AI Data Bias

AI Data Bias — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Blackmagic Design

Blackmagic Design Pty Ltd is an Australian company that develops digital cinema technology and manufactures professional video production hardware and software. Headquartered in South Melbourne, it is known for producing high-end digital movie cameras and a range of broadcast and post-production equipment. The company also develops software applications, including the DaVinci Resolve application for non-linear video editing, color correction, color grading, visual effects, and audio post-production. == History == Blackmagic Design Pty Ltd was founded on 7 September 2001 by Grant Petty. Its first product, DeckLink, introduced in 2002, was a video capture card for macOS that supported uncompressed 10-bit video, marking a shift toward professional-grade yet affordable video workflows. Subsequent versions—including the DeckLink 2, Pro SDI, HD Plus, and Multibridge—added capabilities such as color correction, Windows support, and compatibility with major editing software like Adobe Premiere Pro, to broaden the product's appeal. At the 2012 NAB Show, Blackmagic announced its first Cinema Camera, a digital movie camera. Blackmagic made several acquisitions over the next decade. In 2009, it acquired da Vinci Systems, known for its color-grading tools. In 2010, it acquired Echolab's ATEM switcher line, in 2014, it added eyeon Software (developer of the Blackmagic Fusion compositing software) and London's Cintel (film scanning and restoration), and in 2016, it acquired Fairlight, an audio technology company known for its CMI synthesizers as well as mixing consoles. == Products == List of all products developed by the company. Editing, Color Correction and Audio Post Production DaVinci Resolve (free version) and DaVinci Resolve Studio (paid version), computer software for non-linear video editing, color correction, color grading, visual effects, and audio post-production. Audio/Video Controller Consoles: Editor Keyboard, Speed Editor, DaVinci Resolve Replay Editor, Micro Panel, Mini Panel, DaVinci Resolve Micro Color Panel, Advanced Panel, Fairlight Console Channel Fader, Fairlight Console Channel Control, Fairlight Console LCD Monitor, Fairlight Console Audio Editor, Fairlight Desktop Audio Editor, Fairlight Desktop Console, Fairlight Audio Interface Cintel Film Scanner (Generations 1-3) Live Production Home Streaming: ATEM Mini, ATEM Mini Pro/ISO, ATEM Mini Extreme, ATEM Mini Extreme ISO (The ATEM Mini series has both HDMI and SDI variants) Production Switchers: ATEM 1,2 & 4 M/E Constellation HD, ATEM 1,2 & 4 M/E Constellation 4K, ATEM Constellation 8K, ATEM 1,2 & 4 M/E Production Studio 4K, ATEM Television Studio HD8 & HD8 ISO Switcher & Camera Controllers: ATEM Camera Control Panel, ATEM 1 M/E Advanced Panel, ATEM 2 M/E Advanced Panel, ATEM 4 M/E Advanced Panel Chroma Keyers: Ultimatte 12 HD Mini, Ultimatte 12 HD, Ultimatte 12 4K, Ultimatte 12 8K Recording and Storage: HyperDeck Studio HD Mini, HyperDeck Studio HD Plus, HyperDeck Studio HD Plus, HyperDeck Studio 4K Pro, HyperDeck Extreme 8K HDR, HyperDeck Extreme 4K HDR, HyperDeck Extreme Control, HyperDeck Shuttle HD, Duplicator 4K, MultiDock 10G, Video Assist 7" 12G HDR, Video Assist 5" 12G HDR Capture and Playback UltraStudio: 3G, HD Mini, 4K Mini, 4K Extreme 3 DeckLink (PCIe cards): Mini Recorder, Mini Monitor, Mini Monitor 4K, Mini Recorder 4K, Duo 2 Mini, Duo 2, Quad 2, SDI 4K, Studio 4K, 4K Extreme 12G, 8K Pro, Quad HDMI Recorder Network Storage Cloud Store Cloud Pod Broadcast Converters Micro Converter: BiDirectional SDI/HDMI 3G wPSU, HDMI to SDI 3G wPSU, SDI to HDMI 3G wPSU, BiDirectional SDI/HDMI 3G, HDMI to SDI 3G, SDI to HDMI 3G Mini Converters: Audio to SDI, Optical Fiber 12G, SDI Multiplex 4K, Quad SDI to HDMI 4K, SDI Distribution 4K, SDI to Analog 4K, Audio to SDI 4K, SDI to Audio 4K, HDMI to SDI 6G, SDI to HDMI 6G Teranex Mini: SDI Distribution 12G, SDI to HDMI 12G, Audio to SDI 12G, SDI to Analog 12G, SDI to HDMI 8K HDR, SDI to DisplayPort 8K HDR 2110 IP Converters Routing and Distribution Videohub
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Responsible AI Safety and Education Act

