The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words. It was released on 19 June 2019, under the Apache 2.0 license. It achieved state-of-the-art results on a variety of natural language processing tasks, including language modeling, question answering, and natural language inference. == Architecture == The main idea of XLNet is to model language autoregressively like the GPT models, but allow for all possible permutations of a sentence. Concretely, consider the following sentence:My dog is cute.In standard autoregressive language modeling, the model would be tasked with predicting the probability of each word, conditioned on the previous words as its context: We factorize the joint probability of a sequence of words x 1 , … , x T {\displaystyle x_{1},\ldots ,x_{T}} using the chain rule: Pr ( x 1 , … , x T ) = Pr ( x 1 ) Pr ( x 2 | x 1 ) Pr ( x 3 | x 1 , x 2 ) … Pr ( x T | x 1 , … , x T − 1 ) . {\displaystyle \Pr(x_{1},\ldots ,x_{T})=\Pr(x_{1})\Pr(x_{2}|x_{1})\Pr(x_{3}|x_{1},x_{2})\ldots \Pr(x_{T}|x_{1},\ldots ,x_{T-1}).} For example, the sentence "My dog is cute" is factorized as: Pr ( My , dog , is , cute ) = Pr ( My ) Pr ( dog | My ) Pr ( is | My , dog ) Pr ( cute | My , dog , is ) . {\displaystyle \Pr({\text{My}},{\text{dog}},{\text{is}},{\text{cute}})=\Pr({\text{My}})\Pr({\text{dog}}|{\text{My}})\Pr({\text{is}}|{\text{My}},{\text{dog}})\Pr({\text{cute}}|{\text{My}},{\text{dog}},{\text{is}}).} Schematically, we can write it as
Smartphone kill switch
A smartphone kill switch is a software-based security feature that allows a smartphone's owner to remotely render it inoperable if it is lost or stolen, thereby deterring theft. There have been a number of initiatives to legally require kill switches on smartphones. Smartphones have high resale value, and are therefore often the target of theft, with thieves selling them to cartels for resale. A kill switch can deter theft by making devices worthless. == Legal requirements == In the United States, Minnesota was the first state to pass a bill requiring smartphones to have such a feature, and California was the first to require that the feature be turned on by default. The California law requires the kill switch to be resistant to reinstallation of the phone's operating system. The CTIA initially resisted the legislation, fearing that it would make phones easier to hack, but later supported kill switches. There is evidence that this legislation has been effective, with smartphone theft declining by 50% between 2013 and 2017 in San Francisco. Secure Our Smartphones (S.O.S.), a New York State and San Francisco initiative started by New York State Attorney General Eric Schneiderman and San Francisco District Attorney George Gascón. The initiative is co-chaired by Schneiderman, Gascón and Boris Johnson, and has 105 members. == Examples == An Android phone signed into a Google account can be remotely locked and erased via Google's Find My Device service, as long as it is connected to the Internet. To prevent this, a thief must sign the device out of Google before the owner locks or erases it. iPhones have a similar service.
Algorithmic logic
Algorithmic logic is a calculus of programs that allows the expression of semantic properties of programs by appropriate logical formulas. It provides a framework that enables proving the formulas from the axioms of program constructs such as assignment, iteration and composition instructions and from the axioms of the data structures in question see Mirkowska & Salwicki (1987), Banachowski et al. (1977). The following diagram helps to locate algorithmic logic among other logics. [ P r o p o s i t i o n a l l o g i c o r S e n t e n t i a l c a l c u l u s ] ⊂ [ P r e d i c a t e c a l c u l u s o r F i r s t o r d e r l o g i c ] ⊂ [ C a l c u l u s o f p r o g r a m s o r Algorithmic logic ] {\displaystyle \qquad \left[{\begin{array}{l}\mathrm {Propositional\ logic} \\or\\\mathrm {Sentential\ calculus} \end{array}}\right]\subset \left[{\begin{array}{l}\mathrm {Predicate\ calculus} \\or\\\mathrm {First\ order\ logic} \end{array}}\right]\subset \left[{\begin{array}{l}\mathrm {Calculus\ of\ programs} \\or\\{\mbox{Algorithmic logic}}\end{array}}\right]} The formalized language of algorithmic logic (and of algorithmic theories of various data structures) contains three types of well formed expressions: Terms - i.e. expressions denoting operations on elements of data structures, formulas - i.e. expressions denoting the relations among elements of data structures, programs - i.e. algorithms - these expressions describe the computations. For semantics of terms and formulas consult pages on first-order logic and Tarski's semantics. The meaning of a program K {\displaystyle K} is the set of possible computations of the program. Algorithmic logic is one of many logics of programs. Another logic of programs is dynamic logic, see dynamic logic, Harel, Kozen & Tiuryn (2000).
