StatCrunch is a web-based statistical software application from Pearson Education. StatCrunch was originally created for use in college statistics courses. As a full-featured statistics package, it is now also used for research and for other statistical analysis purposes. == History == American statistics professor Webster West created StatCrunch in 1997. Over the next 19 years West assisted by others added many more statistical procedures and graphing capabilities, and made user interface improvements. In 2005, West received two awards for StatCrunch: the CAUSEweb Resource of the Year Award and the MERLOT Classics Award. In 2013, the StatCrunch Java code was rewritten in JavaScript in order to avoid Java browser security problems, and so that it would run on iOS and Android. In 2015, new ways of importing data were added, including importing multi-page data directly from Wikipedia tables and other Web sources, and also importing with drag-and-drop for various data formats. In 2016, StatCrunch was acquired by Pearson Education, which had already been serving as the primary distributor of StatCrunch for several years. == Software == A StatCrunch license is included with many of Pearson's statistical textbooks. Because StatCrunch is a web application, it works on multiple platforms, including Windows, macOS, iOS, and Android. Data in StatCrunch is represented in a "data table" view, which is similar to a spreadsheet view, but unlike spreadsheets, the cells in a data table can only contain numbers or text. Formulas cannot be stored in these cells. There are many ways to import data into StatCrunch. Data can be typed directly into cells in the data table. Entire blocks of data may be cut-and-pasted into the data table. Text files (.csv, .txt, etc.) and Microsoft Excel files (.xls and .xlsx) can be drag-and-dropped into the data table. Data can be pulled into StatCrunch directly from Wikipedia tables or other Web tables, including multi-page tables. Data can be loaded directly from Google Drive and Dropbox. Shared data sets saved by other StatCrunch community users can be searched for by title or keyword and opened in a data table. Graphs, results, and reports created by StatCrunch can be shared with other users, in addition to the sharing of data sets. StatCrunch has a library of data transformation functions. StatCrunch can also recode and reorganize data. All data is stored in memory, and all processing happens on the client, so response is fast, even with large data sets. StatCrunch can interact with multiple graphs simultaneously. If a user selects a data point on one graph, then that same data point is highlighted on all other displayed graphs. In addition to standard statistical and graphing procedures, StatCrunch has a collection of about forty "applets" which illustrate statistical concepts interactively.
Timeline of artificial intelligence risks in global finance
The following article is a broad timeline of the course of events related to artificial intelligence risks in global finance. The AI boom has led to concerns including the existential risk from artificial intelligence, as the uptake on applications of artificial intelligence increases. By late 2025, global finance and artificial intelligence were "deeply intertwined". A June 2025 Menlo Ventures report raised concerns about the sustainability of future revenue and long-term profitability of AI, given the relatively low rate of consumer monetization. == 2017 == 30 NovemberThe New York Times said that new AI reports by McKinsey & Company, the National Bureau of Economic Research, and an AI Index created by university researchers, indicated an early AI boom. The Index built on a project—"The One Hundred Year Study on Artificial Intelligence" launched in 2014. == 2018 == 2018 was a year of incremental AI growth in finance. == 2022 == The release of ChatGPT by OpenAI became the catalyst for an artificial intelligence boom that continues to remake the global economy. According to a European Central Bank report, public interest in AI increased rapidly as evidenced with rising Google searches, AI jobs, models, patents, and innovations since late 2022. At that time Europe led the US in the size of its AI workforce. == 2023 == The regulatory body, the International Monetary Fund (IMF), published their report, "Generative Artificial Intelligence in Finance: Risk Considerations", drawing attention to oversight gaps and the need for regulations. The report explores the risks posed by using generative artificial intelligence (GenAI) systems in the financial sector including "broader risks to financial stability." == 2024 == January 12 In January 2024 Bloomberg's published its list of the "Magnificent Seven" Big Tech companies on the stock market based on their strength, size and market capitalization:Apple, Microsoft, Alphabet (Google), Amazon, Meta Platforms (Facebook), Nvidia, and Tesla. 21 June During the AI boom, Nvidia became the world's most valuable company, surpassing Microsoft, as its value increased to over US$4 trillion. In 2023 and 2024, the "Magnificent Seven" stocks were the primary drivers behind the increase in equity indexes, according to Reuters. == 2025 == === January === 23 January President Donald Trump's AI policy was announced calling for United States global leadership in artificial intelligence. The Economist noted that this politic shift in which the United States seeks "global dominance" in AI includes trimming regulations and assisting in expansion of infrastructure and increase in number of AI workers. Governments of Gulf nations were also investing trillions of dollars in AI. 27 January Against the backdrop of a tech war between China and the United States over AI dominance, within days of the launch of China's free DeepSeek App, it was the most downloaded app in the United States, rising to the first place in the Apple app store. President Trump responded immediately, saying this "sudden rise" should be a "wake-up" call to the United States, and called on US companies to be more competitive. === June === 26 June In their June 2025 report, Menlo Ventures estimated that only about 3% of consumers paid for artificial intelligence-related services, representing about $USD12 billion in annual spending. This is relatively low in contrast to the massive capital expenditure by AI infrastructure companies, which raises