AI Generator Tool

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  • Glossary of computer graphics

    Glossary of computer graphics

    This is a glossary of terms relating to computer graphics. For more general computer hardware terms, see glossary of computer hardware terms. == 0–9 == 2D convolution Operation that applies linear filtering to image with a given two-dimensional kernel, able to achieve e.g. edge detection, blurring, etc. 2D image 2D texture map A texture map with two dimensions, typically indexed by UV coordinates. 2D vector A two-dimensional vector, a common data type in rasterization algorithms, 2D computer graphics, graphical user interface libraries. 2.5D Also pseudo 3D. Rendering whose result looks 3D while actually not being 3D or having great limitations, e.g. in camera degrees of freedom. 3D graphics pipeline A graphics pipeline taking 3D models and producing a 2D bitmap image result. 3D paint tool A 3D graphics application for digital painting of multiple texture map image channels directly onto a rotated 3D model, such as zbrush or mudbox, also sometimes able to modify vertex attributes. 3D scene A collection of 3D models and lightsources in world space, into which a camera may be placed, describing a scene for 3D rendering. 3D unit vector A unit vector in 3D space. 4D vector A common datatype in graphics code, holding homogeneous coordinates or RGBA data, or simply a 3D vector with unused W to benefit from alignment, naturally handled by machines with 4-element SIMD registers. 4×4 matrix A matrix commonly used as a transformation of homogeneous coordinates in 3D graphics pipelines. 7e3 format A packed pixel format supported by some graphics processing units (GPUs) where a single 32-bit word encodes three 10-bit floating-point color channels, each with seven bits of mantissa and three bits of exponent. == A == AABB Axis-aligned bounding box (sometimes called "axis oriented"), a bounding box stored in world coordinates; one of the simplest bounding volumes. Additive blending A compositing operation where d s t = d s t + s r c , {\displaystyle dst=dst+src,} without the use of an alpha channel, used for various effects. Also known as linear dodge in some applications. Affine texture mapping Linear interpolation of texture coordinates in screen space without taking perspective into account, causing texture distortion. Aliasing Unwanted effect arising when sampling high-frequency signals, in computer graphics appearing e.g. when downscaling images. Antialiasing methods can prevent it. Alpha channel An additional image channel (e.g. extending an RGB image) or standalone channel controlling alpha blending. Ambient lighting An approximation to the light entering a region from a wide range of directions, used to avoid needing an exact solution to the rendering equation. Ambient occlusion (AO) Effect approximating, in an inexpensive way, one aspect of global illumination by taking into account how much ambient light is blocked by nearby geometry, adding visual clues about the shape. Analytic model A mathematical model for a phenomenon to be simulated, e.g. some approximation to surface shading. Contrasts with Empirical models based purely on recorded data. Anisotropic filtering Advanced texture filtering improving on mipmapping, preventing aliasing while reducing blur in textured polygons at oblique angles to the camera. Anti-aliasing Methods for filtering and sampling to avoid visual artifacts associated with the uniform pixel grid in 3D rendering. Array texture A form of texture map containing an array of 2D texture slices selectable by a 3rd 'W' texture coordinate; used to reduce state changes in 3D rendering. Augmented reality Computer-rendered content inserted into the user's view of the real world. AZDO Approaching zero driver overhead, a set of techniques aimed at reducing the CPU overhead in preparing and submitting rendering commands in the OpenGL pipeline. A compromise between the traditional GL API and other high-performance low-level rendering APIs. == B == Back-face culling Culling (discarding) of polygons that are facing backwards from the camera. Baking Performing an expensive calculation offline, and caching the results in a texture map or vertex attributes. Typically used for generating lightmaps, normal maps, or low level of detail models. Barycentric coordinates Three-element coordinates of a point inside a triangle. Beam tracing Modification of ray tracing which instead of lines uses pyramid-shaped beams to address some of the shortcomings of traditional ray tracing, such as aliasing. Bicubic interpolation Extension of cubic interpolation to 2D, commonly used when scaling textures. Bilinear interpolation Linear interpolation extended to 2D, commonly used when scaling textures. Binding Selecting a resource (texture, buffer, etc.) to be referenced by future commands. Billboard A textured rectangle that keeps itself oriented towards the camera, typically used e.g. for vegetation or particle effects. Binary space partitioning (BSP) A data structure that can be used to accelerate visibility determination, used e.g. in Doom engine. Bit depth The number of bits per pixel, sample, or texel in a bitmap image (holding one or more image channels, typical values being 4, 8, 16, 24, 32) Bitmap Image stored by pixels. Bit plane A format for bitmap images storing 1 bit per pixel in a contiguous 2D array; Several such parallel arrays combine to produce the a higher-bit-depth image. Opposite of packed-pixel format. Blend operation A render state controlling alpha blending, describing a formula for combining source and destination pixels. Bone Coordinate systems used to control surface deformation (via Weight maps) during skeletal animation. Typically stored in a hierarchy, controlled by key frames, and other procedural constraints. Bounding box One of the simplest type of bounding volume, consisting of axis-aligned or object-aligned extents. Bounding volume A mathematically simple volume, such as a sphere or a box, containing 3D objects, used to simplify and accelerate spatial tests (e.g. for visibility or collisions). BRDF Bidirectional reflectance distribution functions (BRDFs), empirical models defining 4D functions for surface shading indexed by a view vector and light vector relative to a surface. Bump mapping Technique similar to normal mapping that instead of normal maps uses so called bump maps (height maps). BVH Bounding volume hierarchy is a tree structure on a set of geometric objects. == C == Camera A virtual camera from which rendering is performed, also sometimes referred to as 'eye'. Camera space A space with the camera at the origin, aligned with the viewer's direction, after the application of the world transformation and view transformation. Cel shading Cartoon-like shading effect. Clipping Limiting specific operations to a specific region, usually the view frustum. Clipping plane A plane used to clip rendering primitives in a graphics pipeline. These may define the view frustum or be used for other effects. Clip space Coordinate space in which clipping is performed. Clip window A rectangular region in screen space, used during clipping. A clip window may be used to enclose a region around a portal in portal rendering. CLUT A table of RGB color values to be indexed by a lower-bit-depth image (typically 4–8 bits), a form of vector quantization. Color bleeding Unwanted effect in texture mapping. A color from a border of unmapped region of the texture may appear (bleed) in the mapped result due to interpolation. Color channels The set of channels in a bitmap image representing the visible color components, i.e. distinct from the alpha channel or other information. Color resolution Command buffer A region of memory holding a set of instructions for a graphics processing unit for rendering a scene or portion of a scene. These may be generated manually in bare metal programming, or managed by low level rendering APIs, or handled internally by high level rendering APIs. Command list A group of rendering commands ready for submission to a graphics processing unit, see also Command buffer. Compute API An API for efficiently processing large amounts of data. Compute shader A compute kernel managed by a rendering API, with easy access to rendering resources. Cone tracing Modification of ray tracing which instead of lines uses cones as rays in order to achieve e.g. antialiasing or soft shadows. Connectivity information Indices defining [rendering primitive]s between vertices, possibly held in index buffers. describes geometry as a graph or hypergraph. CSG Constructive solid geometry, a method for generating complex solid models from boolean operations combining simpler modelling primitives. Cube mapping A form of environment reflection mapping in which the environment is captured on a surface of a cube (cube map). Culling Before rendering begins, culling removes objects that don't significantly contribute to the rendered result (e.g. being obscured or outside camera view). == D == Decal A "sticker" picture applied onto a surface (e.g. a

