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  • Environmental impact of AI

    Environmental impact of AI

    The environmental impact of the design, training, deployment and use of artificial intelligence includes the greenhouse gas emissions from generating electricity for data centres and computing hardware, operational and upstream water use, and material impacts from hardware manufacturing, mining and electronic waste. Estimating AI's environmental effects can be difficult because results depend on how impacts are measured, including whether accounting includes only model computation or also data-centre overhead, idle capacity, hardware manufacture, and local electricity supply. As these issues have received greater attention, governments and regulators have increasingly considered data-centre reporting requirements, energy-efficiency standards, and broader transparency measures for AI-related resource use. == Carbon footprint and energy use == AI-related energy use arises at multiple stages, including model training, fine-tuning, inference, storage, networking, and supporting infrastructure such as cooling and power conversion. === Individual level === Published estimates of energy use per AI request vary widely across models, tasks and measurement methods. A benchmark study presented at the 2024 ACM Conference on Fairness, Accountability, and Transparency found substantial differences between task types, with lower energy use for some text tasks and much higher energy use for image generation in the study's test conditions. In that benchmark, simple classification tasks consumed about 0.002–0.007 Wh per prompt on average (about 9% of a smartphone charge for 1,000 prompts), while text generation and text summarisation each used about 0.05 Wh per prompt; image generation averaged 2.91 Wh per prompt, and the least efficient image model in the study used 11.49 Wh per image (roughly equivalent to half a smartphone charge). First-party measurements in production environments have also been published. A 2025 Google study on Gemini assistant serving reported median per-prompt energy, emissions, and water-use estimates under the authors' accounting framework, while noting that different system boundaries can produce substantially different results. The study reported a median text-prompt estimate of about 0.24 Wh, which is roughly as much energy as watching nine seconds of television. The study also stated that software and infrastructure improvements reduced energy use by a factor of 33 and carbon emissions by a factor of 44 for a typical prompt over one year within the authors' framework. Researchers at the University of Michigan measured the energy consumption of various Meta Llama 3.1 models released in 2024 and found that smaller language models (8 billion parameters) use about 114 joules (0.03167 Wh) per response, while larger models (405 billion parameters) require up to 6,700 joules (1.861 Wh) per response. This corresponds to the energy needed to run a microwave oven for roughly one-tenth of a second and eight seconds, respectively. Comparisons between AI systems and human labour for specific tasks have produced mixed results and remain sensitive to assumptions about output quality, workload and system boundaries. A 2024 study in Scientific Reports reported 130 to 2900 times lower estimated carbon emissions for selected AI systems than for human writers and illustrators under its assumptions. A later Scientific Reports paper reported a counterexample for programming tasks under its assumptions, finding 5 to 19 times higher estimated emissions for the evaluated AI system than for human programmers on the benchmark used in that study. === System level === ==== Energy use and efficiency ==== AI electricity intensity depends not only on model architecture but also on hardware and facility efficiency. Data-centre operators commonly report Power usage effectiveness (PUE), which measures the ratio of total facility energy to IT equipment energy; a lower PUE indicates less overhead energy for cooling and other supporting infrastructure. Operators may also publish metrics and case studies on hardware efficiency, cooling systems and power sourcing. In its 2024 environmental report, Google stated that its 2023 total greenhouse gas emissions increased 13% year over year, primarily because of increased data-centre energy consumption and supply-chain emissions, while also reporting lower PUE than industry averages for its own facilities. The International Energy Agency has also reported that data centres remain a relatively small share of global electricity use overall, but that their local effects can be much more pronounced because demand is geographically concentrated. ==== Carbon footprint ==== At system level, AI contributes to rising electricity demand in data centres and related infrastructure. The International Energy Agency estimated that data centres used about 415 TWh of electricity in 2024, or around 1.5% of global electricity consumption, and projected that data-centre electricity use could rise to about 945 TWh by 2030, with AI identified as the main driver of that growth alongside other digital services. The carbon footprint of AI systems depends strongly on electricity sources, hardware efficiency, utilisation rates, and what stages are included in the accounting. Training large models can require substantial electricity, while total lifecycle impacts also depend on deployment scale and the amount of inference performed after training. Early analyses of frontier-model development reported rapid historical growth in training compute for selected systems, although later trends have depended on changes in model design, hardware and efficiency gains. Accounting methods that include upstream or embodied impacts, such as hardware manufacture and facilities construction, can materially affect estimates of AI-related emissions. === Decisions and strategies by individual companies === Large technology companies have reported that the expansion of AI and cloud infrastructure affects their sustainability targets, electricity demand, and resource use. Google, for example, attributed part of its emissions growth in 2023 to increased data-centre energy consumption and supply-chain emissions in its 2024 environmental report. Cloud and AI companies have also announced measures intended to reduce environmental impacts, including investment in more efficient hardware, low-carbon electricity procurement, alternative cooling systems, and water stewardship programmes. The extent, comparability, and third-party verification of such disclosures vary between firms and jurisdictions. == Water usage == Data centres can use water directly for cooling and indirectly through the water used in electricity generation, depending on the local energy mix. Public reporting on data-centre water use has often been inconsistent, making comparisons between operators and regions difficult. To standardise operational reporting, The Green Grid proposed the metric water usage effectiveness (WUE), defined as annual site water use divided by IT equipment energy use. WUE does not by itself measure local water stress, source sustainability, or all upstream water impacts. Studies of AI water use also distinguish between water withdrawal and water consumption. Research on AI-specific water use has argued that the water footprint of AI systems can be difficult to observe and may vary substantially by location, cooling design, and electricity source. A 2025 Communications of the ACM article summarised methods for estimating AI water footprints and emphasised the distinction between water withdrawal and water consumption. Li and colleagues estimated that global AI water withdrawal could reach 4.2–6.6 billion cubic metres in 2027 under the scenarios examined in their article. Using GPT-3, released by OpenAI in 2020, as an example, they estimated that training the model in Microsoft's U.S. data centres could consume about 700,000 litres of onsite water and about 5.4 million litres in total when offsite electricity-related water use was included; they also estimated that 10–50 medium-length GPT-3 responses could consume about 500 mL of water, depending on when and where the model was deployed. Published prompt-level estimates have also varied by system and accounting framework: the 2025 Google study on Gemini assistant serving reported a median text-prompt estimate of about 0.26 mL under its framework. Location can materially affect the significance of data-centre water use. Research on U.S. data centres found that one-fifth of servers' direct water footprint came from moderately to highly water-stressed watersheds, while nearly half of servers were fully or partially powered by plants located in water-stressed regions. A 2025 Reuters report, citing data from Verisk Maplecroft and NatureFinance, said that an average mid-sized data centre uses about 1.4 million litres of water per day for cooling and that Phoenix would experience a 32% increase in annual water stress if currently pl

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  • Enterprise information integration