Geospatial metadata
Geospatial metadata (also geographic metadata) is a type of metadata applicable to geographic data and information. Such objects may be stored in a geographic information system (GIS) or may simply be documents, data-sets, images or other objects, services, or related items that exist in some other native environment but whose features may be appropriate to describe in a (geographic) metadata catalog (may also be known as a data directory or data inventory). == Definition == ISO 19115:2013 "Geographic Information – Metadata" from ISO/TC 211, the industry standard for geospatial metadata, describes its scope as follows: [This standard] provides information about the identification, the extent, the quality, the spatial and temporal aspects, the content, the spatial reference, the portrayal, distribution, and other properties of digital geographic data and services. ISO 19115:2013 also provides for non-digital mediums: Though this part of ISO 19115 is applicable to digital data and services, its principles can be extended to many other types of resources such as maps, charts, and textual documents as well as non-geographic data. The U.S. Federal Geographic Data Committee (FGDC) describes geospatial metadata as follows: A metadata record is a file of information, usually presented as an XML document, which captures the basic characteristics of a data or information resource. It represents the who, what, when, where, why and how of the resource. Geospatial metadata commonly document geographic digital data such as Geographic Information System (GIS) files, geospatial databases, and earth imagery but can also be used to document geospatial resources including data catalogs, mapping applications, data models and related websites. Metadata records include core library catalog elements such as Title, Abstract, and Publication Data; geographic elements such as Geographic Extent and Projection Information; and database elements such as Attribute Label Definitions and Attribute Domain Values. == History == The growing appreciation of the value of geospatial metadata through the 1980s and 1990s led to the development of a number of initiatives to collect metadata according to a variety of formats either within agencies, communities of practice, or countries/groups of countries. For example, NASA's "DIF" metadata format was developed during an Earth Science and Applications Data Systems Workshop in 1987, and formally approved for adoption in 1988. Similarly, the U.S. FGDC developed its geospatial metadata standard over the period 1992–1994. The Spatial Information Council of Australia and New Zealand (ANZLIC), a combined body representing spatial data interests in Australia and New Zealand, released version 1 of its "metadata guidelines" in 1996. ISO/TC 211 undertook the task of harmonizing the range of formal and de facto standards over the approximate period 1999–2002, resulting in the release of ISO 19115 "Geographic Information – Metadata" in 2003 and a subsequent revision in 2013. As of 2011 individual countries, communities of practice, agencies, etc. have started re-casting their previously used metadata standards as "profiles" or recommended subsets of ISO 19115, occasionally with the inclusion of additional metadata elements as formal extensions to the ISO standard. The growth in popularity of Internet technologies and data formats, such as Extensible Markup Language (XML) during the 1990s led to the development of mechanisms for exchanging geographic metadata on the web. In 2004, the Open Geospatial Consortium released the current version (3.1) of Geography Markup Language (GML), an XML grammar for expressing geospatial features and corresponding metadata. With the growth of the Semantic Web in the 2000s, the geospatial community has begun to develop ontologies for representing semantic geospatial metadata. Some examples include the Hydrology and Administrative ontologies developed by the Ordnance Survey in the United Kingdom. == ISO 19115: Geographic information – Metadata == ISO 19115 is a standard of the International Organization for Standardization (ISO). The standard is part of the ISO geographic information suite of standards (19100 series). ISO 19115 and its parts define how to describe geographical information and associated services, including contents, spatial-temporal purchases, data quality, access and rights to use. The objective of this International Standard is to provide a clear procedure for the description of digital geographic data-sets so that users will be able to determine whether the data in a holding will be of use to them and how to access the data. By establishing a common set of