The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant. In this framework the input is a number of samples drawn from a distribution that belongs to a specific class of distributions. The goal is to find an efficient algorithm that, based on these samples, determines with high probability the distribution from which the samples have been drawn. Because of its generality, this framework has been used in a large variety of different fields like machine learning, approximation algorithms, applied probability and statistics. This article explains the basic definitions, tools and results in this framework from the theory of computation point of view. == Definitions == Let X {\displaystyle \textstyle X} be the support of the distributions of interest. As in the original work of Kearns et al. if X {\displaystyle \textstyle X} is finite it can be assumed without loss of generality that X = { 0 , 1 } n {\displaystyle \textstyle X=\{0,1\}^{n}} where n {\displaystyle \textstyle n} is the number of bits that have to be used in order to represent any y ∈ X {\displaystyle \textstyle y\in X} . We focus in probability distributions over X {\displaystyle \textstyle X} . There are two possible representations of a probability distribution D {\displaystyle \textstyle D} over X {\displaystyle \textstyle X} . probability distribution function (or evaluator) an evaluator E D {\displaystyle \textstyle E_{D}} for D {\displaystyle \textstyle D} takes as input any y ∈ X {\displaystyle \textstyle y\in X} and outputs a real number E D [ y ] {\displaystyle \textstyle E_{D}[y]} which denotes the probability that of y {\displaystyle \textstyle y} according to D {\displaystyle \textstyle D} , i.e. E D [ y ] = Pr [ Y = y ] {\displaystyle \textstyle E_{D}[y]=\Pr[Y=y]} if Y ∼ D {\displaystyle \textstyle Y\sim D} . generator a generator G D {\displaystyle \textstyle G_{D}} for D {\displaystyle \textstyle D} takes as input a string of truly random bits y {\displaystyle \textstyle y} and outputs G D [ y ] ∈ X {\displaystyle \textstyle G_{D}[y]\in X} according to the distribution D {\displaystyle \textstyle D} . Generator can be interpreted as a routine that simulates sampling from the distribution D {\displaystyle \textstyle D} given a sequence of fair coin tosses. A distribution D {\displaystyle \textstyle D} is called to have a polynomial generator (respectively evaluator) if its generator (respectively evaluator) exists and can be computed in polynomial time. Let C X {\displaystyle \textstyle C_{X}} a class of distribution over X, that is C X {\displaystyle \textstyle C_{X}} is a set such that every D ∈ C X {\displaystyle \textstyle D\in C_{X}} is a probability distribution with support X {\displaystyle \textstyle X} . The C X {\displaystyle \textstyle C_{X}} can also be written as C {\displaystyle \textstyle C} for simplicity. In order to evaluate learnability, it is necessary to have a way to measure how well an approximated distribution D ′ {\displaystyle \textstyle D'} fits the sampled distribution D {\displaystyle \textstyle D} . There are several ways to measure the divergence between two distributions. Three common possibilities are Kullback–Leibler divergence Total variation distance of probability measures Kolmogorov distance Total variation and Kolmogorov distance are true metrics, while KL divergence is not (it lacks symmetry). These measures are ordered by convergence strength: closeness in KL divergence implies closeness in total variation (via Pinsker's inequality), which in turn implies closeness in Kolmogorov distance. Therefore, a learnability result proven under KL divergence automatically holds under the weaker measures, but not vice versa. Since certain measures may be more appropriate in specific applications, we will use d ( D , D ′ ) {\displaystyle \textstyle d(D,D')} to denote a selected divergence between the distribution D {\displaystyle \textstyle D} and the distribution D ′ {\displaystyle \textstyle D'} . The basic input that we use in order to learn a distribution is a number of samples drawn by this distribution. For the computational point of view the assumption is that such a sample is given in a constant amount of time. So it's like having access to an oracle G E N ( D ) {\displaystyle \textstyle GEN(D)} that returns a sample from the distribution D {\displaystyle \textstyle D} . Sometimes the interest is, apart from measuring the time complexity, to measure the number of samples that have to be used in order to learn a specific distribution D {\displaystyle \textstyle D} in class of distributions C {\displaystyle \textstyle C} . This quantity is called sample complexity of the learning algorithm. In order for the problem of distribution learning to be more clear consider the problem of supervised learning as defined in. In this framework of statistical learning theory a training set S = { ( x 1 , y 1 ) , … , ( x n , y n ) } {\displaystyle \textstyle S=\{(x_{1},y_{1}),\dots ,(x_{n},y_{n})\}} and the goal is to find a