AI Face Natural

AI Face Natural — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Automated negotiation

    Automated negotiation

    Automated negotiation is a form of interaction in systems that are composed of multiple autonomous agents, in which the aim is to reach agreements through an iterative process of making offers. Automated negotiation can be employed for many tasks human negotiators regularly engage in, such as bargaining and joint decision making. The main topics in automated negotiation revolve around the design of protocols and negotiating strategies. == History == Through digitization, the beginning of the 21st century has seen a growing interest in the automation of negotiation and e-negotiation systems, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents being able to negotiate on behalf of human negotiators, and to find better outcomes than human negotiators. == Examples == Examples of automated negotiation include: Online dispute resolution, in which disagreements between parties are settled. Sponsored search auction, where bids are placed on advertisement keywords. Content negotiation, in which user agents negotiate over HTTP about how to best represent a web resource. Negotiation support systems, in which negotiation decision-making activities are supported by an information system.

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  • NeOn Toolkit

    NeOn Toolkit

    The NeOn Toolkit is an open source, multi-platform ontology editor, which supports the development of ontologies in F-Logic and OWL/RDF. The editor is based on the Eclipse platform and provides a set of plug-ins (currently 20 plug-ins are available for the latest version, v2.4) covering a number of ontology engineering activities, including Annotation and Documentation, Modularization and Customization, Reuse, Ontology Evolution, translation and others. The NeOn Toolkit has been developed in the course of the EU-funded NeOn project and is currently maintained and distributed by the NeOn Technologies Foundation.

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  • Comet (browser)

    Comet (browser)

    Comet is an AI-powered web browser based on Chromium. It was released by Perplexity AI for Microsoft Windows and macOS on July 9, 2025, for Android on November 20, 2025, and for iOS on March 18, 2026. Initial access to the browser was limited to users subscribed to Perplexity's most expensive tier, with broader availability expected over time. The browser was released for free download in October 2025. == Features == Comet is integrated with Perplexity's AI-assisted search engine. The browser features an assistant which enables users to perform a variety of tasks such as generating article summaries, sending emails, or buying products. == Security concerns == Researchers at LayerX Security identified a malicious attack vector which they call CometJacking. The exploit could possibly exfiltrate a user's personal sensitive data to a remote server controlled by the attacker. LayerX attempted to responsibly disclose their findings to Comet's developer Perplexity AI in August 2025. Perplexity responded that they saw no security impact and marked the disclosure as not applicable.

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  • Aurora (supercomputer)

    Aurora (supercomputer)

    Aurora is an exascale supercomputer that was sponsored by the United States Department of Energy (DOE) and designed by Intel and Cray for Argonne National Laboratory. It was briefly the second fastest supercomputer in the world from November 2023 to June 2024. The cost was estimated in 2019 to be US$500 million. Olivier Franza is the chief architect and principal investigator of this design. == History == In 2013 DOE presented a proposal for an "exascale" supercomputer, capable of speeds in the neighborhood of 1 exaFLOP (1018 floating point mathematical operations per second) with a maximum power consumption of 20 megawatts (MW) by 2020. Aurora was first announced in 2015 and to be finished in 2018. It was expected to have a speed of 180 petaFLOPS which would be around the speed of Summit. Aurora was meant to be the most powerful supercomputer at the time of its launch and to be built by Cray with Intel processors. Later, in 2017, Intel announced that Aurora would be delayed to 2021 but scaled up to 1 exaFLOP. In March 2019, DOE said that it would build the first supercomputer with a performance of one exaFLOP in the United States in 2021. In October 2020, DOE said that Aurora would be delayed again for a further six months, and would no longer be the first exascale computer in the US. In late October 2021 Intel announced that Aurora would now exceed 2 exaFLOPS in peak double-precision compute – That claim however never was realized. The system was fully installed on June 22, 2023. In May 2024, Aurora appeared at number two on the Top500 supercomputer list, with a performance of 1.012 exaFLOPS, marking the second entry of an exascale capable system on the Top500. == Usage == Functions include research on brain structure, nuclear fusion, low carbon technologies, subatomic particles, cancer and cosmology. It will also develop new materials that will be useful for batteries and more efficient solar cells. It is to be available to the general scientific community. == Architecture == Aurora has 10,624 nodes, with each node being composed of two Intel Xeon Max processors, six Intel Max series GPUs and a unified memory architecture, providing a maximum computing power of 130 teraFLOPS per node. It has around 10 petabytes of memory and 230 petabytes of storage. The machine is stated to consume around 39 MW of power. For comparison, the fastest computer in the world today, El Capitan uses 30 MW, while another Top 500 System, Frontier uses 24 MW.