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  • Lukas Biewald

    Lukas Biewald

    Lukas Biewald (born 1981) is an American entrepreneur and a prominent figure in artificial intelligence. He is recognized for his contributions to machine learning and as the CEO and co-founder of Weights & Biases, a company that builds developer tools for AI, that sold to CoreWeave in 2025 for $1.7B. He previously founded and was CEO of Figure Eight, a human-in-the-loop machine learning platform. He has co-authored 26 AI research papers from 2004 through 2018. == Early life and education == Biewald was born in Boston, Massachusetts in 1981. He attended Cambridge Rindge and Latin School and later earned both a Bachelor's and Master's degree in Computer science from Stanford University. == Early Career and Founding Figure Eight == After graduation, Biewald joined Yahoo! as an engineer, working on machine translations to improve search results, and eventually led the Search Relevance Team for Yahoo! Japan. He later joined Powerset, a natural language search technology company, as their Senior Scientist, which was acquired by Microsoft in 2008 for an estimated $100M. In 2007, Biewald co-founded Figure Eight (formerly CrowdFlower), a data labeling and crowdsourcing company that created datasets for training machine learning models. Figure Eight was acquired by Appen in 2019 for $300 million. == Weights and Biases == In 2017, Biewald co-founded Weights & Biases with Chris Van Pelt and Shawn Lewis. The company provides tools for tracking machine learning experiments, model management, and collaborative AI and LLM app development. The platform has been adopted by organizations such as OpenAI, Salesforce, and Microsoft. In March 2025 Coreweave acquired Weights and Biases at $1.7 billion, with the transaction closing on May 5, 2025. == Gradient Dissent == Biewald hosts the bi-weekly podcast Gradient Dissent. Guest have included: Anthony Goldbloom – Co-founder & CEO of Kaggle. “How to Win Kaggle Competitions” (podcast, Sep. 9, 2020). Shared tips on data-science competitions from the founder of the largest ML community. Richard Socher – Founder & CEO of You.com; former Chief Scientist at Salesforce. “The Challenges of Making ML Work in the Real World” (podcast, September 28, 2020). A leading NLP researcher, he spoke on multimodal search engines powered by large language models. Jensen Huang – Founder & CEO of NVIDIA. “NVIDIA’s CEO on the Next Generation of AI and MLOps” (podcast, March 3, 2022). Huang’s GPUs power modern ML research and production. Emad Mostaque – Co-founder & CEO of Stability AI. “Stable Diffusion, Stability AI, and What’s Next” (podcast, Nov. 15, 2022). Leads the company behind Stable Diffusion, which helped spark the generative-AI imaging boom. Drago Anguelov – Head of Research at Waymo. “Robustness, Safety, and Scalability at Waymo” (podcast, July 14, 2022). Covered Waymo’s self-driving AI advances and deployment challenges. Jeremy Howard – Co-founder of fast.ai. “The Simple but Profound Insight Behind Diffusion” (podcast, Jan. 5, 2023). Known for democratizing deep-learning education; discussed diffusion models and accessible AI tooling. Aidan Gomez – Co-founder & CEO of Cohere. “Scaling LLMs and Accelerating Adoption” (podcast, April 20, 2023). Co-author of “Attention Is All You Need,” he shared how Cohere delivers large-scale NLP models as a service. Chelsea Finn – Stanford Assistant Professor (AI & Robotics). “Shaping the World of Robotics with Chelsea Finn” (podcast, February 15, 2024). A pioneer in meta-learning and robotics, she detailed robots learning complex tasks like cooking. Andrew Feldman – Co-founder & CEO of Cerebras Systems. "Launching the Fastest AI Inference Solution" (podcast, August 27, 2024). Described wafer-scale AI chips achieving new training performance records. Thomas Dohmke – CEO of GitHub. “GitHub CEO on Copilot and the Future of Software Development” (podcast, June 10, 2025). Discussed building Copilot and the future of AI-assisted coding. Martin Shkreli – Founder of Godel Terminal. “From Pharma to AGI Hype, and Developing AI in Finance: Martin Shkreli’s Journey” (podcast, May 20, 2025). Shkreli reflects on his pharma controversies, prison experience, and his new AI-driven trading platform. Jarek Kutylowski – Founder & CEO of DeepL. “How DeepL Built a Translation Powerhouse with AI” (podcast, July 8, 2025). Shared how DeepL’s neural-MT rivals Google Translate through model and infrastructure innovation. == Awards and recognition == In 2010, Lukas Biewald won the Netexplorateur Award for creating the GiveWork iPhone app, which allows users to perform small tasks that assist refugees and people in developing countries. In 2010, Inc Magazine included Biewald and Van Pelt on its list of the Top 30 Entrepreneurs Under 30. == Publications == Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution by John Le, Andy Edmonds, Vaughn Hester, Lukas Biewald. SIGIR 2010 Workshop on Crowdsourcing for Search Evaluation, July 2010. Superficial Data Analysis: Exploring Millions of Social Stereotypes by Lukas Biewald, Brendan O’Connor. O’Reilly July 2009 Biewald has co-authored 26 AI research papers from 2004 through 2018.

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  • Kindwise

    Kindwise

    FlowerChecker, also known as Kindwise, is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms. It is based in Brno, Czech Republic. It was founded in 2014 by Ondřej Veselý, Jiří Řihák, and Ondřej Vild, at the time Ph.D. students. == Features & Tools == FlowerChecker offers multiple products. Plant.id is a machine learning-based plant identification API launched in 2018, with the plant disease identification API, plant.health, released in April 2022. The plant.id API is suitable for integration into other software, such as mobile apps or urban trees from remote-sensing imagery. Other products include insect.id, mushroom.id and crop.health are machine learning-based identification APIs for the identification of insects, fungi and economically important plants, respectively, and include also online public demos. The FlowerChecker app was discontinued in October 2024 after 10 years of successful operation. == Recognition == In 2019, FlowerChecker won the Idea of the Year award in the AI Awards organized by the Confederation of Industry of the Czech Republic. In 2020, an academic study comparing ten free automated image recognition apps showed that plant.id's performance excelled in most of the parameters studied. In an independent study comparing different image-based species recognition models and their suitability for recognizing invasive alien species, the plant.id achieved the highest accuracy compared to other tools. In a subsequent study, plant.id was utilized to evaluate urban forest biodiversity using remote-sensing imagery, achieving the highest accuracy in tree species identification among compared methods. The technology has also been referenced as an example of practical integration of AI-based plant identification into cross-platform precision agriculture systems. == Research activities == Flowerchecker cooperates with the Nature Conservation Agency of the Czech Republic on a biodiversity mapping project. FlowerChecker plans to adapt its services to participate in the control of invasive species. In 2022, the company entered a consortium to develop a weeder capable of in-row weed detection and removal. In 2025, it received funding for the development of a technology for the removal of invasive species.

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  • AI Futures Project

    AI Futures Project

    The AI Futures Project is a nonprofit research organization based in the United States that specializes in forecasting the development and societal impact of advanced artificial intelligence. The organization is best known for its 2025 scenario forecast, AI 2027, which examines the potential near-term emergence of artificial general intelligence (AGI) and its possible global consequences. == History == The AI Futures Project was founded in 2025 by Daniel Kokotajlo, a former researcher in the governance division of OpenAI. Kokotajlo resigned from OpenAI in April 2024, expressing concerns that the company prioritized rapid product development over AI safety and was advancing without sufficient safeguards. He founded the nonprofit to conduct independent forecasting and policy research. The organization is registered as a 501(c)(3) nonprofit in the United States and is funded through donations. It operates with a small research staff and network of advisors drawn from fields including AI policy, forecasting, and risk analysis. == Activities == The mission of the AI Futures Project is to develop detailed scenario forecasts of the trajectory of advanced AI systems to inform policymakers, researchers, and the public. In addition to written reports, the group has conducted tabletop exercises and workshops based on its scenarios, involving participants from academia, technology, and public policy. == AI 2027 == In April 2025, the AI Futures Project released AI 2027, a detailed scenario forecast describing possible developments in AI between 2025 and 2027. The report was authored by Daniel Kokotajlo along with Eli Lifland, Thomas Larsen, and Romeo Dean, with editing assistance from blogger Scott Alexander. The scenario depicts very rapid progress in AI capabilities, including the development of autonomous AI systems capable of recursive self-improvement. AI 2027 presents two alternative endings: one in which international competition over advanced AI leads to catastrophic loss of human control, and another in which coordinated global action slows down development and averts imminent disaster. The authors emphasize that the narratives are hypothetical and intended as planning tools rather than literal forecasts. == Reception == AI 2027 attracted attention from technology journalists and AI researchers. Some commentators praised the report for its level of detail and its usefulness as a strategic planning exercise, while others criticized the scenario as implausibly aggressive in its timelines. The report was cited in policy discussions about AI governance. U.S. Vice President JD Vance reportedly read AI 2027 and referenced its warnings in conversations about international AI coordination. More recent reporting noted that the authors of AI 2027 had publicly revised some of their timelines. According to Kokotajlo, developments since the report's original publication suggested a slower path toward fully autonomous AI research systems than initially forecasted.