target function f : X → Y {\displaystyle \textstyle f:X\rightarrow Y} that minimizes some loss function, e.g. the square loss function. More formally f = arg min g ∫ V ( y , g ( x ) ) d ρ ( x , y ) {\displaystyle f=\arg \min _{g}\int V(y,g(x))d\rho (x,y)} , where V ( ⋅ , ⋅ ) {\displaystyle V(\cdot ,\cdot )} is the loss function, e.g. V ( y , z ) = ( y − z ) 2 {\displaystyle V(y,z)=(y-z)^{2}} and ρ ( x , y ) {\displaystyle \rho (x,y)} the probability distribution according to which the elements of the training set are sampled. If the conditional probability distribution ρ x ( y ) {\displaystyle \rho _{x}(y)} is known then the target function has the closed form f ( x ) = ∫ y y d ρ x ( y ) {\displaystyle f(x)=\int _{y}yd\rho _{x}(y)} . So the set S {\displaystyle S} is a set of samples from the probability distribution ρ ( x , y ) {\displaystyle \rho (x,y)} . Now the goal of distributional learning theory if to find ρ {\displaystyle \rho } given S {\displaystyle S} which can be used to find the target function f {\displaystyle f} . Definition of learnability A class of distributions C {\displaystyle \textstyle C} is called efficiently learnable if for every ϵ > 0 {\displaystyle \textstyle \epsilon >0} and 0 < δ ≤ 1 {\displaystyle \textstyle 0<\delta \leq 1} given access to G E N ( D ) {\displaystyle \textstyle GEN(D)} for an unknown distribution D ∈ C {\displaystyle \textstyle D\in C} , there exists a polynomial time algorithm A {\displaystyle \textstyle A} , called learning algorithm of C {\displaystyle \textstyle C} , that outputs a generator or an evaluator of a distribution D ′ {\displaystyle \textstyle D'} such that Pr [ d ( D , D ′ ) ≤ ϵ ] ≥ 1 − δ {\displaystyle \Pr[d(D,D')\leq \epsilon ]\geq 1-\delta } If we know that D ′ ∈ C {\displaystyle \textstyle D'\in C} then A {\displaystyle \textstyle A} is called proper learning algorithm, otherwise is called improper learning algorithm. In some settings the class of distributions C {\displaystyle \textstyle C} is a class with well known distributions which can be described by a set of parameters. For instance C {\displaystyle \textstyle C} could be the class of all the Gaussian distributions N ( μ , σ 2 ) {\displaystyle \textstyle N(\mu ,\sigma ^{2})} . In this case the algorithm A {\displaystyle \textstyle A} should be able to estimate the parameters μ , σ {\displaystyle \textstyle \mu ,\sigma } . In this case A {\displaystyle \textstyle A} is called parameter learning algorithm. Obviously the parameter learning for simple distributions is a very well studied field that is called statistical estimation and there is a very long bibliography on different estimators for different kinds of simple known distributions. But distributions learning theory deals with learning class of distributions that have more complicated description. == First results == In their seminal work, Kearns et al. deal with the case where A {\displaystyle \textstyle A} is described in term of a finite polynomial sized circuit and they proved the following for some specific classes of distribution. O R {\displaystyle \textstyle OR} gate distributions for this kind of distributions there is no polynomial-sized evaluator, unless # P ⊆ P / poly {\displaystyle \textstyle \#P\subseteq P/{\text{poly}}} . On the other hand, this class is efficiently learnable with generator. Parity gate distributions this class is efficiently learnable with both generator and evaluator. Mixtures of Hamming Balls this class is efficiently learnable with both generator and evaluator. Probabilistic Finite Automata this class is not efficiently learnable with evaluator under the Noisy Parity Assumption which is an impossibility assumption in the PAC learning fram
Straight-Through Quality
Straight-Through Quality (STQ) are approaches and outputs of test automation that have quality and deliver business benefit. STQ takes its name from the business concept of straight-through processing (STP). Also acting as a tool and enabler for STP. Traditional techniques for testing and delivery have often required a great deal of manual support and intervention. These approaches are subject to human error, cost of delay and lack of reuse. These also have the negative side-effect of being unable to deliver 'fail-fast' approaches, which have proven popular with Agile practitioners. Previous traditional approaches have been typically expensive where whole silo'ed departments are created within commercial companies to deliver Quality and Deployment alone. Thus STQ as an approach hopes to resolve this problem. == Examples == Tangible examples of STQ approaches in the software industry are present and often known as continuous integration (CI) and continuous delivery (CD). These combined can ensure that software delivery is integrated, automatically tested and ready for automatic delivery at any time. Together CI/CD can enable STQ which can be used as Business output terminology for business users who do not understand the technical complexities of CI/CD.