metadata terminology, definitions and extension procedures, this standard promotes the proper use and effective retrieval of geographic data. ISO 19115 was revised in 2013 to accommodate growing use of the internet for metadata management, as well as add many new categories of metadata elements (referred to as codelists) and the ability to limit the extent of metadata use temporally or by user. == ISO 19139 Geographic information Metadata XML schema implementation == ISO 19139:2012 provides the XML implementation schema for ISO 19115 specifying the metadata record format and may be used to describe, validate, and exchange geospatial metadata prepared in XML. The standard is part of the ISO geographic information suite of standards (19100 series), and provides a spatial metadata XML (spatial metadata eXtensible Mark-up Language (smXML)) encoding, an XML schema implementation derived from ISO 19115, Geographic information – Metadata. The metadata includes information about the identification, constraint, extent, quality, spatial and temporal reference, distribution, lineage, and maintenance of the digital geographic data-set. == Metadata directories == Also known as metadata catalogues or data directories. (need discussion of, and subsections on GCMD, FGDC metadata gateway, ASDD, European and Canadian initiatives, etc. etc.) GIS Inventory – National GIS Inventory System which is maintained by the US-based National States Geographic Information Council (NSGIC) as a tool for the entire US GIS Community. Its primary purpose is to track data availability and the status of geographic information system (GIS) implementation in state and local governments to aid the planning and building of statewide spatial data infrastructures (SSDI). The Random Access Metadata for Online Nationwide Assessment (RAMONA) database is a critical component of the GIS Inventory. RAMONA moves its FGDC-compliant metadata (CSDGM Standard) for each data layer to a web folder and a Catalog Service for the Web (CSW) that can be harvested by Federal programs and others. This provides far greater opportunities for discovery of user information. The GIS Inventory website was originally created in 2006 by NSGIC under award NA04NOS4730011 from the Coastal Services Center, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The Department of Homeland Security has been the principal funding source since 2008 and they supported the development of the Version 5 during 2011/2012 under Order Number HSHQDC-11-P-00177. The Federal Emergency Management Agency and National Oceanic and Atmospheric Administration have provided additional resources to maintain and improve the GIS Inventory. Some US Federal programs require submission of CSDGM-Compliant Metadata for data created under grants and contracts that they issue. The GIS Inventory provides a very simple interface to create the required Metadata. GCMD - Global Change Master Directory's goal is to enable users to locate and obtain access to Earth science data sets and services relevant to global change and Earth science research. The GCMD database holds more than 20,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences. ECHO - The EOS Clearing House (ECHO) is a spatial and temporal metadata registry, service registry, and order broker. It allows users to more efficiently search and access data and services through the Reverb Client or Application Programmer Interfaces (APIs). ECHO stores metadata from a variety of science disciplines and domains, totalling over 3400 Earth science data sets and over 118 million granule records. GoGeo - GoGeo is a service run by EDINA (University of Edinburgh) and is supported by Jisc. GoGeo allows users to conduct geographically targeted searches to discover geospatial datasets. GoGeo searches many data portals from the HE and FE community and beyond. GoGeo also allows users to create standards compliant metadata through its Geodoc metadata editor. == Geospatial metadata tools == There are many proprietary GIS or geospatial products that support metadata viewing and editing on GIS resources. For example, ESRI's ArcGIS Desktop, SOCET GXP, Autodesk's AutoCAD Map 3D 2008, Arcitecta's Mediaflux and Intergraph's Geo
XOR swap algorithm
