Timnit W. Gebru (Amharic and Tigrinya: ትምኒት ገብሩ; 1982/1983) is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. She is a co-founder of Black in AI, an advocacy group that has pushed for more Black roles in AI development and research. She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR). In December 2020, public controversy erupted over the circumstances surrounding Gebru's departure from Google, where she was technical co-lead of the Ethical Artificial Intelligence Team. Gebru had coauthored a paper on the risks of large language models (LLMs) acting as stochastic parrots, and submitted it for publication. According to Jeff Dean, head of Google AI, the paper was submitted without waiting for Google's internal review, which then asserted that it ignored too much relevant research. Google management requested that Gebru either withdraw the paper or remove the names of all the authors employed by Google. Gebru requested the identity and feedback of every reviewer, and stated that if Google refused, she would talk to her manager about "a last date". Google terminated her employment immediately, stating that they were accepting her resignation. Gebru maintained that she had not formally offered to resign, and only threatened to. Gebru has been widely recognized for her expertise in the ethics of artificial intelligence. She was named one of the World's 50 Greatest Leaders by Fortune and one of Nature's ten people who shaped science in 2021, and in 2022, one of Time's most influential people. == Early life and education == Gebru was raised in Addis Ababa, Ethiopia. Her father, an electrical engineer with a Doctor of Philosophy (PhD), died when she was five years old, and she was raised by her mother, an economist. Both her parents are from Eritrea. When Gebru was 15, during the Eritrean–Ethiopian War, she fled Ethiopia after some of her family were deported to Eritrea and compelled to fight in the war. She was initially denied a U.S. visa and briefly lived in Ireland, but she eventually received political asylum in the U.S., an experience she said was "miserable". Gebru settled in Somerville, Massachusetts to attend high school, where she says she immediately started to experience racial discrimination, with some teachers refusing to allow her to take certain Advanced Placement courses, despite being a high-achiever. After she completed high school, an encounter with the police set Gebru on a course toward a focus on ethics in technology. A friend of hers, a Black woman, was assaulted in a bar, and Gebru called the police to report it. She says that instead of filing the assault report, her friend was arrested and remanded to a cell. Gebru called it a pivotal moment and a "blatant example of systemic racism." In 2001, Gebru was accepted at Stanford University. There, she earned her Bachelor of Science and Master of Science degrees in electrical engineering and her PhD in computer vision in 2017. Gebru was advised during her PhD program by Fei-Fei Li. During the 2008 United States presidential election, Gebru canvassed in support of Barack Obama. Gebru presented her doctoral research at the 2017 LDV Capital Vision Summit competition, where computer vision scientists present their work to members of industry and venture capitalists. Gebru won the competition, starting a series of collaborations with other entrepreneurs and investors. Both during her PhD program in 2016 and in 2018, Gebru returned to Ethiopia with Jelani Nelson's programming campaign, AddisCoder. While working on her PhD, Gebru authored a paper that was never published about her concern over the future of AI. She wrote of the dangers of the lack of diversity in the field, centered on her experiences with the police and on a ProPublica investigation into predictive policing, which revealed a projection of human biases in machine learning. In the paper, she scathed the "boy's club culture", reflecting on her experiences at conference gatherings of drunken male attendees sexually harassing her, and criticized the hero worship of the field's celebrities. == Career == === 2004–2013: Software development at Apple === Gebru joined Apple as an intern while at Stanford, working in their hardware division making circuitry for audio components, and was offered a full-time position the following year. Of her work as an audio engineer, her manager told Wired she was "fearless", and well-liked by her colleagues. During her tenure at Apple, Gebru became more interested in building software, namely computer vision that could detect human figures. She went on to develop signal processing algorithms for the first iPad. At the time, she said she did not consider the potential use for surveillance, saying "I just found it technically interesting." Long after leaving the company, during the #AppleToo movement in the summer of 2021, which was led by Apple engineer Cher Scarlett, who consulted with Gebru, Gebru revealed she experienced "so many egregious things" and "always wondered how they manage[d] to get out of the spotlight." She said that accountability at Apple was long overdue, and warned they could not continue to fly under the radar for much longer. Gebru also criticized the way the media covers Apple and other tech giants, saying that the press helps shield such companies from public scrutiny. === 2013–2017: Research at Stanford and Microsoft === In 2013, Gebru joined Fei-Fei Li's lab at Stanford, where she