Infer.NET
Infer.NET is a free and open source .NET software library for machine learning. It supports running Bayesian inference in graphical models and can also be used for probabilistic programming. == Overview == Infer.NET follows a model-based approach and is used to solve different kinds of machine learning problems including standard problems like classification, recommendation or clustering, customized solutions and domain-specific problems. The framework is used in various different domains such as bioinformatics, epidemiology, computer vision, and information retrieval. Development of the framework was started by a team at Microsoft's research centre in Cambridge, UK in 2004. It was first released for academic use in 2008 and later open sourced in 2018. In 2013, Microsoft was awarded the USPTO's Patents for Humanity Award in Information Technology category for Infer.NET and the work in advanced machine learning techniques. Infer.NET is used internally at Microsoft as the machine learning engine in some of their products such as Office, Azure, and Xbox. The source code is licensed under MIT License and available on GitHub. It is also available as NuGet package.
Ian Goodfellow
Ian J. Goodfellow (born 1987) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He is a research scientist at Google DeepMind, was previously employed as a research scientist at Google Brain and director of machine learning at Apple as well as one of the first employees at OpenAI, and has made several important contributions to the field of deep learning, including the invention of the generative adversarial network (GAN). Goodfellow co-wrote, as the first author, the textbook Deep Learning (2016) and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach (used in more than 1,500 universities in 135 countries). == Education == Goodfellow obtained his BSc and MSc in computer science from Stanford University under the supervision of Andrew Ng, and his PhD in machine learning from the Université de Montréal in February 2015, under the supervision of Yoshua Bengio and Aaron Courville. Goodfellow's thesis is titled Deep learning of representations and its application to computer vision. == Career == After graduation, Goodfellow joined Google as part of the Google Brain research team. In March 2016, he left Google to join the newly founded OpenAI research laboratory. 11 months later, in March 2017, Goodfellow returned to Google Research, but left again in 2019. In 2019, Goodfellow joined Apple as director of machine learning in the Special Projects Group. He resigned from Apple in April 2022 to protest Apple's plan to require in-person work for its employees. Shortly after, Goodfellow then joined Google DeepMind as a research scientist. In 2025, Goodfellow left Google. As of July 2026, based on information on Goodfellow's LinkedIn profile, he is co-founding a startup company. == Research == Goodfellow is best known for inventing generative adversarial networks (GANs), using deep learning to generate images. This approach uses two neural networks to competitively improve an image's quality. A “generator” network creates a synthetic image based on an initial set of images such as a collection of faces. A “discriminator” network tries to determine whether images are authentic or created by the generator. The generate-detect cycle is repeated. For each iteration, the generator and the discriminator use the other's feedback to improve or detect the generated images, until the discriminator can no longer distinguish between generated and authentic images. However, GANs have also been used to create deepfakes. At Google, Goodfellow developed a system enabling Google Maps to automatically transcribe addresses from photos taken by Street View cars and demonstrated security vulnerabilities of machine learning systems. == Recognition == In 2017, Goodfellow was cited in MIT Technology Review's 35 Innovators Under 35. In 2019, he was included in Foreign Policy's list of 100 Global Thinkers.