In computer programming, the exclusive or swap (sometimes shortened to XOR swap) is an algorithm that uses the exclusive or bitwise operation to swap the values of two variables without using the temporary variable which is normally required. The algorithm is primarily a novelty and a way of demonstrating properties of the exclusive or operation. It is sometimes discussed as a program optimization, but there are almost no cases where swapping via exclusive or provides benefit over the standard, obvious technique. == The algorithm == Conventional swapping requires the use of a temporary storage variable. Using the XOR swap algorithm, however, no temporary storage is needed. The algorithm is as follows: Since XOR is a commutative operation, either X XOR Y or Y XOR X can be used interchangeably in any of the foregoing three lines. Note that on some architectures the first operand of the XOR instruction specifies the target location at which the result of the operation is stored, preventing this interchangeability. The algorithm typically corresponds to three machine-code instructions, represented by corresponding pseudocode and assembly instructions in the three rows of the following table: In the above System/370 assembly code sample, R1 and R2 are distinct registers, and each XR operation leaves its result in the register named in the first argument. Using x86 assembly, values X and Y are in registers eax and ebx (respectively), and xor places the result of the operation in the first register (Note: x86 supports XCHG instruction so using triple XOR do not make sense on this architecture). In RISC-V assembly, value X and Y are in registers x10 and x11, and xor places the result of the operation in the first operand. However, in the pseudocode or high-level language version or implementation, the algorithm fails if x and y use the same storage location, since the value stored in that location will be zeroed out by the first XOR instruction, and then remain zero; it will not be "swapped with itself". This is not the same as if x and y have the same values. The trouble only comes when x and y use the same storage location, in which case their values must already be equal. That is, if x and y use the same storage location, then the line: sets x to zero (because x = y so X XOR Y is zero) and sets y to zero (since it uses the same storage location), causing x and y to lose their original values. == Proof of correctness == The binary operation XOR over bit strings of length N {\displaystyle N} exhibits the following properties (where ⊕ {\displaystyle \oplus } denotes XOR): L1. Commutativity: A ⊕ B = B ⊕ A {\displaystyle A\oplus B=B\oplus A} L2. Associativity: ( A ⊕ B ) ⊕ C = A ⊕ ( B ⊕ C ) {\displaystyle (A\oplus B)\oplus C=A\oplus (B\oplus C)} L3. Identity exists: there is a bit string, 0, (of length N) such that A ⊕ 0 = A {\displaystyle A\oplus 0=A} for any A {\displaystyle A} L4. Each element is its own inverse: for each A {\displaystyle A} , A ⊕ A = 0 {\displaystyle A\oplus A=0} . Suppose that we have two distinct registers R1 and R2 as in the table below, with initial values A and B respectively. We perform the operations below in sequence, and reduce our results using the properties listed above. === Linear algebra interpretation === As XOR can be interpreted as binary addition and a pair of bits can be interpreted as a vector in a two-dimensional vector space over the field with two elements, the steps in the algorithm can be interpreted as multiplication by 2×2 matrices over the field with two elements. For simplicity, assume initially that x and y are each single bits, not bit vectors. For example, the step: which also has the implicit: corresponds to the matrix ( 1 1 0 1 ) {\displaystyle \left({\begin{smallmatrix}1&1\\0&1\end{smallmatrix}}\right)} as ( 1 1 0 1 ) ( x y ) = ( x + y y ) . {\displaystyle {\begin{pmatrix}1&1\\0&1\end{pmatrix}}{\begin{pmatrix}x\\y\end{pmatrix}}={\begin{pmatrix}x+y\\y\end{pmatrix}}.} The sequence of operations is then expressed as: ( 1 1 0 1 ) ( 1 0 1 1 ) ( 1 1 0 1 ) = ( 0 1 1 0 ) {\displaystyle {\begin{pmatrix}1&1\\0&1\end{pmatrix}}{\begin{pmatrix}1&0\\1&1\end{pmatrix}}{\begin{pmatrix}1&1\\0&1\end{pmatrix}}={\begin{pmatrix}0&1\\1&0\end{pmatrix}}} (working with binary values, so 1 + 1 = 0 {\displaystyle 1+1=0} ), which expresses the elementary matrix of switching two rows (or columns) in terms of the transvections (shears) of adding one element to the other. To generalize to where X and Y are not single bits, but instead bit vectors of length n, these 2×2 matrices are replaced by 2n×2n block matrices such as ( I n I n 0 I n ) . {\displaystyle \left({\begin{smallmatrix}I_{n}&I_{n}\\0&I_{n}\end{smallmatrix}}\right).