SYMAN is an artificial intelligence technology that uses data from social media profiles to identify trends in the job market. SYMAN is designed to organize actionable data for products and services including recruiting, human capital management, CRM, and marketing. SYMAN was developed with a $21 million series B financing round secured by Identified, which was led by VantagePoint Capital Partners and Capricorn Investment Group.
Couchbase Server
Couchbase Server, originally known as Membase, is a source-available, distributed (shared-nothing architecture) multi-model NoSQL document-oriented database software package optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value, or JSON document access, with low latency and high sustainability throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines. Couchbase Server provided client protocol compatibility with memcached, but added disk persistence, data replication, live cluster reconfiguration, rebalancing and multitenancy with data partitioning. == Product history == Membase was developed by several leaders of the memcached project, who had founded a company, NorthScale, to develop a key-value store with the simplicity, speed, and scalability of memcached, but also the storage, persistence and querying capabilities of a database. The original membase source code was contributed by NorthScale, and project co-sponsors Zynga and Naver Corporation (then known as NHN) to a new project on membase.org in June 2010. On February 8, 2011, the Membase project founders and Membase, Inc. announced a merger with CouchOne (a company with many of the principal players behind CouchDB) with an associated project merger. The merged company was called Couchbase, Inc. In January 2012, Couchbase released Couchbase Server 1.8. In September of 2012, Orbitz said it had changed some of its systems to use Couchbase. In December of 2012, Couchbase Server 2.0 (announced in July 2011) was released and included a new JSON document store, indexing and querying, incremental MapReduce and replication across data centers. == Architecture == Every Couchbase node consists of a data service, index service, query service, and cluster manager component. Starting with the 4.0 release, the three services can be distributed to run on separate nodes of the cluster if needed. In the parlance of Eric Brewer's CAP theorem, Couchbase is normally a CP type system meaning it provides consistency and partition tolerance, or it can be set up as an AP system with multiple clusters. === Cluster manager === The cluster manager supervises the configuration and behavior of all the servers in a Couchbase cluster. It configures and supervises inter-node behavior like managing replication streams and re-balancing operations. It also provides metric aggregation and consensus functions for the cluster, and a RESTful cluster management interface. The cluster manager uses the Erlang programming language and the Open Telecom Platform. ==== Replication and fail-over ==== Data replication within the nodes of a cluster can be controlled with several parameters. In December of 2012, support was added for replication between different data centers. === Data manager === The data manager stores and retrieves documents in response to data operations from applications. It asynchronously writes data to disk after acknowledging to the client. In version 1.7 and later, applications can optionally ensure data is written to more than one server or to disk before acknowledging a write to the client. Parameters define item ages that affect when data is persisted, and how max memory and migration from main-memory to disk is handled. It supports working sets greater than a memory quota per "node" or "bucket". External systems can subscribe to filtered data streams, supporting, for example, full text search indexing, data analytics or archiving. ==== Data format ==== A document is the most basic unit of data manipulation in Couchbase Server. Documents are stored in JSON document format with no predefined schemas. Non-JSON documents can also be stored in Couchbase Server (binary, serialized values, XML, etc.) ==== Object-managed cache ==== Couchbase Server includes a built-in multi-threaded object-managed cache that implements memcached compatible APIs such as get, set, delete, append, prepend etc. ==== Storage engine ==== Couchbase Server has a tail-append storage design that is immune to data corruption, OOM killers or sudden loss of power. Data is written to the data file in an append-only manner, which enables Couchbase to do mostly sequential writes for update, and provide an optimized access patterns for disk I/O. === Performance === A performance benchmark done by Altoros in 2012, compared Couchbase Server with other technologies. Cisco Systems published a benchmark that measured the latency and throughput of Couchbase Server with a mixed workload in 2012. == Licensing and support == Couchbase Server is a packaged version of Couchbase's open source software technology and is available in a community edition without recent bug fixes with an Apache 2.0 license and an edition for commercial use. Couchbase Server builds are available for Ubuntu, Debian, Red Hat, SUSE, Oracle Linux, Microsoft Windows and macOS operating systems. Couchbase has supported software developers' kits for the programming languages .NET, PHP, Ruby, Python, C, Node.js, Java, Go, and Scala. == SQL++ == A query language called