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  • Machine vision

    Machine vision

    Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information. == Definition == Definitions of the term "Machine vision" vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas. The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and industrial robot/process guidance. In more recent times the terms computer vision and machine vision have converged to a greater degree. See glossary of machine vision. == Imaging based automatic inspection and sorting == The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. === Methods and sequence of operation === The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages and programs developed in them then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information. === Equipment === The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices. === Imaging === The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a smart camera or smart sensor. Inclusion of the full processing function into the same enclosure as the camera is often referred to as embedded processing. When separated, the connection may be made to specialized intermediate hardware, a custom processing appliance, or a frame grabber within a computer using either an analog or standardized digital interface (Camera Link, CoaXPress). MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces. While conventional (2D visible light) imaging is most commonly used in MV, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands, line scan imaging, 3D imaging of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color, frame rate, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes. Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry. The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product or image during the imaging process. A laser is projected onto the surfaces of an object. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud. Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras. Other 3D methods used for machine vision are time of flight and grid based. One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012. === Image processing === After an image is acquired, it is processed. Central processing functions are generally done by a CPU, a GPU, a FPGA or a combination of these. Deep learning training and inference impose higher processing performance requirements. Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include; Stitching/Registration: Combining of adjacent 2D or 3D images. Filtering (e.g. morphological filtering) Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value. Pixel counting: counts the number of light or dark pixels Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Edge detection: finding object edges Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color. Blob detection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. Neural network / deep learning / machine learning processing: weighted and self-training multi-variable decision making Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification are object identification,"pass fail" classification of identified objects and OCR. Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size. Barcode, Data Matrix and "2D barcode" reading Optical character recognition: automated reading of text such as serial numbers Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters) Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the

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  • Qualification problem

    Qualification problem

    In philosophy and AI (especially, knowledge-based systems), the qualification problem is concerned with the impossibility of listing all the preconditions required for a real-world action to have its intended effect. It might be posed as how to deal with the things that prevent me from achieving my intended result. It is strongly connected to, and opposite the ramification side of, the frame problem. John McCarthy gives the following motivating example, in which it is impossible to enumerate all the circumstances that may prevent a robot from performing its ordinary function: [T]he successful use of a boat to cross a river requires, if the boat is a rowboat, that the oars and rowlocks be present and unbroken, and that they fit each other. Many other qualifications can be added, making the rules for using a rowboat almost impossible to apply, and yet anyone will still be able to think of additional requirements not yet stated.

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  • Chris Olah

    Chris Olah

    Christopher Olah (born 1992 or 1993) is a Canadian machine learning researcher and a co-founder of Anthropic. He is known for his work on neural network interpretability, particularly mechanistic interpretability, and for research and tools that visualise internal representations in neural networks. In 2025, Forbes reported he had become a billionaire due to his ownership in Anthropic. == Early life and education == Olah was born in Canada. According to Wired, he left university at age 18 without earning a degree and later received a Thiel Fellowship, which supported him in pursuing independent work. == Career == Olah has worked on interpretability research at Google Brain, OpenAI, and Anthropic. Time called him one of the pioneers of mechanistic interpretability and noted that he pursued this research line first at Google, then at OpenAI, and later at Anthropic, which he co-founded. Wired reported that Olah was involved in neural network visualisation work including DeepDream in 2015, as part of efforts to better understand what neural networks learn. Later coverage linked him to more structured interpretability approaches such as "activation atlases". The Verge covered activation atlases as a collaboration between Google and OpenAI researchers to help inspect neural network representations. At Anthropic, Olah has been identified in major press coverage as leading interpretability work aimed at mapping internal "features" in large language models and relating interpretability findings to AI safety. Quanta Magazine has also quoted Olah in reporting on interpretability and the internal structure of modern language models. Time included Olah in its TIME100 AI list in 2024. === Vatican address on AI ethics === On May 25, 2026, Olah spoke at the Vatican during the official presentation of Magnifica Humanitas, the first encyclical of Pope Leo XIV, which addresses artificial intelligence and human dignity. Olah said AI could lead to large-scale displacement of human labor and exacerbate global inequality. He said the commercial and geopolitical incentives driving frontier AI labs often conflict with the public good, and described AI systems as "grown" rather than strictly engineered. Olah called for external moral oversight from religious institutions, scholars, and civil society to hold the technology sector accountable.