} These matrices are operating on values, not on variables (with storage locations), hence this interpretation abstracts away from issues of storage location and the problem of both variables sharing the same storage location. == Code example == A C function that implements the XOR swap algorithm: The code first checks if the addresses are distinct and uses a guard clause to exit the function early if they are equal. Without that check, if they were equal, the algorithm would fold to a triple x ^= x resulting in zero. == Reasons for avoidance in practice == On modern CPU architectures, the XOR technique can be slower than using a temporary variable to do swapping. At least on recent x86 CPUs, both by AMD and Intel, moving between registers regularly incurs zero latency. (This is called MOV-elimination.) Even if there is not any architectural register available to use, the XCHG instruction will be at least as fast as the three XORs taken together. Another reason is that modern CPUs strive to execute instructions in parallel via instruction pipelines. In the XOR technique, the inputs to each operation depend on the results of the previous operation, so they must be executed in strictly sequential order, negating any benefits of instruction-level parallelism. === Aliasing === The XOR swap is also complicated in practice by aliasing. If an attempt is made to XOR-swap the contents of some location with itself, the result is that the location is zeroed out and its value lost. Therefore, XOR swapping must not be used blindly in a high-level language if aliasing is possible. This issue does not apply if the technique is used in assembly to swap the contents of two registers. Similar problems occur with call by name, as in Jensen's Device, where swapping i and A[i] via a temporary variable yields incorrect results due to the arguments being related: swapping via temp = i; i = A[i]; A[i] = temp changes the value for i in the second statement, which then results in the incorrect i value for A[i] in the third statement. == Variations == The underlying principle of the XOR swap algorithm can be applied to any operation meeting criteria L1 through L4 above. Replacing XOR by addition and subtraction gives various slightly different, but largely equivalent, formulations. For example: Unlike the XOR swap, this variation requires that the underlying processor or programming language uses a method such as modular arithmetic or bignums to guarantee that the computation of X + Y cannot cause an error due to integer overflow. Therefore, it is seen even more rarely in practice than the XOR swap. However, the implementation of AddSwap above in the C programming language always works even in case of integer overflow, since, according to the C standard, addition and subtraction of unsigned integers follow the rules of modular arithmetic, i. e. are done in the cyclic group Z / 2 s Z {\displaystyle \mathbb {Z} /2^{s}\mathbb {Z} } where s {\displaystyle s} is the number of bits of unsigned int. Indeed, the correctness of the algorithm follows from the fact that the formulas ( x + y ) − y = x {\displaystyle (x+y)-y=x} and ( x + y ) − ( ( x + y ) − y ) = y {\displaystyle (x+y)-((x+y)-y)=y} hold in any abelian group. This generalizes the proof for the XOR swap algorithm: XOR is both the addition and subtraction in the abelian group ( Z / 2 Z ) s {\displaystyle (\mathbb {Z} /2\mathbb {Z} )^{s}} (which is the direct sum of s copies of Z / 2 Z {\displaystyle \mathbb {Z} /2\mathbb {Z} } ). This doesn't hold when dealing with the signed int type (the default for int). Signed integer overflow is an undefined behavior in C and thus modular arithmetic is not guaranteed by the standard, which may lead to incorrect results. The sequence of operations in AddSwap can be expressed via matrix multiplication as: ( 1 − 1 0 1 ) ( 1 0 1 − 1 ) ( 1 1 0 1 ) = ( 0 1 1 0 ) {\displaystyle {\begin{pmatrix}1&-1\\0&1\end{pmatrix}}{\begin{pmatrix}1&0\\1&-1\end{pmatrix}}{\begin{pmatrix}1&1\\0&1\end{pmatrix}}={\begin{pmatrix}0&1\\1&0\end{pmatrix}}} == Application to register allocation == On architectures lacking a dedicated swap instruction, because it avoids the extra temporary register, the XOR swap algorithm is required for optimal register allocatio
Moral outsourcing
Moral outsourcing is the placing of responsibility for ethical decision-making onto external entities, often algorithms. The term is often used in discussions of computer science and algorithmic fairness, but it can apply to any situation in which one appeals to outside agents in order to absolve themselves of responsibility for their actions. In this context, moral outsourcing specifically refers to the tendency of society to blame technology, rather than its creators or users, for any harm it may cause. == Definition == The term "moral outsourcing" was first coined by Dr. Rumman Chowdhury, a data scientist concerned with the overlap between artificial intelligence and social issues. Chowdhury used the term to describe looming fears of a so-called “Fourth Industrial Revolution” following the rise of artificial intelligence. Moral outsourcing