SQL++ (formerly called N1QL), is used for manipulating the JSON data in Couchbase, just like SQL manipulates data in RDBMS. It has SELECT, INSERT, UPDATE, DELETE, MERGE statements to operate on JSON data. It was initially announced in March 2015 as "SQL for documents". The SQL++ data model is non-first normal form (N1NF) with support for nested attributes and domain-oriented normalization. The SQL++ data model is also a proper superset and generalization of the relational model. === Example === Like query SELECT FROM `bucket` WHERE email LIKE "%@example.org"; Array query SELECT FROM `bucket` WHERE ANY x IN friends SATISFIES x.name = "Pavan" END; == Couchbase Mobile == Couchbase Mobile / Couchbase Lite is a mobile database providing data replication. Couchbase Lite (originally TouchDB) provides native libraries for offline-first NoSQL databases with built-in peer-to-peer or client-server replication mechanisms. Sync Gateway manages secure access and synchronization of data between Couchbase Lite and Couchbase Server. Couchbase Lite added support for Vector Search in version 3.2, allowing cloud to edge support for vector search in mobile applications. == Uses == Couchbase began as an evolution of Memcached, a high-speed data cache, and can be used as a drop-in replacement for Memcached, providing high availability for memcached application without code changes. Couchbase is used to support applications where a flexible data model, easy scalability, and consistent high performance are required, such as tracking real-time user activity or providing a store of user preferences or online applications. Couchbase Mobile, which stores data locally on devices (usually mobile devices) is used to create “offline-first” applications that can operate when a device is not connected to a network and synchronize with Couchbase Server once a network connection is re-established. The Catalyst Lab at Northwestern University uses Couchbase Mobile to support the Evo application, a healthy lifestyle research program where data is used to help participants improve dietary quality, physical activity, stress, or sleep. Amadeus uses Couchbase with Apache Kafka to support their “open, simple, and agile” strategy to consume and integrate data on loyalty programs for airline and other travel partners. High scalability is needed when disruptive travel events create a need to recognize and compensate high value customers. Starting in 2012, it played a role in LinkedIn's caching systems, including backend caching for recruiter and jobs products, counters for security defense mechanisms, for internal applications. == Alternatives == For caching, Couchbase competes with Memcached and Redis. For document databases, Couchbase competes with other document-oriented database systems. It is commonly compared with MongoDB, Amazon DynamoDB, Oracle RDBMS, DataStax, Google Bigtable, MariaDB, IBM Cloudant, Redis Enterprise, SingleStore, and MarkLogic.
Application permissions
Permissions are a means of controlling and regulating access to specific system- and device-level functions by software. Typically, types of permissions cover functions that may have privacy implications, such as the ability to access a device's hardware features (including the camera and microphone), and personal data (such as storage devices, contacts lists, and the user's present geographical location). Permissions are typically declared in an application's manifest, and certain permissions must be specifically granted at runtime by the user—who may revoke the permission at any time. Permission systems are common on mobile operating systems, where permissions needed by specific apps must be disclosed via the platform's app store. == Mobile devices == On mobile operating systems for smartphones and tablets, typical types of permissions regulate: Access to storage and personal information, such as contacts, calendar appointments, etc. Location tracking. Access to the device's internal camera and/or microphone. Access to biometric sensors, including fingerprint readers and other health sensors.. Internet access. Access to communications interfaces (including their hardware identifiers and signal strength where applicable, and requests to enable them), such as Bluetooth, Wi-Fi, NFC, and others. Making and receiving phone calls. Sending and reading text messages The ability to perform in-app purchases. The ability to "overlay" themselves within other apps. Installing, deleting and otherwise managing applications. Authentication tokens (e.g., OAuth tokens) from web services stored in system storage for sharing between apps. Prior to Android 6.0 "Marshmallow", permissions were automatically granted to apps at runtime, and they were presented upon installation in Google Play Store. Since Marshmallow, certain permissions now require the app to request permission at runtime by the user. These permissions may also be revoked at any time via Android's settings menu. Usage of permissions on Android are sometimes abused by app developers to gather personal information and deliver advertising; in particular, apps for using a phone's camera flash as a flashlight (which have grown largely redundant due to the integration of such functionality at the system level on later versions of Android) have been known to require a large array of unnecessary permissions beyond what is actually needed for the stated functionality. iOS imposes a similar requirement for