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Wilkinson's Grammar of Graphics

The Grammar of Graphics (GoG) is a grammar-based system for representing graphics to provide grammatical constraints on the composition of data and information visualizations. A graphical grammar differs from a graphics pipeline as it focuses on semantic components such as scales and guides, statistical functions, coordinate systems, marks and aesthetic attributes. For example, a bar chart can be converted into a pie chart by specifying a polar coordinate system without any other change in graphical specification. The grammar of graphics concept was launched by Leland Wilkinson in 2001 (Wilkinson et al., 2001; Wilkinson, 2005) and graphical grammars have since been written in a variety of languages with various parameterisations and extensions. The major implementations of graphical grammars are nViZn created by a team at SPSS/IBM, followed by Polaris focusing on multidimensional relational databases which is commercialised as Tableau, a revised Layered Grammar of Graphics by Hadley Wickham in Ggplot2, and Vega-Lite which is a visualisation grammar with added interactivity. The grammar of graphics continues to evolve with alternate parameterisations, extensions, or new specifications. == Wilkinson's Grammar of Graphics == === Theory === Wilkinson conceived the seven elements of a graphics to be Variables: mapping of objects to values represented in a graphic Algebra: operations to combine variables and specify dimensions of graphs Geometry: creation of geometric graphs from variables Aesthetics: sensory attributes Statistics: functions to change the appearance and representation of graphs Scales: represent variables on measured dimensions Coordinates: mapping to coordinate systems With these, Wilkinson hypothesised that These seven constructs are orthogonal and virtually all known statistical charts can be generated relatively parsimoniously This computational system is not a taxonomy of charts and rather it describes the meaning of what we do when we construct statistical graphics. === Implementations === Wilkinson wrote SYSTAT, a statistical software package, in the early 1980s. This program was noted for its comprehensive graphics, including the first software implementation of the heatmap display now widely used among biologists. After his company grew to 50 employees, he sold it to SPSS in 1995. At SPSS, he assembled a team of graphics programmers who developed the nViZn platform that produces the visualizations in SPSS, Clementine, and other analytics products. While at Stanford, Tableau founders Hanrahan and Stolte, as well as Diane Tang, created the predecessor to Tableau, named Polaris. Polaris was a data visualization software tool, built with the support of a United States Department of Energy defense program, the Accelerated Strategic Computing Initiative (ASCI). The main differences between Wilkinson's system and Polaris are the use of SQL relational algebra for database services and using shelves instead of cross and nest operators. == Wickham's Layered Grammar of Graphics == === Theory === Hadley Wickham conceived an alternate parameterisation of the syntax Wilkinson had derived, creating a layered grammar of graphics which he implemented as ggplot2 for R (programming language) users. This added a hierarchy of defaults based around the idea of building up a graphic from multiple layers. Wickham conceived these elements to be: Defaults: consists of data and mapping Data: dataset Mapping: aesthetic mappings Layer: consists of data, mapping, geom, stat, and position Data: dataset, or inherit from defaults Mapping: aesthetic mappings, or inherit from defaults Geom: geometric object Stat: statistical transformation Position: position adjustment Scale: mapping of data to aesthetic attributes Coord: mapping of data to the plane of the plot Facet: split up the data === Reception === Wilkinson is generally positive on Wickham's parameterisation and implementation of ggplot2, praising its elegance and expressivity whilst claiming that his original Grammar of Graphics is capable of representing a wider range of statistical graphics. === Implementations === ggplot2 is the first implementation of a layered grammar of graphics in R and implementations in other programming languages have ensued. These include direct ports plotnine for Python, gramm for MATLAB, Lets-Plot for Kotlin and gadfly for Julia. Projects inspired by elements of Wickham's grammar include Vega-Lite which specifies plots in JSON and uses a JavaScript engine. Implementations for Python include Vega-Altair (built on top of Vega-Lite). == Vega-Lite: A Grammar of Interactive Graphics == === Theory === Vega-Lite combines ideas from Wilkinson's Grammar of Graphics and Wickham's Layered Grammar of Graphics with a composition algebra for layered and multi-view displays with a grammar of interaction. The Vega-Lite specification is instantiated in JSON and rendered by the lower-level Vega. The graphical grammar implemented by Vega-Lite is composed of the following: Unit: consists of data, transforms, mark-type and encoding Data: relational table consisting of records (rows) and named attributes (columns) Transforms: data transformations Mark-type: geometric object for visual encoding Encodings: mapping of data attributes to visual marks properties where each encoding consists of: Channel: e.g. colour, shape, size, or text Field: data attribute Data-type: e.g. nominal, ordinal, quantitative, or temporal Value: use a literal instead of a data-type Functions: e.g. binning, aggregation, and sorting Scale: maps from data domain to visual range Guide: axis or legend for visualising scale Composite Views: compose views from multiple unit specifications with operators: Layer: charts plotted on top of each other Hconcat/Vconcat: place views side-by-side Facet: subset data to produce a trellis plot Repeat: multiple plots similar to facet but with full data replication in each cell Interaction: selections identify the set of points a user is interested in manipulating, with components: Selection: get the minimal number of backing points Name: reference Type: how many backing values are stored Predicate: determine the set of selected points e.g. single, list, interval Domain|Range: store data domain or visual range Event: e.g. mouseover, mousedown, mouseup, Init: initialise with specific backing points Transforms: e.g. project, toggle, translate, zoom, and nearest Resolve: resolve selections to union or intersect ==== Implementations ==== Whilst Vega-Lite is the sole implementation of this graphics grammar specification with compilation to Vega, other implementations do create JSON files which can be interpreted by Vega-Lite. == Related projects == Ggplot2 is an R package for plotting Tableau Software (originally known as Polaris) is a commercial software built using the Grammar of Graphics nViZn built by Wilkinson. SYSTAT (statistics package) built by Wilkinson ggpy, ggplot for Python, but has not been updated since 20 November 2016 plotnine started as an effort to improve the scalability of ggplot for Python and is largely compatible with ggplot2 syntax. Plotly - Interactive, online ggplot2 graphs gramm, a plotting class for MATLAB inspired by ggplot2 gadfly, a system for plotting and visualization written in Julia, based largely on ggplot2 Chart::GGPlot - ggplot2 port in Perl, but has not been updated since 16 March 2023 The Lets-Plot for Python library includes a native backend and a Python API, which was mostly based on the ggplot2 package. Lets-Plot Kotlin API is an open-source plotting library for statistical data implemented using the Kotlin programming language, and is built on the principles of layered graphics first described in the Leland Wilkinson's work The Grammar of Graphics. ggplotnim, plotting library using the Nim programming language inspired by ggplot2. Vega and Vega-Lite are plotting libraries that use JSON to specify plots. Vega-Altair, a Python library built on top of Vega-Lite chart-parts - React-friendly Grammar of Graphics, but has not been updated since 10 Dec 2021 g2 - a JavaScript library
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Automatic1111