is often applied by technologists to shrink away from their part in building offensive products. In her TED Talk, Chowdhury gives the example of a creator excusing their work by saying they were simply doing their job. This is a case of moral outsourcing and not taking ownership for the consequences of creation. When it comes to AI, moral outsourcing allows for creators to decide when the machine is human and when it is a computer - shifting the blame and responsibility of moral plights off of the technologists and onto the technology. Conversations around AI and bias and its impacts require accountability to bring change. It is difficult to address these biased systems if their creators use moral outsourcing to avoid taking any responsibility for the issue. One example of moral outsourcing is the anger that is directed at machines for “taking jobs away from humans” rather than companies for employing that technology and jeopardizing jobs in the first place. The term "moral outsourcing" refers to the concept of outsourcing, or enlisting an external operation to complete specific work for another organization. In the case of moral outsourcing, the work of resolving moral dilemmas or making choices according to an ethical code is supposed to be conducted by another entity. == Real-world applications == In the medical field, AI is increasingly involved in decision-making processes about which patients to treat, and how to treat them. The responsibility of the doctor to make informed decisions about what is best for their patients is outsourced to an algorithm. Sympathy is also noted to be an important part of medical practice; an aspect that artificial intelligence, glaringly, is missing. This form of moral outsourcing is a major concern in the medical community. Another field of technology in which moral outsourcing is frequently brought up is autonomous vehicles. California Polytechnic State University professor Keith Abney proposed an example scenario: "Suppose we have some [troublemaking] teenagers, and they see an autonomous vehicle, they drive right at it. They know the autonomous vehicle will swerve off the road and go off a cliff, but should it?" The decision of whether to sacrifice the autonomous vehicle (and any passengers inside) or the vehicle coming at it will be written into the algorithms defining the car's behavior. In the case of moral outsourcing, the responsibility of any damage caused by an accident may be attributed to the autonomous vehicle itself, rather than the creators who wrote the protocol the vehicle will use to "decide" what to do. Moral outsourcing is also used to delegate the consequences of predictive policing algorithms to technology, rather than the creators or the police. There are many ethical concerns with predictive policing due to the fact that it results in the over-policing of low income and minority communities. In the context of moral outsourcing, the positive feedback loop of sending disproportionate police forces into minority communities is attributed to the algorithm and the data being fed into this system--rather than the users and creators of the predictive policing technology. == Outside of technology == === Religion === Moral outsourcing is also commonly seen in appeals to religion to justify discrimination or harm. In his book What It Means to be Moral, sociologist Phil Zuckerman contradicts the popular religious notion that morality comes from God. Religion is oftentimes cited as a foundation for a moral stance without any tangible relation between the religious beliefs and personal stance. In these cases, religious individuals will "outsource" their personal beliefs and opinions by claiming that they are a result of their religious identification. This is seen where religion is cited as a factor for political beliefs, medical beliefs, and in extreme cases an excuse for violence. === Manufacturing === Moral outsourcing can also be seen in the business world in terms of manufacturing goods and avoiding environmental responsibility. Some companies in the United States will move their production process to foreign countries with more relaxed environmental policies to avoid the pollution laws that exist in the US. A study by the Harvard Business Review found that "in countries with tight environmental regulation, companies have 29% lower domestic emissions on average. On the other hand, such a tightening in regulation results in 43% higher emissions abroad." The consequences of higher pollution rates are then attributed to the loose regulations in these countries, rather than on the companies themselves who purposefully moved into these areas to avoid strict pollution policy.