    Enterprise information integration

    Enterprise information integration (EII) is the ability to support a unified view of data and information for an entire organization. The goal of EII is to get a large set of heterogeneous data sources to appear to a user or system as a single, homogeneous data source. In a data virtualization application of EII, there is a process of information integration, using data abstraction to provide a unified interface (known as uniform data access) for viewing all the data within an organization, and a single set of structures and naming conventions (known as uniform information representation) to represent this data. == Overview == Data within an enterprise can be stored in heterogeneous formats, including relational databases (which themselves come in a large number of varieties), text files, XML files, spreadsheets and a variety of proprietary storage methods, each with their own indexing and data access methods. Standardized data access APIs have emerged that offer a specific set of commands to retrieve and modify data from a generic data source. Many applications exist that implement these APIs' commands across various data sources, most notably relational databases. Such APIs include ODBC, JDBC, XQJ, OLE DB, and more recently ADO.NET. There are also standard formats for representing data within a file that are very important to information integration. The best-known of these is XML, which has emerged as a standard universal representation format. There are also more specific XML "grammars" defined for specific types of data such as Geography Markup Language for expressing geographical features and Directory Service Markup Language for holding directory-style information. In addition, non-XML standard formats exist such as iCalendar for representing calendar information and vCard for business card information. Enterprise Information Integration (EII) applies data integration commercially. Despite the theoretical problems described above, the private sector shows more concern with the problems of data integration as a viable product. EII emphasizes neither correctness nor tractability, but speed and simplicity. === Uniform data access === Uniform data access means connectivity and controllability across numerous target data sources. Necessary to fields such as EII and Electronic Data Interchange (EDI), it is most often used regarding analysis of disparate data types and data sources, which must be rendered into a uniform information representation, and generally must appear homogenous to the analysis tools—when the data being analyzed is typically heterogeneous and widely varying in size, type, and original representation. === Uniform information representation === Uniform information representation allows information from several realms or disciplines to be displayed and worked with as if it came from the same realm or discipline. It takes information from a number of sources, which may have used different methodologies and metrics in their data collection, and builds a single large collection of information, where some records may be more complete than others across all fields of data Uniform information representation is particularly important in EII and Electronic Data Interchange (EDI), where different departments of a large organization may have collected information for different purposes, with different labels and units, until one department realized that data already collected by those other departments could be re-purposed for their own needs—saving the enterprise the effort and cost of re-collecting the same information. === Combining disparate data sets === Each data source is disparate and as such is not designed to support EII. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. === Simplicity of deployment === Even if recognized as a solution to a problem, EII as of 2009 currently takes time to apply and offers complexities in deployment. Proposed schema-less solutions include "Lean Middleware". === Handling higher-order information === Analysts experience difficulty—even with a functioning information integration system—in determining whether the sources in the database will satisfy a given application. Answering these kinds of questions about a set of repositories requires semantic information like metadata and/or ontologies. == Applications == EII products enable loose coupling between homogeneous-data consuming client applications and services and heterogeneous-data stores. Such client applications and services include Desktop Productivity Tools (spreadsheets, word processors, presentation software, etc.), development environments and frameworks (Java EE, .NET, Mono, SOAP or RESTful Web services, etc.), business intelligence (BI), business activity monitoring (BAM) software, enterprise resource planning (ERP), Customer relationship management (CRM), business process management (BPM and/or BPEL) Software, and web content management (CMS). == Data access technologies == Service Data Objects (SDO) for Java, C++ and .Net clients and any type of data source XQuery and XQuery API for Java

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  • Information quality

    Information quality

    Information quality (IQ) is a contextual property of or a perspective to the content within information systems. There exist two complementary yet partially conflicting definitions of high-quality: firstly, information is considered high quality if it is fit for its intended purpose ; secondly, it is deemed high quality if it conforms to specified requirements . The primary distinction between these definitions is that Juran's perspective focuses on the suitability of information for its intended purpose, which can be measured by the success of its application even without direct access to or exact knowledge of the data. For example, a black-box AI with access to English Wikipedia can work well for users' purposes but using Estonian Wikipedia fails for the same purposes. Given that the AI remains the same, it can be concluded that English version data would be of higher quality in comparison to Estonian version, even without exact comparison of data contents and their properties in each version. In contrast, Crosby emphasizes adherence to predefined specifications, assuming specific criteria rather than measuring the success of its use; for instance, information in Wikipedia could be proven to be good based on criteria such as existing peer validation and academic references, even if the AI results are poor. This approach falls into problems when data is not completely accessible or all quality properties cannot be known and measured leading to false impression of quality due to lacking and misleading metrics. Numerous IQ frameworks and methodologies provide tangible approach to assess and measure DQ/IQ in a robust and rigorous manner. == Conceptual problems == Although the foundational definitions are usable for most everyday purposes, specialists often use more complex models for information quality. It has been suggested, however, that higher the quality the greater will be the confidence in meeting more general, less specific contexts. == Dimensions and metrics of information quality == "Information quality" is a measure of its fitness for use or conformance to requirements. In this way, "quality" is considered contextual and it can then vary across users and uses of the information. The exact degree of quality is often described with dimensions such as accuracy, timeliness, completeness, and similar scales. Although a huge amount of academic research has been directed to these dimensions, there does not exist consensus on their definitions or practical usefulness . Historically, Richard Wang and Diane Strong proposed a list of dimensions or elements used in assessing Information Quality is: Intrinsic IQ: accuracy, objectivity, believability, reputation Contextual IQ: relevance, value-added, timeliness, completeness, amount of information Representational IQ: interpretability, format, coherence, compatibility Accessibility IQ: accessibility, access security Other authors propose similar but different lists of dimensions for analysis, and emphasize measurement and reporting as information quality metrics. Larry English prefers the term "characteristics" to dimensions. However, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used. === Quality metrics === Source: Authority/verifiability Authority refers to the expertise or recognized official status of a source. Consider the reputation of the author and publisher. When working with legal or government information, consider whether the source is the official provider of the information. Verifiability refers to the ability of a reader to verify the validity of the information irrespective of how authoritative the source is. To verify the facts is part of the duty of care of the journalistic deontology, as well as, where possible, to provide the sources of information so that they can be verified Scope of coverage Scope of coverage refers to the extent to which a source explores a topic. Consider time periods, geography or jurisdiction and coverage of related or narrower topics. Composition and organization Composition and organization has to do with the ability of the information source to present its particular message in a coherent, logically sequential manner. Objectivity Objectivity is the bias or opinion expressed when a writer interprets or analyze facts. Consider the use of persuasive language, the source's presentation of other viewpoints, its reason for providing the information and advertising. Integrity Adherence to moral and ethical principles; soundness of moral character The state of being whole, entire, or undiminished Comprehensiveness Of large scope; covering or involving much; inclusive: a comprehensive study. Comprehending mentally; having an extensive mental grasp. Insurance. covering or providing broad protection against loss. Validity Validity of some information has to do with the degree of obvious truthfulness which the information carries Uniqueness As much as 'uniqueness' of a given piece of information is intuitive in meaning, it also significantly implies not only the originating point of the information but also the manner in which it is presented and thus the perception which it conjures. The essence of any piece of information we process consists to a large extent of those two elements. Timeliness Timeliness refers to information that is current at the time of publication. Consider publication, creation and revision dates. Beware of Web site scripting that automatically reflects the current day's date on a page. Reproducibility (utilized primarily when referring to instructive information) Means that documented methods are capable of being used on the same data set to achieve a consistent result. == Professional associations == IQ International—the International Association for Information and Data Quality IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession. CDOIQ Society Chief Data Officers and Information Quality Society is a global professional society supporting data leaders with networking, meetings, best practices, experience, certification, and training. == Information quality conferences == A number of major conferences relevant to information quality are held annually: Annual MIT Chief Data Officer & Information Quality (CDOIQ) Symposium Annual conferences held at the Massachusetts Institute of Technology, Cambridge, MA, USA Data Governance and Information Quality Conference Commercial conferences held each year in the USA Data Quality Asia Pacific Commercial conference held annually in Sydney or Melbourne, Australia Enterprise Data and Business Intelligence Conference Europe Commercial conferences held annually in London, England. Information and Data Quality Conference Not for profit conference run annually by IQ International (the International Association for Information and Data Quality) in the USA International Conference on Information Quality Academic Conference launched through MITIQ held annually at a University Master Data Management & Data Governance Conferences Six major conferences are run annually by the MDM Institute in venues such as London, San Francisco, Sydney, Toronto, Madrid, Frankfurt, Shanghai and New York City.