combined deep learning with Google Street View to estimate the demographics of United States neighbourhoods, showing that socioeconomic attributes such as voting patterns, income, race, and education can be inferred from observations of cars. In 2015, Gebru attended the field's top conference, Neural Information Processing Systems (NIPS), in Montreal, Canada. Out of 3,700 attendees, she noted she was one of only a few Black researchers. When she attended again the following year, she kept a tally and noted that there were only five Black men and that she was the only Black woman out of 8,500 delegates. Together with her colleague Rediet Abebe, Gebru founded Black in AI, a community of Black researchers working in artificial intelligence that aims to increase the presence, visibility, and well-being of Black professionals and leaders within the field. In the summer of 2017, Gebru joined Microsoft as a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics in AI (FATE) lab. In 2017, Gebru spoke at the Fairness and Transparency conference, where MIT Technology Review interviewed her about biases that exist in AI systems and how adding diversity in AI teams can fix that issue. In her interview with Jackie Snow, Snow asked Gebru, "How does the lack of diversity distort artificial intelligence and specifically computer vision?" and Gebru pointed out that there are biases that exist in the software developers. While at Microsoft, Gebru co-authored a research paper called Gender Shades, which became the namesake of a project of a broader Massachusetts Institute of Technology project led by co-author Joy Buolamwini. The pair investigated facial recognition software, finding that in one particular implementation Black women were 35% less likely to be recognized than White men. === 2018–2020: Artificial intelligence ethics at Google === Gebru joined Google in 2018, where she co-led a team on the ethics of artificial intelligence with Margaret Mitchell. She studied the implications of artificial intelligence, looking to improve the ability of technology to do social good. In 2019, Gebru and other artificial intelligence researchers "signed a letter calling on Amazon to stop selling its facial-recognition technology to law enforcement agencies because it is biased against women and people of color", citing a study that was conducted by MIT researchers showing that Amazon's facial recognition system had more trouble identifying darker-skinned females than any other technology company's facial recognition software. In a New York Times interview, Gebru has further expressed that she believes facial recognition is too dangerous to be used for law enforcement and security purposes at present. === Exit from Google === In 2020 Gebru and five co-authors wrote a paper titled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜". The paper examined risks of very large language models, including their environmental footprint, financial costs, the inscrutability of large models, the potential for LLMs to display prejudice against certain groups, the inability of LLMs to understand the language they process, and the use of LLMs to spread disinformation. In December 2020, her employment with Google ended after Google management asked her to either withdraw the paper before publication, or remove the names of all the Google employees from
Message queuing service
A message queueing service is a message-oriented middleware or MOM deployed in a compute cloud using software as a service model. Service subscribers access queues and or topics to exchange data using point-to-point or publish and subscribe patterns. It's important to differentiate between event-driven and message-driven (aka queue driven) services: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers. Message queues can be a good buffer to handle spiky workloads but they have a finite capacity. According to Gregor Hohpe, message queues require proper mechanisms (aka flow controls) to avoid filling the queue beyond its manageable capacity and to keep the system stable. == Ordering Guarantees in Message Queues == Amazon SQS FIFO and Azure Service Bus sessions are queue-based messaging systems that provide ordering guarantees within a message group or session attempt but do not necessarily guarantee ordered delivery in cases of retries or failures. In SQS FIFO, messages in the same message group are processed in order, with subsequent messages held until the preceding message is successfully processed or moved to the dead-letter queue (DLQ). Once a message is placed in the DLQ, it is no longer retried, creating a gap in the sequence. However, the remaining messages continue to be delivered in order. Azure Service Bus sessions function similarly by maintaining ordering within a session, provided a single consumer processes messages sequentially. The implementation differs from SQS FIFO but follows the same fundamental ordering principle. In contrast, Apache Kafka is a distributed log-based messaging system that guarantees ordering within individual partitions rather than across the entire topic. Unlike queue-based systems, Kafka retains messages in a durable, append-only log, allowing multiple consumers to read at different offsets. Kafka uses manual offset management, giving consumers control over retries and failure handling. If a consumer fails to process a message, it can delay committing the offset, preventing further progress in that