Aidoc
Aidoc Medical is an Israeli technology company that develops computer-aided simple triage and notification systems. Aidoc has obtained U.S. Food and Drug Administration and CE mark approval for its stroke, pulmonary embolism, cervical fracture, intracranial hemorrhage, intra-abdominal free gas, and incidental pulmonary embolism algorithms. Aidoc algorithms are in use in more than 900 hospitals and imaging centers, including Montefiore Nyack Hospital, LifeBridge Health, LucidHealth, Yale New Haven Hospital, Cedars-Sinai Medical Center, University of Rochester Medical Center, and Sheba Medical Center. == History == Aidoc was founded in 2016 by Elad Walach as the CEO, Michael Braginsky as the CTO and Guy Reiner as the VP. In April 2017, the company raised $7M, led by TLV Partners, and in April 2019, the company raised another $27M, led by Square Peg capital. There have been several additional rounds of funding as well, bringing Aidoc's total investment to $370M as of July 2025. In August 2018, Aidoc gained FDA clearance for its intracranial hemorrhage system, and in May 2019 it received clearance for the pulmonary embolism system. In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations obtained FDA clearance. In October 2024, it was reported that Aidoc is working with NVIDIA to develop a framework for deployment and integration of artificial intelligence tools in healthcare. The Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE) is a guideline to facilitate AI adoption in the healthcare industry. == Products and market == Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid for a broad set of pathologies. The company offers an array of algorithms that span across the body, including for intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism, and more. It developed "Always-on AI", a term coined by Elad Walach that refers to a type of artificial intelligence that is "Always-on—constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings, and sparing radiologists from "drowning" in vast amounts of irrelevant data. Aidoc's solutions cover medical conditions prevalent in all settings (ED/inpatient/outpatient), including level 1 trauma centers, outpatient imaging centers, teleradiology groups and, are set up in over 200 medical centers worldwide. Notable customers include the University of Rochester Medical Center and Global Diagnostics Australia. Aidoc announced in 2024 that its new Clinical AI Reasoning Engine (CARE1) had been submitted for FDA approval. In September 2025 Aidoc received a "Breakthrough Device Designation" from the FDA for a new multi-triage solution that spans numerous acute findings in CT scans. Aidoc's CARE1 foundation model was the basis of the workflow on which the designation was made, enabling simultaneous coverage of multiple pathologies. This new designation allows parallel FDA review of multiple indications under a single submission. In April 2026, Aidoc raised million in a Series E funding round led by Growth Equity at Goldman Sachs Alternatives, with participation from General Catalyst and NVentures. The financing brought the company's total funding to over million. == Clinical Research == A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity. Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved 93% sensitivity (91/98, 7 false negatives) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Additional clinical research on Aidoc's Intracranial hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain. The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm.