Computational semantics is a subfield of computational linguistics. Its goal is to elucidate the cognitive mechanisms supporting the generation and interpretation of meaning in humans. It usually involves the creation of computational models that simulate particular semantic phenomena, and the evaluation of those models against data from human participants. While computational semantics is a scientific field, it has many applications in real-world settings and substantially overlaps with Artificial Intelligence. Broadly speaking, the discipline can be subdivided into areas that mirror the internal organization of linguistics. For example, lexical semantics and frame semantics have active research communities within computational linguistics. Some popular methodologies are also strongly inspired by traditional linguistics. Most prominently, the area of distributional semantics, which underpins investigations into embeddings and the internals of Large Language Models, has roots in the work of Zellig Harris. Some traditional topics of interest in computational semantics are: construction of meaning representations, semantic underspecification, anaphora resolution, presupposition projection, and quantifier scope resolution. Methods employed usually draw from formal semantics or statistical semantics. Computational semantics has points of contact with the areas of lexical semantics (word-sense disambiguation and semantic role labeling), discourse semantics, knowledge representation and automated reasoning (in particular, automated theorem proving). Since 1999 there has been an ACL special interest group on computational semantics, SIGSEM.
Biorobotics
Biorobotics is an interdisciplinary science that combines the fields of biomedical engineering, cybernetics, and robotics to develop new technologies that integrate biology with mechanical systems to develop more efficient communication, alter genetic information, and create machines that imitate biological systems. == Cybernetics == Cybernetics focuses on the communication and system of living organisms and machines that can be applied and combined with multiple fields of study such as biology, mathematics, computer science, engineering, and much more. This discipline falls under the branch of biorobotics because of its combined field of study between biological bodies and mechanical systems. Studying these two systems allows for advanced analysis on the functions and processes of each system as well as the interactions between them. === History === Cybernetic theory is a concept that has existed for centuries, dating back to the era of Plato where he applied the term to refer to the "governance of people". The term cybernetique is seen in the mid-1800s used by physicist André-Marie Ampère. The term cybernetics was popularized in the late 1940s to refer to a discipline that touched on, but was separate, from established disciplines, such as electrical engineering, mathematics, and biology. === Science === Cybernetics is often misunderstood because of the breadth of disciplines it covers. In the early 20th century, it was coined as an interdisciplinary field of study that combines biology, science, network theory, and engineering. Today, it covers all scientific fields with system related processes. The goal of cybernetics is to analyze systems and processes of any system or systems in an attempt to make them more efficient and effective. === Applications === Cybernetics is used as an umbrella term so applications extend to all systems related scientific fields such as biology, mathematics, computer science, engineering, management, psychology, sociology, art, and more. Cybernetics is used amongst several fields to discover principles of systems, adaptation of organisms, information analysis and much more. == Genetic engineering == Genetic engineering is a field that uses advances in technology to modify biological organisms. Through different methods, scientists are able to alter the genetic material of microorganisms, plants and animals to provide them with desirable traits. For example, making plants grow bigger, better, and faster. Genetic engineering is included in biorobotics because it uses new technologies to alter biology and change an organism's DNA for their and society's benefit. === History === Although humans have modified genetic material of animals and plants through artificial selection for millennia (such as the genetic mutations that developed teosinte into corn and wolves into dogs), genetic engineering refers to the deliberate alteration or insertion of specific genes to an organism's DNA. The first successful case of genetic engineering occurred in 1973 when Herbert Boyer and Stanley Cohen were able to transfer a gene with antibiotic resistance to a bacterium. === Science === There are three main techniques used in genetic engineering: The plasmid method, the vector method and the biolistic method. ==== Plasmid method ==== This technique is used mainly for microorganisms such as bacteria. Through this method, DNA molecules called plasmids are extracted from bacteria and placed in a lab where restriction enzymes break them down. As the enzymes do this, some develop a rough edge that resembles that of a staircase which is considered 'sticky' and capable of reconnecting. These 'sticky' molecules are inserted into another bacteria where they will connect to the DNA rings with the altered genetic material. ==== Vector method ==== The vector method is considered a more precise technique than the plasmid method as it involves the transfer of a specific gene instead of a whole sequence. In the vector method, a specific gene from a DNA strand is isolated through restriction enzymes in a laboratory and is inserted into a vector. Once the vector accepts the genetic code, it is inserted into the host cell where the DNA will be transferred. ==== Biolistic method ==== The biolistic method is typically used to alter the genetic material of plants. This method embeds the desired DNA with a metallic particle such as gold or tungsten in a high speed gun. The particle is then bombarded into the plant. Due to the high velocities and the vacuum generated during bombardment, the particle is able to penetrate the cell wall and inserts the new DNA into the cell. === Applications === Genetic engineering has many uses in the fields of medicine, research and agriculture. In the medical field, genetically modified bacteria are used to produce drugs such as insulin, human growth hormones and vaccines. In research, scientists genetically modify organisms to observe physical and behavioral changes to understand the function of specific genes. In agriculture, genetic engineering is extremely important as it is used by farmers to grow crops that are resistant to herbicides and to insects such as BTCorn. == Bionics == Bionics is a medical engineering field and a branch of biorobotics consisting of electrical and mechanical systems that imitate biological systems, such as prosthetics and hearing aids. It's a portmanteau that combines biology and electronics. === History === The history of bionics goes as far back in time as ancient Egypt. A prosthetic toe made out of wood and leather was found on the foot of a mummy. The time period of the mummy corpse was estimated to be from around the fifteenth century B.C. Bionics can also be witnessed in ancient Greece and Rome. Prosthetic legs and arms were made for amputee soldiers. In the early 16th century, a French military surgeon by the name of Ambroise Pare became a pioneer in the field of bionics. He was known for making various types of upper and lower prosthetics. One of his most famous prosthetics, Le Petit Lorrain, was a mechanical hand operated by catches and springs. During the early 19th century, Alessandro Volta further progressed bionics. He set the foundation for the creation of hearing aids with his experiments. He found that electrical stimulation could restore hearing by inserting an electrical implant to the saccular nerve of a patient's ear. In 1945, the National Academy of Sciences created the Artificial Limb Program, which focused on improving prosthetics since there were a large number of World War II amputee soldiers. Since this creation, prosthetic materials, computer design methods, and surgical procedures have improved, creating modern-day bionics. === Science === ==== Prosthetics ==== The important components that make up modern-day prosthetics are the pylon, the socket, and the suspension system. The pylon is the internal frame of the prosthetic that is made up of metal rods or carbon-fiber composites. The socket is the part of the prosthetic that connects the prosthetic to the person's missing limb. The socket consists of a soft liner that makes the fit comfortable, but also snug enough to stay on the limb. The suspension system is important in keeping the prosthetic on the limb. The suspension system is usually a harness system made up of straps, belts or sleeves that are used to keep the limb attached. The operation of a prosthetic could be designed in various ways. The prosthetic could be body-powered, externally-powered, or myoelectrically powered. Body-powered prosthetics consist of cables attached to a strap or harness, which is placed on the person's functional shoulder, allowing the person to manipulate and control the prosthetic as he or she deems fit. Externally-powered prosthetics consist of motors to power the prosthetic and buttons and switches to control the prosthetic. Myoelectrically powered prosthetics are new, advanced forms of prosthetics where electrodes are placed on the muscles above the limb. The electrodes will detect the muscle contractions and send electrical signals to the prosthetic to move the prosthetic. The downside to this type of prosthetic is that if the sensors are not placed correctly on the limb then the electrical impulses will fail to move the prosthetic. TrueLimb is a specific brand of prosthetics that uses myoelectrical sensors which enable a person to have control of their bionic limb. ==== Hearing aids ==== Four major components make up the hearing aid: the microphone, the amplifier, the receiver, and the battery. The microphone takes in outside sound, turns that sound to electrical signals, and sends those signals to the amplifier. The amplifier increases the sound and sends that sound to the receiver. The receiver changes the electrical signal back into sound and sends the sound into the ear. Hair cells in the ear will sense the vibrations from the sound, convert the vibrations into nerve signals, and send it to the brain so