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  • Record sealing

    Record sealing

    Record sealing is the process of making public records inaccessible to the public. In many cases, a person with a sealed record gains the legal right to deny or not acknowledge anything to do with the arrest and the legal proceedings from the case itself. Records are commonly sealed in a number of situations: Sealed birth records (typically after adoption or determination of paternity) Juvenile criminal records may be sealed Other types of cases involving juveniles may be sealed, anonymized, or pseudonymized ("impounded"); e.g., child sex offense or custody cases Cases using witness protection information may be partly sealed Cases involving trade secrets Cases involving state secrets == Filing under seal in US court == Normally, records should not be filed under seal without a court permission. However, FRCP 5.2 requires that sensitive text – like Social Security number, Taxpayer Identification Number, birthday, bank accounts, and children’s names – should be redacted off the filings made with the court and accompanying exhibits. A person making a redacted filing can file an unredacted copy under seal, or the Court can choose to order later that an additional filing be made under seal without redaction. Alternately, the filing party may ask the court’s permission to file some exhibits completely under seal. When the document is filed "under seal", it should have a clear indication for the court clerk to file it separately – most often by stamping words "Filed Under Seal" on the bottom of each page. Person making filing should also provide instructions to the court clerk that the document needs to be filed "under seal". Courts often have specific requirements to these filings in their Local Rules. == Difference from expungement == Expungement, which is a physical destruction, namely a complete erasure of one's criminal records, and therefore usually carries a higher standard, differs from record sealing, which is only to restrict the public's access to records, so that only certain law enforcement agencies or courts, under special circumstances, will have access to them. A record seal will greatly improve the chance of employment, as employers will not have access to damning records. There are occasions, like expungement, where one can truthfully state under oath that they have never been convicted before. Most of the time, a record seal has more relaxed requirements than an expungement. If an expungement is not allowed with a case, then sealing a record may be the best bet. Different states have different terms for what constitutes sealing of a record. == Cybersecurity incidents involving sealed records == Several cybersecurity incidents have demonstrated that sealed court documents are not always secure in practice, with vulnerabilities and data breaches exposing sensitive information. In January 2021, following the SolarWinds cyber attack, the U.S. Bankruptcy Court United States District Court for the District of Nevada announced that its Case Management/Electronic Case Files CM/ECF system had been potentially compromised. The judiciary stated that additional safeguards were being implemented to protect filings, and that the review of the incident and its impact was ongoing. Reports noted that the breach raised concerns about exposure of highly sensitive and sealed documents submitted through the CM/ECF system. In 2023, security researcher Jason Parker, following a tip from an activist, identified flaws in online court systems that exposed sealed records including confidential testimony and medical records through publicly accessible portals. In 2024, a cyber intrusion targeting attorneys in a civil case involving Representative Matt Gaetz led to the unauthorized access and leak of sealed depositions and related records. The breach exposed confidential testimony and financial records, some of which were later reported by news outlets, raising concerns about the security of electronically stored legal materials and the handling of sealed filings. In 2025, multiple reports confirmed that the federal judiciary's CM/ECF and PACER (law) filing system was compromised, exposing sealed indictments, confidential informant information, and other sensitive filings. Some courts temporarily reverted to paper-based filing to mitigate the risks of further disclosure. The FBI later confirmed that the breach had exposed sealed records, and investigators suspected foreign state actors were involved. == GAO publications referencing sealed records == Closed Criminal Plea and Sentencing Proceedings (1983) – Reviewed Department of Justice policies on closing plea and sentencing hearings. GAO noted that sealed transcripts should be unsealed once the reasons for closure no longer applied. Information on Plea Agreements and Settlements in Defense Procurement Fraud Cases (1992) – Examined outcomes of procurement fraud prosecutions. GAO observed that in some instances the results were sealed from public access. Military Recruiting: More Needs to Be Done to Better Screen Applicants and Detect Fraud (1999) – Investigated fraudulent enlistments in the armed forces. The report highlighted that sealed juvenile records often prevented recruiters from discovering prior offenses. Social Security Numbers: Governments Could Do More to Reduce Display in Public Records (2004) – Analyzed risks associated with SSN availability in state and local records. GAO pointed out that some categories of records, such as adoption proceedings, were sealed and less likely to expose identifiers. Social Security Numbers: Stronger Safeguards Needed to Protect Privacy (2005 testimony) – Testimony before Congress reiterating concerns over SSN exposure in public records, while noting that sealed categories (e.g., adoption) were exceptions. U.S. Supreme Court: Policies and Perspectives on Video and Audio Coverage of Appellate Court Proceedings (2016) – Surveyed appellate court policies on courtroom media coverage. The report acknowledged distinctions between public filings, confidential submissions, and sealed materials. Evictions: National Data Are Limited and Challenging to Collect (2024) – Examined nationwide eviction data. GAO reported that in some states eviction records may be sealed or expunged, limiting researchers' ability to compile datasets. DOD Fraud Risk Management: Enhanced Data and Collaboration Could Improve Efforts (2024) – Reviewed Department of Defense fraud-risk management. GAO noted that some adjudicative records in its dataset were sealed, restricting completeness of oversight data.

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  • Automatic1111

    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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  • IEEE Transactions on Pattern Analysis and Machine Intelligence

    IEEE Transactions on Pattern Analysis and Machine Intelligence

    IEEE Transactions on Pattern Analysis and Machine Intelligence (sometimes abbreviated as IEEE PAMI or simply PAMI) is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. == Background == The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition. The editor-in-chief is Kyoung Mu Lee (Seoul National University). According to the Journal Citation Reports, the journal has a 2023 impact factor of 20.8.

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  • Learning vector quantization

    Learning vector quantization

    In computer science, learning vector quantization (LVQ) is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems. LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach. It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN). LVQ was invented by Teuvo Kohonen. == Definition == An LVQ system is represented by prototypes W = ( w ( i ) , . . . , w ( n ) ) {\displaystyle W=(w(i),...,w(n))} which are defined in the feature space of observed data. In winner-take-all training algorithms one determines, for each data point, the prototype which is closest to the input according to a given distance measure. The position of this so-called winner prototype is then adapted, i.e. the winner is moved closer if it correctly classifies the data point or moved away if it classifies the data point incorrectly. An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the respective application domain. LVQ systems can be applied to multi-class classification problems in a natural way. A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009) and references therein. LVQ can be a valuable aid in classifying text documents. == Algorithm == The algorithms are presented as in. Set up: Let the data be denoted by x i ∈ R D {\displaystyle x_{i}\in \mathbb {R} ^{D}} , and their corresponding labels by y i ∈ { 1 , 2 , … , C } {\displaystyle y_{i}\in \{1,2,\dots ,C\}} . The complete dataset is { ( x i , y i ) } i = 1 N {\displaystyle \{(x_{i},y_{i})\}_{i=1}^{N}} . The set of code vectors is w j ∈ R D {\displaystyle w_{j}\in \mathbb {R} ^{D}} . The learning rate at iteration step t {\displaystyle t} is denoted by α t {\displaystyle \alpha _{t}} . The hyperparameters w {\displaystyle w} and ϵ {\displaystyle \epsilon } are used by LVQ2 and LVQ3. The original paper suggests ϵ ∈ [ 0.1 , 0.5 ] {\displaystyle \epsilon \in [0.1,0.5]} and w ∈ [ 0.2 , 0.3 ] {\displaystyle w\in [0.2,0.3]} . === LVQ1 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out the code vector w j {\displaystyle w_{j}} , such that x i {\displaystyle x_{i}} falls within the Voronoi cell of w j {\displaystyle w_{j}} . If its label y i {\displaystyle y_{i}} is the same as that of w j {\displaystyle w_{j}} , then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} , otherwise, w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} . === LVQ2 === LVQ2 is the same as LVQ3, but with this sentence removed: "If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} .". If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then nothing happens. === LVQ3 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out two code vectors w j , w k {\displaystyle w_{j},w_{k}} closest to it. Let d j := ‖ x i − w j ‖ , d k := ‖ x i − w k ‖ {\displaystyle d_{j}:=\|x_{i}-w_{j}\|,d_{k}:=\|x_{i}-w_{k}\|} . If min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , then If w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have the same class, and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} and w k ← w k − α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}-\alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} . If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − ϵ α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\epsilon \alpha _{t}(x_{i}-w_{j})} and w k ← w k + ϵ α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\epsilon \alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then the original paper simply does not explain what happens in this case, but presumably nothing happens in this case. Otherwise, skip. Note that condition min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , precisely means that the point x i {\displaystyle x_{i}} falls between two Apollonian spheres.

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  • AARON

    AARON

    AARON is the collective name for a series of computer programs written by artist Harold Cohen that create original artistic images autonomously, which set it apart from previous programs. Proceeding from Cohen's initial question "What are the minimum conditions under which a set of marks functions as an image?", AARON was in development between 1972 and the 2010s. As the software is not open source, its development effectively ended with Cohen's death in 2016. The name "AARON" does not seem to be an acronym; rather, it was a name chosen to start with the letter "A" so that the names of successive programs could follow it alphabetically. However, Cohen did not create any other major programs. Initial versions of AARON created abstract drawings that grew more complex through the 1970s. More representational imagery was added in the 1980s; first rocks, then plants, then people. In the 1990s more representational figures set in interior scenes were added, along with color. AARON returned to more abstract imagery, this time in color, in the early 2000s. Cohen used machines that allowed AARON to produce physical artwork. The first machines drew in black and white using a succession of custom-built "turtle" and flatbed plotter devices. Cohen would sometimes color these images by hand in fabric dye (Procion), or scale them up to make larger paintings and murals. In the 1990s Cohen built a series of digital painting machines to output AARON's images in ink and fabric dye. His later work used a large-scale inkjet printer on canvas. Development of AARON began in the C programming language then switched to Lisp in the early 1990s. Cohen credits Lisp with helping him solve the challenges he faced in adding color capabilities to AARON. An article about Cohen appeared in Computer Answers that describes AARON and shows two line drawings that were exhibited at the Tate gallery. The article goes on to describe the workings of AARON, then running on a DEC VAX 750 minicomputer. Raymond Kurzweil's company has produced a downloadable screensaver of AARON for Microsoft Windows PCs. This version of AARON can also produce printable images. AARON's source code is not publicly available, but Cohen has described AARON's operations in various essays and it is discussed in abstract in Pamela McCorduck's book. AARON cannot learn new styles or imagery on its own; each new capability must be hand-coded by Cohen. It is capable of producing a practically infinite supply of distinct images in its own style. Examples of these images have been exhibited in galleries worldwide. AARON's artwork has been used as an artistic equivalent of the Turing test. It does seem however that AARON's output follows a noticeable formula (figures standing next to a potted plant, framed within a colored square is a common theme). Cohen is very careful not to claim that AARON is creative. But he does ask "If what AARON is making is not art, what is it exactly, and in what ways, other than its origin, does it differ from the 'real thing?' If it is not thinking, what exactly is it doing?" — The further exploits of AARON, Painter. The Whitney Museum featured AARON in 2024, showcasing the evolution of AARON as the earliest artificial intelligence (AI) program for artmaking.

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  • Emospark

    Emospark

    EmoSpark is an artificial intelligence console created in London, United Kingdom by Patrick Levy-Rosenthal. The device uses facial recognition and language analysis to evaluate human emotion and convey responsive content according to the emotion. The console measures 90 mm x 90 mm x 90 mm and is cube shaped. It operates on an "Emotional Processing Unit", an emotion chip developed by Emoshape Inc. that enables the system to create emotional profile graphs of its surroundings. The emotional processing unit is a patent pending technology that is said to create synthesised emotional responses in machines. EmoSpark was funded through an Indiegogo campaign which aimed to raise $200,000. == Product overview == EmoSpark was created by French inventor Patrick Levy-Rosenthal, as an emotionally intelligent artificial life unit for the home that can interact with people. It is powered by Android and can communicate with users through typed input from a computer, tablet, smartphone or TV as well as through spoken commands. The EmoSpark's features are categorized into two types: functional and emotional. EmoSpark is said to have the ability to perform practical software-based tasks. Through the smartphone interface, it is able to gauge a person’s emotions and is reported to have a conversational library of over 2 million sentences. The face-tracking technology identifies users likes and dislikes to categorize their emotional responses to stimuli such as videos and music. The device has an emotional spectrum that is composed of eight emotions which are surprise, sadness, joy, trust, fear, disgust, anger and anticipation. EmoSpark monitors a person's facial expressions and emotions through images from an external camera, which are then processed through an emotion text analysis and content analysis. The New Scientist reported that EmoSpark had the ability to work on the best way to cheer up its users, emotionally. === Connectivity === EmoSpark is able to connect to Facebook and YouTube to present users with content designed to improve their mood, or to Wikipedia for collaborative knowledge that can be shared when users ask questions of it. Through Android OS, EmoSpark is able to be customized with Google Play store apps. The cube is expected to develop its own personality based on the communications it has had with the people using it. == EmoShape == The Emotion Chip (EPU) used in the cube is created by the US company Emoshape Inc, founded by Levy-Rosenthal. EmoShape Ltd (UK) was the company that developed EmoSpark cube. Patrick Levy-Rosenthal also received the IST Prize in 2005 from the European Council for Applied Science, Technology and Engineering.

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  • MacSpeech Scribe

    MacSpeech Scribe

    MacSpeech Scribe is speech recognition software for Mac OS X designed specifically for transcription of recorded voice dictation. It runs on Mac OS X 10.6 Snow Leopard. The software transcribes dictation recorded by an individual speaker. Typically, the speaker will record their dictation using a digital recording device such as a handheld digital recorder, mobile smartphone (e.g. iPhone), or desktop or laptop computer with a suitable microphone. MacSpeech Scribe supports specific audio file formats for recorded dictation: .aif, .aiff, .wav, .mp4, .m4a, and .m4v. MacSpeech Scribe was originally developed by MacSpeech, Inc. and released February 11, 2010, at Macworld Expo in San Francisco. The product is now owned by Nuance Communications which acquired MacSpeech on February 16, 2010. Nuance is the developer of other speech recognition products including Dragon NaturallySpeaking for Windows, Dragon Dictate for Mac (formerly "MacSpeech Dictate"), and Dragon Dictation apps for iOS. Jeffery Battersby of Macworld noted in his September 2010 review of MacSpeech Scribe, v1.1: Small foibles aside, MacSpeech Scribe is a powerful and intelligent tool for transcribing your recorded speech. A simple training process and access to a wide variety of standard audio formats mean that you’ll be moving your spoken text to the printed page in a matter of minutes and with a minimum of hassle. Scribe is the best, simplest way for you to get your spoken word to the printed page. == Release history ==

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  • National Security Commission on Artificial Intelligence