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  • Common Crawl

    Common Crawl

    The Common Crawl Foundation (Common Crawl) is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl was founded by Gil Elbaz. The data had mostly been primarily used by researchers and some startups until the 2020s, when AI companies started training large language models using the data. In November 2025, an investigation by The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and it was not honoring requests from publishers to have their content removed from its databases. == History == Common Crawl was founded in 2007 in San Francisco. It began publishing its crawls in 2011. By 2013, sites like TinEye were building their products off of Common Crawl. The crawl reduces the reliance of companies and researchers on Google, which has the biggest dataset. Common Crawl was designed to have more and fresher data that was more efficient to analyze and utilize than the Wayback Machine created by the Internet Archive. By 2015, 1.8 billion webpages were on the Common Crawl, which started by crawling a list of URLs donated by the search engine Blekko. They use Amazon Web Services, which provides some of its services for free, allowing computing costs to average $2-4000/month. The Common Crawl website listed 30 studies based on Common Crawl data. Before 2023, Common Crawl was not very well known outside of academic researchers who utilize the data. Common Crawl received its first requests to redact information in 2023 and increasingly started seeing its crawler, CCBot, blocked. In 2023, it began receiving significant financial support from AI companies, including Anthropic and OpenAI, each of which donated $250,000. It was also used to train Google DeepMind's large language model Gemini. By April 2023, Common Crawl was capturing 3.1 billion webpages, with an estimated 5% of pages before 2021 containing hate speech or slurs. As of 2024, Common Crawl had been cited in more than 10,000 academic studies. By 2024, The Pile and Common Crawl had been the two main training datasets being used to train AI models. In November 2025, an investigation by technology journalist Alex Reisner for The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and when it said that it was honoring requests from publishers to have their content removed from its databases. It included misleading results in the public search function on its website that showed no entries for websites that had requested their archives be removed, when in fact those sites were still included in its scrapes used by AI companies. As of 2025, Reisner found that CCBot was the most widely-blocked bot by the top 1000 websites. A 2026 article in LWN.net discussed an advantage to services like Common Crawl being that it can limit the scraping costs to websites by allowing companies and researchers to download the data from Common Crawl instead of scraping it themselves. In April 2026, Common Crawl experimentally began to distribute its data through Hugging Face Storage Bucket, in addition to its standard storage on Amazon S3. == Organization == Peter Norvig and Joi Ito have served on the advisory board. Rich Skrenta is the executive director. It has received funding almost exclusively from the Elbaz Family Foundation Trust until 2023 when it started receiving donations from the AI industry. == Refined versions == A number of organizations take raw Common Crawl data and refine it into datasets that exclude edgy content or are otherwise higher-quality for their purposes, such as FineWeb, DCLM and C4. === Colossal Clean Crawled Corpus === Google version of the Common Crawl is called the Colossal Clean Crawled Corpus, or C4 for short. It was constructed for the training of the T5 language model series in 2019. As of 2023, there were some concerns over copyrighted content in the C4 as well as racist content. A 2024 study found that 45% of content was explicitly restricted by websites' terms of service to be used for purposes like AI training by for-profit companies.

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  • Video editing software

    Video editing software

    Video editing software or a video editor is software used for performing the post-production video editing of digital video sequences on a non-linear editing system (NLE). It has replaced traditional flatbed celluloid film editing tools and analog video tape editing machines. Video editing software serves a lot of purposes, such as filmmaking, audio commentary, and general editing of video content. In NLE software, the user manipulates sections of video, images, and audio on a sequence. These clips can be trimmed, cut, and manipulated in many different ways. When editing is finished, the user exports the sequence as a video file. == Components == === Timeline === NLE software is typically based on a timeline interface where sections moving image video recordings, known as clips, are laid out in sequence and played back. The NLE offers a range of tools for trimming, splicing, cutting, and arranging clips across the timeline. Another kind of clip is a text clip, used to add text to a video, such as title screens or movie credits. Audio clips can additionally be mixed together, such as mixing a soundtrack with multiple sound effects. Typically, the timeline is divided into multiple rows on the y-axis for different clips playing simultaneously, whereas the x-axis represents the run time of the video. Effects such as transitions can be performed on each clip, such as a crossfade effect going from one scene to another. === Exporting === Since video editors represent a project with a file format specific to the program, one needs to export the video file in order to publish it. Once a project is complete, the editor can then export to movies in a variety of formats in a context that may range from broadcast tape formats to compressed video files for web publishing (such as on an online video platform or personal website), optical media, or saved to mobile devices. To facilitate editing, source video typically has a higher resolution than the desired output. Therefore, higher resolution video needs to be downscaled during exporting, or after exporting in a process known as transsizing. === Visual effects === As digital video editing advanced, visual effects became possible, and is part of the standard toolkit, usually found in prosumer and professional grade software. A common ability is to do compositing techniques such as chroma keying or luma keying, among others, which allow different objects to look as if they are in the same scene. A different kind of visual effects is motion capture. Software such as Blender can perform motion capture to make animated objects follow an actor's movements. === Additional features === Most professional video editors are able to do color grading, which is to manipulate visual attributes of a video such as contrast to enhance output, and improve emotional impact. Some video editors such as iMovie include stock footage available for use. == Hardware requirements == As video editing puts great demands on storage and graphics performance, especially at high resolutions such as 4K, and for videos with many visual effects, powerful hardware is often required. It is not uncommon for a computer built for video editing to have a lot of drive capacity, and a powerful graphics processing unit, which optimally has hardware accelerated video encoding. Having sufficient disk space is important since videos can take up large amounts of storage, depending on the resolution and compression format used. Each minute of a Full HD (1080p) video at 30 fps takes up 60MB of space. When visual effects are used, a server farm can be employed to speed up the rendering process. == Examples == Video editing software can be divided into consumer grade, which focuses on ease-of-use, along with professional grade software, which focuses on feature availability, and advanced editing techniques. The typical use case for the former is to edit personal videos on the go, when more advanced editing is not required. === Consumer grade === Photos (Apple) Google Photos YouTube Create === Prosumer grade === ==== Proprietary software ==== iMovie CyberLink PowerDirector === Professional grade === ==== Proprietary software ==== Final Cut Pro Adobe Premiere Pro DaVinci Resolve Vegas Pro Lightworks Camtasia Media Composer ==== Free and open source software ==== Avidemux Blender Cinelerra Flowblade Kdenlive OpenShot Shotcut While most video editing software has been separate from the operating systems, some operating systems have had a video editor installed by default, such as Windows Movie Maker in Windows XP, or as a component of the default photo viewer, such as the Photos app on iOS. Some social media platforms, such as TikTok and Instagram may include a rudimentary video editor to trim clips.