partition while other partitions remain unaffected. This partition-based design enables fault isolation and parallel processing while allowing ordering to be maintained within partitions, depending on consumer handling. == Vendors == Apache Kafka Apache Kafka is a distributed system consisting of servers that store and forward messages between producer client and consumer applications. IBM MQ IBM MQ offers a managed service that can be used on IBM Cloud and Amazon Web Services. Microsoft Azure Service Bus Service Bus offers queues, topics & subscriptions, and rules/actions in order to support publish-subscribe, temporal decoupling, and load balancing scenarios. Azure Service Bus is built on AMQP allowing any existing AMQP 1.0 client stack to interact with Service Bus directly or via existing .Net, Java, Node, and Python clients. Standard and Premium tiers allow for pay as you go or isolated resources at massive scale. Oracle Messaging Cloud Service This service provides a messaging solution for applications for asynchronous communication and is influenced by the Java Message Service (JMS) API specification. Any application platform that understands HTTP can also use Oracle Messaging Cloud Service through the REST interface. For Java applications, Oracle Messaging Cloud Service provides a Java library that implements and extends the JMS 1.1 interface. The Java library implements the JMS API by acting as a client of the REST API. Amazon Simple Queue Service Supports messages natively up to 256K, or up to 2GB by transmitting payload via S3. Highly scalable, durable and resilient. Provides loose-FIFO and 'at least once' delivery in order to provide massive scale. Supports REST API and optional Java Message Service client. Low latency. Utilizes Amazon Web Services. IronMQ Supports messages up to 64k; guarantees order; guarantees once only delivery; no delays retrieving messages. Supports REST API and beanstalkd open source protocol. Runs on multiple clouds including AWS and Rackspace. Scaling must be managed by user. RabbitMQ RabbitMQ is a reliable and mature messaging and streaming broker, which is easy to deploy on cloud environments, on-premises, and on your local machine. Supports AMQP, STOMP, MQTT StormMQ Open platform supports messages up to 50Mb. Uses AMQP to avoid vendor lock-in and provide language neutrality. Locate-It Option allows customers to audit the location of their data at all times and satisfy data protection principles. AnypointMQ An enterprise multi-tenant, cloud messaging service that performs advanced asynchronous messaging scenarios between applications. Anypoint MQ is fully integrated with Anypoint Platform, offering role based access control, client application management, and connectors.
Unified Modeling Language
The Unified Modeling Language (UML) is a general-purpose, object-oriented, visual modeling language that provides a way to visualize the architecture and design of a system, similar to the function of a blueprint. UML defines notation for many types of diagrams which focus on aspects such as behavior, interaction, and structure. UML is both a formal metamodel and a collection of graphical templates. The metamodel defines the elements in an object-oriented model such as classes and properties. It is essentially the same thing as the metamodel in object-oriented programming (OOP), however for OOP, the metamodel is primarily used at run time to dynamically inspect and modify an application object model. The UML metamodel provides a mathematical, formal foundation for the graphic views used in the modeling language to describe an emerging system. UML was created in an attempt to define a standard language for object-oriented programming at the OOPSLA '95 Conference. Originally, Grady Booch and James Rumbaugh merged their models into a unified model. This was followed by Booch's company Rational Software purchasing Ivar Jacobson's Objectory company and merging their model into the UML. At the time Rational and Objectory were two of the dominant players in the small world of independent vendors of object-oriented tools and methods. The Object Management Group (OMG) then took ownership of UML. The creation of UML was motivated by the desire to standardize the disparate nature of notational systems and approaches to software design at the time. In 1997, UML was adopted as a standard by the Object Management Group (OMG) and has been managed by this organization ever since. In 2005, UML was also published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) as the ISO/IEC 19501 standard. Since then the standard has been periodically revised to cover the latest revision of UML. Most developers do not use UML per se, but instead produce more informal diagrams, often hand-drawn. These diagrams, however, often include elements from UML. == Use == UML is primarily used for software development (in any industry or domain) but also used outside elsewhere including business processes, system functions, database schemas, workflow in the legal systems, medical electronics, Health care systems, and hardware design.. The UML is used by the OMG itself to define other OMG products such as the Unified Architecture Framework (UAF) and the Systems Modelling Language (SysML) v1. UML is designed for use with many object-oriented software development methods, both today and for the methods when it was first developed – including OMT, Booch method, Objectory, and especially RUP, which it was originally intended to be used with when work began at Rational Software. Although originally intended for object-oriented design documentation, UML has been used effectively in other contexts such as modeling business process. As UML is not inherently linked to a particular programming language, it can be used for modeling a system independent of language. Some UML tools generate source code from a UML model. === Elements === UML diagrams support visualizing system aspects like: Use case diagram for specifying user interactions with systems Class diagram for specifying structures, including data structures Activity diagram for specifying business process workflows Component diagram for specifying how components interface with other components Deployment diagram for specifying how components are deployed and executed on computational nodes In addition to syntactical (notational) elements with well-defined semantics, UML diagrams also allow for free-form comments (notes) that explain aspects such as usage, constraints, and intents. === Sharing === UML models can be exchanged among UML tools via the XML Metadata Interchange (XMI) format. === Cardinality notation === As with database Chen, Bachman, and ISO ER diagrams, class models are specified to use "look-across" cardinalities, even though several authors (Merise, Elmasri & Navathe, amongst others) prefer same-side or "look-here" for roles and both minimum and maximum cardinalities. Recent researchers (Feinerer and Dullea et al.) have shown that the "look-across" technique used by UML and ER diagrams is less effective and less coherent when applied to n-ary relationships of order strictly greater than 2. Feinerer says: "Problems arise if we operate under the look-across semantics as used for UML associations. Hartmann investigates this situation and shows how and why different transformations fail.", and: "As we will see on the next few pages, the look-across interpretation introduces several difficulties which prevent the extension of simple mechanisms from binary to n-ary associations." === Artifacts === An artifact is the "specification of a physical piece of information that is used or produced by a software development process, or by deployment and operation of a system" including models, source code, scripts, executables, tables in database systems, development deliverables, a design documents, and email messages. An artifact is the physical entity that is deployed to a node. Other UML elements such as classes and components are first manifest into artifacts and instances of these artifacts are then deployed. Artifacts can be composed of other artifacts. === Metamodeling === The OMG developed a metamodeling architecture to define UML, called the Meta-Object Facility (MOF). MOF is designed as a four-layered architecture, as shown in the image at right. It provides a meta-meta model at the top, called the M3 layer. This M3-model is the language used by Meta-Object Facility to build metamodels, called M2-models. The most prominent example of a Layer 2 Meta-Object Facility model is the UML metamodel, which describes UML itself. These M2-models describe elements of the M1-layer, and thus M1-models. These would be, for example, models written in UML. The last layer is the M0-layer or data layer. It is used to describe runtime instances of the system. The metamodel can be extended using a mechanism called stereotyping. This has been criticized as being insufficient/untenable by Brian Henderson-Sellers and Cesar Gonzalez-Perez in "Uses and Abuses of the Stereotype Mechanism in UML 1.x and 2.0". == Diagrams == UML 2 defines many types of diagrams – shown as a taxonomy in the image. === Structure diagrams === Structure diagrams emphasize the structure of the system – using objects, classifiers, relationships, attributes and operations. They are used to document software architecture. Class diagram – Describes the structure of a class Component diagram – Describes how a software system is split into components and dependencies between the components Composite structure diagram Deployment diagram Object diagram Package diagram Profile diagram === Behavior diagrams === Behavior diagrams emphasize the behavior of a system by showing collaborations among objects and changes to the internal states of objects. They are used to describe the functionality of a system. Activity diagram – Describes the business and operational activities of components State machine diagram Use case diagram – Depicts of a user's interaction with a system === Interaction diagrams === Interaction diagrams, a subset of behavior diagrams, emphasize the flow of control and data between components of a system. Communication diagram – shows communication between components Interaction overview diagram Sequence diagram – shows interactions arranged in time sequence; can be drawn via tools such as Lucidchart and Draw.io Timing diagram – focuses on timing constraints === Examples === == Adoption == In 2013, UML had been marketed by OMG for many contexts, but aimed primarily at software development with limited success. It has been treated, at times, as a design silver bullet, which leads to problems. UML misuse includes overuse (designing every part of the system with it, which is unnecessary) and assuming that novices can design with it. It is considered a large language, with many constructs. Some people (including Jacobson) feel that UML's size hinders learning and therefore uptake. Visual Studio removed support for UML in 2016 due to lack of use. == History == UML has evolved since the second half of the 1990s and has its roots in the object-oriented programming methods developed in the late 1980s and early 1990s. The image shows a timeline of the history of UML and other object-oriented modeling methods and notation. === Origin === Rational Software hired James Rumbaugh from General Electric in 1994 and after that, the company became the source for two of the most popular object-oriented modeling approaches of the day: Rumbaugh's object-modeling technique (OMT) and Grady Booch's method. They were soon assisted in their efforts by Ivar Jacobson, the creator of the object-oriented software engineeri
Plinian Core
Plinian Core is a set of vocabulary terms that can be used to describe different aspects of biological species information. Under "biological species Information" all kinds of properties or traits related to taxa—biological and non-biological—are included. Thus, for instance, terms pertaining descriptions, legal aspects, conservation, management, demographics, nomenclature, or related resources are incorporated. == Description == The Plinian Core is aimed to facilitate the exchange of information about the species and upper taxa. What is in scope? Species level catalogs of any kind of biological objects or data. Terminology associated with biological collection data. Striving for compatibility with other biodiversity-related standards. Facilitating the addition of components and attributes of biological data. What is not in scope? Data interchange protocols. Non-biodiversity-related data. Occurrence level data. This standard is named after Pliny the Elder, a very influential figure in the study of the biological species. Plinian Core design requirements includes: ease of use, to be self-contained, able to support data integration from multiple databases, and ability to handle different levels of granularity. Core terms can be grouped in its current version as follows: Metadata Base Elements Record Metadata Nomenclature and Classification Taxonomic description Natural history Invasive species Habitat and Distribution Demography and Threats Uses, Management and Conservation associatedParty, MeasurementOrFact, References, AncillaryData == Background == Plinian Core started as a collaborative project between Instituto Nacional de Biodiversidad and GBIF Spain in 2005. A series of iterations in which elements were defined and implanted in different projects resulted in a "Plinian Core Flat" [deprecated]. As a result, a new development was impulse to overcome them in 2012. New formal requirements, additional input and a will to better support the standard and its documentation, as well as to align it with the processes of TDWG, the world reference body for biodiversity information standards. A new version, Plinian Core v3.x.x was defined. This provides more flexibility to fully represent the information of a species in a variety of scenarios. New elements to deal with aspects such as IPR, related resources, referenced, etc. were introduced, and elements already included were better-defined and documented. Partner for the development of Plinian Core in this new phase incorporated the University of Granada (UG, Spain), the Alexander von Humboldt Institute (IAvH, Colombia), the National Commission for the Knowledge and Use of Biodiversity (Conabio, Mexico) and the University of São Paulo (USP, Brazil). A "Plinian Core Task Group" within TDWG "Interest Group on species Information" was constituted and currently working on its development. == Levels of the standard == Plinian Core is presented in to levels: the abstract model and the application profiles. The abstract model (AM), comprising the abstract model schema(xsd) and the terms' URIs, is the normative part. It is all comprehensive, and allows for different levels of granularity in describing species properties. The AM should be taken as a "menu" from which to choose terms and level of detail needed in any specific project. The subsets of the abstract model intended to be implemented in specific projects are the "application profiles" (APs). Besides containing part of the elements of the AM, APs can impose additional specifications on the included elements, such as controlled vocabularies. Some examples of APs in use follow: Application profile CONABIO Application profile INBIO Application profile GBIF.ES Application profile Banco de Datos de la Naturaleza.Spain Application profile SIB-COLOMBIA == Relation to other standards == Plinian incorporates a number of elements already defined by other standards. The following table summarizes these standards and the elements used in Plinian Core:
Texas Senate Bill 20
Texas Senate Bill 20 (S.B. 20), also known as the "Stopping AI-Generated Child Pornography Act", is a 2025 law in the state of Texas that creates new criminal offenses for those who possess, promote, or view visual material deemed obscene, which is said to depict a child, whether it is an actual person, animated or cartoon depiction, or an image of someone created through computer software or artificial intelligence. It was passed by the Texas Legislature on May 28, 2025, unanimously in both chambers. It was signed into law by Governor Greg Abbott on June 20, 2025. It went into effect on September 1, 2025. It was authored by Pete Flores and co-sponsored by Brent Hagenbuch, Juan Hinojosa, Joan Huffman, Phil King, and Tan Parker, as part of a package of legislation in the Texas House and Senate about A.I. and child pornography. Some supporters called it "common-sense" legislation falling within the "proper role" of government, protecting children and the "common good" within the state, with Heidi Ruiz, a police sergeant in Houston, describing the bill as "fantastic" and "fabulous." The bill drew comparisons to language, within Texas state legislation, which aimed to institute state-level book bans. Critics described the law as unconstitutional, saying it violated the Free Speech Clause of the First Amendment which prohibits abridgement of freedom of speech and the press, including the legal precedent set in Ashcroft v. Free Speech Coalition. The Comic Book Legal Defense Fund vowed to support those wrongly accused under the law. Much of the controversy regarding S.B. 20 involves the broad language pertaining to "obscene" pornographic images as including A.I.-created, animated, and cartoon depictions, with some critics arguing it could have a chilling effect on anime, manga, graphic novels, and other media produced, distributed, or created within Texas. == Provisions == S.B. 20 gives Texas police more provisions to restrict artificial intelligence-created child pornography, creating new criminal charge for possessing material depicting an underage person, under age 18, whether this child is an actual person or not. Those charged with this felony offense could go to state jail, but this could be elevated if the person charged has a prior conviction, of a $10,000 fine and two years in prison. == Reactions == === Support === Lieutenant Governor Dan Patrick applauded the unanimous passage of the law in the Texas Senate and called it "a priority" to protect children in Texas, and Texas citizens and thanked Pete Flores for his work on "this important issue". He later described the bill as part of the "bold, conservative agenda" that the Texas legislature passed during the 2025 legislative session. Phil King, one of the bill's co-sponsors, said that issue of child pornography had "infiltrated" the state's schools and said he was proud that the Texas legislature had "taken decisive action to protect our vulnerable Texans". Another co-sponsor of the legislation, Tan Parker described the law as "decisive action" to protect the children within Texas, and said he looked "forward to advancing this critical legislation" onward from the Texas Senate Criminal Justice Committee. He also described the legislation as "critical" action to protect the state's children from A.I.-generated child pornography and an "effective tool for law enforcement" to crack down on child porn perpetrators. Other supporters, such as police, and prosecutors, called the legislation an "important step" to ensure that images generated with A.I., along with deepfakes, "can't be shared with impunity" and necessary to ensure children's protection. Flores told senators that technology which enabled the production of "offensive" material by child predators had "no redeeming value whatsoever" and asserted that the materials had often been "used to groom and abuse children". John Leigh, a co-founder of Anime Matsuri, one of the largest conventions for anime within Texas, reassured those who contacted him, saying that the law is not targeted at anime and manga fans, stated that he supported the legislation, describing it as a step "in the right direction," and said that he did not believe it would "negatively impact" anime or related art in the state. Also, State Representative Dade Phelan emphasized the legislation's urgency to deal with A.I. and child pornography, adding that they need to "put some guardrails on it to where the public is being taken care of". The Texas Policy Research Foundation supported the legislation, saying that although it may lead to increased demands on state and local governmental resources, higher costs for local governments, and possible "civil liberty concerns" around online censorship, it represents a "necessary legal update" to address exploitation of children online, while "modernizing enforcement mechanisms" and recommended that lawmakers vote in favor of the law. Additionally, the group Texans for Fiscal Responsibility supported the law, arguing that it strengthened state law, upheld public safety, protected minors, and called it a "common-sense bill" protecting and promoting the "common good", children, and fell within the "proper role" of government. The Texas Public Policy Foundation also expressed their support for the law. A policy director for aforementioned conservative think tank, Zach Whiting, told the Texas Senate Committee on Criminal Justice, on March 4, 2025, that the foundation would assist legislators ans staff to "advance any and all measures to protect kids online" and shared an excerpt from of research paper about threats posed by A.I. in creating "sexually explicit deepfakes of children". === Opposition === Although the bill passed both chambers unanimously, there were some reports that the bill stalled due to opposition from Democratic lawmakers. Additionally, some individuals expressed concerns about the broad nature of the law's provisions. Anime Matsuri co-founder Deneice Leigh called for the law's wording to be clarified because "artists are anxious about displaying or selling fan art" even if the intention