The Responsible AI Safety and Education Act (RAISE Act) is a New York State law that imposes transparency, safety, and reporting requirements on developers of large frontier artificial intelligence models. The law was signed by Governor Kathy Hochul on December 19, 2025. It was sponsored by State Senator Andrew Gounardes and Assemblymember Alex Bores. The RAISE Act is the second U.S. state law to regulate frontier AI model developers, following California's Transparency in Frontier Artificial Intelligence Act (TFAIA), which was signed in September 2025. Hochul signed the bill on the condition that the legislature would pass chapter amendments to bring the law closer to the California model. The amending bills (A9449/S8828) were introduced in January 2026; as of February 2026 they remain in committee, though the Governor's office and legal commentators treat the agreed-upon amendments as representing the final form of the law. == Provisions == The following describes the RAISE Act as it is expected to operate after the agreed-upon chapter amendments take effect. The law is expected to take effect on January 1, 2027. === Scope === The law applies to "large frontier developers," defined as companies with annual revenues exceeding $500 million that develop "frontier models," which are foundation models trained using more than 1026 floating-point operations (FLOPs). The version passed by the legislature in June 2025 had instead defined large developers based on having spent over $100 million in aggregate compute costs, and also included a provision prohibiting deployment of frontier models posing "unreasonable risk of critical harm"; both were removed as part of the negotiations between Hochul and the legislature. Accredited colleges and universities engaged in academic research are exempt, as is the state's Empire AI consortium. === Safety and transparency framework === Large frontier developers must write, implement, and publicly publish a "frontier AI framework" describing how they assess and mitigate catastrophic risks, secure unreleased model weights against unauthorized access, use third-party evaluators, govern internal use of frontier models, and respond to safety incidents. The framework must describe these measures "in detail," a requirement that goes beyond the California TFAIA's requirement to describe a developer's "approach." The framework must be reviewed at least annually, and material modifications must be published with justification within 30 days. Before or concurrently with deploying a new or substantially modified frontier model, developers must publish a transparency report including the model's release date, supported languages and output modalities, intended uses, and any restrictions on use. Large frontier developers must additionally include summaries of catastrophic risk assessments and the extent of third-party involvement. === Catastrophic risk and incident reporting === The law defines "catastrophic risk" as a foreseeable and material risk that a frontier model will contribute to the death of or serious injury to more than 50 people, or more than $1 billion in property damage, arising from a frontier model providing expert-level assistance in creating chemical, biological, radiological, or nuclear weapons; engaging in cyberattacks or conduct equivalent to crimes such as murder, assault, or theft without meaningful human oversight; or evading the control of its developer or user. Loss of equity value is explicitly excluded from the definition of property damage. "Critical safety incidents" include unauthorized access to model weights resulting in death or injury, materialization of a catastrophic risk, loss of control of a frontier model causing death or injury, and a model using deceptive techniques to subvert developer controls outside of an evaluation context in a manner that increases catastrophic risk. Frontier developers must report critical safety incidents within 72 hours, or within 24 hours if the incident poses an imminent risk of death or serious physical injury. === Enforcement === The chapter amendments establish a new office within the New York State Department of Financial Services to oversee compliance, receive incident reports, and publish annual reports on AI safety beginning in 2028. Large frontier developers must file disclosure statements with this office and pay pro rata assessments to fund its operations. The New York Attorney General may bring civil actions, with penalties of up to $1 million for a first violation and $3 million for subsequent violations. The version passed by the legislature in June 2025 had set penalties at up to $10 million and $30 million respectively. The law does not create a private right of action. == Legislative history == The bill was introduced in the Assembly on March 5, 2025, by Assemblymember Alex Bores, and in the Senate on March 27, 2025, by Senator Andrew Gounardes. After a series of amendments, the legislature passed the bill in June 2025. Governor Hochul did not immediately sign the bill, using nearly all the time available under New York law before acting; had she not signed by the end of 2025, the bill would have been pocket vetoed. The tech industry lobbied against the bill during this period, and Hochul initially proposed a near-complete rewrite modeled on California's TFAIA. Legislators resisted the extent of the changes, and the two sides ultimately agreed on a version that used the California law as a base but preserved several provisions that went beyond it, including the 72-hour incident reporting timeline and the creation of a dedicated enforcement office. Hochul signed the original bill (S6953-B/A6453-B) on December 19, 2025, with the legislature committing to pass chapter amendments formalizing the agreed changes in the January 2026 session. The amending bills (A9449 in the Assembly, S8828 in the Senate) were introduced on January 6 and January 8, 2026. OpenAI and Anthropic expressed support for the law. Anthropic's head of external affairs Sarah Heck said the two state laws "should inspire Congress to build on them." The super PAC network Leading the Future, backed by Andreessen Horowitz and OpenAI president Greg Brockman, subsequently announced plans to challenge Bores in a future election. == Federal preemption debate == Hochul signed the RAISE Act eight days after President Donald Trump issued an executive order on December 11, 2025, directing the Department of Justice to challenge state AI laws deemed to conflict with a "minimally burdensome" national AI policy. On January 9, 2026, the Department of Justice announced the establishment of an AI Litigation Task Force as called for by the executive order. The executive order also threatened states with loss of certain federal broadband funding if their AI laws were found to be onerous. Legal commentators have noted several potential avenues for federal challenge, including arguments that the law constitutes compelled speech, violates the dormant Commerce Clause by creating a patchwork of state regulations, or is preempted by federal AI policy. == Comparison with California's TFAIA == The RAISE Act was designed to align with California's Transparency in Frontier Artificial Intelligence Act, signed on September 29, 2025. Both laws use the same 1026 FLOP threshold to define frontier models and the same $500 million revenue threshold to define large developers. Both require public safety frameworks, transparency reports, and incident reporting. The RAISE Act's 72-hour incident reporting window is stricter than the TFAIA's 15-day window, though both require faster reporting for incidents posing imminent physical risk (24 hours under the RAISE Act, immediate under the TFAIA). The RAISE Act establishes a dedicated enforcement office within the Department of Financial Services, whereas California routes reports through the Office of Emergency Services. The RAISE Act requires developers to describe their safety measures "in detail" and how they "handle" various risks, whereas the TFAIA requires developers to describe their "approach."
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Early-exit network

Early-exit networks are a class of dynamic neural networks designed for efficient inference by allowing models to make confident predictions at intermediate layers, rather than processing the full network. Early-exit mechanisms are methods for deep neural networks that add intermediate classifiers, allowing inference to stop at earlier layers for inputs assessed as low uncertainty. Decisions to exit are typically based on confidence measures such as softmax-derived scores, classification margins, or entropy-based criteria, with the goal of reducing computational cost. These approaches are commonly paired with specialized training procedures and system-level optimizations to improve efficiency while preserving accuracy. The main idea behind the technology is to stop excessive calculations when a good answer can already be given with a high degree of probability, which can save both computation and time. Early-exit networks have also been extended with expert-based exit criteria, where intermediate classifiers are treated as multiple “experts” whose predictions and confidence scores can be aggregated to decide whether to stop computation early. Hardware implementations are also being developed.
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GENESIS (software)

GENESIS (The General Neural Simulation System) is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including: sub-cellular processes, individual neurons, networks of neurons, and neuronal systems. These simulations are “computer-based implementations of models whose primary objective is to capture what is known of the anatomical structure and physiological characteristics of the neural system of interest”. GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.” Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University. == Neurons and Neural Systems == GENESIS works by creating simulation environments for constructing models of neurons or neural systems. "Nerve cells are capable of communicating with each other in such a highly structured manner as to form neuronal networks. To understand neural networks, it is necessary to understand the ways in which one neuron communicates with another through synaptic connections and the process called synaptic transmission". Neurons have a specialized structure for their function, they "are different from most other cells in the body in that they are polarized and have distinct morphological regions, each with specific functions". The two important regions of a neuron are the dendrite and the axon. "Dendrites are the region where one neuron receives connections from other neurons. The cell body or soma contains the nucleus and the other organelles necessary for cellular function. The axon is a key component of nerve cells over which information is transmitted from one part of the neuron (e.g., the cell body) to the terminal regions of the neuron". The third important piece of a neuron is the synapse. "The synapse is the terminal region of the axon this is where one neuron forms a connection with another and conveys information through the process of synaptic transmission". Neural networks like the ones simulated with GENESIS software can quickly become highly complex and difficult to understand. "Just a few interconnected neurons (a microcircuit) can perform sophisticated tasks such as mediate reflexes, process sensory information, generate locomotion and mediate learning and memory. Even more complex networks, macrocircuits, consist of multiple embedded microcircuits. Macrocircuits mediate higher brain functions such as object recognition and cognition". GENESIS endeavors to simulate neural systems as they are found in nature. Often, "a neuron can receive contacts from up to 10,000 presynaptic neurons, and, in turn, any one neuron can contact up to 10,000 postsynaptic neurons. The combinatorial possibility could give rise to enormously complex neuronal circuits or network topologies, which might be very difficult to understand". == History == GENESIS was developed by Dr. James M. Bower, in the Caltech laboratory, and first released to the public in 1988 in association with the first Methods in Computational Neuroscience Course at the Marine Biological Laboratory in Woods Hole, MA. Full source code for the software was released in the same year under an open software model for development. It's now supported by the Computational Biology Initiative at the University of Texas at San Antonio and is available free along with tutorial guides on its use. P-GENESIS, a parallel version of GENESIS, was first run in 1990 on the Intel Delta, which was the prototype for the Intel Paragon family of massively parallel supercomputers. == How GENESIS Works == GENESIS is useful in creating a simulation environment for constructing models of neurobiological systems, such as: sub-cellular processes individual neurons networks of neurons neuronal systems The GENESIS system is complicated, but relatively easy to use. An individual can input commands through one of three ways: script files, graphical user interface, or the GENESIS command shell. These commands are then processed by the script language interpreter. "The Script Language Interpreter processes commands entered through the keyboard, script files, or the graphical user interface, and passes them to the GENESIS simulation engine. The simulation engine also loads compiled object libraries, reads and writes data files, and interacts with the graphical user interface". Below is a graphical representation of the user input process and a sample GENESIS output. == Applications == Most current applications for GENESIS involve realistic simulations of biological systems. It is usually used to simulate the behavior of larger brain structures, for example the cerebral cortex. These studies most often occur in lab courses in neural simulation at Caltech and the Marine Biological Laboratory at Woods Hole, Massachusetts. GENESIS can be used in combination with Yale University’s software called NEURON as a means for scientists to collaborate to construct a physical description of the nervous system. The GENESIS software can also be used with Kinetikit in the modeling of signal transduction pathways. GENESIS has been used in many studies. Some of these studies involve research that focuses on the development of software that would be useful across many disciplines. Others are studies of neurons, such as Purkinje cells. These studies used GENESIS to simulate Purkinje cells and could be useful for the planning and development of later experiments using the GENESIS software. There may also be biomedical applications of the software. For example, St. Jude Medical in Europe has developed an implanted GENESIS device.
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Clubdjpro