permissions to be granted at runtime, with particular controls offered for enabling of Bluetooth, Wi-Fi, and location tracking. == WebPermissions == WebPermissions is a permission system for web browsers. When a web application needs some data behind permission, it must request it first. When it does it, a user sees a window asking him to make a choice. The choice is remembered, but can be cleared lately. Currently the following resources are controlled: geolocation desktop notifications service workers sensors audio capturing devices, like sound cards, and their model names and characteristics video capturing devices, like cameras, and their identifiers and characteristics == Analysis == The permission-based access control model assigns access privileges for certain data objects to application. This is a derivative of the discretionary access control model. The access permissions are usually granted in the context of a specific user on a specific device. Permissions are granted permanently with few automatic restrictions. In some cases permissions are implemented in 'all-or-nothing' approach: a user either has to grant all the required permissions to access the application or the user can not access the application. There is still a lack of transparency when the permission is used by a program or application to access the data protected by the permission access control mechanism. Even if a user can revoke a permission, the app can blackmail a user by refusing to operate, for example by just crashing or asking user to grant the permission again in order to access the application. The permission mechanism has been widely criticized by researchers for several reasons, including; Intransparency of personal data extraction and surveillance, including the creation of a false sense of security; End-user fatigue of micro-managing access permissions leading to a fatalistic acceptance of surveillance and intransparency; Massive data extraction and personal surveillance carried out once the permissions are granted. Some apps, such as XPrivacy and Mockdroid spoof data in order to act as a measure for privacy. Further transparency methods include longitudinal behavioural profiling and multiple-source privacy analysis of app data access.
Neural processing unit
A neural processing unit (NPU), also known as an AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. == Use == Their purpose is either to efficiently execute already trained AI models (inference) or to train AI models. NPUs can be more efficient in terms of speed or power consumption. NPU applications include algorithms for robotics, Internet of things, and data-intensive or sensor-driven tasks. They are often manycore or spatial designs and focus on low-precision arithmetic, novel dataflow architectures, or in-memory computing capability. As of 2024, a widely used datacenter-grade AI integrated circuit chip, the Nvidia H100 GPU, contains tens of billions of MOSFETs. === Consumer devices === AI accelerators are used in Apple silicon, Qualcomm, Samsung, Huawei, and Google Tensor smartphone processors. Vision processing units are accelerators specialized for machine vision algorithms such as CNN (convolutional neural networks) and SIFT (scale-invariant feature transform). They are used in devices that need to keep track of objects visually such as AR headsets and drones. It is more recently (circa 2017) added to processors from Apple and (circa 2022) to processors from Intel and AMD. All models of Intel Meteor Lake processors have a built-in versatile processor unit (VPU) for accelerating inference for computer vision and deep learning. On consumer devices, the NPU is intended to be small, power-efficient, but reasonably fast when used to run small models. To do this they are designed to support low-bitwidth operations using data types such as INT4, INT8, FP8, and FP16. A common metric is trillions of operations per second (TOPS). Although TOPS does not explicitly specify the kind of operations, it is typically INT8 additions and multiplications. === Datacenters === Accelerators are used in cloud computing servers: e.g., tensor processing units (TPU) for Google Cloud Platform, and Trainium and Inferentia chips for Amazon Web Services. Many vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design. Since the late 2010s, graphics processing units designed by companies such as Nvidia and AMD often include AI-specific hardware in the form of dedicated functional units for low-precision matrix-multiplication operations. These GPUs are commonly used as AI accelerators, both for training and inference. === Scientific computation === Although NPUs are tailored for low-precision (e.g., FP16, INT8) matrix multiplication operations, they can be used to emulate higher-precision matrix multiplications in scientific computing. As modern GPUs place much focus on making the NPU part fast, using emulated FP64 (Ozaki scheme) on NPUs can potentially outperform native FP64. This has been demonstrated using FP16-emulated FP64 on NVIDIA TITAN RTX and using INT8-emulated FP64 on NVIDIA consumer GPUs and the A100 GPU. Consumer GPUs especially benefited as they have limited FP64 hardware capacity, showing a 6× speedup. Since CUDA Toolkit 13.0 Update 2, cuBLAS automatically uses INT8-emulated FP64 matrix multiplication of the equivalent precision if it is faster than native. This is in addition to the FP16-emulated FP32 feature introduced in version 12.9. == Programming == An operating system or a higher-level library may provide application programming interfaces such as TensorFlow with LiteRT Next (Android), CoreML (iOS, macOS) or DirectML (Windows). Formats such as ONNX are used to represent trained neural networks. Consumer CPU-integrated NPUs are accessible through vendor-specific APIs. AMD (Ryzen AI), Intel (OpenVINO), Apple silicon (CoreML), and Qualcomm (SNPE) each have their own APIs, which can be built upon by a higher-level library. GPUs generally use existing GPGPU pipelines such as CUDA and OpenCL adapted for lower precisions and specialized matrix-multiplication operations. Vulkan is also being used. Custom-built systems such as the Google TPU use private interfaces. There are a large number of separate underlying acceleration APIs and compilers/runtimes in use in the AI field, causing a great increase in software development effort due to the many combinations involved. As of 2025, the open standard organization Khronos Group is pursuing standardization of AI-related interfaces to reduce the amount of work needed. Khronos is working on three separate fronts: expansion of data types and intrinsic operations in OpenCL and Vulkan, inclusion of compute graphs in SPIR-V, and a NNEF/SkriptND file format for describing a neural network.
ChromaDB
Chroma or ChromaDB is open-source data infrastructure tailored to applications with large language models. Its headquarters are in San Francisco. In April 2023, it raised 18 million US dollars as seed funding. ChromaDB has been used in academic studies on artificial intelligence, particularly as part of the tech stack for retrieval-augmented generation.
Photo-consistency
In computer vision, photo-consistency determines whether a given voxel is occupied. A voxel is considered to be photo consistent when its color appears to be similar to all the cameras that can see it. Most voxel coloring or space carving techniques require using photo consistency as a check condition in Image-based modeling and rendering applications. == Usage == 3D Volumetric Reconstruction. Image registration. Multi-view reconstruction.
Topological deep learning
Topological deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids and sequences. However, scientific and real-world data often exhibit more intricate data domains encountered in scientific computations, including point clouds, meshes, time series, scalar fields graphs, or general topological spaces like simplicial complexes and CW complexes. TDL addresses this by incorporating topological concepts to process data with higher-order relationships, such as interactions among multiple entities and complex hierarchies. This approach leverages structures like simplicial complexes and hypergraphs to capture global dependencies and qualitative spatial properties, offering a more nuanced representation of data. TDL also encompasses methods from computational and algebraic topology that permit studying properties of neural networks and their training process, such as their predictive performance or generalization properties. The mathematical foundations of TDL are algebraic topology, differential topology, and geometric topology. Therefore, TDL can be generalized for data on differentiable manifolds, knots, links, tangles, curves, etc. == History and motivation == Traditional techniques from deep learning often operate under the assumption that a dataset is residing in a highly-structured space (like images, where convolutional neural networks exhibit outstanding performance over alternative methods) or a Euclidean space. The prevalence of new types of data, in particular graphs, meshes, and molecules, resulted in the development of new techniques, culminating in the field of geometric deep learning, which originally proposed a signal-processing perspective for treating such data types. While originally confined to graphs, where connectivity is defined based on nodes and edges, follow-up work extended concepts to a larger variety of data types, including simplicial complexes and CW complexes, with recent work proposing a unified perspective of message-passing on general combinatorial complexes. An independent perspective on different types of data originated from topological data analysis, which proposed a new framework for describing structural information of data, i.e., their "shape," that is inherently aware of multiple scales in data, ranging from local information to global information. While at first restricted to smaller datasets, subsequent work developed new descriptors that efficiently summarized topological information of datasets to make them available for traditional machine-learning techniques, such as support vector machines or random forests. Such descriptors ranged from new techniques for feature engineering over new ways of providing suitable coordinates for topological descriptors, or the creation of more efficient dissimilarity measures. Contemporary research in this field is largely concerned with either integrating information about the underlying data topology into existing deep-learning models or obtaining novel ways of training on topological domains. == Learning on topological spaces == One of the core concepts in topological deep learning is considering the domain upon which this data is defined and supported. In case of Euclidean data, such as images, this domain is a grid, upon which the pixel value of the image is supported. In a more general setting this domain might be a topological domain. Studying and developing deep learning models that are supported ln topological domains constitute the essence of topological deep learning. Next, we introduce the most common topological domains that are encountered in a deep learning setting. These domains include, but not limited to, graphs, simplicial complexes, cell complexes, combinatorial complexes and hypergraphs. Given a finite set S of abstract entities, a neighborhood function N {\displaystyle {\mathcal {N}}} on S is an assignment that attach to every point x {\displaystyle x} in S a subset of S or a relation. Such a function can be induced by equipping S with an auxiliary structure. Edges provide one way of defining relations among the entities of S. More specifically, edges in a graph allow one to define the notion of neighborhood using, for instance, the one hop neighborhood notion. Edges however, limited in their modeling capacity as they can only be used to model binary relations among entities of S since every edge is connected typically to two entities. In many applications, it is desirable to permit relations that incorporate more than two entities. The idea of using relations that involve more than two entities is central to topological domains. Such higher-order relations allow for a broader range of neighborhood functions to be defined on S to capture multi-way interactions among entities of S. Next we review the main properties, advantages, and disadvantages of some commonly studied topological domains in the context of deep learning, including (abstract) simplicial complexes, regular cell complexes, hypergraphs, and combinatorial complexes. ==== Comparisons among topological domains ==== Each of the enumerated topological domains has its own characteristics, advantages, and limitations: Simplicial complexes Simplest form of higher-order domains. Extensions of graph-based models. Admit hierarchical structures, making them suitable for various applications. Hodge theory can be naturally defined on simplicial complexes. Require relations to be subsets of larger relations, imposing constraints on the structure. Cell Complexes Generalize simplicial complexes. Provide more flexibility in defining higher-order relations. Each cell in a cell complex is homeomorphic to an open ball, attached together via attaching maps. Boundary cells of each cell in a cell complex are also cells in the complex. Represented combinatorially via incidence matrices. Hypergraphs Allow arbitrary set-type relations among entities. Relations are not imposed by other relations, providing more flexibility. Do not explicitly encode the dimension of cells or relations. Useful when relations in the data do not adhere to constraints imposed by other models like simplicial and cell complexes. Combinatorial Complexes : Generalize and bridge the gaps between simplicial complexes, cell complexes, and hypergraphs. Allow for hierarchical structures and set-type relations. Combine features of other complexes while providing more flexibility in modeling relations. Can be represented combinatorially, similar to cell complexes. ==== Hierarchical structure and set-type relations ==== The properties of simplicial complexes, cell complexes, and hypergraphs give rise to two main features of relations on higher-order domains, namely hierarchies of relations and set-type relations. ===== Rank function ===== A rank function on a higher-order domain X is an order-preserving function rk: X → Z, where rk(x) attaches a non-negative integer value to each relation x in X, preserving set inclusion in X. Cell and simplicial complexes are common examples of higher-order domains equipped with rank functions and therefore with hierarchies of relations. ===== Set-type relations ===== Relations in a higher-order domain are called set-type relations if the existence of a relation is not implied by another relation in the domain. Hypergraphs constitute examples of higher-order domains equipped with set-type relations. Given the modeling limitations of simplicial complexes, cell complexes, and hypergraphs, we develop the combinatorial complex, a higher-order domain that features both hierarchies of relations and set-type relations. The learning tasks in TDL can be broadly classified into three categories: Cell classification: Predict targets for each cell in a complex. Examples include triangular mesh segmentation, where the task is to predict the class of each face or edge in a given mesh. Complex classification: Predict targets for an entire complex. For example, predict the class of each input mesh. Cell prediction: Predict properties of cell-cell interactions in a complex, and in some cases, predict whether a cell exists in the complex. An example is the prediction of linkages among entities in hyperedges of a hypergraph. In practice, to perform the aforementioned tasks, deep learning models designed for specific topological spaces must be constructed and implemented. These models, known as topological neural networks, are tailored to operate effectively within these spaces. === Topological neural networks === Central to TDL are topological neural networks (TNNs), specialized architectures designed to operate on data structured in topological domains. Unlike traditional neural networks tailored for grid-like structures, TNNs are adept at handling more intricate data representations, such as graphs