Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. == Computer science == For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007), there are three major dimensions for similarity: syntactic, external, and semantic. Coincidentally, they roughly correspond to the dimensions identified by Cognitive Scientists below. A number of tools and frameworks have been developed for aligning ontologies, some with inspiration from Cognitive Science and some independently. Ontology alignment tools have generally been developed to operate on database schemas, XML schemas, taxonomies, formal languages, entity-relationship models, dictionaries, and other label frameworks. They are usually converted to a graph representation before being matched. Since the emergence of the Semantic Web, such graphs can be represented in the Resource Description Framework line of languages by triples of the form
Deep learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Early forms of neural networks were inspired by information processing and distributed communication nodes in biological systems, particularly the human brain. However, current neural networks do not intend to model the brain function of organisms, and are generally seen as low-quality models for that purpose. == Overview == Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. Fundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a progressively more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place at which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate on. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction. The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than two. CAP of depth two has been shown to be a universal approximator in the sense that it can emulate any function. Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > two) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. Deep learning architectures can be constructed with a greedy layer-by-layer method. Deep learning helps to disentangle these abstractions and pick out which features improve performance. Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data is more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. The term deep learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons. The etymology of the term is more complicated. == Interpretations == Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference. The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. Recent work also showed that universal approximation also holds for non-bounded activation functions such as Kunihiko Fukushima's rectified linear unit. The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al. proved that if the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator. The probabilistic interpretation derives from the field of machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop. == History == === Before 1980 === There are two types of artificial neural network (ANN): feedforward neural network (FNN) or multilayer perceptron (MLP) and recurrent neural networks (RNN). RNNs have cycles in their connectivity structure, whereas FNNs do not. In the 1920s, Wilhelm Lenz and Ernst Ising created the Ising model which is essentially a non-learning RNN architecture consisting of neuron-like threshold elements. In 1972, Shun'ichi Amari made this architecture adaptive. His learning RNN was republished by John Hopfield in 1982. Other early recurrent neural networks were published by Kaoru Nakano in 1971. Already in 1948, Alan Turing produced work on "Intelligent Machinery" that was not published in his lifetime, containing "ideas related to artificial evolution and learning RNNs". Frank Rosenblatt (1958) proposed the perceptron, an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. He later published a 1962 book that also introduced variants and computer experiments, including a version with four-layer perceptrons "with adaptive preterminal networks" where the last two layers have learned weights (here he credits H. D. Block and B. W. Knight). The book cites an earlier network by R. D. Joseph (1960) "functionally equivalent to a variation of" this four-layer system (the book mentions Joseph over 30 times). Should Joseph therefore be considered the originator of proper adaptive multilayer perceptrons with learning hidden units? Unfortunately, the learning algorithm was not a functional one, and fell into oblivion. The first working deep learning algorithm was the Group method of data handling, a method to train arbitrarily deep neural networks, published by Alexey Ivakhnenko and Lapa in 1965. They regarded it as a form of polynomial regression, or a generalization of Rosenblatt's perceptron to handle more complex, nonlinear, and hierarchical relationships. A 1971 paper described a deep network with eight layers trained by this method, which is based on layer by layer training through regression analysis. Superfluous hidden units are pruned using a separate validation set. Since the activation functions of the nodes are Kolmogorov-Gabor polynomials, these were also the first deep networks with multiplicative units or "gates". The first deep learning multilayer perceptron trained by stochastic gradient descent was published in 1967 by Shun'ichi
Ontology components
Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations. == List == Common components of ontologies include: Individuals instances or objects (the basic or "ground level" objects; the tokens). Classes sets, collections, concepts, types of objects, or kinds of things. Attributes aspects, properties, features, characteristics, or parameters that individuals (and classes and relations) can have. Relations ways in which classes and individuals can be related to one another. Relations can carry attributes that specify the relation further. Function terms complex structures formed from certain relations that can be used in place of an individual term in a statement. Restrictions formally stated descriptions of what must be true in order for some assertion to be accepted as input. Rules statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form. Axioms assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements. Events the changing of attributes or relations. Actions types of events. Ontologies are commonly encoded using ontology languages. == Individuals == Individuals (instances) are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract individuals such as numbers and words (although there are differences of opinion as to whether numbers and words are classes or individuals). Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology. In formal extensional ontologies, only the utterances of words and numbers are considered individuals – the numbers and names themselves are classes. In a 4D ontology, an individual is identified by its spatio-temporal extent. Examples of formal extensional ontologies are BORO, ISO 15926 and the model in development by the IDEAS Group. == Classes == == Attributes == Objects in an ontology can be described by relating them to other things, typically aspects or parts. These related things are often called attributes, although they may be independent things. Each attribute can be a class or an individual. The kind of object and the kind of attribute determine the kind of relation between them. A relation between an object and an attribute express a fact that is specific to the object to which it is related. For example, the Ford Explorer object has attributes such as: ⟨has as name⟩ Ford Explorer ⟨as by definition as part⟩ 6-speed transmission ⟨as by definition as part⟩ door (with as minimum and maximum cardinality: 4) ⟨as by definition as part one of⟩ {4.0L engine, 4.6L engine} The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing. Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes). If that is not the case, then you would have either a taxonomy (if hyponym relationships exist between concepts) or a controlled vocabulary. These are useful, but are not considered true ontologies. == Relations == Relations (also known as relationships) between objects in an ontology specify how objects are related to other objects. Typically a relation is of a particular type (or class) that specifies in what sense the object is related to the other object in the ontology. For example, in the ontology that contains the concept Ford Explorer and the concept Ford Bronco might be related by a relation of type ⟨is defined as a successor of⟩. The full expression of that fact then becomes: Ford Explorer is defined as a successor of : Ford Bronco This tells us that the Explorer is the model that replaced the Bronco. This example also illustrates that the relation has a direction of expression. The inverse expression expresses the same fact, but with a reverse phrase in natural language. Much of the power of ontologies comes from the ability to describe relations. Together, the set of relations describes the semantics of the domain: that is, its various semantic relations, such as synonymy, hyponymy and hypernymy, coordinate relation, and others. The set of used relation types (classes of relations) and their subsumption hierarchy describe the expression power of the language in which the ontology is expressed. An important type of relation is the subsumption relation (is-a-superclass-of, the converse of is-a, is-a-subtype-of or is-a-subclass-of). This defines which objects are classified by which class. For example, we have already seen that the class Ford Explorer is-a-subclass-of 4-Wheel Drive Car, which in turn is-a-subclass-of Car. The addition of the is-a-subclass-of relationships creates a taxonomy; a tree-like structure (or, more generally, a partially ordered set) that clearly depicts how objects relate to one another. In such a structure, each object is the 'child' of a 'parent class' (Some languages restrict the is-a-subclass-of relationship to one parent for all nodes, but many do not). Another common type of relations is the mereology relation, written as part-of, that represents how objects combine to form composite objects. For example, if we extended our example ontology to include concepts like Steering Wheel, we would say that a "Steering Wheel is-by-definition-a-part-of-a Ford Explorer" since a steering wheel is always one of the components of a Ford Explorer. If we introduce meronymy relationships to our ontology, the hierarchy that emerges is no longer able to be held in a simple tree-like structure since now members can appear under more than one parent or branch. Instead this new structure that emerges is known as a directed acyclic graph. As well as the standard is-a-subclass-of and is-by-definition-a-part-of-a relations, ontologies often include additional types of relations that further refine the semantics they model. Ontologies might distinguish between different categories of relation types. For example: relation types for relations between classes relation types for relations between individuals relation types for relations between an individual and a class relation types for relations between a single object and a collection relation types for relations between collections Relation types are sometimes domain-specific and are then used to store specific kinds of facts or to answer particular types of questions. If the definitions of the relation types are included in an ontology, then the ontology defines its own ontology definition language. An example of an ontology that defines its own relation types and distinguishes between various categories of relation types is the Gellish ontology. For example, in the domain of automobiles, we might need a made-in type relationship which tells us where each car is built. So the Ford Explorer is made-in Louisville. The ontology may also know that Louisville is-located-in Kentucky and Kentucky is-classified-as-a state and is-a-part-of the U.S. Software using this ontology could now answer a question like "which cars are made in the U.S.?"
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
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."
Smart environment
Smart environments link computers and other smart devices to everyday settings and tasks. Smart environments include smart homes, smart cities, and smart manufacturing. == Introduction == Smart environments are an extension of pervasive computing. According to Mark Weiser, pervasive computing promotes the idea of a world that is connected to sensors and computers. These sensors and computers are integrated with everyday objects in peoples' lives and are connected through networks. == Definition == Cook and Das, define a smart environment as "a small world where different kinds of smart devices are continuously working to make inhabitants' lives more comfortable." Smart environments aim to satisfy the experience of individuals from every environment, by replacing hazardous work, physical labor, and repetitive tasks with automated agents. Poslad differentiates three different kinds of smart environments for systems, services, and devices: virtual (or distributed) computing environments, physical environments, and human environments, or a hybrid combination of these: Virtual computing environments enable smart devices to access pertinent services anywhere and anytime. Physical environments may be embedded with various smart devices of different types including tags, sensors, and controllers, and have different form factors ranging from nano- to micro- to macro-sized. Human environments: humans, either individually or collectively, inherently form a smart environment for devices. However, humans themselves may be accompanied by smart devices such as mobile phones, use surface-mounted devices (wearable computing), and contain embedded devices (e.g., pacemakers to maintain a healthy heart operation or AR contact lenses) == Features == Smart environments encompass a range of features and services across various domains, including smart homes, smart cities, smart health, and smart factories. Some of the key features of smart environments are: Sensors and Actuators: Smart environments are equipped with an assembly of sensors and actuators that collect data and initiate actions to provide services for the betterment of human life. Interconnected Systems: These environments consist of interconnected systems that enable seamless communication and coordination among various devices and components. Data-Driven Technologies: Smart environments leverage data-driven technologies, such as the Internet of Things (IoT), to obtain information from the physical world, process it, and perform actions accordingly. Efficiency and Sustainability: They are designed to improve efficiency, sustainable practices, and resource management across different settings, such as energy efficiency in smart homes and environmental quality management in smart cities. Diverse Requirements: Different types of smart environments have diverse requirements and technology choices, influencing the processing and utilization of data within a specific environment. == Technologies == Building a smart environment involves technologies of Wireless communication Algorithm design, signal prediction & classification, information theory Multilayered software architecture, Corba, middleware Speech recognition Image processing, image recognition Sensors design, calibration, motion detection, temperature, pressure sensors, accelerometers Semantic Web and knowledge graphs Adaptive control, Kalman filters Computer networking Parallel processing Operating systems == Existing projects == The Aware Home Research Initiative at Georgia Tech "is devoted to the multidisciplinary exploration of emerging technologies and services based in the home" and was launched in 1998 as one of the first "living laboratories." The Mav Home (Managing an Adaptive Versatile Home) project, at UT Arlington, is a smart environment-lab with state-of-the-art algorithms and protocols used to provide a customized, personal environment to the users of this space. The Mav Home project, in addition to providing a safe environment, wants to reduce the energy consumption of the inhabitants. Other projects include House at the MIT Media Lab and many others.
Fooocus
Fooocus is an open source generative artificial intelligence program that allows users to generate images from a text prompt. It uses Stable Diffusion XL as the base model for its image capabilities as well as a collection of default settings and prompts to make the image generation process more streamlined. == History == Fooocus was created by Lvmin Zhang, a doctoral student at Stanford University who previously studied at the Chinese University of Hong Kong and Soochow University. He is also the main author of ControlNet, which has been adopted by many other Stable Diffusion interfaces, such as AUTOMATIC1111 and ComfyUI. As of 9 July 2024, the project had 38.1k stars on GitHub. == Features == Fooocus' main feature is that it is easy to set up and does not require users to manually configure model parameters to achieve desirable results. According to the project, it uses GPT-2 to automatically add more detail to the user's prompts. It includes common extensions such LCM low-rank adaptation by default which allows for faster generation speed. Fooocus prefers a photographic style by default, with a list of predefined styles to choose from. While Fooocus aims to provide good results out of the box, it also includes an "advanced" tab that allows for user customization. The user interface is based on Gradio. It appears this project has not been updated in over 1 year. The latest git update for Fooocus was in Aug 12, 2024.