ClubDJPro (often referred to as ClubDJ) is a DJ console and video mixing tool developed by Cube Software Solutions Inc. software. It was released in June 2005. == User interface == ClubDJPro has a GUI that was designed to allow aesthetic revisions via Skins. The skin engine that ClubDJPro uses allows for the ability to expand the software to take up the entire screen. As of 4.4.3.3 there are 3 user changeable skins included in the program which are changeable in the preferences tab. They are called 'AquaLung', 'Eleanor', and 'Grabber'. == Editions == ClubDJPro is available in two different editions, with separate features depending upon their target consumer group. DJ Edition - Can play audio files only. VJ Edition - Contains all of the features of the DJ Edition, in addition to support for video, karaoke, and visualizations. == Supported MIDI Controllers == Supported since version 2.0: Hercules Console Hercules Console MK2 Hercules Control MP3 PCDJ DAC-2 Controller == History == The initial "final release" of ClubDJPro was released on June 24, 2005. On June 26, 2009, the 4th iteration of the ClubDJPro software was released. The development of the software and website appears to have halted. As of March 2018 the website continues to show a new version "Coming Spring 2016".
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DeepSeek

Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence (AI) company that develops large language models (LLMs). Based in Hangzhou, Zhejiang, DeepSeek is owned and funded by High-Flyer, a Chinese hedge fund. DeepSeek was founded in July 2023 by Liang Wenfeng, the co-founder of High-Flyer, who also serves as the CEO for both of the companies. The company launched an eponymous chatbot alongside its DeepSeek-R1 model in January 2025. DeepSeek-R1 provided responses comparable to other contemporary large language models, such as OpenAI's GPT-4 and o1. Its training cost was reported to be significantly lower than other LLMs. The company claims that it trained its V3 model for US$6 million—far less than the US$100 million cost for OpenAI's GPT-4 in 2023—and using approximately one-tenth the computing power consumed by Meta's comparable model, Llama 3.1. DeepSeek's success against larger and more established rivals has been described as "upending AI". DeepSeek's models are described as "open-weight", meaning the exact parameters are openly shared, but the training data is not openly licensed. Since the January 2025 debut of DeepSeek-R1, the company has made its new models available under free and open-source software licenses, primarily the MIT License. The company reportedly recruits AI researchers from top Chinese universities and also hires from outside traditional computer science fields to broaden its models' knowledge and capabilities. DeepSeek significantly reduced training expenses for their R1 model by incorporating techniques such as mixture of experts (MoE) layers. The company also trained its models during ongoing trade restrictions on AI chip exports to China, using weaker AI chips intended for export and employing fewer units overall. Observers say this breakthrough sent "shock waves" through the industry which were described as triggering a "Sputnik moment" for the US in the field of artificial intelligence, particularly due to its open-source, cost-effective, and high-performing AI models. This threatened established AI hardware leaders such as Nvidia; Nvidia's share price dropped sharply, losing US$600 billion in market value, the largest single-company decline in U.S. stock market history. == History == === Founding and early years (2016–2023) === In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had been trading since the 2008 financial crisis while attending Zhejiang University. The company began stock trading using a GPU-dependent deep learning model on 21 October 2016; before then, it had used CPU-based linear models. By the end of 2017, most of its trading was driven by AI. Liang established High-Flyer as a hedge fund focused on developing and using AI trading algorithms, and by 2021 the firm was using AI exclusively, often using Nvidia chips. In 2019, the company began constructing its first computing cluster, Fire-Flyer, at a cost of 200 million yuan; it contained 1,100 GPUs interconnected at 200 Gbit/s and was retired after 1.5 years in operation. By 2021, Liang had started buying large quantities of Nvidia GPUs for an AI project, reportedly obtaining 10,000 Nvidia A100 GPUs before the United States restricted chip sales to China. Computing cluster Fire-Flyer 2 began construction in 2021 with a budget of 1 billion yuan. It was reported that in 2022, Fire-Flyer 2's capacity had been used at over 96%, totaling 56.74 million GPU hours. 27% was used to support scientific computing outside the company. During 2022, Fire-Flyer 2 had 5,000 PCIe A100 GPUs in 625 nodes, each containing 8 GPUs. At the time, it exclusively used PCIe instead of the DGX version of A100, since at the time the models it trained could fit within a single 40 GB GPU VRAM and so there was no need for the higher bandwidth of DGX (i.e., it required only data parallelism but not model parallelism). Later, it incorporated NVLinks and NCCL (Nvidia Collective Communications Library) to train larger models that required model parallelism. On 14 April 2023, High-Flyer announced the launch of an artificial general intelligence (AGI) research lab, stating that the new lab would focus on developing AI tools unrelated to the firm's financial business. Two months later, on 17 July 2023, that lab was spun off into an independent company, DeepSeek, with High-Flyer as its principal investor and backer. Venture capital investors were reluctant to provide funding, as they considered it unlikely that the venture would be able to quickly generate an "exit". === Model releases since 2023 === DeepSeek released its first model, DeepSeek Coder, on 2 November 2023, followed by the DeepSeek-LLM series on 29 November 2023. In January 2024, it released two DeepSeek-MoE models (Base and Chat), and in April 3 DeepSeek-Math models (Base, Instruct, and RL). DeepSeek-V2 was released in May 2024, followed a month later by the DeepSeek-Coder V2 series. In September 2024, DeepSeek V2.5 was introduced and revised in December. On 20 November 2024, the preview of DeepSeek-R1-Lite became available via chat. In December, DeepSeek-V3-Base and DeepSeek-V3 (chat) were released. On 20 January 2025, DeepSeek launched the DeepSeek chatbot—based on the DeepSeek-R1 model—free for iOS and Android. By 27 January, DeepSeek surpassed ChatGPT as the most downloaded freeware app on the iOS App Store in the United States, triggering an 18% drop in Nvidia's share price. On 24 March 2025, DeepSeek released DeepSeek-V3-0324 under the MIT License. On 28 May 2025, DeepSeek released DeepSeek-R1-0528 under the MIT License. The model has been noted for more tightly following official Chinese Communist Party ideology and censorship in its answers to questions than prior models. On 21 August 2025, DeepSeek released DeepSeek V3.1 under the MIT License. This model features a hybrid architecture with thinking and non-thinking modes. It also surpasses prior models like V3 and R1, by over 40% on certain benchmarks like SWE-bench and Terminal-bench. It was updated to V3.1-Terminus on 22 September 2025. V3.2-Exp was released on 29 September 2025. It uses DeepSeek Sparse Attention, a more efficient attention mechanism based on previous research published in February. DeepSeek-V3.2 was released on 1 December 2025, alongside a DeepSeek-V3.2-Speciale variant that focused on reasoning. In February 2026, Anthropic accused DeepSeek of using thousands of fraudulent accounts to generate millions of conversations with Claude to train its own large language models. In April 2026, investors began speaking with DeepSeek for a $300 million funding round, which would bring DeepSeek to a total valuation of $10 billion. On April 24, 2026, DeepSeek released a preview of its V4 series, including the 1.6-trillion parameter DeepSeek-V4-Pro and the 284-billion parameter DeepSeek-V4-Flash, both featuring a 1-million token context window, under the MIT License. DeepSeek's V4 LLM has been adopted by key semiconductor manufacturers and artificial intelligence chipmakers such as Huawei and Cambricon. == Company operation == DeepSeek is headquartered in Hangzhou, Zhejiang, and is owned and funded by High-Flyer. Its co-founder, Liang Wenfeng, serves as CEO. As of May 2024, Liang personally held an 84% stake in DeepSeek through two shell corporations. === Strategy === DeepSeek has stated that it focuses on research and does not have immediate plans for commercialization. This posture also means it can skirt certain provisions of China's AI regulations aimed at consumer-facing technologies. DeepSeek's hiring approach emphasizes skills over lengthy work experience, resulting in many hires fresh out of university. The company likewise recruits individuals without computer science backgrounds to expand the range of expertise incorporated into the models, for instance in poetry or advanced mathematics. According to The New York Times, dozens of DeepSeek researchers have or have previously had affiliations with People's Liberation Army laboratories and the Seven Sons of National Defence. Due to the impact of United States restrictions on chips, DeepSeek refined its algorithms to maximise computational efficiency and thereby leveraged older hardware and reduced energy consumption. DeepSeek also expanded on the African continent as it offers more affordable and less power-hungry AI solutions. The company has bolstered African language models and generated a number of startups, for example in Nairobi. Along with Huawei's storage and cloud computing services, the impact on the tech scene in sub-saharan Africa is considerable. DeepSeek offers local data sovereignty and more flexibility compared to Western AI platforms. == Training framework == High-Flyer/DeepSeek had operated at least two primary computing clusters: Fire-Flyer (萤火一号) and Fire-Flyer 2 (萤火二号). Fire-Flyer 1 was constructed in 2019 and was retired after 1.5 years of operation. Fi
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Mycin