    National Security Commission on Artificial Intelligence

    The National Security Commission on Artificial Intelligence (NSCAI) was an independent commission of the United States of America from 2018 to 2021. Its mission was to make recommendations to the President and Congress to "advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States". The commission's 15 members were nominated by the United States Congress. The NSCAI was dissolved on 1 October 2021. == History and reporting == The NSCAI began working in March 2019 and by November 2019 it had received more than 200 classified and unclassified briefings to help with the creation of its final report due in 2021.On 4 November 2019, the NSCAI shared its interim report with Congress, where it explained the 27 initial judgements to base its ongoing work. In the interim report the commission also agreed on seven principles: Global leadership in AI technology is a national security priority AI adoption is an urgent imperative for national security A shared sense of responsibility for the American peoples security must be created from government officials and private sector leaders. It needs to find local AI talent and use it to attract the world’s best minds Actions used for the protection of America’s AI leadership against foreign threats needs to follow the principles of free enterprise, free inquiry and free flow of ideas. The technical limitations of AI are universally known, however, a strong desire remains for powerful, dependable, and secure AI systems. United States used AI must follow American values including the rule of law Fundamental areas of effort for the preservation of U.S. advantages were also agreed upon in the interim report of 2019. The NSCAI released its first report of recommendations in March 2020, most of which were included in the 2021 National Defense Authorization Act. In July 2020, the commission published the second report to Congress. It identified 35 actions for both Executive and Legislative branches, which were focused on six fundamental areas. This report was available to the public. In January 2021, a draft of the final report was presented at a panel led by Schmidt. The report recommended the US to use AI technology for military use and development. It issued its final report in March 2021, saying that the U.S. is not sufficiently prepared to defend or compete against China in the AI era. It was broken up into two parts, the first titled “Defending America in the AI Era”, and the second “Winning the Technology Competition”. The report spoke about China’s efforts and investments into integration and that it could very well take the lead in AI in the next few years. Additional suggestions were made to concentrate on AI in everything we do and to implement it into US national security on multiple levels, as well as focus on bringing in new talent to develop AI and to introduce it to the working force on both civilian and military levels. Another recommendation of the NSCAI report was to develop and provide China and Russia with alternative models that are based on norms and democratic values. The final report also included a proposed $40 billion budget for government spending. On 14 April 2021, NSCAI executive director Ylli Bajraktari and director of Research and Analysis Justin Lynch participated in an event held by the Center for Security and Emerging Technology (CSET) to discuss the final report findings. In October 2021, NSCAI chair Eric Schmidt founded the bipartisan, non-profit Special Competitive Studies Project (SCSP) through his family led non-profit Eric & Wendy Schmidt Fund for Strategic Innovation in order to carry on the NSCAI’s efforts and expand beyond national security. The Foundation for Defense of Democracies held an event in June 2023, called “Thinking Forward After the NSCAI and CSC: A Discussion on AI and Cyber Policy”, with former members of NSCAI on the moderation panel, including Eric Schmidt and Ylli Bajraktari. == Members == Members of the National Security Commission on Artificial Intelligence: Eric Schmidt (chair), former CEO of Google Robert Work (Vice Chair), former Deputy Secretary of Defense Mignon Clyburn, former Commissioner of the Federal Communications Commission Chris Darby, CEO of In-Q-Tel Kenneth M. Ford, CEO of the Florida Institute for Human and Machine Cognition Jose-Marie Griffiths, President of Dakota State University Eric Horvitz, Technical Fellow at Microsoft Katrina G. McFarland, former Assistant Secretary of Defense for Acquisition Jason Matheny, Director of the Center for Security and Emerging Technology at Georgetown University Gilman Louie, partner at Alsop Louie Partners William Mark, vice president at SRI International Andy Jassy, CEO of Amazon Web Services (AWS) Safra Catz, CEO of Oracle Steve Chien, Technical Fellow at Jet Propulsion Laboratory (JPL) Andrew Moore, Google/Alphabet == Recommendations == The report's recommendations include: Dramatically increasing non-defense federal spending on AI research and development, doubling every year from $2 billion in 2022, to $32 billion in 2026. That would bring it up to a level similar to spending on biomedical research A dramatic increase in undergraduate scholarship and graduate studies fellowships in AI Creation of a Digital Corps to bring skilled tech workers into government Founding of a Digital Service Academy: an accredited university providing subsidized education in exchange for a commitment to work for a time in government Include civil rights and civil liberty reports for new AI systems or major updates to existing systems Expanding allocations of employment-based green cards, and giving them to every AI PhD graduate from an accredited U.S. university Reforming the acquisition management system Department of Defense to make it faster and easier to introduce new technologies == Transparency == In December 2019, a ruling was made under the Freedom of Information Act (FOIA) that the NSCAI must also provide historical documents upon request. The Electronic Privacy Information Center (EPIC) filed the lawsuit against the NSCAI in September 2019 after being refused information about the upcoming meetings and prepared records of the commission under FOIA and the Federal Advisory Committee Act (FACA). The U.S. District Court for the District of Columbia ruled in June 2020 that the NSCAI must comply with FACA and therefore hold open meetings and provide records to the public. The lawsuit was also filed by EPIC.

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  • IEEE Transactions on Visualization and Computer Graphics

    IEEE Transactions on Visualization and Computer Graphics

    IEEE Transactions on Visualization and Computer Graphics is a peer-reviewed scientific journal published by the IEEE Computer Society. It covers subjects related to computer graphics and visualization techniques, systems, software, hardware, and user interface issues. TVCG has been considered the top journal in the field of visualization. Since 2011, TVCG has allowed authors to present recently accepted papers at partner conferences. These include: IEEE Visualization (VIS), including VAST, InfoVis, and SciVis. IEEE Virtual Reality Conference (IEEE VR) IEEE International Symposium on Mixed and Augmented Reality (ISMAR) ACM Symposium on Interactive 3D Graphics and Games (I3D) IEEE Pacific Visualization Conference (IEEE PacificVis) ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) Eurographics Symposium on Geometry Processing (SGP) Pacific Graphics Conference (PG) Eurovis - The EG and VGTC Conference on Visualization Graphics Interfaces (GI)

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  • Frame (artificial intelligence)

    Frame (artificial intelligence)

    Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets. Frames are also an extensive part of knowledge representation and reasoning schemes. They were originally derived from semantic networks and are therefore part of structure-based knowledge representations. According to Russell and Norvig's Artificial Intelligence: A Modern Approach, structural representations assemble "facts about particular object and event types and [arrange] the types into a large taxonomic hierarchy analogous to a biological taxonomy". == Frame structure == The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met. Some information in the frame is generally unchanged while other information, stored in "terminals", usually change. Terminals can be considered as variables. Top-level frames carry information, that is always true about the problem in hand, however, terminals do not have to be true. Their value might change with the new information encountered. Different frames may share the same terminals. Each piece of information about a particular frame is held in a slot. The information can contain: Facts or Data Values (called facets) Procedures (also called procedural attachments) IF-NEEDED: deferred evaluation IF-ADDED: updates linked information Default Values For Data For Procedures Other Frames or Subframes == Features and advantages == A frame's terminals are already filled with default values, which is based on how the human mind works. For example, when a person is told "a boy kicks a ball", most people will visualize a particular ball (such as a familiar soccer ball) rather than imagining some abstract ball with no attributes. One particular strength of frame-based knowledge representations is that, unlike semantic networks, they allow for exceptions in particular instances. This gives frames a degree of flexibility that allows representations to reflect real-world phenomena more accurately. Like semantic networks, frames can be queried using spreading activation. Following the rules of inheritance, any value given to a slot that is inherited by subframes will be updated (IF-ADDED) to the corresponding slots in the subframes and any new instances of a particular frame will feature that new value as the default. Because frames are based on structures, it is possible to generate a semantic network given a set of frames even though it lacks explicit arcs. References to Noam Chomsky and his generative grammar of 1950 are generally missing from Minsky's work. The simplified structures of frames allow for easy analogical reasoning, a much prized feature in any intelligent agent. The procedural attachments provided by frames also allow a degree of flexibility that makes for a more realistic representation and gives a natural affordance for programming applications. == Example == Worth noticing here is the easy analogical reasoning (comparison) that can be done between a boy and a monkey just by having similarly named slots. Also notice that Alex, an instance of a boy, inherits default values like "Sex" from the more general parent object Boy, but the boy may also have different instance values in the form of exceptions such as the number of legs. == Frame language == A frame language is a technology used for knowledge representation in artificial intelligence. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information hiding. Frames originated in AI research and objects primarily in software engineering. However, in practice, the techniques and capabilities of frame and object-oriented languages overlap significantly. === Example === A simple example of concepts modeled in a frame language is the Friend of A Friend (FOAF) ontology defined as part of the Semantic Web as a foundation for social networking and calendar systems. The primary frame in this simple example is a Person. Example slots are the person's email, home page, phone, etc. The interests of each person can be represented by additional frames describing the space of business and entertainment domains. The slot knows links each person with other persons. Default values for a person's interests can be inferred by the web of people they are friends of. === Implementations === The earliest frame-based languages were custom developed for specific research projects and were not packaged as tools to be re-used by other researchers. Just as with expert system inference engines, researchers soon realized the benefits of extracting part of the core infrastructure and developing general-purpose frame languages that were not coupled to specific applications. One of the first general-purpose frame languages was KRL. One of the most influential early frame languages was KL-ONE. KL-ONE spawned several subsequent Frame languages. One of the most widely used successors to KL-ONE was the Loom language developed by Robert MacGregor at the Information Sciences Institute. In the 1980s, Artificial Intelligence generated a great deal of interest in the business world fueled by expert systems. This led to the development of many commercial products for the development of knowledge-based systems. These early products were usually developed in Lisp and integrated constructs such as IF-THEN rules for logical reasoning with Frame hierarchies for representing data. One of the most well known of these early Lisp knowledge-base tools was the Knowledge Engineering Environment (KEE) from Intellicorp. KEE provided a full Frame language with multiple inheritance, slots, triggers, default values, and a rule engine that supported backward and forward chaining. As with most early commercial versions of AI software KEE was originally deployed in Lisp on Lisp machine platforms but was eventually ported to PCs and Unix workstations. The research agenda of the Semantic Web spawned a renewed interest in automatic classification and frame languages. An example is the Web Ontology Language (OWL) standard for describing information on the Internet. OWL is a standard to provide a semantic layer on top of the Internet. The goal is that rather than searching the web using keywords as most search engines (e.g. Google) do today, the web can be organized by concepts organized in an ontology, like a directory structure. The name of the OWL language itself provides a good example of the value of a Semantic Web. If one were to search for "OWL" using the Internet today most of the pages retrieved would be on the bird Owl rather than the standard OWL. With a Semantic Web it would be possible to specify the concept "Web Ontology Language" and the user would not need to worry about the various possible acronyms or synonyms as part of the search. Likewise, the user would not need to worry about homonyms crowding the search results with irrelevant data such as information about birds of prey as in this simple example. In addition to OWL, various standards and technologies that are relevant to the Semantic Web and were influenced by Frame languages include OIL and DAML. The Protege Open Source software tool from Stanford University provides an ontology editing capability that is built on OWL and has the full capabilities of a classifier. However it ceased to explicitly support frames as of version 3.5 (which is maintained for those preferring frame orientation), with the current version being 5.6.8 as of 2025. The justification for moving from explicit frames being that OWL DL is more expressive and "industry standard". === Comparison of frames and objects === Frame languages have a significant overlap with object-oriented languages. The terminologies and goals of the two communities were different but as they moved from the academic world and labs to the commercial world developers tended to not care about philosophical issues and focused primarily on specific capabilities, taking the best from either camp regardless of where the idea began. What both paradigms have in common is a desire to reduce the distance between concepts in the real world and their implementation in software. As such both paradigms arrived at the idea of representing the primary software objects in taxonomies starting with very general types and progressing to more specific types. The following table illustrates the correlation between standard terminology from the object-oriented and frame language communities: The primary difference between the two paradigms was in the degree that encapsulation was considered a majo

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  • Safe and Secure Innovation for Frontier Artificial Intelligence Models Act