AUTOMATIC1111 Stable Diffusion Web UI (SD WebUI, A1111, or Automatic1111) is an open source generative artificial intelligence program that allows users to generate images from a text prompt. It uses Stable Diffusion as the base model for its image capabilities together with a large set of extensions and features to customize its output. == History == SD WebUI was released on GitHub on August 22, 2022, by AUTOMATIC1111, 1 month after the initial release of Stable Diffusion. At the time, Stable Diffusion could only be run via the command line. SD WebUI quickly rose in popularity and has been described as "the most popular tool for running diffusion models locally." SD WebUI is one of the most popular user interfaces for Stable Diffusion, together with ComfyUI. In February 2024, a book was published by ja:Gijutsu Hyoronsha on using Stable Diffusion with SD WebUI in Japanese. As of July 2024, the project had 136,000 stars on GitHub. == Features == SD WebUI uses Gradio for its user interface. Each parameter in the Stable Diffusion program is exposed via a UI interface within SD WebUI. SD WebUI contains additional parameters not included in Stable Diffusion itself, such as support for Low-rank adaptations, ControlNet and custom variational autoencoders. SD WebUI supports prompt weighting, image-to-image based generation, inpainting, outpainting and image scaling. It supports over 20 samplers including DDIM, Euler, Euler a, DPM++ 2M Karras, and UniPC. It is also used for its various optimizations over the base Stable Diffusion. == Stable Diffusion WebUI Forge == Stable Diffusion WebUI Forge (Forge) is a notable fork of SD WebUI started by Lvmin Zhang, who is also the creator of ControlNet and Fooocus. The initial goal of Forge was to improve the performance and features of SD WebUI with the intention to upstream changes back to SD WebUI. One of Forge's optimizations allowed users with low VRAM to generate images faster on some versions of Stable Diffusion. It improved generation speed for users with 8GB and 6GB VRAM by 30-45% and 60-75%, respectively. Forge also includes extra features such as support for more samplers than standard SD WebUI. Some of Forge's optimizations were borrowed from ComfyUI, and others were developed by the Forge team. In August 2024, Forge added support for the Flux diffusion model developed by Black Forest Labs, which is not yet supported by SD WebUI.
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Aidan Gomez

Aidan Gomez is a British-Canadian computer scientist working in the field of artificial intelligence, with a focus on natural language processing. He is the co-founder and CEO of the technology company Cohere. == Early life and education == Gomez grew up in Brighton, Ontario. He graduated from the University of Toronto with a bachelor's degree in computer science and mathematics. He was pursuing a PhD in computer science from the University of Oxford. He paused his studies to launch Cohere. He was granted the PhD in 2024. == Career == In 2017, as a 20 year-old intern at Google Brain, Gomez was one of eight authors of the research paper "Attention Is All You Need", which is credited with changing the AI industry and helping lead to the creation of ChatGPT. The paper proposed a novel deep learning architecture called the transformer, that enables machine learning models to analyze large amounts of data for patterns, and then use those patterns to make predictions while leveraging GPU parallelization. It has been commonly adopted for training large language models and in the development of generative AI. In the same year, Gomez founded FOR.ai, a program to help researchers learn machine learning techniques in a collaborative format. An outgrowth of this project was Cohere For AI (now Cohere Labs), which released Aya, an open-source multilingual LLM. As a PhD student, Gomez worked as a machine learning researcher at Google Brain. At that time, he co-authored the paper "One Model to Learn Them All" about multi-task learning by a single neural network. In 2019, Gomez left Google Brain to launch Cohere, an enterprise-focused company that helps businesses implement AI into chatbots, search engines, and other products. As of Sept 2025, Cohere has raised about US$1.6 billion at valuation north of $7 billion, as Gomez leads the company as its CEO. Gomez was named to the 2023 Time 100/AI list of the most influential people in the field of artificial intelligence. He and his fellow Cohere founders Ivan Zhang and Nick Frosst were named number 1 on 2023 Maclean's AI Trailblazers Power List. In April 2025, Gomez was elected to the board of Rivian. == Views on AI == Gomez has stated that warnings regarding the existential risk from artificial intelligence are overblown, and that real risks involve the automated spread of misinformation on social media. He said that the United States would win the AI arms race over China.
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Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy

The Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy is an international norms and arms control proposal by the U.S. government for artificial intelligence in the military. It was announced at the Summit on Responsible Artificial Intelligence in the Military Domain by Bonnie Jenkins, Under Secretary of State for Arms Control. As of January 2024, fifty-one countries have signed the declaration. The US government sees it as an extension of the Department of Defense Directive 3000.09 which is the current US policy on autonomous weapons. It covers areas such as Lethal autonomous weapons and weapons decision-making.
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Paprika (app)