MYCIN was an early backward chaining expert system that used black box to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases. MYCIN was developed over five or six years in the early 1970s at Stanford University. It was written in Lisp as the doctoral dissertation of Edward Shortliffe under the direction of Bruce G. Buchanan, Stanley N. Cohen and others. MYCIN emerged from the Stanford Heuristic Programming Project. MYCIN demonstrated the potential for expert systems in building high-performance medical reasoning programs. MYCIN is often viewed as a pioneer in the field of expert systems, even being referred to as the "grandaddy of them all-the one that launched the field" by Dr. Allen Newell. MYCIN led to the EMYCIN expert system shell ("essential MYCIN") for acquiring knowledge, reasoning with it, and explaining the results, without the specific medical knowledge. It can be described as "EMYCIN = Prolog + uncertainty + caching + questions + explanations + contexts - variables". An introduction is in Chapter 16 of Paradigms of Artificial Intelligence Programming (PAIP). == Method == MYCIN operated using a fairly simple inference engine and a knowledge base of ~600 rules by obtaining individual inferential facts identified by experts and encoding such facts as individual production rules. No other AI program at the time contained as much domain-specific knowledge clearly separated from its inference procedures as MYCIN. It would query the physician running the program via a long series of simple yes/no or textual questions. At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis' probability, the reasoning behind each diagnosis (that is, MYCIN would also list the questions and rules which led it to rank a diagnosis a particular way), and its recommended course of drug treatment. MYCIN could additionally respond to queries by physicians related to why it asked the user a certain question, how it arrived at a conclusion, and why it did not consider certain factors. The developers performed studies showing that MYCIN's performance was minimally affected by perturbations in the uncertainty metrics associated with individual rules, suggesting that the power in the system was related more to its knowledge representation and reasoning scheme than to the details of its numerical uncertainty model. Some observers felt that it should have been possible to use classical Bayesian statistics. MYCIN's developers argued that this would require either unrealistic assumptions of probabilistic independence, or require the experts to provide estimates for an unfeasibly large number of conditional probabilities. Subsequent studies later showed that the certainty factor model could indeed be interpreted in a probabilistic sense, and highlighted problems with the implied assumptions of such a model. However the modular structure of the system would prove very successful, leading to the development of graphical models such as Bayesian networks. === Context === A context in MYCIN determines what types of objects can be reasoned about. They are similar to variables in Prolog, or environment variables in operating systems. === Evidence combination === In MYCIN it was possible that two or more rules might draw conclusions about a parameter with different weights of evidence. For example, one rule may conclude that the organism in question is E. Coli with a certainty of 0.8 whilst another concludes that it is E. Coli with a certainty of 0.5 or even −0.8. In the event the certainty is less than zero the evidence is actually against the hypothesis. In order to calculate the certainty factor MYCIN combined these weights using the formula below to yield a single certainty factor: C F ( x , y ) = { X + Y − X Y if X , Y > 0 X + Y + X Y if X , Y < 0 X + Y 1 − min ( | X | , | Y | ) otherwise {\displaystyle CF(x,y)={\begin{cases}X+Y-XY&{\text{if }}X,Y>0\\X+Y+XY&{\text{if }}X,Y<0\\{\frac {X+Y}{1-\min(|X|,|Y|)}}&{\text{otherwise}}\end{cases}}} Where X and Y are the certainty factors. This formula can be applied more than once if more than two rules draw conclusions about the same parameter. It is commutative, so it does not matter in which order the weights were combined. The combination formula was designed to have the following desirable properties: −1 can be interpreted as "false", +1 as "true", and 0 as "uncertain". Combining unknown with anything leaves it unchanged. Combining true with anything (except false) gives true. Similarly for false. Combining true and false is a division-by-zero error. Combining +x and -x gives unknown. Combining two positives (except true) gives a larger positive. Similarly for negatives. Combining a positive and a negative gives something in between. === Examples === The following examples come from Chapter 16 of PAIP, which contains an implementation in Common Lisp of a modified and simplified version of MYCIN for pedagogical purposes. A rule, and an English paraphrase generated by the system: == Results == An evaluation of MYCIN was conducted at the Stanford Medical School. The first phase of the evaluation consisted of 10 test cases of diverse origin, chosen by a physician who was not acquainted with MYCIN's methods or knowledge base. These cases were presented to 7 physicians and 1 senior medical student. 10 prescriptions were compiled for each of the cases, 1 recommended by MYCIN, 1 prescribed by the treating physician at the county hospital, and 8 by the aforementioned individuals. The second phase of the evaluation consisted of eight infectious disease specialists being provided the clinical summary and set of 10 prescriptions for each of the 10 cases and tasked to provide their own recommendations for each case and assess the 10 prescriptions. MYCIN received an acceptability rating of 65%, which was comparable to the 42.5% to 62.5% rating of five faculty members. This study is often cited as showing the potential for disagreement about therapeutic decisions, even among experts, when there is no "gold standard" for correct treatment. == Practical use == MYCIN was never actually used in practice. This wasn't because of any weakness in its performance. Some observers raised ethical and legal issues related to the use of computers in medicine, regarding the responsibility of the physicians in case the system gave wrong diagnosis. However, the greatest problem, and the reason that MYCIN was not used in routine practice, was the state of technologies for system integration, especially at the time it was developed. MYCIN was a stand-alone system that required a user to enter all relevant information about a patient by typing in responses to questions MYCIN posed. MYCIN ran on the DEC KI10 PDP-10, supporting a large time-shared system available over the early Internet (ARPANet), before personal computers were developed. MYCIN's greatest influence was accordingly its demonstration of the power of its representation and reasoning approach. Rule-based systems in many non-medical domains were developed in the years that followed MYCIN's introduction of the approach. In the 1980s, expert system "shells" were introduced (including one based on MYCIN, known as E-MYCIN (followed by Knowledge Engineering Environment - KEE)) and supported the development of expert systems in a wide variety of application areas. A difficulty that rose to prominence during the development of MYCIN and subsequent complex expert systems has been the extraction of the necessary knowledge for the inference engine to use from the human expert in the relevant fields into the rule base (the so-called "knowledge acquisition bottleneck").
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NLWeb