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  • Fei-Fei Li

    Fei-Fei Li

    Fei-Fei Li (Chinese: 李飞飞; pinyin: Lǐ Fēifēi; born July 3, 1976) is a Chinese-born American computer scientist best known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is a professor of computer science at Stanford University, with research expertise in artificial intelligence, machine learning, deep learning, computer vision, and cognitive neuroscience. Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab, and served as Chief Scientist of AI/ML at Google Cloud and the director of the Stanford Artificial Intelligence Laboratory from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity in the field of artificial intelligence. In 2023, Li was named one of the Time 100 AI Most Influential People. Li received the Intel Lifetime Achievements Innovation Award in 2017 for her contributions to artificial intelligence, and was elected member of the National Academy of Engineering, the National Academy of Medicine in 2020 and the American Academy of Arts and Sciences in 2021. In 2025, she was named as one of the "Architects of AI" for Time's Person of the Year. On August 3, 2023, Li was appointed to the United Nations Scientific Advisory Board, established by Secretary-General Antonio Guterres. In 2024, Li was included on the Gold House's most influential Asian A100 list. In 2024, she raised $230 million for a startup called World Labs, which she and three colleagues founded to develop a "spatial intelligence" AI technology that can understand how the three-dimensional physical world works. In 2026, World Labs raised $1 Billion. == Early life and education == Li was born in Beijing, China, in 1976 and grew up in Chengdu, Sichuan. She studied at Sichuan Chengdu No.7 High School. When she was 12, her father immigrated to Parsippany, New Jersey. When she was 16, Li and her mother joined him in the United States. While attending Parsippany High School, Li worked weekends at her family's dry-cleaning shop. She graduated from Parsippany High School in 1995. She was inducted into the hall of fame at Parsippany High School in 2017. Li pursued undergraduate study at Princeton University, where she received a Bachelor of Arts with a major in physics in 1999. Li completed her senior thesis, "Auditory binaural correlogram difference: a new computational model for Huggins dichotic pitch", under the supervision of Bradley Dickinson, professor of electrical engineering. During her years at Princeton, Li returned home most weekends to help run her family's dry cleaning business and worked as a dishwasher to supplement the family income. Li pursued graduate study at the California Institute of Technology, where she received a Master of Science in electrical engineering in 2001 and a Doctor of Philosophy in electrical engineering in 2005. Li completed her dissertation, "Visual Recognition: Computational Models and Human Psychophysics", under the primary supervision of Pietro Perona and secondary supervision of Christof Koch. Her graduate studies were supported by the National Science Foundation Graduate Research Fellowship and The Paul & Daisy Soros Fellowships for New Americans. == Career and research == From 2005 to 2006, Li was an assistant professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign, and from 2007 to 2009, she was an assistant professor in the Computer Science Department at Princeton University. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012, and then full professor in 2018. At Stanford, Li served as the director of Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018. Her research has focused on computer vision, deep learning, and cognitive neuroscience, with over 300 peer-reviewed publications. She became the founding co-director of Stanford's University-level initiative - the Human-Centered AI Institute, along with co-director Dr. John Etchemendy, former provost of Stanford University. The institute aligns with Li's aims to advance AI research, education, policy, and practice to improve the human condition. While at Princeton in 2007, Li led the development of ImageNet, a massive visual database designed to advance object recognition in AI. The project involved labeling over 14 million images using Amazon Mechanical Turk and inspired the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which catalyzed progress in deep learning and led to dramatic improvements in image classification performance. The database addressed a key bottleneck in computer vision: the lack of large, annotated datasets for training machine learning models. Today, ImageNet is credited as a cornerstone innovation that underpins advancements in autonomous vehicles, facial recognition, and medical imaging. On her sabbatical from Stanford University from January 2017 to fall of 2018, Li joined Google Cloud as its Chief Scientist of AI/ML and Vice President. At Google, her team focused on democratizing AI technology and lowering the barrier for entrance to businesses and developers, including the developments of products like AutoML. In September 2017, Google secured a contract from the Department of Defense called Project Maven, which aimed to use AI techniques to interpret images captured by drone cameras. Google told employees who protested the company's work on Project Maven that their role was "specifically scoped to be for non-offensive purposes". In June 2018, Google told employees it would not seek renewal of the contract. In internal emails which were later leaked to reporters, Li expressed enthusiasm for the Google Cloud role in Project Maven, but warned against mentioning its AI component, saying that military AI is linked in the public mind with the danger of autonomous weapons. Asked about those leaked emails, Li told The New York Times, "I believe in human-centered AI to benefit people in positive and benevolent ways. It is deeply against my principles to work on any project that I think is to weaponize AI." In the fall of 2018, Li left Google and returned to Stanford University to continue her professorship. In 2023, Li co-led the launch of the RAISE-Health (Responsible AI for Safe and Equitable Health) initiative at Stanford University in collaboration with Stanford medicine. The initiative aims to develop frameworks for the responsible use of artificial intelligence in healthcare, including clinical care, biomedical research, and patient safety. According to her Stanford profile, she has been on partial academic leave from January 2024 through the end of 2025 to focus on entrepreneurial ventures. In 2024, Li said there was a disparity between private-sector investment in AI and support for academic and government research, and called for greater public funding for scientific uses of the technology and for studying its risks. Li is also known for her non-profit work as the co-founder and chairperson of nonprofit organization AI4ALL, whose mission is to educate the next generation of AI technologists, thinkers and leaders by promoting diversity and inclusion through human-centered AI principles. The program was created in collaboration with Melinda French Gates and Jensen Huang. Prior to establishing AI4ALL in 2017, Li and her former student Olga Russakovsky, currently an assistant professor in Princeton University, co-founded and co-directed the precursor program at Stanford called SAILORS (Stanford AI Lab OutReach Summers). SAILORS was an annual summer camp at Stanford dedicated to 9th grade high school girls in AI education and research, established in 2015 till it changed its name to AI4ALL @Stanford in 2017. In 2018, AI4ALL has successfully launched five more summer programs in addition to Stanford, including Princeton University, Carnegie Mellon University, Boston University, University of California Berkeley, and Canada's Simon Fraser University. We are at a turning point. AI's influence continues to grow, but representation and inclusion of a diversity of researchers in the field does not. It's critical that we seize this moment to create structures that will support long-term, positive changes. This won't happen via a single mechanism or quick fix. It starts with early education and extends to the existing structures of power within academia, work cultures among current AI researchers, and gatekeeping functions of research publishing, to name a few levers of change. Li has been described as a "researcher bringing humanity to AI". Li was elected as a member of the American Academy of Arts and Sciences in 2021, the National Academy of Engineering in 2020, and the National Academy of Medicine in 2020. In a November 2023 interview with The Guardian, Li said that while she would not refer to herself as the "godmother