Paprika is an app and website that helps users organize recipes, produce meal plans, and create grocery lists. The app is available for Android, iOS, macOS, and Windows devices. == Overview == The app allows users to import recipes from various sources, including websites and other apps. The app also allows users to automatically generate meal plans, which are also customizable, in order to achieve specific objectives such as weight loss, muscle gain, adherence to various dietary preferences, or personal taste. The app is also capable of generating grocery lists based on the daily or weekly meal plans chosen by the user. All the recipes, menus, and grocery lists of each user are accessible from smartphones, tablets, and computers. The app is part of a broader category of mobile apps focused on meal planning, recipe management, and shopping list automation, which have grown in popularity with the expansion of smartphone usage and digital cooking tools. == History == Paprika Recipe Manager for iPad version 1.0 was initially released in September 2010 by Hindsight LLC. Paprika 2.0 was released for iPhone and iPad in November 2013, and Paprika 3.0 was released for iOS and macOS in November 2017. == Reception == Paprika has been featured in technology and lifestyle publications as a recipe management and meal planning application. Coverage has noted features such as importing recipes from websites, ingredient scaling, and cross-platform synchronization. The app has also appeared in lists of cooking and meal planning tools published by outlets including The Verge and The Kitchn.
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National Security Memorandum on Artificial Intelligence

The Memorandum on Advancing the United States' Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence is a memorandum signed by U.S. president Joe Biden. The memorandum is described as seeking to advance U.S. leadership in the development of safe, secure, and trustworthy artificial intelligence (AI); enable the U.S. government to use AI for national security; and contribute to international AI governance.
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Semantic analysis (knowledge representation)

Semantic analysis is a method for eliciting and representing knowledge about organisations. Initially the problem must be defined by domain experts and passed to the project analyst(s). The next step is the generation of candidate affordances. This step will generate a list of semantic units that may be included in the schema. The candidate grouping follows where some of the semantic units that will appear in the schema are placed in simple groups. Finally the groups will be integrated together into an ontology chart. Semantic analysis always starts from the problem definition which if not clear, require the analyst to employ relevant literature, interviews with the stakeholders and other techniques towards collecting supplementary information. All assumptions made must be genuine and not limiting the system.
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OntoCAPE

OntoCAPE is a large-scale ontology for the domain of Computer-Aided Process Engineering (CAPE). It can be downloaded free of charge via the OntoCAPE Homepage. OntoCAPE is partitioned into 62 sub-ontologies, which can be used individually or as an integrated suite. The sub-ontologies are organized across different abstraction layers, which separate general knowledge from knowledge about particular domains and applications. The upper layers have the character of an upper ontology, covering general topics such as mereotopology, systems theory, quantities and units. The lower layers conceptualize the domain of chemical process engineering, covering domain-specific topics such as materials, chemical reactions, or unit operations.
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STIT logic

STIT logic (from seeing to it that) is a family of modal and branching-time logics for reasoning about agency and choice. A typical STIT operator has the form [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} , usually read as "agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } ", and is interpreted in models where agents choose between alternative possible futures. STIT logics are used in action theory, deontic logic, epistemic logic, and the theory of intelligent agents to formalise notions such as "could have done otherwise", responsibility, joint action, and strategic ability in an indeterministic world. == Etymology == The acronym STIT comes from the English phrase "seeing to it that", introduced in influential work by Nuel Belnap and Michael Perloff on the logical analysis of agentive expressions. In this tradition, "to see to it that φ {\displaystyle \varphi } " is treated as a primitive agency operator, rather than being reduced to ordinary modal necessity. == History == Modern STIT logic arose in the 1980s in the context of branching-time semantics and formal theories of agency. Belnap and Perloff's article "Seeing to it that: A canonical form for agentives" introduced the idea of treating expressions of the form "agent i sees to it that φ" as a primitive modal operator, and analysed such sentences using a branching tree of moments and histories. This approach was further developed in a series of papers on indeterminism and agency and provided the conceptual core for later STIT formalisms. In the 1990s the basic formal systems of STIT logic were worked out. Horty and Belnap's influential paper on the deliberative STIT operator distinguished between a "Chellas" STIT that merely records the result of an agent's present choice and a "deliberative" STIT that requires the agent's choice to make a difference, and connected STIT with issues of action, omission, ability and obligation. Around the same time, Ming Xu proved completeness and decidability results for basic STIT systems, including a single-agent logic with Kripke-style semantics and axiomatizations for multi-agent deliberative STIT, thereby establishing STIT as a well-behaved normal modal framework. This early work was systematised in Belnap, Perloff and Xu's monograph Facing the Future: Agents and Choices in Our Indeterminist World, which presents a general branching-time semantics for individual and group STIT operators, discusses independence-of-agents conditions and articulates the metaphysical picture of an indeterministic "tree" of moments. At roughly the same time, Horty's book Agency and Deontic Logic developed deontic STIT logics in which obligations are tied to agents' available choices rather than to static states of affairs, and used the resulting systems to analyse "ought implies can", contrary-to-duty obligations and deontic paradoxes. These works helped to position STIT at the intersection of action theory, temporal logic and deontic logic. From the late 1990s and 2000s onward, STIT logics were combined with epistemic, temporal and strategic modalities. Broersen introduced complete STIT logics for knowledge and action and deontic-epistemic STIT systems that distinguish different modes of mens rea, with applications to responsibility and the specification of multi-agent systems. Work on group and coalitional agency investigated axiomatisations and complexity results for group STIT logics, and related STIT-based analyses of agency to coalition logic and alternating-time temporal logic (ATL) by exhibiting formal embeddings between the frameworks. Explicit temporal operators were added to STIT in so-called temporal STIT logics. Lorini proposed a temporal STIT with "next" and "until" operators along histories and showed how it can be applied to normative reasoning about ongoing behaviour and commitments. Ciuni and Lorini compared different semantics for temporal STIT, clarifying the relationships between branching-time, game-based and epistemic approaches, while Boudou and Lorini gave a semantics for temporal STIT based on concurrent game structures, thus strengthening links with standard models of multi-agent interaction used for ATL and strategy logic. In parallel, complexity-theoretic work by Balbiani, Herzig and Troquard and by Schwarzentruber and co-authors investigated the satisfiability and model-checking problems for various STIT fragments, showing for instance that many expressive group STIT logics are undecidable or of high computational complexity. In the 2010s, STIT ideas were combined with justification logic, imagination operators and refined deontic notions. Justification STIT logics, developed by Olkhovikov and others, merge explicit justifications with STIT-style agency so that producing a proof can itself be treated as an action that brings about knowledge, and they come with completeness and decidability results. Olkhovikov and Wansing introduced STIT imagination logics, together with axiomatic systems and tableau calculi, to model acts of voluntary imagining and their role in doxastic control. Other authors have proposed STIT-based logics of responsibility, blameworthiness and intentionality for use in philosophical and AI settings. Xu's survey article "Combinations of STIT with Ought and Know" (2015) reviews many of these developments and emphasises the interplay between deontic and epistemic STIT logics. Current research on STIT focuses on proof theory, automated reasoning and richer expressive resources. Lyon and van Berkel, building on earlier work on labelled calculi for STIT, have developed cut-free sequent systems and proof-search algorithms that yield syntactic decision procedures for a range of deontic and non-deontic multi-agent STIT logics and support applications such as duty checking and compliance checking in autonomous systems. Sawasaki has proposed first-order cstit-based STIT logics that can distinguish de re and de dicto readings of agency statements and has proved strong completeness results for Hilbert systems over finite models, moving the STIT programme beyond the purely propositional level. Further work investigates interpreted-system and computationally grounded semantics for STIT and its extensions in order to model the behaviour of autonomous agents in multi-agent settings, and proposes STIT-based semantics for epistemic notions based on patterns of information disclosure in interactive systems. == Branching-time semantics == STIT logics are usually interpreted over branching-time models. A standard STIT frame consists of: a non-empty set of moments T {\displaystyle T} , partially ordered by < {\displaystyle <} so that ( T , < ) {\displaystyle (T,<)} forms a tree (every pair of moments with a common predecessor has a greatest lower bound); a set of histories, each history being a maximal linearly ordered subset of T {\displaystyle T} ; a non-empty set of agents A g {\displaystyle Ag} ; for each agent i ∈ A g {\displaystyle i\in Ag} and moment m {\displaystyle m} , a choice function c h o i c e i m {\displaystyle {\mathsf {choice}}_{i}^{m}} that partitions the set of histories passing through m {\displaystyle m} into choice cells. The idea is that a moment represents a time at which choices are made, and histories represent complete possible future courses of events. At each moment, each agent's choice corresponds to selecting one of the available cells of histories determined by their choice function. Formulas are evaluated at pairs ( m , h ) {\displaystyle (m,h)} of a moment and a history through that moment (sometimes written m / h {\displaystyle m/h} ). A valuation assigns truth-values to atomic propositions at such indices; Boolean connectives are interpreted pointwise as in Kripke-style modal logic. == Chellas and deliberative STIT operators == Several STIT operators have been distinguished in the literature. A common approach uses two closely related operators, often called Chellas STIT and deliberative STIT. Let H m {\displaystyle H_{m}} be the set of histories passing through a moment m {\displaystyle m} , and write H m {\displaystyle H_{m}} ⟦ φ ⟧ m = { h ∈ H m ∣ M , m / h ⊨ φ } {\displaystyle {\text{⟦}}\varphi {\text{⟧}}_{m}=\{h\in H_{m}\mid M,m/h\models \varphi \}} for the set of histories at m {\displaystyle m} where φ {\displaystyle \varphi } holds. The Chellas STIT operator, often written [ i c s t i t : φ ] {\displaystyle [i\ {\mathsf {cstit}}:\varphi ]} , is given by M , m / h ⊨ [ i c s t i t : φ ] iff c h o i c e i m ( h ) ⊆ ⟦ φ ⟧ m . {\displaystyle M,m/h\models [i\ {\mathsf {cstit}}:\varphi ]\quad {\text{iff}}\quad {\mathsf {choice}}_{i}^{m}(h)\subseteq {\text{⟦}}\varphi {\text{⟧}}_{m}.} Intuitively, agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } if φ {\displaystyle \varphi } holds at all histories compatible with their present choice. The deliberative STIT operator, [ i d s t i t : φ ] {\displaystyle [i\ {\mathsf {dstit}}:\varphi ]} , adds
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Semantic parameterization

Semantic parameterization is a conceptual modeling process for expressing natural language descriptions of a domain in first-order predicate logic. The process yields a formalization of natural language sentences in Description Logic to answer the who, what and where questions in the Inquiry-Cycle Model (ICM) developed by Colin Potts and his colleagues at the Georgia Institute of Technology. The parameterization process complements the Knowledge Acquisition and autOmated Specification (KAOS) method, which formalizes answers to the when, why and how ICM questions in Temporal Logic, to complete the ICM formalization. The artifacts used in the parameterization process include a dictionary that aligns the domain lexicon with unique concepts, distinguishing between synonyms and polysemes, and several natural language patterns that aid in mapping common domain descriptions to formal specifications. == Relationship to other theories == Semantic Parameterization defines a meta-model consisting of eight roles that are domain-independent and reusable. Seven of these roles correspond to Jeffrey Gruber's thematic relations and case roles in Charles Fillmore's case grammar: The Inquiry-Cycle Model (ICM) was introduced to drive elicitation between engineers and stakeholders in requirements engineering. The ICM consists of who, what, where, why, how and when questions. All but the when questions, which require a Temporal Logic to represent such phenomena, have been aligned with the meta-model in semantic parameterization using Description Logic (DL). == Introduction with Example == The semantic parameterization process is based on Description Logic, wherein the TBox is composed of words in a dictionary, including nouns, verbs, and adjectives, and the ABox is partitioned into two sets of assertions: 1) those assertions that come from words in the natural language statement, called the grounding, and 2) those assertions that are inferred by the (human) modeler, called the meta-model. Consider the following unstructured natural language statement (UNLS) (see Breaux et al. for an extended discussion): UNLS1.0 The customer1,1 must not share2,2 the access-code3,3 of the customer1,1 with someone4,4 who is not the provider5,4. The modeler first identifies intensional and extensional polysemes and synonyms, denoted by the subscripts: the first subscript uniquely refers to the intensional index, i.e., the same first index in two or more words refer to the same concept in the TBox; the second subscript uniquely refers to the extensional index, i.e., two same second index in two or more words refer to the same individual in the ABox. This indexing step aligns words in the statement and concepts in the dictionary. Next, the modeler identifies concepts from the dictionary to compose the meta-model. The following table illustrates the complete DL expression that results from applying semantic parameterization.
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Greg Brockman