    Safe and Secure Innovation for Frontier Artificial Intelligence Models Act

    The Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, or SB 1047, was a failed 2024 California bill intended to "mitigate the risk of catastrophic harms from AI models so advanced that they are not yet known to exist". Specifically, the bill would have applied to models which cost more than $100 million to train and were trained using a quantity of computing power greater than 1026 integer or floating-point operations. SB 1047 would have applied to all AI companies doing business in California—the location of the company would not matter. The bill would have created protections for whistleblowers and required developers to perform risk assessments of their models prior to release, with guidance from the Government Operations Agency. It would also have established CalCompute, a University of California public cloud computing cluster for startups, researchers and community groups. == Background == The rapid increase in capabilities of AI systems in the 2020s, including the release of ChatGPT in November 2022, caused some researchers and members of the public to become concerned about the existential risks associated with increasingly powerful AI systems. Hundreds of tech executives and AI researchers, including two of the so-called "Godfathers of AI", Geoffrey Hinton and Yoshua Bengio, signed a statement in May 2023 calling for the mitigation of the "risk of extinction from AI" to be a global priority alongside "pandemics and nuclear war". However, the plausibility of these risks is still widely debated. Strong regulation of AI has been criticized for purportedly causing regulatory capture by large AI companies like OpenAI, a phenomenon in which regulation advances the interest of larger companies at the expense of smaller competition and the public in general, although OpenAI ended up opposing the bill. Other advocates of AI regulation aim to prevent bias and privacy violations, rather than existential risks. For example, some experts who view existential concerns as overblown and unrealistic view them as a distraction from near-term harms of AI like discriminatory automated decision making. In the face of existential concerns, technology companies have made voluntary commitments to conduct safety testing, for example at the AI Safety Summit and AI Seoul Summit. In 2023, not long before the bill was proposed, Governor Newsom of California and President Biden issued executive orders on artificial intelligence. State Senator Wiener said SB 1047 draws heavily on the Biden executive order, and is motivated by the absence of unified federal legislation on AI safety. Historically, California has passed regulation on several tech issues itself, including consumer privacy and net neutrality, in the absence of action by Congress. == History == === Proposal and voting === The bill was authored by State Senator Scott Wiener. Wiener first proposed AI legislation for California through an intent bill called SB 294, the Safety in Artificial Intelligence Act, in September 2023. On February 7, 2024, Wiener introduced SB 1047. On May 21, SB 1047 passed the Senate 32–1. The bill was significantly amended by Wiener on August 15, 2024, in response to industry advice. Amendments included adding clarifications, and removing the creation of a "Frontier Model Division" and the penalty of perjury. On August 28, the bill passed the State Assembly 48–16. Then, due to the amendments, the bill was once again voted on by the Senate, passing 30–9. === Veto by governor === On September 29, Governor Gavin Newsom vetoed the bill. The deadline for California lawmakers to overrule Newsom's veto was November 30, 2024. Newsom cited concerns over the bill's regulatory framework targeting only large AI models based on their computational size, while not taking into account whether the models are deployed in high-risk environments. Newsom emphasized that this approach could create a false sense of security, overlooking smaller models that might present equally significant risks. He acknowledged the need for AI safety protocols but stressed the importance of adaptability in regulation as AI technology continues to evolve rapidly. Governor Newsom also committed to working with technology experts, federal partners, and research institutions, including the Carnegie Endowment for International Peace, led by former California Supreme Court Justice Mariano-Florentino Cuéllar; and Stanford University's Human-Centered AI (HAI) Institute, led by Dr. Fei-Fei Li. He announced plans to collaborate with these entities to advance responsible AI development, aiming to protect the public while fostering innovation. == Provisions == SB 1047 would have covered AI models with training compute over 1026 integer or floating-point operations and a cost of over $100 million. If a covered model is fine-tuned using more than $10 million, the resulting model would also have been covered. The bill would have defined critical harms with respect to four categories: Creation or use of a chemical, biological, radiological, or nuclear weapon Cyberattacks on critical infrastructure causing mass casualties or at least $500 million of damage Autonomous crimes causing mass casualties or at least $500 million of damage Other harms of comparable severity Developers would have needed to create a "safety and security protocol" before training covered models. Before deployment, they would have submitted a statement of compliance, confirming they took reasonable care to take measures to prevent covered models that pose an unreasonable risk of critical harms. The statement would have included risk assessments and descriptions of their compliance process. These rules would have applied to both covered models and their derivatives, including post-training modifications, with annual third-party audits required starting in 2026. Safeguards to reduce risk included the ability to shut down the model, which has been variously described as a "kill switch" and "circuit breaker". Whistleblowing provisions would have protected employees who report safety problems and incidents. Additionally, SB 1047 would have created a public cloud computing cluster called CalCompute, associated with the University of California, to support startups, researchers, and community groups that lack large-scale computing resources. === Compliance and supervision === SB 1047 would have required developers, beginning January 1, 2026, to annually retain a third-party auditor to perform an independent audit of compliance with the requirements of the bill, as provided. The Government Operations Agency would have reviewed the results of safety tests and incidents, and issue guidance, standards, and best practices. The bill would have created a Board of Frontier Models to supervise the application of the bill by the Government Operations Agency. It is would be composed of 9 members. == Reception == === Subjects of debate === Proponents of the bill described its provisions as simple and narrowly focused, with Sen. Scott Weiner describing it as a "light-touch, basic safety bill". This was disputed by critics of the bill, who described the bill's language as vague and criticized it as consolidating power in the largest AI companies at the expense of smaller ones. Proponents, in turn, argued that the bill only applies to models trained using more than 1026 FLOPS and with over $100 million, or fine-tuned with more than $10 million, and that the threshold could be increased if needed. The penalty of perjury was also a subject of debate, and was eventually removed through an amendment. The scope of the "kill switch" requirement was also reduced, following concerns from open-source developers. The use of the term "reasonable assurance" in the bill was also controversial, and it was eventually amended to "reasonable care". Critics then argued that "reasonable care" imposed an excessive burden by requiring confidence that models could not be used to cause catastrophic harm; proponents claimed that the standard did not require certainty and that it already applied to AI developers under existing law. === Support and opposition === Individual supporters of the bill included Turing Award recipients Yoshua Bengio and Geoffrey Hinton, Elon Musk, Bill de Blasio, Kevin Esvelt, Dan Hendrycks, Vitalik Buterin, OpenAI whistleblowers Daniel Kokotajlo and William Saunders, Lawrence Lessig, Sneha Revanur, Stuart Russell, Jan Leike, actors Mark Ruffalo, Sean Astin, and Rosie Perez, Scott Aaronson, and Max Tegmark. Over 120 Hollywood celebrities, including Mark Hamill, Jane Fonda, and J. J. Abrams, also signed a statement in support of the bill. Max Tegmark likened the bill's focus on holding companies responsible for the harms caused by their models to the FDA requiring clinical trials before a company can release a drug to the market. Organizations sponsoring the bill included the Center for AI Safety, Economic Security California and Encode. The la

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