Data validation and reconciliation
Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation. == Models, data and measurement errors == Industrial processes, for example chemical or thermodynamic processes in chemical plants, refineries, oil or gas production sites, or power plants, are often represented by two fundamental means: Models that express the general structure of the processes, Data that reflects the state of the processes at a given point in time. Models can have different levels of detail, for example one can incorporate simple mass or compound conservation balances, or more advanced thermodynamic models including energy conservation laws. Mathematically the model can be expressed by a nonlinear system of equations F ( y ) = 0 {\displaystyle F(y)=0\,} in the variables y = ( y 1 , … , y n ) {\displaystyle y=(y_{1},\ldots ,y_{n})} , which incorporates all the above-mentioned system constraints (for example the mass or heat balances around a unit). A variable could be the temperature or the pressure at a certain place in the plant. === Error types === Data originates typically from measurements taken at different places throughout the industrial site, for example temperature, pressure, volumetric flow rate measurements etc. To understand the basic principles of PDR, it is important to first recognize that plant measurements are never 100% correct, i.e. raw measurement y {\displaystyle y\,} is not a solution of the nonlinear system F ( y ) = 0 {\displaystyle F(y)=0\,\!} . When using measurements without correction to generate plant balances, it is common to have incoherencies. Measurement errors can be categorized into two basic types: random errors due to intrinsic sensor accuracy and systematic errors (or gross errors) due to sensor calibration or faulty data transmission. Random errors means that the measurement y {\displaystyle y\,\!} is a random variable with mean y ∗ {\displaystyle y^{}\,\!} , where y ∗ {\displaystyle y^{}\,\!} is the true value that is typically not known. A systematic error on the other hand is characterized by a measurement y {\displaystyle y\,\!} which is a random variable with mean y ¯ {\displaystyle {\bar {y}}\,\!} , which is not equal to the true value y ∗ {\displaystyle y^{}\,} . For ease in deriving and implementing an optimal estimation solution, and based on arguments that errors are the sum of many factors (so that the Central limit theorem has some effect), data reconciliation assumes these errors are normally distributed. Other sources of errors when calculating plant balances include process faults such as leaks, unmodeled heat losses, incorrect physical properties or other physical parameters used in equations, and incorrect structure such as unmodeled bypass lines. Other errors include unmodeled plant dynamics such as holdup changes, and other instabilities in plant operations that violate steady state (algebraic) models. Additional dynamic errors arise when measurements and samples are not taken at the same time, especially lab analyses. The normal practice of using time averages for the data input partly reduces the dynamic problems. However, that does not completely resolve timing inconsistencies for infrequently-sampled data like lab analyses. This use of average values, like a moving average, acts as a low-pass filter, so high frequency noise is mostly eliminated. The result is that, in practice, data reconciliation is mainly making adjustments to correct systematic errors like biases. === Necessity of removing measurement errors === ISA-95 is the international standard for the integration of enterprise and control systems It asserts that: Data reconciliation is a serious issue for enterprise-control integration. The data have to be valid to be useful for the enterprise system. The data must often be determined from physical measurements that have associated error factors. This must usually be converted into exact values for the enterprise system. This conversion may require manual, or intelligent reconciliation of the converted values [...]