Gregory Brockman (born November 29, 1987) is an American entrepreneur and software engineer. He is co-founder and president of OpenAI. He began his career at Stripe in 2010, upon leaving MIT, and became CTO in 2013. He left Stripe in 2015 to co-found OpenAI, where he also served as CTO. == Early life == Brockman was born in Thompson, North Dakota, and attended Red River High School, where he excelled in mathematics, chemistry, and computer science. He won a silver medal in the 2006 International Chemistry Olympiad and became the first finalist from North Dakota to participate in the Intel science talent search since 1973. In 2003, 2005, and 2007, he attended Canada/USA Mathcamp, a summer program for mathematically talented high-school students. In 2008, Brockman enrolled at Harvard University but left a year later, briefly enrolling at the Massachusetts Institute of Technology. == Career == In 2010, he dropped out of MIT to join Stripe, a company founded by Patrick Collison, his MIT classmate, and John Collison. In 2013, he became Stripe's first CTO, while the company grew from 5 to 205 employees. Brockman left Stripe in May 2015. === OpenAI === Brockman met with Sam Altman and Elon Musk, and led the recruiting of the OpenAI founding team. Many of its members, including Ilya Sutskever, were top AI research talent that left high paying jobs for the opportunity at OpenAI. He co-founded OpenAI in December 2015 alongside Altman, Sutskever and others. The company initially operated from Brockman's living room. He led various projects at OpenAI, including OpenAI Gym and OpenAI Five, a Dota 2 bot. On February 14, 2019, OpenAI announced that they had developed a new large language model called GPT-2, but kept it private due to their concern for its potential misuse. They released the model to a limited group of beta testers in May 2019. On March 14, 2023, in a live video demo, Brockman unveiled GPT-4, the fourth iteration in the GPT series. On November 17, 2023, alongside the firing of Sam Altman, Brockman was told he had been removed from the board. Sutskever supplied the board with a document of alleged bullying by Brockman. Mira Murati said Brockman's relationship with Altman made it impossible for her to do her job, and Altman had already "fielded many requests from OpenAI employees to rein in Brockman". Brockman was to report to Murati, but on November 17, he announced that he had quit the company. On November 20, 2023, Microsoft CEO Satya Nadella announced that Brockman and Altman would join Microsoft to lead a new advanced AI research team. The following day, after a deal was reached to reinstate Altman as CEO, Brockman returned to OpenAI. Brockman took a sabbatical from August to November 2024. === Elon Musk lawsuit === Jury selection for OpenAI cofounder Elon Musk's lawsuit against OpenAI and its current executives, including Brockman, began on April 27, 2026. On April 28, 2026, trial testimony was by now underway, with Elon Musk beginning his testimony against Altman and OpenAI. On April 30, 2026 Musk would enter his third day of testimony. == Personal life == In November 2019 after a year of dating, Brockman married Anna at OpenAI's offices on a workday. Ilya Sutskever officiated. == Political activities == Brockman and his wife were the biggest donors to Donald Trump's Super PAC, MAGA Inc., in 2025 with each of them donating US$12.5 million. Brockman and his wife also donated $50 million to Leading the Future, a super PAC dedicated to AI deregulation that he helped found with Andreessen Horowitz co-founders Marc Andreessen and Ben Horowitz. OpenAI publicly expressed openness to increased regulatory oversight and has a policy against donating to such Super PACs.
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Embodied cognition