Natural Language Web or NLWeb was introduced by Microsoft in 2025. It is an open Python project designed to simplify the creation of natural language interfaces for websites. It enables users to query website contents using natural language, similar to interacting with an AI assistant. Every instance functions as a Model Context Protocol (MCP) server allowing websites to make their content discoverable and accessible to AI agents and other participants. NLWeb leverages existing web standards like Schema.org and RSS to build conversational capabilities of processing user queries through language models, performing semantic searches against website content and generating natural responses. It is platform-agnostic, running on all major systems and connecting to any vector database. Content to be indexed by NLWeb works best when it is organized in an AI friendly way. This means short, interlinked and semantically annotated articles work best. Initial adopters of NLWeb include TripAdvisor, Shopify, Eventbrite, and Hearst.
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Ciscogate

Ciscogate, also known as the Black Hat Bug, is the name given to a legal incident that occurred at the Black Hat Briefings security conference in Las Vegas, Nevada, on July 27, 2005. On the morning of the first day of the conference, July 26, 2005, some attendees noticed that 30 pages of text had been physically ripped out of the extensive conference presentation booklet the night before at the request of Cisco Systems and the CD-ROM with presentation slides was not included. It was determined the pages covered a talk to be given by Michael Lynn, a security researcher with Atlanta-based IBM Internet Security Systems (ISS). Instead of the pages with the details, attendees found a photographed copy of a notice from Black Hat saying "Due to some last minute changes beyond Black Hat's control, and at the request of the presenter, the included materials aren't up to the standards Black Hat tries to meet. Black Hat will be the first to apologize. We hope the vendors involved will follow suit." According to Lynn's lawyer, his employer had approved of the talk leading up to the conference but changed their minds two days before the scheduled talk, forbidding him from presenting. Lynn's original presentation was to cover a vulnerability in Cisco routers. The presentation was one of four scheduled to follow Jeff Moss' keynote address on the first day of the conference, titled "Cisco IOS Security Architecture". After being told by his employer that he could not present on the topic, Lynn chose an alternate topic. Cisco and ISS had offered to give new joint presentation but this was turned down by Black Hat because the original speaking slot was given to Lynn, not Cisco. Lynn's presentation began by covering security issues in services that allow users to make Voice over IP telephone calls. Shortly after beginning the presentation Lynn changed back to his original topic and began disclosing some technical details of the vulnerability he found in Cisco routers stating that he would rather resign from his job at ISS than keep the details private. == Lawsuit == Shortly after Lynn concluded his talk he met Jennifer Granick, who would soon become his lawyer. During their initial meeting Lynn told Granick that he expected to be sued. Later in the evening Lynn had heard that Cisco and ISS had filed a lawsuit and requested a temporary restraining order against Black Hat but not himself. A public relations representative from Black Hat told Granick that the lawsuit was against both Black Hat and Lynn and that the companies had scheduled an Ex parte hearing in San Francisco the next morning to request the restraining order. That night, Andrew Valentine, an attorney for ISS and Cisco called Lynn who directed them to Granick. During the conversation Valentine explained the claims and accusations against Lynn, which included three things: 1) ISS claimed copyright over the presentation that Lynn gave, 2) Cisco claimed copyright over the decompiled machine code obtained from the router which was included in the presentation, and 3) Cisco claimed the presentation contained trade secrets. These complaints were outlined in a civil complaint at the U.S. Northern District of California and filed against both Lynn and Black Hat. According to Granick, she and Valentine were able agree to an injunction to settle the case without court proceedings. This deal was almost called off due to an inadvertent mistake by Black Hat in which they had restored Lynn's presentation on their web server. Black Hat, Granick, and the plaintiff's lawyers were able to resolve this problem and the deal stood. One condition of the settlement required Lynn to provide an image of all computer data he used in his research to be provided to a third party for forensic analysis before erasing his research and any Cisco data from his systems. The settlement also stipulated that Lynn was prohibited from talking about the vulnerability in the future. == FBI Investigation == Shortly after lawyers for Lynn and ISS / Cisco filed settlement papers, FBI agents from the Las Vegas office arrived at the conference to begin asking questions. According to Granick, they were there at the request of the Atlanta FBI office and Lynn was not of interest. Granick asserted the Fifth and Sixth amendment rights on behalf of her client, Lynn. Granick asserted his rights for the Atlanta office and asked if an arrest warrant had been issued for Lynn. Over the next 24 hours Granick was not able to ascertain the status of a warrant but ultimately determined no warrant was issued. When the FBI was asked about the case by a journalist, spokesman Paul Bresson declined to discuss the case saying "Our policy is to not make any comment on anything that is ongoing. That's not to confirm that something is, because I really don't know". Granick would only confirm to journalists that the "investigation has to do with the presentation". == Response == === Attendees === Attendees of Black Hat Briefings, as well as many that also attended DEF CON, were not happy with vendors threatening legal action over vulnerability disclosure. The term "Ciscogate" was coined quickly by an unknown person, but some attendees were quick to create shirts to commemorate the incident. === Cisco === Mojgan Khalili, a senior manager for corporate PR at Cisco, issued a statement to the press saying "It is important to note that the information Mr. Lynn presented was not a disclosure of a new vulnerability or a flaw with Cisco IOS software. Mr. Lynn's research explores possible ways to expand exploitations of existing security vulnerabilities impacting routers." === ISS === Kim Duffy, managing director of ISS Australia, was asked about ISS's response to the incident. Duffy responded that it was "business as usual" as the company handled the incident "strictly by the book". He gave a brief statement to ZDNet UK saying "ISS has published rules for disclosure and that is what we stick to. We didn't care to publish [the disclosure] because we were not ready. We had not completed the research to our satisfaction so it was not ready to be disclosed". ISS spokesperson Roger Fortier confirmed that Lynn was no longer employed with the company and that ISS was still working with Cisco on the matter. He gave a statement to the Washington Post saying "ISS and Cisco have been working on this in the background and didn't feel at this time that the material was ready for publication. The decision was made on Monday to pull the presentation because we wanted to make sure the research was fully baked."
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Liang Wenfeng

