can be made as is acknowledged in most Sokolov papers. The journal does not even have an impact factor this is not where any rigorous or important science is done. This could have been another interesting Sokolov study but the clear prejudice of the authors to try and force a non-sequitur political commentary into this piece is shocking and completely in supported by this paper. I am amazing this was published and endorsed in the skeptic magazine, a great publication I respected for sticking to hard facts and logic and removing bias, and I hope the skeptic magazine does better in future •. Congratulations, you managed to perpetrate the same hoax a number of other authors have played on a number of other pay-to-publish journals, thus hammering home the message that peer-review in all sciences is a flawed system. However, your little expose says NOTHING about the field of gender studies. I am not a student of gender studies, i am a neuroscientist and physiologist, so i am not familiar with the relevant literature and cannot speak of the validity of legitimate gender studies papers.

But if you think that this hoax proves anything about gender studies then these other hoaxes prove that computer science and theoretical physics are also bogus: •. As insane as gender studies are at many points —>managing to publish a hoax hardly proves anything, and you know it.

If we assume good intentions but dont understand a text, we give it the benefit of the doubt. We dont just dismiss it as nonesense. That’s actually a good thing. What uve done proves that people tend to read and hear things they wanna hear and read. That’s not exactly new, and pretty much the principle of any horoscope. Use vague and complex language, double sided statements and poeple will interpret it in a way that fits them.

It’s quite possible that some people are able to interpret something meaning full into your bullshit. A randomly generated scentence CAN make sense. It’s just unlikely. Trying to turn this into a political thing by accusing “academic political left” (whomever that is supposed to be) of beeing more susceptible to this stuff than others is just stupid. People just generally are. I wouldn’t even consider the genderfield “left” at all.

They may define themselves like that, but often the way they argue about everything beeing “constructed” is actually very close to the extreme right. They are “hip” at the moment but thats about it. A number of years ago, James Randi tested a dowser who claimed a 100% success rate. Randi agreed to pay the dowser $10,000 if the dowser succeeded in five out of ten tests. After the fifth test, the dowser stated that there was no need to run the remaining tests since he had already gotten five correct, but Randi said to run the remaining tests anyway. As it turns out, the dowser scored zero.

Asked if this would cause him to revise his claim of 100% success rate, he said it would not. His explaination for the ten failures was: “My power wasn’t working then.” Peter Boghossian and James Lindsay set out to demonstrate that something about the field of gender studies which I won’t try to characterize precisely. To test their hypothesis, they write a paper filled with nonsense, and submit it to an academic journal.

Their hypothesis is that the the paper will be accepted. Instead, the paper is rejected. Here I don’t want to get into the problems with their hypothesis or their test. And I certainly don’t want to get into the merits of gender studies, a field that I know absolutely nothing about. Instead, I want to focus on what they did when their test failed. Rather than admitting failure, they decided to try again with a different journal. It was apparently chosen, not because it made any sense in terms of the hypothesis (since it wasn’t a gender studies journal), but purely because they were given a strong indication that the journal would accept their paper.

Michael Shermer should be embarrassed that he published this paper. I wouldn’t expect an editor of Skeptic to understand the ins and outs of academic publishing.

I would expect him to understand the problem of accepting a test and then ignoring the results when they don’t turn out the way you want. What this has in common with Sokal’s hoax is that both were written by scientists (and one philosopher in the recent case) – that’s all.

Sokal’s was not only published in a more established journal, it offered a genuine and timely critique when and where it was much needed: it showed up the unreality of the language being employed by some practitioners of the social sciences, as a substitute for intellectual rigour. But the current case shows a complete lack of genuine engagement with gender studies, its issues and debates, its engagement with some of the most sensitive areas of human experience, etc. Instead, the writers simply set up a straw man – literally – to castrate him. The lack of intellectual – and political – rigour in this case, lies with the pranksters, not with its practitioners in the field. This is not to say that all who engage in gender studies are intellectually rigorous, always.

There is much mediocrity in this field, as in any field. Gender studies is not privileged to have more mediocrity simply because it is gender studies – unless one is of the opinion that thinking about gender is itself foolhardy.

But it seems that is exactly how these two writers would have it: if there has ever been a more obvious attempt to delegitimize the field of gender studies itself, I do not know of it. To some extent, the social sciences in general are always going to be vulnerable to such pranks from scientists. Unlike the latter, whose disciplines are premised on the possibility of epistemological certitude, the social sciences are in fact premised on the insubstantiality of such certitude in human and social matters. While there are probably much more comprehensible and lucid ways (than espoused by the votaries of po-stru, pomo, poco, etc) of thinking about, articulating and representing those uncertain social matters, the inarticulacies of those votaries do not the field of social science – or even the discipine of gender studies – make. In fact, given the routine reports one hears of plagiarized work and cooked up lab findings, which also find their way into very reputed publications in the ‘natural sciences’, there is perhaps some need for scientists to police their own intellectual fields, instead of wasting time – theirs and the worlds – with farce that is unwarranted. It speaks volumes for the depths of gender prejudices, that all those who are hailing this ‘hoax’, without exception, are men.

It only reaffirms the arguments of the field of gender studies, and underlines the urgent need for greater gender sensitization in the sciences (notorious for the lack of it) – which evidently remain committed to frivolous displays of machismo. There authors of this article illuminated quite clearly how gender studies and social-theory based “science” is not science at all, but in fact philosophical discourse. Social science involves experiments, surveys, research, test subjects, not philosophizing using arcane field-specific neologisms. That this is was done while simultaneously showing some of the cracks in the wall of “peer review” is simply a bonus. Hard science does not respect social theory sciences because, well, its not science! It is not a quest for truth via experimental process, but is in fact a workshop for disseminating ideology. While what you’re saying may be true, I think the problem here is that the authors of this did not carefully select their target in a way that was anywhere near as effective as Sokal, which really weakens their argument.

Based on my experience in the field, there is a serious problem with these sorts of philosophies, but it’s imperative to carefully plan a counter against them. As others have pointed out before, this study has been used against science for global warming deniers, so careful tact is necessary. You guys are heroes. Such great heroes. Slayers for truth, defilers of evil.

It is such an amazing work that you have wrought; it will be long remembered among the finest efforts of humanity. We are all better that such great heroes live among us!

Thousands of words of heroism, and not an ounce of hubris! Heroes such as these put the humility in every man’s bones, and every woman’s; self-improvement is the virtue that every woman and man aspires to in the wake of such heroes. They have so little hubris, nary any at all if we go looking for it. I am grateful to be alive with such great heroes.

Although the publication of this paper exposes the problem of the quality of open-access journals, it does not, in any significant way, establish the main conclusion that gender studies is an illegitimate field of study. Michael Shermer, Peter Boghossian, and James Lindsay appear to either have a huge blind spot when it comes to criticism of fields they think are problematic, or they are simply hoping their audience will. I am extremely disappointed with this example of poor argumentation and analysis. It is not worthy of skeptic magazine or of association with skepticism and good thinking more generally. I’m glad that I am now aware of these problems but why so much text for it?

A lot of it is redundant and filled with hard to read phrases and obscure vocabulary (although, it is all correctly used as far as I’ve checked). Maybe it would be fine if there was a better conclusion at the end or a suggestion or theory on how we could improve the situation, most of the text is just repeating the problem over and over again. Also, the last few paragraphs sound like you are confident that something is very wrong with the current system but the confidence is not argumented that well. Empirically, I can agree but it’s far off from being a fact. I do agree that this attempt at a hoax is pathetic. It’s likely that this was a low effort attempt at undermining gender studies. But I find the banter in the comment section just as pathetic – assuming “masculinity” and labeling your opponent as “alt right” shows an absolute lack of self awareness to your own lack of skepticism.

Also, it’s extremely inaccurate to describe Harris or Shermer as “alt right”; it’s a depressing scene to see old-left constantly mislabeled as “alt right” in convenience of a strawman. Shermer’s failure here to achieve proper skepticism is not the same as fitting into a specific set of beliefs. Raging Bee brings up a serious concern – your poorly thought out “stunt” is now being used by science deniers to peddle their nonsense. Great care should be taken with these sorts of issues. To the authors – try a more heavily researched attempt at a Sokal-like hoax presented to more prominent journals. I would love to see what is there. Don’t just aim for low hanging fruit.

I personally find these gender theorists counter intuitive towards actually helping achieve “equality”, and therefore they fail at their own purpose. While I can’t speak in regards to gender or race, the work that is generated by “disability advocates” is embarrassing, non-representative and, for lack of better words, absolute fashionable nonsense. I think that if we really want to help anyone with these issues, we have to take the pursuit of these hoaxes seriously and not settle for such low hanging fruit.