Embodied cognition represents a diverse group of theories which investigate how cognition is shaped by the bodily state and capacities of the organism. These embodied factors include the motor system, the perceptual system, bodily interactions with the environment (situatedness), and the assumptions about the world that shape the functional structure of the brain and body of the organism. Embodied cognition suggests that these elements are essential to a wide spectrum of cognitive functions, such as perception biases, memory recall, comprehension and high-level mental constructs (such as meaning attribution and categories) and performance on various cognitive tasks (reasoning or judgment). The embodied mind thesis challenges other theories, such as cognitivism, computationalism, and Cartesian dualism. It is closely related to the extended mind thesis, situated cognition, and enactivism. The modern version depends on understandings drawn from up-to-date research in psychology, linguistics, cognitive science, dynamical systems, artificial intelligence, robotics, animal cognition, plant cognition, and neurobiology. == Theory == Proponents of the embodied cognition thesis emphasize the active and significant role the body plays in the shaping of cognition and in the understanding of an agent's mind and cognitive capacities. In philosophy, embodied cognition holds that an agent's cognition, rather than being the product of mere (innate) abstract representations of the world, is strongly influenced by aspects of an agent's body beyond the brain itself. An embodied model of cognition opposes the disembodied Cartesian model, according to which all mental phenomena are non-physical and, therefore, not influenced by the body. With this opposition the embodiment thesis intends to reintroduce an agent's bodily experiences into any account of cognition. It is a rather broad thesis and encompasses both weak and strong variants of embodiment. In an attempt to reconcile cognitive science with human experience, the enactive approach to cognition defines "embodiment" as follows: By using the term embodied we mean to highlight two points: first that cognition depends upon the kinds of experience that come from having a body with various sensorimotor capacities, and second, that these individual sensorimotor capacities are themselves embedded in a more encompassing biological, psychological and cultural context. This double sense attributed to the embodiment thesis emphasizes the many aspects of cognition that researchers in different fields—such as philosophy, cognitive science, artificial intelligence, psychology, and neuroscience—are involved with. This general characterization of embodiment faces some difficulties: a consequence of this emphasis on the body, experience, culture, context, and the cognitive mechanisms of an agent in the world is that often distinct views and approaches to embodied cognition overlap. The theses of extended cognition and situated cognition, for example, are usually intertwined and not always carefully separated. And since each of the aspects of the embodiment thesis is endorsed to different degrees, embodied cognition should be better seen "as a research program rather than a well-defined unified theory". Some authors explain the embodiment thesis by arguing that cognition depends on an agent's body and its interactions with a determined environment. From this perspective, cognition in real biological systems is not an end in itself; it is constrained by the system's goals and capacities. Such constraints do not mean cognition is set by adaptive behavior (or autopoiesis) alone, but instead that cognition requires "some kind of information processing... the transformation or communication of incoming information". The acquiring of such information involves the agent's "exploration and modification of the environment". It would be a mistake, however, to suppose that cognition consists simply of building maximally accurate representations of input information...the gaining of knowledge is a stepping stone to achieving the more immediate goal of guiding behavior in response to the system's changing surroundings. Another approach to understanding embodied cognition comes from a narrower characterization of the embodiment thesis. The following narrower view of embodiment avoids any compromises to external sources other than the body and allows differentiating between embodied cognition, extended cognition, and situated cognition. Thus, the embodiment thesis can be specified as follows: Many features of cognition are embodied in that they are deeply dependent upon characteristics of the physical body of an agent, such that the agent's beyond-the-brain body plays a significant causal role, or a physically constitutive role, in that agent's cognitive processing. This thesis points out the core idea that an agent's body plays a significant role in shaping different features of cognition, such as perception, attention, memory, reasoning—among others. Likewise, these features of cognition depend on the kind of body an agent has. The thesis omits direct mention of some aspects of the "more encompassing biological, psychological and cultural context" included in the enactive definition, making it possible to separate embodied cognition, extended cognition, and situated cognition. In contrast to the embodiment thesis, the extended mind thesis limits cognitive processing neither to the brain nor even to the body, it extends it outward into the agent's world. Situated cognition emphasizes that this extension is not just a matter of including resources outside the head but stressing the role of probing and changing interactions with the agent's world. Cognition is situated in that it is inherently dependent upon the cultural and social contexts within which it takes place. This conceptual reframing of cognition as an activity influenced by the body has had significant implications. For instance, the view of cognition inherited by most contemporary cognitive neuroscience is internalist in nature. An agent's behavior along with its capacity to maintain (accurate) representations of the surrounding environment were considered as the product of "powerful brains that can maintain the world models and devise plans". From this perspective, cognizing was conceived as something that an isolated brain did. In contrast, accepting the role the body plays during cognitive processes allows us to account for a more encompassing view of cognition. This shift in perspective within neuroscience suggests that successful behavior in real-world scenarios demands the integration of several sensorimotor and cognitive (as well as affective) capacities of an agent. Thus, cognition emerges in the relationship between an agent and the affordances provided by the environment rather than in the brain alone. In 2002, a collection of positive characterizations summarizing what the embodiment thesis entails for cognition were offered. Professor of Cognitive Psychology Margaret Wilson argues that the general outlook of embodied cognition "displays an interesting co-variation of multiple observations and houses a number of different claims: (1) cognition is situated; (2) cognition is time-pressured; (3) we off-load cognitive work onto the environment; (4) the environment is part of the cognitive system; (5) cognition is for action; (6) offline cognition is bodily-based". According to Wilson, the first three and the fifth claim appear to be at least partially true, while the fourth claim is deeply problematic in that all things that have an impact on the elements of a system are not necessarily considered part of the system. The sixth claim has received the least attention in the literature on embodied cognition, yet it might be the most significant of the six claims as it shows how certain human cognitive capabilities, that previously were thought to be highly abstract, now appear to be leaning towards an embodied approach for their explanation. Wilson also describes at least five main (abstract) categories that combine both sensory and motor skills (or sensorimotor functions). The first three are working memory, episodic memory, and implicit memory; the fourth is mental imagery, and finally, the fifth concerns reasoning and problem solving. == History == The theory of embodied cognition, along with the multiple aspects it comprises, can be regarded as the imminent result of an intellectual skepticism towards the flourishment of the disembodied theory of mind put forth by René Descartes in the 17th century. According to Cartesian dualism, the mind is entirely distinct from the body and can be successfully explained and understood without reference to the body or to its processes. Research has been done to identify the set of ideas that would establish what could be considered as the early stages of embodied cognition around inquiries regarding the mind-body-soul rel
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Stability (learning theory)

Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm output is changed with small perturbations to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One way to modify this training set is to leave out an example, so that only 999 examples of handwritten letters and their labels are available. A stable learning algorithm would produce a similar classifier with both the 1000-element and 999-element training sets. Stability can be studied for many types of learning problems, from language learning to inverse problems in physics and engineering, as it is a property of the learning process rather than the type of information being learned. The study of stability gained importance in computational learning theory in the 2000s when it was shown to have a connection with generalization. It was shown that for large classes of learning algorithms, notably empirical risk minimization algorithms, certain types of stability ensure good generalization. == History == A central goal in designing a machine learning system is to guarantee that the learning algorithm will generalize, or perform accurately on new examples after being trained on a finite number of them. In the 1990s, milestones were reached in obtaining generalization bounds for supervised learning algorithms. The technique historically used to prove generalization was to show that an algorithm was consistent, using the uniform convergence properties of empirical quantities to their means. This technique was used to obtain generalization bounds for the large class of empirical risk minimization (ERM) algorithms. An ERM algorithm is one that selects a solution from a hypothesis space H {\displaystyle H} in such a way to minimize the empirical error on a training set S {\displaystyle S} . A general result, proved by Vladimir Vapnik for an ERM binary classification algorithms, is that for any target function and input distribution, any hypothesis space H {\displaystyle H} with VC-dimension d {\displaystyle d} , and n {\displaystyle n} training examples, the algorithm is consistent and will produce a training error that is at most O ( d n ) {\displaystyle O\left({\sqrt {\frac {d}{n}}}\right)} (plus logarithmic factors) from the true error. The result was later extended to almost-ERM algorithms with function classes that do not have unique minimizers. Vapnik's work, using what became known as VC theory, established a relationship between generalization of a learning algorithm and properties of the hypothesis space H {\displaystyle H} of functions being learned. However, these results could not be applied to algorithms with hypothesis spaces of unbounded VC-dimension. Put another way, these results could not be applied when the information being learned had a complexity that was too large to measure. Some of the simplest machine learning algorithms—for instance, for regression—have hypothesis spaces with unbounded VC-dimension. Another example is language learning algorithms that can produce sentences of arbitrary length. Stability analysis was developed in the 2000s for computational learning theory and is an alternative method for obtaining generalization bounds. The stability of an algorithm is a property of the learning process, rather than a direct property of the hypothesis space H {\displaystyle H} , and it can be assessed in algorithms that have hypothesis spaces with unbounded or undefined VC-dimension such as nearest neighbor. A stable learning algorithm is one for which the learned function does not change much when the training set is slightly modified, for instance by leaving out an example. A measure of Leave one out error is used in a Cross Validation Leave One Out (CVloo) algorithm to evaluate a learning algorithm's stability with respect to the loss function. As such, stability analysis is the application of sensitivity analysis to machine learning. == Summary of classic results == Early 1900s - Stability in learning theory was earliest described in terms of continuity of the learning map L {\displaystyle L} , traced to Andrey Nikolayevich Tikhonov. 1979 - Devroye and Wagner observed that the leave-one-out behavior of an algorithm is related to its sensitivity to small changes in the sample. 1999 - Kearns and Ron discovered a connection between finite VC-dimension and stability. 2002 - In a landmark paper, Bousquet and Elisseeff proposed the notion of uniform hypothesis stability of a learning algorithm and showed that it implies low generalization error. Uniform hypothesis stability, however, is a strong condition that does not apply to large classes of algorithms, including ERM algorithms with a hypothesis space of only two functions. 2002 - Kutin and Niyogi extended Bousquet and Elisseeff's results by providing generalization bounds for several weaker forms of stability which they called almost-everywhere stability. Furthermore, they took an initial step in establishing the relationship between stability and consistency in ERM algorithms in the Probably Approximately Correct (PAC) setting. 2004 - Poggio et al. proved a general relationship between stability and ERM consistency. They proposed a statistical form of leave-one-out-stability which they called CVEEEloo stability, and showed that it is a) sufficient for generalization in bounded loss classes, and b) necessary and sufficient for consistency (and thus generalization) of ERM algorithms for certain loss functions such as the square loss, the absolute value and the binary classification loss. 2010 - Shalev Shwartz et al. noticed problems with the original results of Vapnik due to the complex relations between hypothesis space and loss class. They discuss stability notions that capture different loss classes and different types of learning, supervised and unsupervised. 2016 - Moritz Hardt et al. proved stability of gradient descent given certain assumption on the hypothesis and number of times each instance is used to update the model. == Preliminary definitions == We define several terms related to learning algorithms training sets, so that we can then define stability in multiple ways and present theorems from the field. A machine learning algorithm, also known as a learning map L {\displaystyle L} , maps a training data set, which is a set of labeled examples ( x , y ) {\displaystyle (x,y)} , onto a function f {\displaystyle f} from X {\displaystyle X} to Y {\displaystyle Y} , where X {\displaystyle X} and Y {\displaystyle Y} are in the same space of the training examples. The functions f {\displaystyle f} are selected from a hypothesis space of functions called H {\displaystyle H} . The training set from which an algorithm learns is defined as S = { z 1 = ( x 1 , y 1 ) , . . , z m = ( x m , y m ) } {\displaystyle S=\{z_{1}=(x_{1},\ y_{1})\ ,..,\ z_{m}=(x_{m},\ y_{m})\}} and is of size m {\displaystyle m} in Z = X × Y {\displaystyle Z=X\times Y} drawn i.i.d. from an unknown distribution D. Thus, the learning map L {\displaystyle L} is defined as a mapping from Z m {\displaystyle Z_{m}} into H {\displaystyle H} , mapping a training set S {\displaystyle S} onto a function f S {\displaystyle f_{S}} from X {\displaystyle X} to Y {\displaystyle Y} . Here, we consider only deterministic algorithms where L {\displaystyle L} is symmetric with respect to S {\displaystyle S} , i.e. it does not depend on the order of the elements in the training set. Furthermore, we assume that all functions are measurable and all sets are countable. The loss V {\displaystyle V} of a hypothesis f {\displaystyle f} with respect to an example z = ( x , y ) {\displaystyle z=(x,y)} is then defined as V ( f , z ) = V ( f ( x ) , y ) {\displaystyle V(f,z)=V(f(x),y)} . The empirical error of f {\displaystyle f} is I S [ f ] = 1 n ∑ V ( f , z i ) {\displaystyle I_{S}[f]={\frac {1}{n}}\sum V(f,z_{i})} . The true error of f {\displaystyle f} is I [ f ] = E z V ( f , z ) {\displaystyle I[f]=\mathbb {E} _{z}V(f,z)} Given a training set S of size m, we will build, for all i = 1....,m, modified training sets as follows: By removing the i-th element S | i = { z 1 , . . . , z i − 1 , z i + 1 , . . . , z m } {\displaystyle S^{|i}=\{z_{1},...,\ z_{i-1},\ z_{i+1},...,\ z_{m}\}} By replacing the i-th element S i = { z 1 , . . . , z i − 1 , z i ′ , z i + 1 , . . . , z m } {\displaystyle S^{i}=\{z_{1},...,\ z_{i-1},\ z_{i}',\ z_{i+1},...,\ z_{m}\}} == Definitions of stability == === Hypothesis Stability === An algorithm L {\displaystyle L} has hypothesis stability β with respect to the loss function V if the following holds: ∀ i ∈ { 1 , . . . , m } , E S , z [ | V ( f S , z ) − V ( f S |
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AlphaGo Zero

AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero: surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0; reached the level of AlphaGo Master in 21 days; and exceeded all previous versions in 40 days. Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills, as expert data is "often expensive, unreliable, or simply unavailable." Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge". Furthermore, AlphaGo Zero performed better than standard deep reinforcement learning models (such as Deep Q-Network implementations) due to its integration of Monte Carlo tree search. David Silver, one of the first authors of DeepMind's papers published in Nature on AlphaGo, said that it is possible to have generalized AI algorithms by removing the need to learn from humans. Google later developed AlphaZero, a generalized version of AlphaGo Zero that could play chess and shōgi in addition to Go. In December 2017, AlphaZero beat the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformed AlphaGo Lee on an Elo scale. AlphaZero also defeated a top chess program (Stockfish) and a top Shōgi program (Elmo). == Architecture == The network in AlphaGo Zero is a ResNet with two heads. The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time step go before the beginning of the game, then 0 in all positions.) 8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. The body is a ResNet with either 20 or 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing. Value head outputs a number in the range ( − 1 , + 1 ) {\displaystyle (-1,+1)} , representing the expected score for the current player. -1 represents current player losing, and +1 winning. == Training == AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession. It appeared to develop the skills required to beat top humans within just a few days, whereas the earlier AlphaGo took months of training to achieve the same level. According to Epoch.ai, training cost 3e23 FLOPs. For comparison, the researchers also trained a version of AlphaGo Zero using human games, AlphaGo Master, and found that it learned more quickly, but actually performed more poorly in the long run. DeepMind submitted its initial findings in a paper to Nature in April 2017, which was then published in October 2017. == Hardware cost == The hardware cost for a single AlphaGo Zero system in 2017, including the four TPUs, has been quoted as around $25 million. == Applications == According to Hassabis, AlphaGo's algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding (see AlphaFold) or accurately simulating chemical reactions. AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car. DeepMind stated in October 2017 that it had already started active work on attempting to use AlphaGo Zero technology for protein folding, and stated it would soon publish new findings. == Reception == AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do it—and their ability to train the system in 40 days, on four TPUs". The Guardian called it a "major breakthrough for artificial intelligence", citing Eleni Vasilaki of Sheffield University and Tom Mitchell of Carnegie Mellon University, who called it an impressive feat and an “outstanding engineering accomplishment" respectively. Mark Pesce of the University of Sydney called AlphaGo Zero "a big technological advance" taking us into "undiscovered territory". Gary Marcus, a psychologist at New York University, has cautioned that for all we know, AlphaGo may contain "implicit knowledge that the programmers have about how to construct machines to play problems like Go" and will need to be tested in other domains before being sure that its base architecture is effective at much more than playing Go. In contrast, DeepMind is "confident that this approach is generalisable to a large number of domains". In response to the reports, South Korean Go professional Lee Sedol said, "The previous version of AlphaGo wasn’t perfect, and I believe that’s why AlphaGo Zero was made." On the potential for AlphaGo's development, Lee said he will have to wait and see but also said it will affect young Go players. Mok Jin-seok, who directs the South Korean national Go team, said the Go world has already been imitating the playing styles of previous versions of AlphaGo and creating new ideas from them, and he is hopeful that new ideas will come out from AlphaGo Zero. Mok also added that general trends in the Go world are now being influenced by AlphaGo's playing style. "At first, it was hard to understand and I almost felt like I was playing against an alien. However, having had a great amount of experience, I’ve become used to it," Mok said. "We are now past the point where we debate the gap between the capability of AlphaGo and humans. It’s now between computers." Mok has reportedly already begun analyzing the playing style of AlphaGo Zero along with players from the national team. "Though having watched only a few matches, we received the impression that AlphaGo Zero plays more like a human than its predecessors," Mok said. Chinese Go professional Ke Jie commented on the remarkable accomplishments of the new program: "A pure self-learning AlphaGo is the strongest. Humans seem redundant in front of its self-improvement." == Comparison with predecessors == == AlphaZero == On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case. AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Chess (unlike Go) can end in a tie; therefore AZ can take into account the possibility of a tie game. An open source program, Leela Zero, based on the ideas from the AlphaGo papers is available. It uses a GPU instead of the TPUs recent versions of AlphaGo rely on.
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Transparency in Frontier Artificial Intelligence Act

The Transparency in Frontier Artificial Intelligence Act, also referred to as SB-53, is a 2025 California law which mandates increased transparency for companies building artificial intelligence. SB-53 is primarily focused on assessing and reducing potential catastrophic risks from AI, and is the first bill addressing such risks to be passed into law in America. The bill requires companies to create publicly accessible documents assessing potential "catastrophic risk[s]" from their AI models, as well as publishing documentation on how the model incorporates national and international safety standards. SB-53 also sets up whistleblower protections and procedures for alerting the government to a "critical safety incident". == History == SB-53 was preceded in 2024 by the unsuccessful Safe and Secure Innovation for Frontier Artificial Intelligence Models Act ("SB-1047"), a proposed bill authored by Senator Scott Wiener which was vetoed by Governor Gavin Newsom. Afterwords, Newsom created a "Joint California AI Policy Working Group" to provide recommendations for AI regulation, which guided the drafting of SB-53. Senator Scott Wiener introduced the bill on January 7, 2025, and after a series of amendments, SB-53 passed the Senate 29-8 on September 13. Governor Gavin Newsom approved the bill on September 25, passing it into law. == Provisions == SB-53 applies primarily to companies making at least $500 million in yearly gross revenue. It defines a “frontier model” as any AI trained with over 1026 FLOPS (including fine-tuning), including unreleased internal models. Both the financial and computational thresholds must be met before most of the law is applied, although the threshold can be lowered or otherwise updated by the California Department of Technology in an annual review starting in 2027. Most of the bill's provisions are focused on "catastrophic risks" from AI, which are defined as incidents in which a model contributes to more than 50 deaths or serious injuries, or causes more than one billion dollars ($1,000,000,000) in economic damage from AI-assisted acts (such as cyberattacks or the creation of biological weapons). The bill requires companies to provide publicly accessible safety frameworks for frontier AI models, describing how the company tests for catastrophic risk from its AI, and how it implements protections against such risks. This includes addressing the possibility that the AI may attempt to circumvent internal guardrails or oversight mechanisms. (Certain safety incidents, such as dangerously deceptive model behavior, physical injury, or death, must be reported to California Office of Emergency Services (OES) within 15 days, unless the incident poses imminent physical risk, in which case it must be reported immediately.) The company must follow its published framework, and if any changes are made, the framework should be updated within 30 days, and justification for said changes must also be made public. Additionally, all frontier companies are required to publish basic information about newly released frontier models (such as terms of service, supported languages, and intended use), although only large companies (making over $500 million annually) need to publish full safety frameworks. SB-53 also establishes various whistleblower protections for covered employees. Large companies must have anonymous whistleblowing channels in place which protect employees from retaliation from reporting risks to state or federal authorities if they have reasonable cause to believe that their employer is substantially risking public health and safety.
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