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  • Online analytical processing

    Online analytical processing

    In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints. These viewpoints are sometimes called dimensions (such as looking at the same sales by salesperson, or by date, or by customer, or by product, or by region, etc.). Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP (online transaction processing), which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries (read, insert, update and delete). == Overview of OLAP systems == At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. For example: Sales Fact Table +-------------+----------+ | sale_amount | time_id | +-------------+----------+ Time Dimension | 930.10| 1234 |----+ +---------+-------------------+ +-------------+----------+ | | time_id | timestamp | | +---------+-------------------+ +---->| 1234 | 20080902 12:35:43 | +---------+-------------------+ === Multidimensional databases === Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions". Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. === Aggregations === It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions, using an aggregate function (or aggregation function). The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently. For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end). In other cases, the aggregate function cannot be computed without analyzing the entire set at once, though in some cases approximations can be computed; examples include DISTINCT COUNT, MEDIAN, and MODE; for example, the median of a set is not the median of medians of subsets. These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). == Types == OLAP systems have been traditionally categorized using the following taxonomy. === Multidimensional OLAP (MOLAP) === MOLAP (multi-dimensional online analytical processing) is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing. Such MOLAP tools generally utilize a pre-calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data se

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  • Figure AI

    Figure AI

    Figure AI, Inc. is an American robotics company developing humanoid robots that operate via artificial intelligence. The company was founded in 2022 by Brett Adcock. As of late 2025, the company has a $39 billion valuation. Three generations of humanoid robots (Figure 01–03) have been developed, as well as two iterations of a vision-language-action model (Helix 01–02), which can control up to two robots at once. By 2026, the robots demonstrated the potential ability to perform household work and the company gained publicity when a Figure 03 appeared at a White House event. == History == Figure AI was founded in 2022 by Brett Adcock, also known for founding Archer Aviation and Vettery. That year, the company introduced its prototype, Figure 01, a bipedal robot designed for manual labor, initially targeting the logistics and warehousing sectors. The initial model utilized external cabling for easier maintenance. In May 2023, Figure AI raised $70 million from investors including Adcock, who invested $20 million, and Parkway Venture Capital. In January 2024, Figure AI announced a partnership with BMW to deploy humanoid robots in automotive manufacturing facilities. In February 2024, Figure AI secured $675 million in venture capital funding from a consortium that includes Jeff Bezos, Microsoft, Nvidia, Intel, and the startup-funding divisions of Amazon and OpenAI; the company was then valued at $2.6 billion. Figure AI also announced a partnership with OpenAI, which would build specialized artificial intelligence (AI) models for Figure AI's humanoid robots, enabling its robots to process language; the collaboration ended after a year, with Adcock stating that large language models had become a smaller problem compared to those allowing for "high rate robot control". In August 2024, the company introduced Figure 02, describing it as the next step toward deploying humanoids for industrial use. The machine has 35 degrees of freedom (DOF), while the five-fingered hands have 16 DOF and the ability to carry up to 25 kilograms (55 lb). The model is equipped with cabling integrated into the limbs, a torso-placed battery, six RGB cameras, and an onboard vision-language-action (VLA) model. It has three times the computing power (including inference AI) of the previous model, including two graphics processing units, supported by Nvidia. Microphones, speakers, and custom AI models (developed with OpenAI) enable communication with humans. In early 2025, Figure AI announced BotQ, a manufacturing facility aiming to produce 12,000 humanoids per year with the help of its own humanoid robots, and Helix, a VLA model that can control up to two robots at once. Helix enables a robot to interact with the world without extensive manual training, according to the company allowing it to pick up nearly any small household object. By April, the company issued cease-and-desist letters to at least two secondary brokers promoting its private stock without authorization. In September, a third round of financing exceeded $1 billion, raising the company's total valuation to $39 billion. Investors included Brookfield Asset Management, Intel, Macquarie Capital, Nvidia, Parkway Venture Capital, Qualcomm, Salesforce, and T-Mobile. In October 2025, Figure 03 was introduced. According to the company, its hardware and software redesign aims to create a general-purpose robot able to learn directly from humans. An upgraded camera system delivers twice the frame rate, a quarter the latency, and a 60% wider field of view, in addition to a camera in each hand. Tactile sensors in the fingertips can detect forces as little as 3 grams (0.1 oz). It incorporates soft materials and a protected battery for safety, and removable, washable textiles. It supports wireless inductive charging. In November 2025, the former head of product safety sued the company on the basis of being fired for raising the concern that the company's robots were strong enough to fracture a human skull. By early 2026, Figure 02 had been used in demonstrations showing that it could load a washing machine, sort packages, and fold laundry. That January, Helix 02 was released, expanding the AI model to the entire body to allow for functional autonomy. A Helix 02–powered Figure 02 was shown to be capable of loading and unloading a dishwasher, based on hours of motion-capture data and simulation-based machine learning. In March, U.S. First Lady Melania Trump appeared at the White House with a Figure 03, promoting the presumptive eventual ability of AI to teach children. In May 2026, Figure AI livestreamed a group of their robots processing packages nonstop for almost a week, inspiring a 10-hour competition between their robot and a human, in which the robot performed 98.5% as well as the human.

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  • Tagsistant

    Tagsistant

    Tagsistant is a semantic file system for the Linux kernel, written in C and based on FUSE. Unlike traditional file systems that use hierarchies of directories to locate objects, Tagsistant introduces the concept of tags. == Design and differences with hierarchical file systems == In computing, a file system is a type of data store which could be used to store, retrieve and update files. Each file can be uniquely located by its path. The user must know the path in advance to access a file and the path does not necessarily include any information about the content of the file. Tagsistant uses a complementary approach based on tags. The user can create a set of tags and apply those tags to files, directories and other objects (devices, pipes, ...). The user can then search all the objects that match a subset of tags, called a query. This kind of approach is well suited for managing user contents like pictures, audio recordings, movies and text documents but is incompatible with system files (like libraries, commands and configurations) where the univocity of the path is a security requirement to prevent the access to a wrong content. == The tags/ directory == A Tagsistant file system features four main directories: archive/ relations/ stats/ tags/ Tags are created as sub directories of the tags/ directory and can be used in queries complying to this syntax: tags/subquery/[+/subquery/[+/subquery/]]/@/ where a subquery is an unlimited list of tags, concatenated as directories: tag1/tag2/tag3/.../tagN/ The portion of a path delimited by tags/ and @/ is the actual query. The +/ operator joins the results of different sub-queries in one single list. The @/ operator ends the query. To be returned as a result of the following query: tags/t1/t2/+/t1/t4/@/ an object must be tagged as both t1/ and t2/ or as both t1/ and t4/. Any object tagged as t2/ or t4/, but not as t1/ will not be retrieved. The query syntax deliberately violates the POSIX file system semantics by allowing a path token to be a descendant of itself, like in tags/t1/t2/+/t1/t4/@ where t1/ appears twice. As a consequence a recursive scan of a Tagsistant file system will exit with an error or endlessly loop, as done by Unix find: This drawback is balanced by the possibility to list the tags inside a query in any order. The query tags/t1/t2/@/ is completely equivalent to tags/t2/t1/@/ and tags/t1/+/t2/t3/@/ is equivalent to tags/t2/t3/+/t1/@/. The @/ element has the precise purpose of restoring the POSIX semantics: the path tags/t1/@/directory/ refers to a traditional directory and a recursive scan of this path will properly perform. == The reasoner and the relations/ directory == Tagsistant features a simple reasoner which expands the results of a query by including objects tagged with related tags. A relation between two tags can be established inside the relations/ directory following a three level pattern: relations/tag1/rel/tag2/ The rel element can be includes or is_equivalent. To include the rock tag in the music tag, the Unix command mkdir can be used: mkdir -p relations/music/includes/rock The reasoner can recursively resolve relations, allowing the creation of complex structures: mkdir -p relations/music/includes/rock mkdir -p relations/rock/includes/hard_rock mkdir -p relations/rock/includes/grunge mkdir -p relations/rock/includes/heavy_metal mkdir -p relations/heavy_metal/includes/speed_metal The web of relations created inside the relations/ directory constitutes a basic form of ontology. == Autotagging plugins == Tagsistant features an autotagging plugin stack which gets called when a file or a symlink is written. Each plugin is called if its declared MIME type matches The list of working plugins released with Tagsistant 0.6 is limited to: text/html: tags the file with each word in and <keywords> elements and with document, webpage and html too image/jpeg: tags the file with each Exif tag == The repository == Each Tagsistant file system has a corresponding repository containing an archive/ directory where the objects are actually saved and a tags.sql file holding tagging information as an SQLite database. If the MySQL database engine was specified with the --db argument, the tags.sql file will be empty. Another file named repository.ini is a GLib ini store with the repository configuration. Tagsistant 0.6 is compatible with the MySQL and Sqlite dialects of SQL for tag reasoning and tagging resolution. While porting its logic to other SQL dialects is possible, differences in basic constructs (especially the INTERSECT SQL keyword) must be considered. == The archive/ and stats/ directories == The archive/ directory has been introduced to provide a quick way to access objects without using tags. Objects are listed with their inode number prefixed. The stats/ directory features some read-only files containing usage statistics. A file configuration holds both compile time information and current repository configuration. == Main criticisms == It has been highlighted that relying on an external database to store tags and tagging information could cause the complete loss of metadata if the database gets corrupted. It has been highlighted that using a flat namespace tends to overcrowd the tags/ directory. This could be mitigated introducing triple tags.</p> <a href="https://aizhi.co/news/60b799932.html" class="read-more" title="Tagsistant">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/24d799968.html" class="card-thumb-link" title="Non-personal data"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/99/AdobeSpeechEnhancementScreenshot.png/960px-AdobeSpeechEnhancementScreenshot.png" alt="Non-personal data" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/24d799968.html" title="Non-personal data">Non-personal data</a></h2> <p class="article-excerpt">Non-Personal Data (NPD) is electronic data that does not contain any information that can be used to identify a natural person. Thus, it can either be data that has no personal information to begin with (such as weather data, stock prices, data from anonymous IoT sensors); or it is data that had personal data that was subsequently pseudoanonymized (for example, identifiable strings substituted with random strings) or anonymized (such as by irreversibly removing all personal data). NPD is part of the overall Data Governance Strategy of a region or country. While personal data are covered by Data Protection Legislation such as GDPR, other kinds of data would fall under the scope of NPD Regulation. == Importance of non-personal data == It has been pointed out that the future is data-driven. What this means is that much of the present innovation taking place in domains such as Machine Learning and Artificial Intelligence is fueled by data, which is needed for calibrating the complex models (comprising neural network-based as well as other kinds). The larger the volume, diversity and quality of the data, the higher is the quality of the model, leading to better predictions and explanations. However, there is a flip-side to data availability. The newly-emerging awareness of privacy and the consequent need for powerful Data Protection Regulations (such as GDPR) makes it increasingly difficult or impossible to obtain data in the quantities required. This is a contradiction, and the only way out would be to remove all personal data from data sets (either by Data anonymization or Pseudonymization coupled with noise injection, at which point it becomes NPD. Therefore, many innovation-friendly countries are coming out with regulatory regimes that would ensure that personal data is protected, while, at the same time, non-personal data can be extracted from personal data so that innovation is fostered. In other words, NPD 'unlocks' value that was locked away in data sets that have personally-identifiable information. It is expected that multiple NPD data sets will begin to be available on free or commercial basis from different providers once the regulations are in place. == Emerging regulatory frameworks == Non-Personal Data has significant uses that may be economic, social, political or security-related. Several countries and regions are in the process of regulating the use of NPD. In May 2019, the European Union operationalized its Regulation of the Free Flow of NPD. India announced a nine-member expert committee to make recommendations on the regulation of NPD in 2019, which published its first report in mid-2020. The report was opened for public comments, after which it was revised and published in December 2020. == Proposed NPD regulatory framework in India == The following were the objectives of the proposed Indian regulation as per the revised report: Sovereignty: India has rights over the data of India, its people and organisations. Benefit India: Benefits of data must accrue to India and its people. Benefits the world: Innovation, new models and algorithms for the world. Privacy: Misuse, reidentification and harms must be prevented. Simplicity: The regulations should be simple, digital and unambiguous. Innovation and entrepreneurship: The data should be freely available for innovation and entrepreneurship in India. == Concerns == The major concern in the use of NPD is if there are techniques (statistical or AI-based) by which multiple data sets can be used to extract personally-identifiable data.</p> <a href="https://aizhi.co/news/24d799968.html" class="read-more" title="Non-personal data">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/74f799918.html" class="card-thumb-link" title="Semantic query"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4b/Monica_Lam_at_The_First_Workshop_of_a_Public_AI_Assistant_to_WWK_2025.jpg/960px-Monica_Lam_at_The_First_Workshop_of_a_Public_AI_Assistant_to_WWK_2025.jpg" alt="Semantic query" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/74f799918.html" title="Semantic query">Semantic query</a></h2> <p class="article-excerpt">Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning. Semantic queries work on named graphs, linked data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data. This is in contrast to semantic search, which uses semantics (meaning of language constructs) in unstructured text to produce a better search result. (See natural language processing.) From a technical point of view, semantic queries are precise relational-type operations much like a database query. They work on structured data and therefore have the possibility to utilize comprehensive features like operators (e.g. >, < and =), namespaces, pattern matching, subclassing, transitive relations, semantic rules and contextual full text search. The semantic web technology stack of the W3C is offering SPARQL to formulate semantic queries in a syntax similar to SQL. Semantic queries are used in triplestores, graph databases, semantic wikis, natural language and artificial intelligence systems. == Background == Relational databases represent all relationships between data in an implicit manner only. For example, the relationships between customers and products (stored in two content-tables and connected with an additional link-table) only come into existence in a query statement (SQL in the case of relational databases) written by a developer. Writing the query demands exact knowledge of the database schema. Linked-Data represent all relationships between data in an explicit manner. In the above example, no query code needs to be written. The correct product for each customer can be fetched automatically. Whereas this simple example is trivial, the real power of linked-data comes into play when a network of information is created (customers with their geo-spatial information like city, state and country; products with their categories within sub- and super-categories). Now the system can automatically answer more complex queries and analytics that look for the connection of a particular location with a product category. The development effort for this query is omitted. Executing a semantic query is conducted by walking the network of information and finding matches (also called Data Graph Traversal). Another important aspect of semantic queries is that the type of the relationship can be used to incorporate intelligence into the system. The relationship between a customer and a product has a fundamentally different nature than the relationship between a neighbourhood and its city. The latter enables the semantic query engine to infer that a customer living in Manhattan is also living in New York City whereas other relationships might have more complicated patterns and "contextual analytics". This process is called inference or reasoning and is the ability of the software to derive new information based on given facts. == Articles == Velez, Golda (2008). "Semantics Help Wall Street Cope With Data Overload". Wall Street & Technology. wallstreetandtech.com. Zhifeng, Xiao (2009). "Spatial information semantic query based on SPARQL". In Liu, Yaolin; Tang, Xinming (eds.). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining. Vol. 7492. SPIE. pp. 74921P. Bibcode:2009SPIE.7492E..60X. doi:10.1117/12.838556. S2CID 62191842. Aquin, Mathieu (2010). "Watson, more than a Semantic Web search engine" (PDF). Semantic Web Journal. Dworetzky, Tom (2011). "How Siri Works: iPhone's 'Brain' Comes from Natural Language Processing". International Business Times. Horwitt, Elisabeth (2011). "The semantic Web gets down to business". computerworld.com. Rodriguez, Marko (2011). "Graph Pattern Matching with Gremlin". Marko A. Rodriguez. markorodriguez.com on Graph Computing. Sequeda, Juan (2011). "SPARQL Nuts & Bolts". Cambridge Semantics. Freitas, Andre (2012). "Querying Heterogeneous Datasets on the Linked Data Web" (PDF). IEEE Internet Computing. Kauppinen, Tomi (2012). "Using the SPARQL Package in R to handle Spatial Linked Data". linkedscience.org. Lorentz, Alissa (2013). "With Big Data, Context is a Big Issue". Wired.</p> <a href="https://aizhi.co/news/74f799918.html" class="read-more" title="Semantic query">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/124f199874.html" class="card-thumb-link" title="Cups (app)"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/9/9f/CMAC_system_block_diagram.jpg" alt="Cups (app)" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/124f199874.html" title="Cups (app)">Cups (app)</a></h2> <p class="article-excerpt">Cups (stylized as CUPS) was a mobile app launched in New York City in April 2014. It was a mobile payment and discovery platform for independent coffee shops nearby. The app was active in more than 400 cafes in New York, San Francisco, Philadelphia, Nashville, Minneapolis and Saint Paul, and other U.S. cities. == History == Cups was founded in Israel in 2012 by Gilad Rotem and four other co-founders, who were all high school friends. The company ran a limited beta pilot in Tel Aviv and Jerusalem, featuring 80 locations, from September 2012 until September 2014. Customers received all-you-can-drink coffee at certain coffee shops in Tel Aviv for approximately $45 a month. In October 2013, the founders relocated to New York. Cups participated in the Entrepreneur's Roundtable Accelerator program and went live in New York in 2014, initially working with 50 small coffee shops in Manhattan and Brooklyn. In early 2016, the company launched 30 locations in Philadelphia in February, followed by 40 more locations in San Francisco in March. == Functionality == The Cups app gave the user a list of the nearest participating coffee shops to their current location. The app user can order a drink using the app and pay the cashier with their phone. The cashier would enter a code that entered the purchase into the app's system. The app also allowed for onboard tipping and food purchases. The company reimbursed the coffee shop and kept a portion of their sales. In early 2016, the Cups Café Network was launched, using bulk purchasing power to land discounts with service providers which would normally be reserved for larger chains. In this way, the company aimed to help its café partners compete with the larger coffee chains.</p> <a href="https://aizhi.co/news/124f199874.html" class="read-more" title="Cups (app)">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/383b799609.html" class="card-thumb-link" title="Gutmann method"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/61/Confusion_matrix.png/960px-Confusion_matrix.png" alt="Gutmann method" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/383b799609.html" title="Gutmann method">Gutmann method</a></h2> <p class="article-excerpt">The Gutmann method is an algorithm for securely erasing the contents of computer hard disk drives, such as files. Devised by Peter Gutmann and Colin Plumb and presented in the paper Secure Deletion of Data from Magnetic and Solid-State Memory in July 1996, it involved writing a series of 35 patterns over the region to be erased. The selection of patterns assumes that the user does not know the encoding mechanism used by the drive, so it includes patterns designed specifically for three types of drives. A user who knows which type of encoding the drive uses can choose only those patterns intended for their drive. A drive with a different encoding mechanism would need different patterns. Most of the patterns in the Gutmann method were designed for older MFM/RLL-encoded disks. Gutmann himself has noted that more modern drives no longer use these older encoding techniques, making parts of the method irrelevant. He said "In the time since this paper was published, some people have treated the 35-pass overwrite technique described in it more as a kind of voodoo incantation to banish evil spirits than the result of a technical analysis of drive encoding techniques". Since about 2001, some ATA IDE and SATA hard drive manufacturer designs include support for the ATA Secure Erase standard, obviating the need to apply the Gutmann method when erasing an entire drive. The Gutmann method does not apply to USB sticks: a 2011 study reports that 71.7% of data remained available. On solid state drives it resulted in 0.8–4.3% recovery. == Background == The delete function in most operating systems simply marks the space occupied by the file as reusable (removes the pointer to the file) without immediately removing any of its contents. At this point the file can be fairly easily recovered by numerous recovery applications. However, once the space is overwritten with other data, there is no known way to use software to recover it. It cannot be done with software alone since the storage device only returns its current contents via its normal interface. Gutmann claims that intelligence agencies have sophisticated tools, including magnetic force microscopes, which together with image analysis, can detect the previous values of bits on the affected area of the media (for example hard disk). This claim however seems to be invalid based on the thesis "Data Reconstruction from a Hard Disk Drive using Magnetic Force Microscopy". == Method == An overwrite session consists of a lead-in of four random write patterns, followed by patterns 5 to 31 (see rows of table below), executed in a random order, and a lead-out of four more random patterns. Each of patterns 5 to 31 was designed with a specific magnetic media encoding scheme in mind, which each pattern targets. The drive is written to for all the passes even though the table below only shows the bit patterns for the passes that are specifically targeted at each encoding scheme. The result should obscure any data on the drive so that only the most advanced physical scanning (e.g., using a magnetic force microscope) of the drive is likely to be able to recover any data. The series of patterns is as follows: Encoded bits shown in bold are what should be present in the ideal pattern, although due to the encoding the complementary bit is actually present at the start of the track. == Criticism == Daniel Feenberg of the National Bureau of Economic Research, an American private nonprofit research organization, criticized Gutmann's claim that intelligence agencies are likely to be able to read overwritten data, citing a lack of evidence for such claims. He finds that Gutmann cites one non-existent source and sources that do not actually demonstrate recovery, only partially-successful observations. The definition of "random" is also quite different from the usual one used: Gutmann expects the use of pseudorandom data with sequences known to the recovering side, not an unpredictable one such as a cryptographically secure pseudorandom number generator. Nevertheless, some published government security procedures consider an overwritten disk to still be sensitive. Human factors and potential limitations in the overwriting software create a residual risk that is not considered acceptable at the highest security levels. Gutmann himself has responded to some of these criticisms and also criticized how his algorithm has been abused in an epilogue to his original paper, in which he states: In the time since this paper was published, some people have treated the 35-pass overwrite technique described in it more as a kind of voodoo incantation to banish evil spirits than the result of a technical analysis of drive encoding techniques. As a result, they advocate applying the voodoo to PRML and EPRML drives even though it will have no more effect than a simple scrubbing with random data. In fact performing the full 35-pass overwrite is pointless for any drive since it targets a blend of scenarios involving all types of (normally-used) encoding technology, which covers everything back to 30+-year-old MFM methods (if you don't understand that statement, re-read the paper). If you're using a drive which uses encoding technology X, you only need to perform the passes specific to X, and you never need to perform all 35 passes. For any modern PRML/EPRML drive, a few passes of random scrubbing is the best you can do. As the paper says, "A good scrubbing with random data will do about as well as can be expected". This was true in 1996, and is still true now. Gutmann's statement has been criticized for not recognizing that PRML/EPRML does not replace RLL, with critics claiming PRML/EPRML to be a signal detection method rather than a data encoding method. Polish data recovery service Kaleron has also claimed that Gutmann's publication contains further factual errors and assumptions that do not apply to actual disks.</p> <a href="https://aizhi.co/news/383b799609.html" class="read-more" title="Gutmann method">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/281c799711.html" class="card-thumb-link" title="Artificial intelligence in industry"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5f/Jake_Elwes_Artist_Portrait_-_photo_credit_Gretchen_Andrew_%2852324232098%29.jpg/960px-Jake_Elwes_Artist_Portrait_-_photo_credit_Gretchen_Andrew_%2852324232098%29.jpg" alt="Artificial intelligence in industry" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/281c799711.html" title="Artificial intelligence in industry">Artificial intelligence in industry</a></h2> <p class="article-excerpt">Industrial artificial intelligence, or industrial AI, refers to the application of artificial intelligence to industrial business processes. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery. Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing. == Categories == Possible applications of industrial AI and machine learning in the production domain can be divided into seven application areas: Market and trend analysis Machinery and equipment Intralogistics Production process Supply chain Building Product Each application area can be further divided into specific application scenarios that describe concrete AI/ML scenarios in production. While some application areas have a direct connection to production processes, others cover production adjacent fields like logistics or the factory building. An example from the application scenario Process Design & Innovation are collaborative robots. Collaborative robotic arms are able to learn the motion and path demonstrated by human operators and perform the same task. Predictive and preventive maintenance through data-driven machine learning are application scenarios from the Machinery & Equipment application area. == Challenges == In contrast to entirely virtual systems, in which ML applications are already widespread today, real-world production processes are characterized by the interaction between the virtual and the physical world. Data is recorded using sensors and processed on computational entities and, if desired, actions and decisions are translated back into the physical world via actuators or by human operators. This poses major challenges for the application of ML in production engineering systems. These challenges are attributable to the encounter of process, data and model characteristics: The production domain's high reliability requirements, high risk and loss potential, the multitude of heterogeneous data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes. In particular, production data comprises a variety of different modalities, semantics and quality. Furthermore, production systems are dynamic, uncertain and complex, and engineering and manufacturing problems are data-rich but information-sparse. Besides that, due to the variety of use cases and data characteristics, problem-specific data sets are required, which are difficult to acquire, hindering both practitioners and academic researchers in this domain. === Process and industry characteristics === The domain of production engineering can be considered as a rather conservative industry when it comes to the adoption of advanced technology and their integration into existing processes. This is due to high demands on reliability of the production systems resulting from the potentially high economic harm of reduced process effectiveness due to e.g., additional unplanned downtime or insufficient product qualities. In addition, the specifics of machining equipment and products prevent area-wide adoptions across a variety of processes. Besides the technical reasons, the reluctant adoption of ML is fueled by a lack of IT and data science expertise across the domain. === Data characteristics === The data collected in production processes mainly stem from frequently sampling sensors to estimate the state of a product, a process, or the environment in the real world. Sensor readings are susceptible to noise and represent only an estimate of the reality under uncertainty. Production data typically comprises multiple distributed data sources resulting in various data modalities (e.g., images from visual quality control systems, time-series sensor readings, or cross-sectional job and product information). The inconsistencies in data acquisition lead to low signal-to-noise ratios, low data quality and great effort in data integration, cleaning and management. In addition, as a result from mechanical and chemical wear of production equipment, process data is subject to various forms of data drifts. === Machine learning model characteristics === ML models are considered as black-box systems given their complexity and intransparency of input-output relation. This reduces the comprehensibility of the system behavior and thus also the acceptance by plant operators. Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved, complicating the certification of production equipment. Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data, further risking the reliability of the production system because of lacking robustness and safety. In addition to high development and deployment costs, the data drifts cause high maintenance costs, which is disadvantageous compared to purely deterministic programs. == Standard processes for data science in production == The development of ML applications – starting with the identification and selection of the use case and ending with the deployment and maintenance of the application – follows dedicated phases that can be organized in standard process models. The process models assist in structuring the development process and defining requirements that must be met in each phase to enter the next phase. The standard processes can be classified into generic and domain-specific ones. Generic standard processes (e.g., CRISP-DM, ASUM-DM, or knowledge discovery in databases (KDD)) describe a generally valid methodology and are thus independent of individual domains. Domain-specific processes on the other hand consider specific peculiarities and challenges of special application areas. The Machine Learning Pipeline in Production is a domain-specific data science methodology that is inspired by the CRISP-DM model and was specifically designed to be applied in fields of engineering and production technology. To address the core challenges of ML in engineering – process, data, and model characteristics – the methodology especially focuses on use-case assessment, achieving a common data and process understanding data integration, data preprocessing of real-world production data and the deployment and certification of real-world ML applications. == Industrial data sources == The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models. In other domains, like computer vision, speech recognition or language models, extensive reference datasets (e.g. ImageNet, Librispeech, The People's Speech) and data scraped from the open internet are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability. For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms have no industrial focus and offer limited filtering abilities regarding industrial data sources.</p> <a href="https://aizhi.co/news/281c799711.html" class="read-more" title="Artificial intelligence in industry">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/294f799698.html" class="card-thumb-link" title="Applications of artificial intelligence"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/1/1f/Raspberry_Pi_AI_Kit_for_Raspberry_Pi_5_complete_Kit_07.jpg/960px-Raspberry_Pi_AI_Kit_for_Raspberry_Pi_5_complete_Kit_07.jpg" alt="Applications of artificial intelligence" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/294f799698.html" title="Applications of artificial intelligence">Applications of artificial intelligence</a></h2> <p class="article-excerpt">Artificial intelligence is the capability of computational systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, massive advancements have been made in the field of generative artificial intelligence, which uses generative models to generate text, images, videos, and other forms of data. This article describes applications of AI in different sectors. == Agriculture == In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. == AI-assisted software develoment == == Architecture and design == == Business == A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1-2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services. == Chatbots == == Computer science == === Programming assistance === ==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient. The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices. ==== Neural network design ==== AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet. ==== Quantum computing ==== Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems. === Historical contributions === AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories: Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction == Customer service == === Human resources === AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities. === Online and telephone customer service === AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making. === Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots. == Education == In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. == Energy and environment == === Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid). === Environmental monitoring === Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans. === Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts. === Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas. == Entertainment and media == === Media === AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement. Motion interpolation Pixel-art scaling algorithms Image scaling Imag</p> <a href="https://aizhi.co/news/294f799698.html" class="read-more" title="Applications of artificial intelligence">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/382b099617.html" class="card-thumb-link" title="Anthem medical data breach"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/c9/Sammenligning_av_DP%2C_pt%2C_px%2C_mm_og_in.png/960px-Sammenligning_av_DP%2C_pt%2C_px%2C_mm_og_in.png" alt="Anthem medical data breach" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/382b099617.html" title="Anthem medical data breach">Anthem medical data breach</a></h2> <p class="article-excerpt">The Anthem medical data breach was a medical data breach of information held by Elevance Health, known at that time as Anthem Inc. On February 4, 2015, Anthem, Inc. disclosed that criminal hackers had broken into its servers and had potentially stolen over 37.5 million records that contain personally identifiable information from its servers. On February 24, 2015 Anthem raised the number to 78.8 million people whose personal information had been affected. According to Anthem, Inc., the data breach extended into multiple brands Anthem, Inc. uses to market its healthcare plans, including, Anthem Blue Cross, Anthem Blue Cross and Blue Shield, Blue Cross and Blue Shield of Georgia, Empire Blue Cross and Blue Shield, Amerigroup, Caremore, and UniCare. Healthlink says that it was also a victim. Anthem says users' medical information and financial data were not compromised. Anthem has offered free credit monitoring in the wake of the breach. Michael Daniel, chief adviser on cybersecurity for President Barack Obama, said he would be changing his own password. According to The New York Times, about 80 million company records were hacked, and there is a fear that the stolen data will be used for identity theft. The compromised information contained names, birthdays, medical IDs, social security numbers, street addresses, e-mail addresses and employment information, including income data. == Theft of the data == The data was stolen over a period of weeks the month before the data breach was discovered. Because no medical information was compromised, Anthem was not required by law to encrypt the data. However, Anthem faced several civil class-action lawsuits, which were settled in 2017 at a cost of $115 million. Anthem did not admit any wrongdoing in the settlement. Data from the attack is expected to be sold on the black market. == Impact == Persons whose data was stolen could have resulting problems about identity theft for the rest of their lives. Anthem had a US$100 million insurance policy for cyber problems from American International Group. One report suggested that all of this money could be consumed by the process of notifying customers of the breach. == Responses == Anthem hired Mandiant, a cybersecurity firm, to review their security systems and advised people whose data was stolen to monitor their accounts and remain vigilant. The theft of the data raised fears generally about the theft of medical information. A writer from Harvard Law School suggested that this data breach might spark reform of security practices and government data safety regulation. An investigation conducted by several state insurance commissioners blames the breach on an attacker whose identity was withheld, and claims that the breach was likely ordered by a foreign government whose name was withheld. It also concluded that Anthem had taken reasonable measures to protect its data before the breach and that its remediation plan was effective at shutting down the breach once it was discovered. It also marks the starting date of the breach as February 18, 2014. The lead investigator was the Indiana Department of Insurance (DOI) -- Anthem's principal regulator, because Anthem is headquartered in Indiana. The Indiana DOI hired independent auditors to conduct a security assessment at Anthem, which concluded, "While deficiencies within Anthem’s cybersecurity posture were noted by the Examination Team, these deficiencies were not, in our experience, uncommon to companies comparable to Anthem in size and scope. While the pre-breach deficiencies impacted Anthem’s ability to reduce the likelihood of and quickly detect the Data Breach, the controls implemented subsequent to the Data Breach should improve Anthem’s ability to detect future breaches and enable Anthem to respond more effectively to a future attack than was the case in this instance." Federal regulators also conducted an investigation of the Anthem data breach, resulting in a $16 million settlement between Anthem and the Department of Health and Human Services (HHS) -- by far the largest HHS data breach settlement. An HHS Director overseeing the investigation said, "The largest health data breach in U.S. history fully merits the largest HIPAA settlement in history. Unfortunately, Anthem failed to implement appropriate measures for detecting hackers who had gained access to their system to harvest passwords and steal people's private information." The HHS settlement also required Anthem to perform a risk assessment and correct any identified deficiencies in its cybersecurity, with HHS oversight of Anthem's progress. Approximately 100 private class action lawsuits were filed against Anthem over the data breach and consolidated in California federal court, in front of Judge Koh, a respected authority in data breach litigation. After contested briefing over who should lead the litigation efforts, Judge Koh appoints Eve Cervantez of Altshuler Berzon and Andy Friedman of Cohen Milstein as co-lead counsel, and appointed Eric Gibbs of Gibbs Law Group and Michael Sobel of Lieff Cabraser to head a Plaintiffs' Steering Committee. In 2017, Anthem agreed to settle the litigation for $115 million, the largest ever data breach settlement at the time. The attorneys requested $38 million in fees for their work on the case, but Judge Koh slashed the fee request, finding that only $31 million in fees were merited.</p> <a href="https://aizhi.co/news/382b099617.html" class="read-more" title="Anthem medical data breach">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/415d799577.html" class="card-thumb-link" title="Data Science Africa"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5f/Jake_Elwes_Artist_Portrait_-_photo_credit_Gretchen_Andrew_%2852324232098%29.jpg/960px-Jake_Elwes_Artist_Portrait_-_photo_credit_Gretchen_Andrew_%2852324232098%29.jpg" alt="Data Science Africa" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/415d799577.html" title="Data Science Africa">Data Science Africa</a></h2> <p class="article-excerpt">Data Science Africa (DSA) is a non-profit knowledge sharing professional group that aims at bringing together leading researchers and practitioners working on data science methods or applications relevant to Africa, and providing training on state of the art data science methods to students and others interested in developing practical skills. Since 2013, DSA has been organizing conference, workshops and summer schools on machine learning and data science across East Africa. Facilitators of Summer School and workshops are researchers and practitioners from the academia, private and public institutions across the world. == Summer schools and workshops == The first summer school which started as Gaussian Process Summer School was held at Makerere University in Kampala, Uganda from 6th to 9 August 2013. The First Data Science Summer School and Workshop was held at Dedan Kimathi University of Technology in Nyeri, Kenya from 15th to 19 June 2015. The Second Data Science Summer School was held at Makerere University, Kampala, Uganda from 27th to 29 July 2016, and the workshop was held at Pulse Lab, Kampala, Uganda from 30 July to 1 August 2016. The Third Data Science Summer School and Workshop was held at Nelson Mandela African Institute of Science and Technology, Tanzania from 19th to 21 July 2017. Among the sponsors of the event was ARM</p> <a href="https://aizhi.co/news/415d799577.html" class="read-more" title="Data Science Africa">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://aizhi.co/news/38e799954.html" class="card-thumb-link" title="National Data Repository"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/f/fc/Wikipetan%2C_Flowers_and_Butterfly_%28Layers_Overlapped%29.gif" alt="National Data Repository" loading="lazy"></a> <div class="card-body"> <h2><a href="https://aizhi.co/news/38e799954.html" title="National Data Repository">National Data Repository</a></h2> <p class="article-excerpt">A National Data Repository (NDR) is a data bank that seeks to preserve and promote a country's natural resources data, particularly data related to the petroleum exploration and production (E&P) sector. A National Data Repository is normally established by an entity that governs, controls and supports the exchange, capture, transference and distribution of E&P information, with the final target to provide the State with the tools and information to assure the growth, govern-ability, control, independence and sovereignty of the industry. The two fundamental reasons for a country to establish an NDR are to preserve data generated inside the country by the industry, and to promote investments in the country by utilizing data to reduce the exploration, production, and transportation business risks. Countries take different approaches towards preserving and promoting their natural resources data. The approach varies according to a country's natural resources policies, level of openness, and its attitude towards foreign investment. == Data types == NDRs store a vast array of data related to a country's natural resources. This includes wells, well log data, well reports, core samples, seismic surveys, post-stack seismic, field data/tapes, seismic (acquisition/processing) reports, production data, geological maps and reports, license data and geological models. == Funding models == Some NDRs are financed entirely by a country's government. Others are industry-funded. Still some are hybrid systems, funded in part by industry and government. NDRs typically charge fees for data requests and for data loading. The cost differs significantly between countries. In some cases an annual membership is charged to oil companies to store and access the data in the NDR. == Standards body == Energistics is the global energy standards resource center for the upstream oil and gas industry. Energistics National Data Repository Work Group: The standards body is Energistics. === Energistics-standards-directory === Global regulators of upstream oil and natural gas information, including seismic, drilling, production and reservoir data, formed the National Data Repository (NDR) Work Group in 2008 to collaborate on the development of data management standards and to assist emerging nations with hydrocarbon reserves to better collect, maintain and deliver oil and gas data to the public and to the industry. Ten countries, led by the Netherlands, Norway and the United Kingdom, formed NDR to share best practices and to formalize the development and deployment of data management standards for regulatory agencies. The other countries involved in the NDR Work Group's formation are Australia, Canada, India, Kenya, New Zealand, South Africa and the United States. Annual NDR Conference: Approximately every 18 months Energistics organizes a National Data Repository Conference. The purpose is to provide government and regulatory agencies from around the world an opportunity to attend a series of workshops dedicated to developing data exchange standards, improving communications with the oil and gas industry and learning data management techniques for natural resources information. === Society of Exploration Geophysicists and The International Oil and Gas Producers Association === The SEG is the custodian of the SEG standards which are used for the exchange, retention and release of seismic data. They are commonly used by National Data Repositories with the SEGD and SEGY being the field and processed exchange standards respectively. == NDRs around the world == Click here to see a map of the NDRs around the world</p> <a href="https://aizhi.co/news/38e799954.html" class="read-more" title="National Data Repository">Read more →</a> </div> </article> </li> </ul> <nav class="pagination" aria-label="Pagination"> <a href="https://aizhi.co/aiforbusinessreddit/29/" class="page-num">1</a><a href="https://aizhi.co/aiforbusinessreddit/30/" class="page-num">2</a><a href="https://aizhi.co/aiforbusinessreddit/31/" class="page-num">3</a><a href="https://aizhi.co/aiforbusinessreddit/32/" class="page-num">4</a><a href="https://aizhi.co/aiforbusinessreddit/33/" class="page-num">5</a><a href="https://aizhi.co/aiforbusinessreddit/34/" class="page-num">6</a><a href="https://aizhi.co/aiforbusinessreddit/35/" class="page-num">7</a><a href="https://aizhi.co/aiforbusinessreddit/36/" class="page-num">8</a><a href="https://aizhi.co/aiforbusinessreddit/37/" class="page-num">9</a><a href="https://aizhi.co/aiforbusinessreddit/38/" class="page-num">10</a> </nav> </main> <aside class="sidebar"> <section class="sidebar-section"> <h2>All Categories</h2> <ul> <li><a href="https://aizhi.co/aicodingtools/">AI Coding Tools</a></li><li><a href="https://aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://aizhi.co/aivideotools/">AI Video Tools</a></li><li><a href="https://aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://aizhi.co/aiwritingtools/">AI Writing Tools</a></li><li><a href="https://aizhi.co/ainewsandguides/">AI News and Guides</a></li> </ul> </section> <section class="sidebar-section"> <h2>Trending Guides</h2> <ul> <li><a href="https://aizhi.co/news/451e499544.html" title="Google Research">Google Research</a></li><li><a href="https://aizhi.co/news/29e799963.html" title="Ontology merging">Ontology merging</a></li><li><a href="https://aizhi.co/news/07e799985.html" title="Query language">Query language</a></li><li><a href="https://aizhi.co/news/398d799594.html" title="EdgeRank">EdgeRank</a></li><li><a href="https://aizhi.co/news/258d499737.html" title="Maximum inner-product search">Maximum inner-product search</a></li><li><a href="https://aizhi.co/news/469b799523.html" title="Magic Quadrant">Magic Quadrant</a></li><li><a href="https://aizhi.co/news/383b799609.html" title="Gutmann method">Gutmann method</a></li><li><a href="https://aizhi.co/news/427e799565.html" title="Data drilling">Data drilling</a></li><li><a href="https://aizhi.co/news/240a399756.html" title="Neuro-sama">Neuro-sama</a></li><li><a href="https://aizhi.co/news/206a799786.html" title="AI-assisted reverse engineering">AI-assisted reverse engineering</a></li> </ul> </section> </aside> </div> </div> </div> <footer class="site-footer"> <div class="container"> <div class="footer-cols"> <div class="footer-col footer-about"> <a class="brand" href="https://aizhi.co/" aria-label="Aizhi"> <span class="brand-mark" aria-hidden="true">✦</span> <span class="brand-text">Aizhi</span> </a> <p class="footer-tagline">Hand-picked AI tools, generators and practical how-to guides — independent reviews, updated for 2026.</p> </div> <nav class="footer-col" aria-label="Categories"> <h2 class="footer-h">Categories</h2> <ul> <li><a href="https://aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://aizhi.co/ainewsandguides/">AI News and Guides</a></li><li><a href="https://aizhi.co/aiwritingtools/">AI Writing Tools</a></li><li><a href="https://aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://aizhi.co/aicodingtools/">AI Coding Tools</a></li><li><a href="https://aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://aizhi.co/aivideotools/">AI Video Tools</a></li> </ul> </nav> <nav class="footer-col" aria-label="Site"> <h2 class="footer-h">Site</h2> <ul> <li><a href="https://aizhi.co/">Home</a></li> <li><a href="/sitemap.xml">XML Sitemap</a></li> </ul> </nav> </div> <div class="partner-links" aria-label="Network"> </div> <p class="footer-copy"> © Aizhi. 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