. Systems must be set up to ensure that accurate data are sent to production and from production. Inadvertent operator or clerical errors may result in too much production, too little production, the wrong production, incorrect inventory, or missing inventory. == History == PDR has become more and more important due to industrial processes that are becoming more and more complex. PDR started in the early 1960s with applications aiming at closing material balances in production processes where raw measurements were available for all variables. At the same time the problem of gross error identification and elimination has been presented. In the late 1960s and 1970s unmeasured variables were taken into account in the data reconciliation process., PDR also became more mature by considering general nonlinear equation systems coming from thermodynamic models., , Quasi steady state dynamics for filtering and simultaneous parameter estimation over time were introduced in 1977 by Stanley and Mah. Dynamic PDR was formulated as a nonlinear optimization problem by Liebman et al. in 1992. == Data reconciliation == Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors. From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation. Given n {\displaystyle n} measurements y i {\displaystyle y_{i}} , data reconciliation can mathematically be expressed as an optimization problem of the following form: min x , y ∗ ∑ i = 1 n ( y i ∗ − y i σ i ) 2 subject to F ( x , y ∗ ) = 0 y min ≤ y ∗ ≤ y max x min ≤ x ≤ x max , {\displaystyle {\begin{aligned}\min _{x,y^{}}&\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\\{\text{subject to }}&F(x,y^{})=0\\&y_{\min }\leq y^{}\leq y_{\max }\\&x_{\min }\leq x\leq x_{\max },\end{aligned}}\,\!} where y i ∗ {\displaystyle y_{i}^{}\,\!} is the reconciled value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), y i {\displaystyle y_{i}\,\!} is the measured value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), x j {\displaystyle x_{j}\,\!} is the j {\displaystyle j} -th unmeasured variable ( j = 1 , … , m {\displaystyle j=1,\ldots ,m\,\!} ), and σ i {\displaystyle \sigma _{i}\,\!} is the standard deviation of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), F ( x , y ∗ ) = 0 {\displaystyle F(x,y^{})=0\,\!} are the p {\displaystyle p\,\!} process equality constraints and x min , x max , y min , y max {\displaystyle x_{\min },x_{\max },y_{\min },y_{\max }\,\!} are the bounds on the measured and unmeasured variables. The term ( y i ∗ − y i σ i ) 2 {\displaystyle \left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\,\!} is called the penalty of measurement i. The objective function is the sum of the penalties, which will be denoted in the following by f ( y ∗ ) = ∑ i = 1 n ( y i ∗ − y i σ i ) 2 {\displaystyle f(y^{})=\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}} . In other words, one wants to minimize the overall correction (measured in the least squares term) that is needed in order to satisfy the system constraints. Additionally, each least squares term is weighted by the standard deviation of the corresponding measurement. The standard deviation is related to the accuracy of the measurement. For example, at a 95% confidence level, the standard deviation is about half the accuracy. === Redundancy === Data reconciliation relies strongly on the concept of redundancy to correct the measurements as little as possible in order to satisfy the process constraints. Here, redundancy is defined differently from redundancy in information theory. Instead, redundancy arises from combining sensor data with the model (algebraic constraints), sometimes more specifically called "spatial redundancy", "analytical redundancy", or "topological redundancy". Redundancy can be due to sensor redundancy, where sensors are duplicated in order to have more than one measurement of the same quantity. Redundancy also arises when a single variable can be estimated in several independent ways from separate sets of measurements at a given time or time averaging period, using the algebraic constraints. Redundancy is linked to the concept
Instagram face
Instagram face is a beauty standard based on the filters and influencers popular on Instagram. == Overview == An "Instagram face" has catlike eyes, long lashes, a small nose, high cheekbones, full lips, and a blank expression. Digital filters manipulate photographs and video to create an idealized image that, according to critics, has resulted in an unrealistic and homogeneous beauty standard. According to Jia Tolentino, the face is "distinctly white but ambiguously ethnic". The face has been described as a racial composite of different peoples. In 2024, cosmetic surgeon Paul Banwell said, "People used to come to see me asking to look like a particular celebrity, but many patients come to me now wanting to look like the filtered version of themselves." While based on digital filters, the look is achieved in person using heavy applications of makeup or cosmetic surgery. Plastic surgery, Botox injections, and injectable filler have significantly increased in popularity since the rise of digital filters. Influencers market makeup products designed to recreate the look. == History == The growth of reality