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  • Pattern language

    Pattern language

    A pattern language is an organized and coherent set of patterns, each of which describes a problem and the core of a solution that can be used in many ways within a specific field of expertise. The term was coined by architect Christopher Alexander and popularized by his 1977 book A Pattern Language. A pattern language can also be an attempt to express the deeper wisdom of what brings aliveness within a particular field of human endeavor, through a set of interconnected patterns. Aliveness is one placeholder term for "the quality that has no name": a sense of wholeness, spirit, or grace, that while of varying form, is precise and empirically verifiable. Alexander claims that ordinary people can use this design approach to successfully solve very large, complex design problems. == What is a pattern? == When a designer designs something – whether a house, computer program, or lamp – they must make many decisions about how to solve problems. A single problem is documented with its typical place (the syntax), and use (the grammar) with the most common and recognized good solution seen in the wild, like the examples seen in dictionaries. Each such entry is a single design pattern. Each pattern has a name, a descriptive entry, and some cross-references, much like a dictionary entry. A documented pattern should explain why that solution is good in the pattern's contexts. Elemental or universal patterns such as "door" or "partnership" are versatile ideals of design, either as found in experience or for use as components in practice, explicitly described as holistic resolutions of the forces in recurrent contexts and circumstances, whether in architecture, medicine, software development or governance, etc. Patterns might be invented or found and studied, such as the naturally occurring patterns of design that characterize human environments. Like all languages, a pattern language has vocabulary, syntax, and grammar – but a pattern language applies to some complex activity other than communication. In pattern languages for design, the parts break down in this way: The language description – the vocabulary – is a collection of named, described solutions to problems in a field of interest. These are called design patterns. So, for example, the language for architecture describes items like: settlements, buildings, rooms, windows, latches, etc. Each solution includes syntax, a description that shows where the solution fits in a larger, more comprehensive or more abstract design. This automatically links the solution into a web of other needed solutions. For example, rooms have ways to get light, and ways to get people in and out. The solution includes grammar that describes how the solution solves a problem or produces a benefit. So, if the benefit is unneeded, the solution is not used. Perhaps that part of the design can be left empty to save money or other resources; if people do not need to wait to enter a room, a simple doorway can replace a waiting room. In the language description, grammar and syntax cross index (often with a literal alphabetic index of pattern names) to other named solutions, so the designer can quickly think from one solution to related, needed solutions, and document them in a logical way. In Christopher Alexander's book A Pattern Language, the patterns are in decreasing order by size, with a separate alphabetic index. The web of relationships in the index of the language provides many paths through the design process. This simplifies the design work because designers can start the process from any part of the problem they understand and work toward the unknown parts. At the same time, if the pattern language has worked well for many projects, there is reason to believe that even a designer who does not completely understand the design problem at first will complete the design process, and the result will be usable. For example, skiers coming inside must shed snow and store equipment. The messy snow and boot cleaners should stay outside. The equipment needs care, so the racks should be inside. == Many patterns form a language == Just as words must have grammatical and semantic relationships to each other in order to make a spoken language useful, design patterns must be related to each other in position and utility order to form a pattern language. Christopher Alexander's work describes a process of decomposition, in which the designer has a problem (perhaps a commercial assignment), selects a solution, then discovers new, smaller problems resulting from the larger solution. Occasionally, the smaller problems have no solution, and a different larger solution must be selected. Eventually all of the remaining design problems are small enough or routine enough to be solved by improvisation by the builders, and the "design" is done. The actual organizational structure (hierarchical, iterative, etc.) is left to the discretion of the designer, depending on the problem. This explicitly lets a designer explore a design, starting from some small part. When this happens, it's common for a designer to realize that the problem is actually part of a larger solution. At this point, the design almost always becomes a better design. In the language, therefore, each pattern has to indicate its relationships to other patterns and to the language as a whole. This gives the designer using the language a great deal of guidance about the related problems that must be solved. The most difficult part of having an outside expert apply a pattern language is in fact to get a reliable, complete list of the problems to be solved. Of course, the people most familiar with the problems are the people that need a design. So, Alexander famously advocated on-site improvisation by concerned, empowered users, as a powerful way to form very workable large-scale initial solutions, maximizing the utility of a