Liang Wenfeng (Chinese: 梁文锋; pinyin: Liáng Wénfēng; born 1985) is a Chinese entrepreneur and businessman who is the co-founder of the quantitative hedge fund High-Flyer, as well as the founder and CEO of its artificial intelligence company DeepSeek. Liang attended Zhejiang University, and began his career by applying machine learning methods to quantitative finance. Through High-Flyer, he built large-scale computing infrastructure that was later used to support artificial intelligence research, leading to the creation of DeepSeek in 2023. DeepSeek gained international attention following the release of DeepSeek-R1, which analysts described as demonstrating high-level performance with comparatively limited compute resources. In 2025, Liang was named to Time magazine's list of 100 Most Influential People in AI and Fortune's list of the Most Powerful People in Business. == Early life == Liang was born in 1985 in the village of Mililing (米历岭村), Qinba town (覃巴镇), Wuchuan city (吴川市), Guangdong. His parents were both primary school teachers. Liang was routinely praised by both locals and teachers alike. Even since middle school, Liang was recalled for being well-known for reading comic books, while also being very proficient in mathematics. == Education == After elementary school, Liang attended Wuchuan No. 1 Middle School. There, he quickly excelled in class and ranked highly amongst his peers. He taught himself high school and university-level mathematics courses. Liang then attended Wuchaun No. 1 High School. In these years, he developed hobbies of mathematical modeling and conducting research projects. Compared to his peers, he was always ranked highly. For every mathematics exam, he always ranked within the top three. He was also the top scorer in the Zhanjiang region of Guangdong for the college entrance exam. Thus, in 2002, Liang left high school early to further pursue his education at the university level at the young age of 17. Attending Zhejiang University at the age of 17, Liang earned a Bachelor of Engineering in Electronic Information Engineering in 2007 and his Master of Engineering in Information & Communication Engineering in 2010. His master's dissertation was titled "Study on Object Tracking Algorithm Based on Low-Cost PTZ camera" (基于低成本PTZ摄像机的目标跟踪算法研究). In his college years, DJI founder Wang Tao asked Liang to join as a co-founder. Liang declined the invitation to pursue artificial intelligence methodologies in financial markets. While he states that those around him had entrepreneurial mindsets, he himself valued academics. == Career == === Early career (2008–2016) === During the 2008 financial crisis, Liang formed a team with his classmates to accumulate data related to financial markets. He also led the team to explore quantitative trading using machine learning and other technologies. After his graduation, Liang moved to a cheap flat in Chengdu, Sichuan, where he experimented with ways to apply AI to various fields. These ventures failed, until he tried applying AI to finance. In 2013, Liang attempted to integrate artificial intelligence with quantitative trading and founded Hangzhou Yakebi Investment Management Co Ltd with Xu Jin, an alumnus of Zhejiang University. In 2015, they co-founded Hangzhou Huanfang Technology Co Ltd, which is today's Zhejiang Jiuzhang Asset Management Co Ltd. === High-Flyer (2016–2023) === In February 2016, Liang and two other engineering classmates co-founded Ningbo High-Flyer Quantitative Investment Management Partnership (Limited Partnership). The team relied on mathematics and AI to make investments. Much of the early startup culture was described by former employees to be "geeky" and "quirky," often seen as contrary to the existing culture in large Chinese tech companies. In 2019, Liang founded High-Flyer AI which was dedicated to research on AI algorithms and its basic applications. By this time, High-Flyer had over 10 billion yuan in assets under management. On 30 August 2019, Liang Wenfeng delivered a keynote speech entitled "The Future of Quantitative Investment in China from a Programmer's Perspective" at the Private Equity Golden Bull Award ceremony held by China Securities Journal, and sparked heated discussions. Liang stated that the criterion for determining what is quantitative or non-quantitative is whether the investment decision is made by quantitative methods or by people. Quantitative funds do not have portfolio managers making the decisions and instead are just servers. He also stated High-Flyer's mission is to improve the effectiveness of China's secondary market. In February 2021, Gregory Zuckerman's book The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution was published. Liang wrote the preface for the Chinese edition of the book where he stated that whenever he encountered difficulties at work, he would think of Simons' words "There must be a way to model prices". In January 2025, Zuckerman wrote in The Wall Street Journal where he acknowledged this fact and stated he has been trying to get in touch with Liang but much like Simons, Liang is very secretive and difficult to contact. During 2021, Liang started buying thousands of Nvidia GPUs for his AI side project while running High-Flyer. Liang wanted to build something and it will be a game changer which his business partners thought was only possible from giants such as ByteDance and Alibaba Group. === DeepSeek (since 2023) === ==== DeepSeek begins ==== In May 2023, Liang announced High-Flyer would pursue the development of artificial general intelligence and launched DeepSeek. During that month in an interview with 36Kr, Liang stated that High-Flyer had acquired 10,000 Nvidia A100 GPUs before the US government imposed AI chip restrictions on China. That laid the foundation for DeepSeek to operate as an LLM developer. Liang also stated DeepSeek gets funding from High-Flyer. This was because when DeepSeek was founded, venture capital firms were reluctant in providing funding as it was unlikely that it would be able to generate an exit in a short period of time. Liang only personally holds 1% of the company, with 99% of the company being held by Ningbo High-Flyer Quantitative Investment Management Partnership (Limited Partnership). With DeepSeek's funding model, it lacks commercial pressure and rigid key performance indicators, enabling the company to deviate from previously established model architectures. ==== Early development ==== In July 2024, Liang was interviewed again by 36Kr. He stated that when DeepSeek-V2 was released and triggered an AI price war in China, it came as a huge surprise as the team did not expect pricing to be so sensitive. Liang's aggressive pricing of the language model forced domestic tech giants including Alibaba and Baidu to cut their own rates by over 95%. He also stated that as China's economy develops, it should gradually become a contributor instead of freeriding. What is lacking in China's innovation is not capital but a lack of confidence and knowledge on organizing talent into it. DeepSeek has not hired anyone particularly special and employees tend to be locally educated. When it comes to disruptive technologies, closed source approaches can only temporarily delay others in catching up. As the goal was long-term, DeepSeek sought employees who had ability and passion rather than experience. To retain a high talent density relative to larger firms like Bytedance or Baidu, DeepSeek aimed to maintain a low-hierarchy corporate culture, with members working in project-based groups, as well as competitive compensation. Liang emphasized his vision for DeepSeek employees to bring their "unique experience and ideas" instead of needing to be explicitly directed, with an overall bottom-up approach to division of labor. Liang noted that a significant outcome of this approach was the multi-head latent attention training architecture, which was attributed directly to a young DeepSeek researcher's personal interest. This advancement played a core role in reducing the cost of training the DeepSeek-V3 model, released in December 2024. ==== Release of DeepSeek-R1 ==== Also on 20 January 2025, DeepSeek, the company Liang founded and served as the CEO, released DeepSeek-R1, a 671-billion-parameter open-source reasoning AI model, alongside the publication of a detailed technical paper explaining its architecture and training methodology. The model was built using just 2,048 Nvidia H800 GPUs at a cost of $5.6 million, showcasing a resource-efficient approach that contrasted sharply with the billion-dollar budgets of Western competitors. The development of DeepSeek-R1 occurred amidst U.S. sanctions where Trump limited sales of Nvidia chips to China. By 27 January, DeepSeek surpassed ChatGPT to become the #1 free app on the United States iOS App Store. U.S. stocks plummeted, as more than $1 trillion was erased in market capitalization amid panic over DeepSeek. Technology journ
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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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Responsible AI Safety and Education Act