is "not be to penalize creators". She also described the bill as "vague and open to interpretation" as to what would be considered obscene and offensive while noting that the bill is not aiming to "target artists". Benjamin Napier, owner of Mansfield Comics and Manga in Mansfield, Texas, said that at first he felt the law was "ridiculous" and "kind of frivolous" at first, part of a "misguided puritanical onslaught", and noted that he would not cow "to the puritanical regime" if it was enacted. Kirsten Cather, an Asian Studies scholar at University of Texas, expressed concern at the law's misinterpretation because "many anime characters appear youthful, regardless of their actual age", said that the law could "stifle creative expression", and noted that the law's scope is broad enough to have manga and anime under scrutiny, a "real slippery slope here that's being breached". Marcel Green of Screen Rant said that the law's ambiguity led to concerns from manga and anime fans, and theorized that the law's application to a fan within Texas, who downloaded the 368th chapter of My Hero Academia, which has a "sexualized depiction" of an "underage high school student", would result in a criminal offense of "180 days to two years in state jail, along with a fine of up to $10,000". Green also said the law is problematic because many anime and manga characters are young, with many protagonists as minors and argued that the law could apply in limited cases, if state officials deemed an anime or manga under scrutiny as lacking "artistic value". Evan D. Mullicane, on the same site, said the vague wording of the legislation made it "dangerous" for anime such as Dragon Ball and Naruto, and could impact more than hentai, predicting it will be used against more than its "intended target" and be used to censor stories with "young LGBTQIA characters". Another critic on the same site, Carlyle Edmundson, called for anime fans to step up and prevent the law's enactment "for the good of artists and fans everywhere", saying that the legislation was "draconian" and claimed it was the most extreme case of anime and manga censorship in U.S. history. Nick Valdez of ComicBook.com said that the legislation could lead to censorship of "many anime and manga projects," like Kill la Kill and The 100 Girlfriends Who Really, Really, Really, Really, Really Love You, becoming a crime, and said that even if the law is enforced in a case-by-case basis, it could lead to a "much larger ban of materials in the state" itself due to the content of certain manga and anime. Vanessa Esguerra of The Mary Sue argued that possession of manga like Berserk and Vagabond, or viewing Dandadan, could be deemed illegal under the law, due to various parts of each of these media, and asserted that viewing and owning certain anime and other media, falling under the law's provisions,
Time-inhomogeneous hidden Bernoulli model
Time-inhomogeneous hidden Bernoulli model (TI-HBM) is an alternative to hidden Markov model (HMM) for automatic speech recognition. Contrary to HMM, the state transition process in TI-HBM is not a Markov-dependent process, rather it is a generalized Bernoulli (an independent) process. This difference leads to elimination of dynamic programming at state-level in TI-HBM decoding process. Thus, the computational complexity of TI-HBM for probability evaluation and state estimation is O ( N L ) {\displaystyle O(NL)} (instead of O ( N 2 L ) {\displaystyle O(N^{2}L)} in the HMM case, where N {\displaystyle N} and L {\displaystyle L} are number of states and observation sequence length respectively). The TI-HBM is able to model acoustic-unit duration (e.g. phone/word duration) by using a built-in parameter named survival probability. The TI-HBM is simpler and faster than HMM in a phoneme recognition task, but its performance is comparable to HMM. For details, see [1] or [2].
Sarah Guo
Sarah Guo is an American tech investor. She is the founder of the venture capital firm Conviction and formerly a general partner at Greylock Partners. == Early life and education == Guo grew up in Wisconsin. Her parents worked for Bell Labs. After attending Phillips Academy, she graduated from the University of Pennsylvania and its Wharton School. She received a Bachelor of Arts, a Bachelor of Science, a Master of Business Administration (M.B.A.), and a Master of Arts from the University of Pennsylvania. == Career == As a teenager, Guo worked at Casa Systems, a cloud networking company founded by her parents that launched in 2003 and went public in 2017. She then worked at Goldman Sachs. In 2013, Guo joined Greylock Partners. While still in her twenties, she became the firm's youngest General Partner. Guo left Greylock in July 2022, and in October of that year, launched a new early-stage venture capital firm focused on AI with $101 million. In 2025, Conviction raised a second fund in late 2024 with Mike Vernal. Conviction's investments include early investments in Baseten, Cognition AI, OpenEvidence, Harvey, HeyGen, Mistral AI, Sierra Platform, Sunday Robotics, and Thinking Machines Lab. Guo appears in media outlets, as an expert in AI, infrastructure, business software, cybersecurity, technology policy and software engineering. Guo is on the Midas List and the Midas Seed List of top investors. She co-hosts the podcast No Priors with tech founder and super angel Elad Gil. == Personal life == Guo is married to Pat Grady of Sequoia Capital.