design, and minimizing the design rework. The desire to empower users of architecture was, in fact, what led Alexander to undertake a pattern language project for architecture in the first place. == Design problems in a context == An important aspect of design patterns is to identify and document the key ideas that make a good system different from a poor system (that may be a house, a computer program or an object of daily use), and to assist in the design of future systems. The idea expressed in a pattern should be general enough to be applied in very different systems within its context, but still specific enough to give constructive guidance. The range of situations in which the problems and solutions addressed in a pattern apply is called its context. An important part in each pattern is to describe this context. Examples can further illustrate how the pattern applies to very different situation. For instance, Alexander's pattern "A PLACE TO WAIT" addresses bus stops in the same way as waiting rooms in a surgery, while still proposing helpful and constructive solutions. The "Gang-of-Four" book Design Patterns by Gamma et al. proposes solutions that are independent of the programming language, and the program's application domain. Still, the problems and solutions described in a pattern can vary in their level of abstraction and generality on the one side, and specificity on the other side. In the end this depends on the author's preferences. However, even a very abstract pattern will usually contain examples that are, by nature, absolutely concrete and specific. Patterns can also vary in how far they are proven in the real world. Alexander gives each pattern a rating by zero, one or two stars, indicating how well they are proven in real-world examples. It is generally claimed that all patterns need at least some existing real-world examples. It is, however, conceivable to document yet unimplemented ideas in a pattern-like format. The patterns in Alexander's book also vary in their level of scale – some describing how to build a town or neighbourhood, others dealing with individual buildings and the interior of rooms. Alexander sees the low-scale artifacts as constructive elements of the large-scale world, so they can be connected to a hierarchic network. === Balancing of forces === A pattern must characterize the problems that it is meant to solve, the context or situation where these problems arise, and the conditions under which the proposed solutions can be recommended. Often these problems arise from a conflict of different interests or "forces". A pattern emerges as a dialogue that will then help to balance the forces and finally make a decision. For instance, there could be a pattern suggesting a wireless telephone. The forces would be the need to communicate, and the need to get other things done at the same time (cooking, inspecting the bookshelf). A very specific pattern would be just "WIRELESS TELEPHONE". More general patterns would be "WIRELESS DEVICE" or "SECONDARY ACTIVITY", suggesting that a secondary activity (such as talking on t

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  • The Synthetic Party

    The Synthetic Party

    Det Syntetiske Parti (English: The Synthetic Party) is a political party driven by artificial intelligence (AI), founded in May 2022 in Denmark. The party aims to represent non-voters and fringe political parties while raising awareness of AI's societal role and exploring how it can be integrated into democratic processes. == Founder == The founder and continuous party secretary is Asker Bryld Staunæs, a philosopher from Aarhus University and a conceptual artist. == Main goal == The political goals have been machine learned from texts by Danish fringe parties since 1970 and represent the 20 percent of Danes who do not vote in the election. The party is synthetic; as such, many of the policies, such as universal basic income, can be contradictory to one another. == International collaborations == The Synthetic Party has signed bilateral collaboration agreements with the Finnish AI Party and AI Party (Japan) concerning the development of a global project created around artificial intelligence and politics These collaborations were expanded during the exhibition-event Synthetic Summit (28 February – 13 April 2025) at Kunsthal Aarhus, curated by Computer Lars (Asker Bryld Staunæs) on behalf of The Synthetic Party. The summit staged parliamentary scenography, performances, and computer sculptures, and invited both the public and policymakers to encounter an international line-up of AI parties and virtual politicians. Aarhus University described the event as part of Staunæs's PhD research, positioning it as an international top-meeting of virtual politicians. Participants included the Japanese AI Party, the Swedish AI Party, the Finnish AI Party, Parker Politics (New Zealand), Lex AI (Brazil), the Simiyya collective (Egypt/Sweden), the Synthetic Party (Denmark), and Wiktoria Cukt 2.0 (Poland). As part of the summit, the one-day AI World Congress was held on 1 March 2025, structured as a performative assembly where each group participated through both machinic agents and human delegates. Sessions were chaired by participating parties, with Computer Lars delivering the opening presentation. Throughout the day, contributions were synthesized into a common record using a shared AI system. The congress concluded with the adoption of the Synthetic Summit Resolution, a collectively authored treaty of algorithmic governance. Signatories included Floor Kist and Nick Gerritsen (Parker Politics), Michihito Matsuda (Japanese AI Party), Emma Bexell (Swedish AI Party), Samee Haapa (Finnish AI Party), Pedro Markun (Lex AI), Kristian T. Madsen and Michael Birkebæk Jensen (NextGen Democracy / DemAI), Asker Bryld Staunæs, Benjamin Asger Krog Møller, Caroline Sofie Axelsson, Life with Artificials (The Synthetic Party), and Piotr Wyrzykowski (Wiktoria Cukt 2.0).