The Responsible AI Safety and Education Act (RAISE Act) is a New York State law that imposes transparency, safety, and reporting requirements on developers of large frontier artificial intelligence models. The law was signed by Governor Kathy Hochul on December 19, 2025. It was sponsored by State Senator Andrew Gounardes and Assemblymember Alex Bores. The RAISE Act is the second U.S. state law to regulate frontier AI model developers, following California's Transparency in Frontier Artificial Intelligence Act (TFAIA), which was signed in September 2025. Hochul signed the bill on the condition that the legislature would pass chapter amendments to bring the law closer to the California model. The amending bills (A9449/S8828) were introduced in January 2026; as of February 2026 they remain in committee, though the Governor's office and legal commentators treat the agreed-upon amendments as representing the final form of the law. == Provisions == The following describes the RAISE Act as it is expected to operate after the agreed-upon chapter amendments take effect. The law is expected to take effect on January 1, 2027. === Scope === The law applies to "large frontier developers," defined as companies with annual revenues exceeding $500 million that develop "frontier models," which are foundation models trained using more than 1026 floating-point operations (FLOPs). The version passed by the legislature in June 2025 had instead defined large developers based on having spent over $100 million in aggregate compute costs, and also included a provision prohibiting deployment of frontier models posing "unreasonable risk of critical harm"; both were removed as part of the negotiations between Hochul and the legislature. Accredited colleges and universities engaged in academic research are exempt, as is the state's Empire AI consortium. === Safety and transparency framework === Large frontier developers must write, implement, and publicly publish a "frontier AI framework" describing how they assess and mitigate catastrophic risks, secure unreleased model weights against unauthorized access, use third-party evaluators, govern internal use of frontier models, and respond to safety incidents. The framework must describe these measures "in detail," a requirement that goes beyond the California TFAIA's requirement to describe a developer's "approach." The framework must be reviewed at least annually, and material modifications must be published with justification within 30 days. Before or concurrently with deploying a new or substantially modified frontier model, developers must publish a transparency report including the model's release date, supported languages and output modalities, intended uses, and any restrictions on use. Large frontier developers must additionally include summaries of catastrophic risk assessments and the extent of third-party involvement. === Catastrophic risk and incident reporting === The law defines "catastrophic risk" as a foreseeable and material risk that a frontier model will contribute to the death of or serious injury to more than 50 people, or more than $1 billion in property damage, arising from a frontier model providing expert-level assistance in creating chemical, biological, radiological, or nuclear weapons; engaging in cyberattacks or conduct equivalent to crimes such as murder, assault, or theft without meaningful human oversight; or evading the control of its developer or user. Loss of equity value is explicitly excluded from the definition of property damage. "Critical safety incidents" include unauthorized access to model weights resulting in death or injury, materialization of a catastrophic risk, loss of control of a frontier model causing death or injury, and a model using deceptive techniques to subvert developer controls outside of an evaluation context in a manner that increases catastrophic risk. Frontier developers must report critical safety incidents within 72 hours, or within 24 hours if the incident poses an imminent risk of death or serious physical injury. === Enforcement === The chapter amendments establish a new office within the New York State Department of Financial Services to oversee compliance, receive incident reports, and publish annual reports on AI safety beginning in 2028. Large frontier developers must file disclosure statements with this office and pay pro rata assessments to fund its operations. The New York Attorney General may bring civil actions, with penalties of up to $1 million for a first violation and $3 million for subsequent violations. The version passed by the legislature in June 2025 had set penalties at up to $10 million and $30 million respectively. The law does not create a private right of action. == Legislative history == The bill was introduced in the Assembly on March 5, 2025, by Assemblymember Alex Bores, and in the Senate on March 27, 2025, by Senator Andrew Gounardes. After a series of amendments, the legislature passed the bill in June 2025. Governor Hochul did not immediately sign the bill, using nearly all the time available under New York law before acting; had she not signed by the end of 2025, the bill would have been pocket vetoed. The tech industry lobbied against the bill during this period, and Hochul initially proposed a near-complete rewrite modeled on California's TFAIA. Legislators resisted the extent of the changes, and the two sides ultimately agreed on a version that used the California law as a base but preserved several provisions that went beyond it, including the 72-hour incident reporting timeline and the creation of a dedicated enforcement office. Hochul signed the original bill (S6953-B/A6453-B) on December 19, 2025, with the legislature committing to pass chapter amendments formalizing the agreed changes in the January 2026 session. The amending bills (A9449 in the Assembly, S8828 in the Senate) were introduced on January 6 and January 8, 2026. OpenAI and Anthropic expressed support for the law. Anthropic's head of external affairs Sarah Heck said the two state laws "should inspire Congress to build on them." The super PAC network Leading the Future, backed by Andreessen Horowitz and OpenAI president Greg Brockman, subsequently announced plans to challenge Bores in a future election. == Federal preemption debate == Hochul signed the RAISE Act eight days after President Donald Trump issued an executive order on December 11, 2025, directing the Department of Justice to challenge state AI laws deemed to conflict with a "minimally burdensome" national AI policy. On January 9, 2026, the Department of Justice announced the establishment of an AI Litigation Task Force as called for by the executive order. The executive order also threatened states with loss of certain federal broadband funding if their AI laws were found to be onerous. Legal commentators have noted several potential avenues for federal challenge, including arguments that the law constitutes compelled speech, violates the dormant Commerce Clause by creating a patchwork of state regulations, or is preempted by federal AI policy. == Comparison with California's TFAIA == The RAISE Act was designed to align with California's Transparency in Frontier Artificial Intelligence Act, signed on September 29, 2025. Both laws use the same 1026 FLOP threshold to define frontier models and the same $500 million revenue threshold to define large developers. Both require public safety frameworks, transparency reports, and incident reporting. The RAISE Act's 72-hour incident reporting window is stricter than the TFAIA's 15-day window, though both require faster reporting for incidents posing imminent physical risk (24 hours under the RAISE Act, immediate under the TFAIA). The RAISE Act establishes a dedicated enforcement office within the Department of Financial Services, whereas California routes reports through the Office of Emergency Services. The RAISE Act requires developers to describe their safety measures "in detail" and how they "handle" various risks, whereas the TFAIA requires developers to describe their "approach."
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Yahoo Groups

Yahoo! Groups was a free-to-use system of electronic mailing lists offered by Yahoo!. Prior to February 2020, Yahoo! Groups was one of the world's largest collections of online discussion boards. It allowed members to subscribe to various groups, read subscribed discussions online, view and share photos, files and bookmarks within a group, access a group calendar, create polls for group members, and receive email notifications of new discussion topics. Some groups were simply announcement boards, to which only the group moderators could post, while others were discussion forums. Depending on each group's settings, membership could be open to everyone or only to invited or approved people. On February 1, 2020, Yahoo! removed online access to discussions and all other features except simple membership management, essentially turning all groups into mailing lists, and on October 13, 2020, it announced that Yahoo Groups would shut down completely on December 15, 2020. == History == In 1998 Yahoo! Clubs was launched as an extension of services developed by Yahoo! Messenger. In August 2000 Yahoo acquired eGroups.com. Yahoo! Groups was launched in early 2001 as an integration of technology from eGroups.com and community groups from both eGroups.com and Yahoo! Clubs. In 2001 Yahoo! deleted adult groups from its search directory, making it very difficult to locate Yahoo! groups with adult content. The Groups Updates Email feature was introduced in 2010. It summarized, in a single email, all the updates that occurred every twenty-four hours in all groups. In September 2010, a major facelift was rolled out, making Yahoo! Groups look very similar to Facebook. In December, Yahoo! Groups Japan emailed its users and posted a notice on its homepage, to announce that its service, which commenced in February 2004, would be closing on May 28, 2014. In October 2019, Yahoo! announced that all content that had been posted to Yahoo! Groups will be deleted on December 14, 2019; that date was later amended to January 31, 2020. Yahoo! announced that adding new content would be blocked on October 28, 2019. Once the content was deleted, users of Yahoo! Groups were only able to browse the group directory, request invitations and, if members of a group, send messages to that group. On October 13, 2020, Yahoo! announced they would be shutting down Yahoo! Groups on December 15, 2020. The site was closed down a few days after the advertised date, displaying a message that the service was officially shut down. This message stopped appearing at the end of January 2021 and the Yahoo! Groups web address began redirecting to the main Yahoo! page. === Criticism and controversy === On August 31, 2010, Yahoo! Groups started rolling out a major software change, which was denounced by a large number of users. The re-model was completely abandoned on January 12, 2011. == Site statistics == In August 2008, Yahoo! Group staff reported that there were 113 million users, and nine million Groups using 22 languages. In July 2010, the web analytics website Quantcast reported around 915 thousand unique visitors daily to the Yahoo! Groups website (US). In January 2011, that number had increased to 933 thousand unique visitors daily. The number did not include Yahoo! Group members who accessed the Groups site via email. In September 2010, at its "Product Runway" event, Yahoo! told reporters that Yahoo! Groups had 115 million group members and that there were 10 million Yahoo! groups. == Archives ==
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Drools