television series and social media throughout the 2010s has influenced the popularity of Instagram face. In 2019, The New Yorker referred to this phenomenon as "Instagram Face," identifying Kim Kardashian as its "patient zero." Similarly, her younger sister Kylie Jenner significantly impacted the trend with her 2015 lip filler confession, which acted as a catalyst, introducing Juvéderm to a new generation. Sirin Kale of Vice News has described Jenner as "at the vanguard of an aesthetic that’s swept through British towns and cities," while also pointing towards other celebrities such as Iggy Azalea and Farrah Abraham. In 2018, Americans underwent 7 million neurotoxin injections and 2.5 million filler injections and spent $16.5 billion on cosmetic surgery. 92% of the latter was performed on women. Botox usage has also been on the rise. == Criticism == In her 2021 book The Selfie, Temporality, and Contemporary Photography, Claire Raymond of Princeton University criticised "Instagram faces" for erasing "heritable quirks and lived history; it erases what makes the human face so compelling, whether conventionally beautiful or not," while also arguing that the procedures used to create Instagram faces "numb and freeze the face and skin, rendering less mobile the lips, the eyes, and the neck. Numbness is the central feature of the experience for the woman who gets Instagram face through cosmetic procedures. Others may see her more, but she feels less and less." == Influence on popular culture == The increasing popularity of cosmetic surgeries towards a homogeneous ideal has resulted in the emergence of the "goopcore" sub-genre of body horror. The sub-genre combines graphic violence with body modifications from the beauty industry. Allie Rowbottom's goopcore novel Aesthetica centers around an influencer attempting to undo years of plastic surgery with a new experimental procedure.
Tumblr
Tumblr ( TUM-blər) is a microblogging and social media platform founded by David Karp in 2007 and operated by American company Tumblr, Inc., a subsidiary of Automattic. The service allows users to post multimedia and other content to a short-form blog. It has attracted significant attention and controversy for hosting a wide range of progressive user-generated content. == History == === Beginnings (2006–2012) === Development of Tumblr began in 2006 during a two-week gap between contracts at David Karp's software consulting company, Davidville. Karp had been interested in tumblelogs (short-form blogs, hence the name Tumblr) for some time and was waiting for one of the established blogging platforms to introduce their own tumblelogging platform. As none had done so after a year of waiting, Karp and developer Marco Arment began working on their own platform. Tumblr was launched in February 2007, and within two weeks had gained 75,000 users. Arment left the company in September 2010 to work on Instapaper. In June 2012, Tumblr featured its first major brand advertising campaign in collaboration with Adidas, who launched an official soccer Tumblr blog and bought ad placements on the user dashboard. This launch came only two months after Tumblr announced it would be moving towards paid advertising on its site. === Ownership by Yahoo! (2013–2018) === On May 20, 2013, it was announced that Yahoo and Tumblr had reached an agreement for Yahoo! Inc. to acquire Tumblr for $1.1 billion in cash. Many of Tumblr's users were unhappy with the news, causing some to start a petition, achieving nearly 170,000 signatures. David Karp remained CEO and the deal was finalized on June 20, 2013. Advertising sales goals were not met and in 2016 Yahoo wrote down $712 million of Tumblr's value. Verizon Communications acquired Yahoo in June 2017, and placed Yahoo and Tumblr under its Oath subsidiary. Karp announced in November 2017 that he would be leaving Tumblr by the end of the year. Jeff D'Onofrio, Tumblr's president and COO, took over leading the company. The site, along with the rest of the Oath division (renamed Verizon Media Group in 2019), continued to struggle under Verizon. In March 2019, Similarweb estimated Tumblr had lost 30% of its user traffic since December 2018, when the site had introduced a stricter content policy with heavier restrictions on adult content (which had been a notable draw to the service). In May 2019, it was reported that Verizon was considering selling the site due to its continued struggles since the purchase (as it had done with another Yahoo property, Flickr, via its sale to SmugMug). Following this news, Pornhub's vice president publicly expressed interest in purchasing Tumblr, with a promise to reinstate the previous adult content policies. === Automattic (2019–present) === On August 12, 2019, Verizon Media announced that it would sell Tumblr to Automattic, the operator of blog service WordPress.com and corporate backer of the open source blog software of the same name. The sale was for an undisclosed amount, but Axios