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  • Decorrelation

    Decorrelation

    Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible. Since the minimum possible autocorrelation for a given signal energy is achieved by equalising the power spectrum of the signal to be similar to that of a white noise signal, this is often referred to as signal whitening. == Process == === Signal processing === Most decorrelation algorithms are linear, but there are also non-linear decorrelation algorithms. Many data compression algorithms incorporate a decorrelation stage. For example, many transform coders first apply a fixed linear transformation that would, on average, have the effect of decorrelating a typical signal of the class to be coded, prior to any later processing. This is typically a Karhunen–Loève transform, or a simplified approximation such as the discrete cosine transform. By comparison, sub-band coders do not generally have an explicit decorrelation step, but instead exploit the already-existing reduced correlation within each of the sub-bands of the signal, due to the relative flatness of each sub-band of the power spectrum in many classes of signals. Linear predictive coders can be modelled as an attempt to decorrelate signals by subtracting the best possible linear prediction from the input signal, leaving a whitened residual signal. Decorrelation techniques can also be used for many other purposes, such as reducing crosstalk in a multi-channel signal, or in the design of echo cancellers. In image processing decorrelation techniques can be used to enhance or stretch, colour differences found in each pixel of an image. This is generally termed as 'decorrelation stretching'. === Neuroscience === In neuroscience, decorrelation is used in the analysis of the neural networks in the human visual system. The raw inputs from cone cells and rod cells under go many steps of processing before it is handled by the visual cortex. These steps generally perform decorrelation, both spatial (surround suppression in the retina) and temporal (handling of movement in the lateral geniculate nucleus). === Cryptography === In cryptography, decorrelation is used in cipher design (see Decorrelation theory) and in the design of hardware random number generators.

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  • Ian Goodfellow

    Ian Goodfellow

    Ian J. Goodfellow (born 1987) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He is a research scientist at Google DeepMind, was previously employed as a research scientist at Google Brain and director of machine learning at Apple as well as one of the first employees at OpenAI, and has made several important contributions to the field of deep learning, including the invention of the generative adversarial network (GAN). Goodfellow co-wrote, as the first author, the textbook Deep Learning (2016) and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach (used in more than 1,500 universities in 135 countries). == Education == Goodfellow obtained his BSc and MSc in computer science from Stanford University under the supervision of Andrew Ng, and his PhD in machine learning from the Université de Montréal in February 2015, under the supervision of Yoshua Bengio and Aaron Courville. Goodfellow's thesis is titled Deep learning of representations and its application to computer vision. == Career == After graduation, Goodfellow joined Google as part of the Google Brain research team. In March 2016, he left Google to join the newly founded OpenAI research laboratory. 11 months later, in March 2017, Goodfellow returned to Google Research, but left again in 2019. In 2019, Goodfellow joined Apple as director of machine learning in the Special Projects Group. He resigned from Apple in April 2022 to protest Apple's plan to require in-person work for its employees. Shortly after, Goodfellow then joined Google DeepMind as a research scientist. In 2025, Goodfellow left Google. As of July 2026, based on information on Goodfellow's LinkedIn profile, he is co-founding a startup company. == Research == Goodfellow is best known for inventing generative adversarial networks (GANs), using deep learning to generate images. This approach uses two neural networks to competitively improve an image's quality. A “generator” network creates a synthetic image based on an initial set of images such as a collection of faces. A “discriminator” network tries to determine whether images are authentic or created by the generator. The generate-detect cycle is repeated. For each iteration, the generator and the discriminator use the other's feedback to improve or detect the generated images, until the discriminator can no longer distinguish between generated and authentic images. However, GANs have also been used to create deepfakes. At Google, Goodfellow developed a system enabling Google Maps to automatically transcribe addresses from photos taken by Street View cars and demonstrated security vulnerabilities of machine learning systems. == Recognition == In 2017, Goodfellow was cited in MIT Technology Review's 35 Innovators Under 35. In 2019, he was included in Foreign Policy's list of 100 Global Thinkers.

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  • Ethics of artificial intelligence

    Ethics of artificial intelligence

    The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks. Some application areas may also have particularly important ethical implications, like healthcare, education, criminal justice, or the military. == Machine ethics == Machine ethics (or machine morality) is the field of research concerned with designing Artificial Moral Agents (AMAs), robots or artificially intelligent computers that behave morally or as though moral. To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency, which are related to the concept of AMAs. There are discussions on creating tests to see if an AI is capable of making ethical decisions. Alan Winfield concludes that the Turing test is flawed and the requirement for an AI to pass the test is too low. A proposed alternative test is one called the Ethical Turing Test, which would improve on the current test by having multiple judges decide if the AI's decision is ethical or unethical. Neuromorphic AI could be one way to create morally capable robots, as it aims to process information similarly to humans, nonlinearly and with millions of interconnected artificial neurons. Similarly, whole-brain emulation (scanning a brain and simulating it on digital hardware) could also in principle lead to human-like robots, thus capable of moral actions. And large language models are capable of approximating human moral judgments. Inevitably, this raises the question of the environment in which such robots would learn about the world and whose morality they would inherit – or if they end up developing human 'weaknesses' as well: selfishness, pro-survival attitudes, inconsistency, scale insensitivity, etc. In Moral Machines: Teaching Robots Right from Wrong, Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation. As one example, it has introduced normative ethicists to the controversial issue of which specific learning algorithms to use in machines. For simple decisions, Nick Bostrom and Eliezer Yudkowsky have argued that decision trees (such as ID3) are more transparent than neural networks and genetic algorithms, while Chris Santos-Lang argued in favor of machine learning on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable to criminal "hackers". Some researchers frame machine ethics as part of the broader AI control or value alignment problem: the difficulty of ensuring that increasingly capable systems pursue objectives that remain compatible with human values and oversight. Stuart Russell has argued that beneficial systems should be designed to (1) aim at realizing human preferences, (2) remain uncertain about what those preferences are, and (3) learn about them from human behaviour and feedback, rather than optimizing a fixed, fully specified goal. Some authors argue that apparent compliance with human values may reflect optimization for evaluation contexts rather than stable internal norms, complicating the assessment of alignment in advanced language models. == Challenges == === Algorithmic biases === AI has become increasingly inherent in facial and voice recognition systems. These systems may be vulnerable to biases and errors introduced by their human creators. Notably, the data used to train them can have biases. According to Allison Powell, associate professor at LSE and director of the Data and Society programme, data collection is never neutral and always involves storytelling. She argues that the dominant narrative is that governing with technology is inherently better, faster and cheaper, but proposes instead to make data expensive, and to use it both minimally and valuably, with the cost of its creation factored in. Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias. In natural language processing, problems can arise from the text corpus—the source material the algorithm uses to learn about the relationships between different words. Large companies such as IBM, Google, etc. that provide significant funding for research and development have made efforts to research and address these biases. One potential solution is to create documentation for the data used to train AI systems. Process mining can be an important tool for organizations to achieve compliance with proposed AI regulations by identifying errors, monitoring processes, identifying potential root causes for improper execution, and other functions. However, there are also limitations to the current landscape of fairness in AI, due to the intrinsic ambiguities in the concept of discrimination, both at the philosophical and legal level. ==== Racial and gender biases ==== Bias can be introduced through historical data used to train AI systems. For instance, Amazon terminated their use of AI hiring and recruitment because the algorithm favored male candidates over female ones. This was because Amazon's system was trained with data collected over a 10-year period that included mostly male candidates. The algorithms learned the biased pattern from the historical data, and generated predictions where these types of candidates were most likely to succeed in getting the job. Therefore, the recruitment decisions made by the AI system turned out to be biased against female and minority candidates. The performance of facial recognition and computer vision models may vary based on race and gender. Facial recognition algorithms made by Microsoft, IBM and Face++ all performed significantly worse on darker-skinned women. Facial recognition was shown to be biased against those with darker skin tones. AI systems may be less accurate for black people, as was the case in the development of an AI-based pulse oximeter that overestimated blood oxygen levels in patients with darker skin, causing issues with their hypoxia treatment. In 2015, controversy erupted after a Black couple were labeled "Gorillas" by Google Photos. Oftentimes the systems are able to easily detect the faces of white people while being unable to register the faces of people who are black. This has led to the ban of police usage of AI materials or software in some U.S. states. The reason for these biases is that AI pulls information from across the internet to influence its responses in each situation. For example, if a facial recognition system was only tested on people who were white, it would make it much harder for it to interpret the facial structure and tones of other races and ethnicities. Biases often stem from the training data rather than the algorithm itself, notably when the data represents past human decisions. A 2020 study that reviewed voice recognition systems from Amazon, Apple, Google, IBM, and Microsoft found that they have higher error rates when transcribing black people's voices than white people's. Injustice in the use of AI is much harder to eliminate within healthcare systems, as oftentimes diseases and conditions can affect different races and genders differently. This can lead to confusion as the AI may be making decisions based on statistics showing that one patient is more likely to have problems due to their gender or race. This can be perceived as a bias because each patient is a different case, and AI is making decisions based on what it is programmed to group that individual into. This leads to a discussion about what should be considered a biased decision in the distribution of treatment. While it is known that there are differences in how diseases and injuries affect different genders and races, there is a discussion on whether it is fairer to incorporate this into healthcare treatments, or to examine each patient without this knowledge. In modern society there are certain tests for diseases, such as breast cancer, that are recommended to certain groups of people over others because they are more likely to contract the disease in question. If AI implements these statistics

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