Drools is a business rule management system (BRMS) with a forward and backward chaining inference-based rules engine, more correctly known as a production rule system, using an enhanced implementation of the Rete algorithm. Drools supports the Java Rules Engine API (Java Specification Request 94) standard for its business rule engine and enterprise framework for the construction, maintenance, and enforcement of business policies in an organization, application, or service. == Drools in Apache Kie == Drools, as part of the Kie Community has entered Apache Incubator in January, 2023. == Red Hat Decision Manager == Red Hat Decision Manager (formerly Red Hat JBoss BRMS) is a business rule management system and reasoning engine for business policy and rules development, access, and change management. JBoss Enterprise BRMS is a productized version of Drools with enterprise-level support available. JBoss Rules is also a productized version of Drools, but JBoss Enterprise BRMS is the flagship product. Components of the enterprise version: JBoss Enterprise Web Platform – the software infrastructure, supported to run the BRMS components only JBoss Enterprise Application Platform or JBoss Enterprise SOA Platform – the software infrastructure, supported to run the BRMS components only Business Rules Engine – Drools Expert using the Rete algorithm and the Drools Rule Language (DRL) Business Rules Manager – Drools Guvnor - Guvnor is a centralized repository for Drools Knowledge Bases, with rich web-based GUIs, editors, and tools to aid in the management of large numbers of rules. Business Rules Repository – Drools Guvnor Drools and Guvnor are JBoss Community open source projects. As they are mature, they are brought into the enterprise-ready product JBoss Enterprise BRMS. Components of the JBoss Community version: Drools Guvnor (Business Rules Manager) – a centralized repository for Drools Knowledge Bases Drools Expert (rule engine) – uses the rules to perform reasoning Drools Flow (process/workflow), or jBPM 5 – provides for workflow and business processes Drools Fusion (event processing/temporal reasoning) – provides for complex event processing Drools Planner/OptaPlanner (automated planning) – optimizes automated planning, including NP-hard planning problems == Example == This example illustrates a simple rule to print out information about a holiday in July. It checks a condition on an instance of the Holiday class, and executes Java code if that condition is true. The purpose of dialect "mvel" is to point the getter and setters of the variables of your Plain Old Java Object (POJO) classes. Consider the above example, in which a Holiday class is used and inside the circular brackets (parentheses) "month" is used. So with the help of dialect "mvel" the getter and setters of the variable "month" can be accessed. Dialect "java" is used to help us write our Java code in our rules. There is one restriction or characteristic on this. We cannot use Java code inside the "when" part of the rule but we can use Java code in the "then" part. We can also declare a Reference variable $h1 without the $ symbol. There is no restriction on this. The main purpose of putting the $ symbol before the variable is to mark the difference between variables of POJO classes and Rules.
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Leabra

Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathematically predict outcomes based on inputs and previous learning influences. Leabra is heavily influenced by and contributes to neural network designs and models, including emergent. == Background == It is the default algorithm in emergent (successor of PDP++) when making a new project, and is extensively used in various simulations. Hebbian learning is performed using conditional principal components analysis (CPCA) algorithm with correction factor for sparse expected activity levels. Error-driven learning is performed using GeneRec, which is a generalization of the recirculation algorithm, and approximates Almeida–Pineda recurrent backpropagation. The symmetric, midpoint version of GeneRec is used, which is equivalent to the contrastive Hebbian learning algorithm (CHL). See O'Reilly (1996; Neural Computation) for more details. The activation function is a point-neuron approximation with both discrete spiking and continuous rate-code output. Layer or unit-group level inhibition can be computed directly using a k-winners-take-all (KWTA) function, producing sparse distributed representations. A feedforward and feedback (FFFB) form of inhibition has now replaced the KWTA form of inhibition. FFFB inhibition can be efficiently implemented by using the average excitatory input and activity levels in a given layer. The net input is computed as an average, not a sum, over connections, based on normalized, sigmoidally transformed weight values, which are subject to scaling on a connection-group level to alter relative contributions. Automatic scaling is performed to compensate for differences in expected activity level in the different projections. Documentation about this algorithm can be found in the book "Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain" published by MIT press. and in the Emergent Documentation Archived 2009-04-16 at the Wayback Machine == Overview of the leabra algorithm == The pseudocode for Leabra is given here, showing exactly how the pieces of the algorithm described in more detail in the subsequent sections fit together. Iterate over minus and plus phases of settling for each event. o At start of settling, for all units: - Initialize all state variables (activation, v_m, etc.). - Apply external patterns (clamp input in minus, input & output in plus). - Compute net input scaling terms (constants, computed here so network can be dynamically altered). - Optimization: compute net input once from all static activations (e.g., hard-clamped external inputs). o During each cycle of settling, for all non-clamped units: - Compute excitatory netinput (g_e(t), aka eta_j or net) -- sender-based optimization by ignoring inactives. - Compute kWTA inhibition for each layer, based on g_i^Q: Sort units into two groups based on g_i^Q: top k and remaining k+1 -> n. If basic, find k and k+1th highest If avg-based, compute avg of 1 -> k & k+1 -> n. Set inhibitory conductance g_i from g^Q_k and g^Q_k+1 - Compute point-neuron activation combining excitatory input and inhibition o After settling, for all units, record final settling activations as either minus or plus phase (y^-_j or y^+_j). After both phases update the weights (based on linear current weight values), for all connections: o Compute error-driven weight changes with CHL with soft weight bounding o Compute Hebbian weight changes with CPCA from plus-phase activations o Compute net weight change as weighted sum of error-driven and Hebbian o Increment the weights according to net weight change. == Implementations == Emergent Archived 2015-10-03 at the Wayback Machine is the original implementation of Leabra; its most recent implementation is written in Go. It was written chiefly by Dr. O'Reilly, but professional software engineers were recently hired to improve the existing codebase. This is the fastest implementation, suitable for constructing large networks. Although emergent has a graphical user interface, it is very complex and has a steep learning curve. If you want to understand the algorithm in detail, it will be easier to read non-optimized code. For this purpose, check out the MATLAB version. There is also an R version available, that can be easily installed via install.packages("leabRa") in R and has a short introduction to how the package is used. The MATLAB and R versions are not suited for constructing very large networks, but they can be installed quickly and (with some programming background) are easy to use. Furthermore, they can also be adapted easily. == Special algorithms == Temporal differences and general dopamine modulation. Temporal differences (TD) is widely used as a model of midbrain dopaminergic firing. Primary value learned value (PVLV). PVLV simulates behavioral and neural data on Pavlovian conditioning and the midbrain dopaminergic neurons that fire in proportion to unexpected rewards (an alternative to TD). Prefrontal cortex basal ganglia working memory (PBWM). PBWM uses PVLV to train prefrontal cortex working memory updating system, based on the biology of the prefrontal cortex and basal ganglia.
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