reported that the sale price was less than $3 million, less than 0.3% of Yahoo's original purchase price. Automattic CEO Matt Mullenweg stated that the site will operate as a complementary service to WordPress.com, and that there were no plans to reverse the content policy decisions made during Verizon ownership. In November 2022, Mullenweg stated that Tumblr will add support for the decentralized social networking protocol ActivityPub. In November 2023, most of Tumblr's product development and marketing teams were transferred to other groups within Automattic. Mullenweg stated that focus would shift to core functionality and streamlining existing features. In February 2024, Automattic announced that it would begin selling user data from Tumblr and WordPress.com to Midjourney and OpenAI. Tumblr users are opted-in by default, with an option to opt out. In August 2024, Automattic announced that it would migrate Tumblr's backend to an architecture derived from WordPress, in order to ease development and code sharing between the platforms. The company stated that this migration would not impact the service's user experience and content, and that users "won't even notice a difference from the outside". In January 2025, Mullenweg stated that the migration, once completed, would also "unlock" ActivityPub access for Tumblr, including native support for the company's official ActivityPub plugin for WordPress. In April 2025, Automattic announced layoffs for 16% of its workforce, reducing a large portion of Tumblr staff. On March 16, 2026, Tumblr implemented a change to how notes were assigned to reblogs, making it more similar to sites like Twitter and Bluesky. The change was rolled back the next day after heavy user backlash. == Features == === Blog management === Dashboard: The dashboard is the primary tool for the typical Tumblr user. It is a live feed of recent posts from blogs that they follow. Through the dashboard, users are able to comment, reblog, and like posts from other blogs that appear on their dashboard. The dashboard allows the user to upload text posts, images, videos, quotes, or links to their blog with a click of a button displayed at the top of the dashboard. Users are also able to connect their blogs to their Twitter and Facebook accounts, so that whenever they make a post, it will also be sent as a tweet and a status update. As of June 2022, users can also turn off reblogs on specific posts through the dashboard. Queue: Users are able to set up a schedule to delay posts that they make. They can spread their posts over several hours or even days. Tags: Users can help their audience find posts about certain topics by adding tags. If someone were to upload a picture to their blog and wanted their viewers to find pictures, they would add the tag #picture, and their viewers could use that word to search for posts with the tag #picture. HTML editing: Tumblr allows users to edit their blog's theme using HTML to control the appearance of their blog. Custom themes are able to be shared and used by other users, or sold. Custom domains: Tumblr allows users to use custom domains for their blogs. Users must purchase a domain from Tumblr Domains, an in-house registrar that provides domains that can only be used with Tumblr unless removed from the user's blog and transferred to another registrar. Blogs previously were able to be linked with any domain/subdomain from any registrar, however following the introduction of the Tumblr Domains service, now requires you to purchase a domain directly from Tumblr to be used with a blog. Users who kept their blogs connected to a domain after the introduction got to keep their custom domain, as long as they do not disconnect it from Tumblr or let the domain expire. === Tags === The tagging system on the website operates on a hybrid tagging system, involving both self-tagging (user write their own tags on their posts) and an auto-manual function (the website will recommend popular tags and ones that the user has used before.) Only the first 20 tags added to any post will be indexed by the site. The tags are prefaced by a hashtag and separated by commas, and spaces and special characters are allowed, but only up to 140 characters total per tag. There are two main types used by Tumblr users: descriptive tagging, and opinion or commentary tagging. Descriptive tags are usually introduced by the original poster, and describe what is in the post (e.g. #art, #sky). These are important for the original poster to use, so their post will be indexed and searchable by others wishing to view that subject of content. Tags used as a form of communication are unique to Tumblr, and are typically more personal, expressing opinions, reactions, meta-commentary, background information, and more. Instead of adding onto the reblogged post (with their comments becoming an addition to each subsequent reblog from them) a user may add their comments in the tags, not changing the content or appearance