RealSense
RealSense is an American technology company that develops depth cameras and computer-vision systems used in robotics, access control, industrial automation and healthcare. The company’s stereoscopic 3D cameras and software are marketed as a perception platform for “physical AI”, particularly for humanoid robots and autonomous mobile robots (AMRs). RealSense was incubated for more than a decade inside Intel’s perceptual computing and depth-sensing group before being spun out as an independent company in July 2025 with a US$50 million Series A round backed by a semiconductor-focused private equity firm and strategic investors including Intel Capital and the MediaTek Innovation Fund. Following the spin-out, RealSense announced a strategic collaboration with Nvidia to integrate its AI depth cameras with the Nvidia Jetson Thor robotics platform, the Isaac Sim simulation environment and the Holoscan Sensor Bridge for low-latency sensor fusion. In November 2025, Swiss access-solutions provider dormakaba acquired a minority stake in RealSense and formed a partnership to develop AI-powered biometric access-control and security systems for data centres, airports and other critical infrastructure. == History == === Origins in Intel Perceptual Computing === Intel began developing depth-sensing and perceptual-computing technologies in the early 2010s under the Perceptual Computing brand, with research spanning gesture control, facial recognition and eye-tracking systems. The work led to a series of 3D cameras and developer challenge programmes intended to stimulate software ecosystems for natural-user interfaces. In 2014 Intel rebranded the effort as Intel RealSense, positioning the technology as a family of depth cameras and vision processors for PCs, mobile devices and embedded systems. Early devices such as the F200 and R200 were integrated into laptops and tablets from OEMs including Asus, HP, Dell, Lenovo and Acer, and were also sold as standalone webcams by partners such as Razer and Creative. === Refocus on robotics and near-closure === By the late 2010s Intel had steered RealSense away from mainstream PC peripherals toward robotics, industrial and embedded applications, adding stereo and lidar-based depth cameras to the portfolio. In August 2021, trade publication CRN reported that Intel planned to wind down the RealSense business as part of a broader restructuring, raising questions about the future of the product line. Despite that announcement, Intel continued to invest in new custom silicon for depth cameras, and RealSense remained widely used in mobile robots and automation projects. === Spin-out as RealSense Inc. (2025) === On 11 July 2025, Intel completed the spin-out of its RealSense 3D-camera business into a new privately held company, RealSense Inc., and the new entity announced a US$50 million Series A funding round. The round was led by a semiconductor-focused private equity investor with participation from Intel Capital, MediaTek Innovation Fund and other strategics. Independent coverage described RealSense as serving more than 3,000 active customers and supplying depth cameras to a large share of global AMR and humanoid robot platforms. The company stated that it would continue to support the existing Intel RealSense product roadmap while accelerating development of AI-enabled cameras and perception software. === Strategic partnerships and investments === In October 2025 RealSense and Nvidia announced a strategic collaboration centered on integrating RealSense AI depth cameras with Nvidia’s Jetson Thor robotics compute modules, the Isaac Sim simulation environment and the Holoscan Sensor Bridge for multi-sensor streaming. The collaboration is positioned as enabling “physical AI” workloads such as whole-body humanoid control, real-time mapping and safety-critical human–robot interaction. On 19 November 2025, dormakaba announced that it had acquired a minority stake in RealSense and entered into a partnership to co-develop intelligent access-control solutions, including biometric gates for airports and enterprise facilities. The partnership aims to combine RealSense’s depth and facial-authentication technology with dormakaba’s installed base of sensors, doors and turnstiles. == Products == === Depth-camera families === RealSense’s products are sold as modular components (depth modules, vision processors and complete cameras) and as integrated systems with on-device AI. The company continues to offer and support the Intel RealSense D400 family of active-stereo depth cameras (including the D415, D435 and D455), which are widely used in robotics and automation. These devices combine a RealSense Vision Processor from the D4 family with dual infrared imagers and, on some models, an RGB camera. Earlier generations of Intel RealSense cameras, including the F200, R200, SR300 and the L515 lidar camera, remain in use in niche and legacy applications but are no longer the focus of the independent company’s roadmap. === D555 PoE depth camera === The first new hardware platform announced after the spin-out was the RealSense Depth Camera D555, a ruggedised stereo-depth device aimed at industrial and robotics deployments. The D555 uses the longer-range D450 optical module with a global shutter and integrates RealSense’s Vision SoC V5, a new generation of vision processor optimised for neural-network inference and depth computation. Key features highlighted in technical coverage include: Power over Ethernet (PoE), allowing power and data to be delivered over a single cable and supporting both RJ45 and ruggedised M12 connections; an IP-rated enclosure designed for harsh indoor and outdoor environments; a built-in inertial measurement unit (IMU) to support simultaneous localisation and mapping (SLAM) and motion tracking; native support for ROS 2 and integration with the open-source RealSense SDK. According to independent reporting, the D555 is used in AI-enabled embedded-vision applications in mobile robots and fixed industrial systems, and was among the first RealSense products to be tightly integrated with Nvidia’s Jetson Thor and Holoscan platforms for low-latency sensor fusion. === Software and SDK === RealSense cameras are supported by a cross-platform, open-source software stack historically branded as Intel RealSense SDK 2.0. The SDK provides device drivers, depth and point-cloud processing, tracking and calibration tools, and bindings for languages such as C++, Python and C#. The independent company has continued to maintain and extend the SDK for new hardware, including D555 and other Vision SoC V5-based devices, and publishes reference integrations for ROS 2 and industrial-automation frameworks. === Biometrics and access-control products === In addition to general-purpose depth cameras, RealSense offers facial-authentication hardware and software, commonly referred to as RealSense ID, for biometric access control and identity verification. These products combine an active depth sensor with a dedicated neural-network pipeline running on embedded processors, aimed at applications such as secure doors, turnstiles and kiosks. Use-case material published by partners describes deployments of RealSense-based biometric readers in school lunch programmes, agricultural biosecurity checkpoints and enterprise facilities. The dormakaba partnership announced in 2025 extends this portfolio to integrated biometric gates and sensor-equipped doors in airports and data centres. == Applications == === Robotics and automation === RealSense depth cameras are used in autonomous mobile robots, humanoid robots, drones and industrial automation systems for tasks such as obstacle avoidance, navigation and manipulation. Reuters reported in 2025 that RealSense cameras were embedded in around 60 percent of the world’s AMRs and humanoid robots, citing customers including Unitree Robotics and ANYbotics. Developers and integrators use RealSense systems with platforms such as Nvidia Jetson, ROS and proprietary motion-planning stacks. === Biometrics and security === RealSense technology is also applied in biometric access control and surveillance, where depth and infrared imaging are used to improve anti-spoofing performance for facial recognition. The dormakaba investment and collaboration is aimed at integrating these capabilities into boarding gates, staff entrances and secure facilities, with RealSense providing perception hardware and algorithms and dormakaba providing access-control infrastructure and global distribution. == Reception == Early coverage of Intel RealSense for consumer PCs noted that the technology’s impact would depend on the availability of compelling software and use cases for depth-sensing cameras. Later reporting on the spin-out has characterised the new company as part of a broader wave of investment in robotics and physical AI, with some analysts suggesting that RealSense’s installed base and patent portfolio give it an advantage as dep
Level-set method
The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes. LSM can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. LSM makes it easier to perform computations on shapes with sharp corners and shapes that change topology (such as by splitting in two or developing holes). These characteristics make LSM effective for modeling objects that vary in time, such as an airbag inflating or a drop of oil floating in water. == Overview == The figure on the right illustrates several ideas about LSM. In the upper left corner is a bounded region with a well-behaved boundary. Below it, the red surface is the graph of a level set function φ {\displaystyle \varphi } determining this shape, and the flat blue region represents the X-Y plane. The boundary of the shape is then the zero-level set of φ {\displaystyle \varphi } , while the shape itself is the set of points in the plane for which φ {\displaystyle \varphi } is positive (interior of the shape) or zero (at the boundary). In the top row, the shape's topology changes as it is split in two. It is challenging to describe this transformation numerically by parameterizing the boundary of the shape and following its evolution. An algorithm can be used to detect the moment the shape splits in two and then construct parameterizations for the two newly obtained curves. On the bottom row, however, the plane at which the level set function is sampled is translated upwards, on which the shape's change in topology is described. It is less challenging to work with a shape through its level-set function rather than with itself directly, in which a method would need to consider all the possible deformations the shape might undergo. Thus, in two dimensions, the level-set method amounts to representing a closed curve Γ {\displaystyle \Gamma } (such as the shape boundary in our example) using an auxiliary function φ {\displaystyle \varphi } , called the level-set function. The curve Γ {\displaystyle \Gamma } is represented as the zero-level set of φ {\displaystyle \varphi } by Γ = { ( x , y ) ∣ φ ( x , y ) = 0 } , {\displaystyle \Gamma =\{(x,y)\mid \varphi (x,y)=0\},} and the level-set method manipulates Γ {\displaystyle \Gamma } implicitly through the function φ {\displaystyle \varphi } . This function φ {\displaystyle \varphi } is assumed to take positive values inside the region delimited by the curve Γ {\displaystyle \Gamma } and negative values outside. == The level-set equation == If the curve Γ {\displaystyle \Gamma } moves in the normal direction with a speed v {\displaystyle v} , then by chain rule and implicit differentiation, it can be determined that the level-set function φ {\displaystyle \varphi } satisfies the level-set equation ∂ φ ∂ t = v | ∇ φ | . {\displaystyle {\frac {\partial \varphi }{\partial t}}=v|\nabla \varphi |.} Here, | ⋅ | {\displaystyle |\cdot |} is the Euclidean norm (denoted customarily by single bars in partial differential equations), and t {\displaystyle t} is time. This is a partial differential equation, in particular a Hamilton–Jacobi equation, and can be solved numerically, for example, by using finite differences on a Cartesian grid. However, the numerical solution of the level set equation may require advanced techniques. Simple finite difference methods fail quickly. Upwinding methods such as the Godunov method are considered better; however, the level set method does not guarantee preservation of the volume and shape of the set level in an advection field that maintains shape and size, for example, a uniform or rotational velocity field. Instead, the shape of the level set may become distorted, and the level set may disappear over a few time steps. Therefore, high-order finite difference schemes, such as high-order essentially non-oscillatory (ENO) schemes, are often required, and even then, the feasibility of long-term simulations is questionable. More advanced methods have been developed to overcome this; for example, combinations of the leveling method with tracking marker particles suggested by the velocity field. == Example == Consider a unit circle in R 2 {\textstyle \mathbb {R} ^{2}} , shrinking in on itself at a constant rate, i.e. each point on the boundary of the circle moves along its inwards pointing normally at some fixed speed. The circle will shrink and eventually collapse down to a point. If an initial distance field is constructed (i.e. a function whose value is the signed Euclidean distance to the boundary, positive interior, negative exterior) on the initial circle, the normalized gradient of this field will be the circle normal. If the field has a constant value subtracted from it in time, the zero level (which was the initial boundary) of the new fields will also be circular and will similarly collapse to a point. This is due to this being effectively the temporal integration of the Eikonal equation with a fixed front velocity. == Applications == In mathematical modeling of combustion, LSM is used to describe the instantaneous flame surface, known as the G equation. Level-set data structures have been developed to facilitate the use of the level-set method in computer applications. Computational fluid dynamics Trajectory planning Optimization Image processing Computational biophysics Discrete complex dynamics (visualization of the parameter plane and the dynamic plane) == History == The level-set method was developed in 1979 by Alain Dervieux, and subsequently popularized by Stanley Osher and James Sethian. It has since become popular in many disciplines, such as image processing, computer graphics, computational geometry, optimization, computational fluid dynamics, and computational biology.
Semantic similarity network
A semantic similarity network (SSN) is a special form of semantic network. designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances. Bendeck (2004, 2008) introduced the concept of semantic similarity networks (SSN) as the specialization of a semantic network to measure semantic similarity from ontological representations. Implementations include genetic information handling. The concept is formally defined (Bendeck 2008) as a directed graph, with concepts represented as nodes and semantic similarity relations as edges. The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity between concepts. The semantic similarity relationships of the SSN represent several of the general relationship types of the standard Semantic network, reducing the complexity of the (normally, very large) network for calculations of semantics. SSNs define relation types as templates (and taxonomy of relations) for semantic similarity attributes that are common to relations of the same type. SSN representation allows propagation algorithms to faster calculate semantic similarities, including stop conditions within a specified threshold. This reduces the computation time and power required for calculation. A more recent publications on Semantic Matching and Semantic Similarity Networks could be found in (Bendeck 2019). Specific Semantic Similarity Network application on healthcare was presented at the Healthcare information exchange Format (FHIR European Conference) 2019. The latest evolution in Artificial Intelligence (like ChatGPT, based on Large language model), relay strongly on evolutionary computation, the next level will be to include semantic unification (like in the Semantic Networks and this Semantic similarity network) to extend the current models with more powerful understanding tools.