of the original post in any way. Not all users choose to use tags this way, but those who do use tags for commentary may prefer it over adding a comment on the actual post. === Mobile === With Tumblr's 2009 acquisition of Tumblerette, an iOS application created by Jeff Rock and Garrett Ross, the service launched its official iPhone app. The site became available to BlackBerry smartphones on April 17, 2010, via a Mobelux application in BlackBerry World. In June 2012, Tumblr released a new version of its iOS app, Tumblr 3.0, allowing support for Spotify integration, hi-res images and offline access. An app for Android is also available. A Windows Phone app was released on April 23, 2013. An app for Google Glass was released on May 16, 2013. === Inbox and messaging === Tumblr blogs have the option to allow users to submit questions, either as themselves or anonymously, to the blog for a response. Tumblr
Universal psychometrics
Universal psychometrics encompasses psychometrics instruments that could measure the psychological properties of any intelligent agent. Up until the early 21st century, psychometrics relied heavily on psychological tests that require the subject to cooperate and answer questions, the most famous example being an intelligence test. Such methods are only applicable to the measurement of human psychological properties. As a result, some researchers have proposed the idea of universal psychometrics - they suggest developing testing methods that allow for the measurement of non-human entities' psychological properties. For example, it has been suggested that the Turing test is a form of universal psychometrics. This test involves having testers (without any foreknowledge) attempt to distinguish a human from a machine by interacting with both (while not being to see either individuals). It is supposed that if the machine is equally intelligent to a human, the testers will not be able to distinguish between the two, i.e., their guesses will not be better than chance. Thus, Turing test could measure the intelligence (a psychological variable) of an AI. Other instruments proposed for universal psychometrics include reinforcement learning and measuring the ability to predict complexity.
Philco computers
Philco was one of the pioneers of transistorized computers, also known as second-generation computers. After the company developed the surface-barrier transistor, which was much faster than previous point-contact types, it was awarded contracts for military and government computers. Commercialized derivatives of some of these designs became successful business and scientific computers. The TRANSAC (Transistor Automatic Computer) Model S-1000 was released as a scientific computer. The TRANSAC S-2000 mainframe computer system was first produced in 1958, and a family of compatible machines, with increasing performance, was released over the next several years. However, the mainframe computer market was dominated by IBM. Other companies could not deploy resources for development, customer support and marketing on the scale that IBM could afford, making competition in this segment difficult after the introduction of the IBM 360 family. Philco went bankrupt and was purchased in 1961 by Ford Motor Company, but the computer division carried on until the Philco division of Ford exited the computer business in 1963. The Ford company maintained one Philco mainframe in use until 1981. == The surface-barrier transistor == The surface-barrier transistor developed by Philco in 1953 had a much higher frequency response than the original point-contact transistors. The transistor was made of a thin crystal of germanium, which was electrolytically etched with pits on either side forming a very thin base region, on the order of 5 micrometers. Philco's process for etching was United States patent number 2,885,571. Philco surface-barrier transistors were used in TX-0, and in early models of what would become the DEC PDP product line. Although relatively fast, the small size of the devices limited their power to circuits operating at a few tens of milliwatts. == Military and government == Between 1955 and 1957, Philco built transistor computers for use in aircraft, models C-1000, C-1100, and C-1102, intended for airborne real-time applications. By 1957, the C-1102 had been used by a civilian sector customer. The BASICPAC AN/TYK 6V (first delivery in 1961), COMPAC AN/TYK 4V (not completed), and LOGICPAC systems were built for the US Army as transportable computer systems for use with their Fieldata concept of integrated information management. BASICPAC was a transistorized computer with up to 28,672 words of 38-bit core memory (including sign and parity), available in several configurations from a minimum system, to a truck-borne mobile version, to a fully expanded system. Basic clock periods was 1 microsecond (which gives a clock rate of 1 MHz), with 12 microsecond memory access and a fixed-point multiplication taking 242 microseconds. Input/output was by paper tape reader and punch, or through a teletypewriter. With additional hardware, magnetic tape storage was also available, with up to seven I/O devices. The instruction set had 31 basic operation codes and nine opcodes for I/O === CXPQ === Philco was contracted by the US Navy to build the CXPQ computer. One model was completed and installed at the David Taylor Model Basin. This design was later adapted to become the commercial TRANSAC S-2000. Only one CXPQ was built. The CXPQ is a 48-bit transistorized computer. === SOLO === In 1955, the National Security Agency through the US Navy contracted with Philco to produce a computer suitable for use as a workstation, with an architecture based on the vacuum-tube computer system called Atlas II already in use at the NSA, and similar to the commercial UNIVAC 1103. At the time, Philco was the largest producer of surface barrier transistors, which were the only type available with the speed and quantities required for a computer. The SOLO prototype was delivered in 1958, but required extensive debugging at NSA. Difficulties were encountered with core memory and power supplies. SOLO used paper tape and teleprinter machines for input and output. SOLO cost about $1 million US, and contained 8,000 transistors. While the system was extensively used for training, testing, research and development, no additional units were ordered. SOLO was removed from active service in 1963. The design of the SOLO became commercialized as Philco's TRANSAC Model S-1000. == Commercial == === S-1000 === The TRANSAC S-1000 was a scientific computer with a 36-bit word length and 4096 words of core memory. It was packaged in a container about the size of a large office desk, and used only 1.2 kilowatts, much less than vacuum-tube-based computers of similar capacity. In a 1961 survey, about 15 S-1000 computer installations had been identified. It weighed about 1,650 pounds (750 kg). === S-2000 === The TRANSAC S-2000 was a large mainframe system intended for both business and scientific work. It had a 48-bit word length and supported calculations in fixed point, floating point and binary-coded decimal formats. The original S-2000 "TRANSAC" (Transistor Automatic Computer) released in 1958 was later designated Model 210; it was used internally at Philco. Similar to the Control Data Corporation Model 1604, it was a 48-bit fully transistorized computer. Three succeeding models were released in the series, all compatible with the software of the original model. The Model 211 was introduced in 1960, using micro-alloy diffused field-effect transistors, requiring significant redesign of circuits compared to the original. The TRANSAC S-2000/Philco 210/211 weighed about 2,000 pounds (910 kg). By 1964, eighteen Model 210, eighteen Model 211 and seven Model 212 systems had been sold. After Philco was purchased by Ford Motor Company, the Model 212 was introduced in 1962 and released in 1963. It had 65,535 words of 48-bit memory. Initially made with 6-microsecond core memory, it had better performance than the IBM 7094 transistor computer. It was later upgraded in 1964 to 2-microsecond core memory, which gave the machine floating-point performance greater than the IBM 7030 Stretch computer. A Model 213 was announced in 1964 but never built. By that time competition from IBM had made the Philco computer operations no longer profitable for Ford, and the division was closed down. The Model 212 could carry out a floating-point multiplication in 22 microseconds. Each word contained two 24-bit instructions with 16 bits of address information and eight bits for the opcode. There were 225 different valid opcodes in the Model 212; invalid opcodes were detected and halted the machine. The CPU had an accumulator register of 48 bits, three general-purpose registers of 24 bits, and 32 index registers of 15 bits. Main memory size ranged from 4K words to 64K words. Only the first model had a magnetic drum memory; later editions used tape drives. The Model 212 weighed about 6,500 pounds (3.3 short tons; 2.9 t). Software for the S-2000 initially consisted of TAC (Translator-Assembler-Compiler), and ALTAC, a FORTRAN II-like language with some differences from the IBM 704 FORTRAN implementation. A COBOL compiler was also available, targeted at business applications. The Philco 2400 was the input/output system for the S-2000. Operations such as reading cards or printing were carried out through magnetic tapes, thereby offloading the S-2000 from relatively slow input/output processing. The 2400 had a 24-bit word length and could be supplied with 4K to 32K characters (1K to 8K words) of core memory, rated at 3-microsecond cycle time. The instruction set was aimed at character I/O use. The idea of base registers, implemented in Philco computers, influenced the design of IBM/360. The last Philco TRANSAC S-2000 Model 212 was taken out of service in December 1981, after 19 years of service at Ford.