concerns about revenue sustainability and long-term profitability. === July === 23 July The Trump administration launched the US AI Action Plan, positioning the United States in a high-stakes technological race with China for global dominance in artificial intelligence, emphasizing that neither nation can afford to fall behind due to the exponential nature of AI advancement. The plan, a new government website and policy speech called for accelerated AI adoption across federal agencies, and a number of initiatives to make is easier for AI infrastructure expansion, and other measures to ensure American leadership in AI standards. Some leading experts warned that the administration failed to provide sufficient regulations and safeguards for AI safety. Concerns were raised about the negative impacts of cuts to research funding and tightened visa policies for scientists, potentially undermining public trust and America's ability to compete internationally. === September === 7 September The Economist cautioned that AI revenues are relatively modest compared to the high cost and investments in the creation of new data centers. Even Sam Altman, OpenAI CEO and one of the leading figures of the AI boom,, raised concerns about investors' outsized hopes for financial returns. At the same time, history has shown that new technologies, like railways and electricity, endured and spread after the initial hype faded. 12 September Economists warn that U.S. households' direct and indirect investments—mutual funds or retirement plans—in the stock market reached an unprecedented historically high level, now representing 45% of all financial assets, or about $USD51.2 trillion. Compared to the Dot-com bubble this represents a sharp increase in exposure. This makes U.S. households vulnerable to market downturns which in turn would result in decreasing consumer spending. U.S. household net worth rose to a record $176.3 trillion in the second quarter, an increase of $7.3 trillion since early 2025 and about $46 trillion higher than before the pandemic. Federal Reserve data attribute the surge primarily to gains in stock markets and housing values. However, the rise in wealth on paper coincided with increased household borrowing and growing government debt. 18 September Questions were being raised about how quickly the data centers, chips, servers, and GPUs assets of major AI companies will depreciate in value. Comparisons have been made to the Railway Mania in the aftermath of the stock market bubble where a valuable physical infrastructure remained standing, and the telecoms crash after the dot-com bubble which left fiber networks. 28 September There were warnings that record-high American stock ownership during the AI-fueled market boom is a red flag for systemic risk, as the current concentration in equities exceeds levels seen before the dot-com bubble burst in 2000, and could amplify the impact of any future stock market correction. === October === 3 October In 2025 alone, venture capitalists invested almost $USD200 billion in the artificial intelligence sector. 29 October Nvidia was the first company in the world to be valued at US$5 trillion, largely due to AI demand and strategic partnerships with leading technology and AI firms. Nvidia's increase in value was "meteoric". === November === 2 November Forbes reported that, since April, the 'Magnificent Seven' tech giants together contributed over 40% of the S&P 500's return, highlighting their outsized influence and the growing impact of AI on market valuations. CNN warned that while there is a current benefit to investors, with such a high concentration in the S&P 500, they are highly exposed to the fate of the Mag Seven. 2 November Globally there are 11,000 datacentres—huge campuses for AI infrastructure, including thousands of chips, GPUS, and servers. This represents a 500% increase over the last two decades. It is anticipated that $3USDtn more will be spent on increasing that number over the next two or three years. 5 November Concerns about the potential for a market bubble were raised as six of the AI-related Big Tech "Magnificent Seven"—that contribute to the AI boom—reported losing ground in the stock market. Global markets and artificial intelligence have become "deeply intertwined", according to a Reuters report. As of November 2025, more than 50% of the 20 largest S&P firms were deeply exposed to AI. In contrast, in 2000, the 20 S&P 500 firms represented 39% of its total value only 11 of these companies were exposed to the internet. If AI fails to deliver strong returns on their investments, these top S&P firms would be significantly impacted, according to the Economist. Analysts suggest that the AI market in 2025 may not behave like a traditional one, as investors are simultaneously aware of the risks and driven by the potential for outsized rewards. Leading AI labs may believe that the first company to achieve artificial general intelligence (AGI), when an AI system surpasses all human cognitive abilities and becomes capable of self-improvement—could dominate the future of technology and finance. While some have estimated that the potential value of such a breakthrough could be as high as $1.46 quadrillion, this figure is speculative and widely debated. 5 November Bloomberg described Nvidia's H100 Hopper-Blackwell AI chips as the "King of AI chips". Nvidia dominates the AI chip market with over 78% of the market share because of both speed and cost. According to B
Cognitive Technologies
Cognitive Technologies is a Russian software corporation that develops corporate business applications, AI-based advanced driver assistance systems. Founded in 1993 in Moscow (Russia), the company has offices in Eastern Europe, with R&D Centers in Russia. == History == Cognitive Technologies was founded in 1993 by Olga Uskova and Vladimir Arlazarov. The first employees previously worked in the team that developed the first world computer chess champion "Kaissa". The first programs developed by Cognitive Technologies were optical image and character recognition software – Tiger and CuneiForm. In February 2015 Cognitive Technologies and Kamaz, Russian Dakar Rally-winning truck manufacturer, started working on the self-driving Kamaz truck project. The first field tests took place in June 2015. In 2015 Andrey Chernogorov was appointed CEO of the company. == Products == Cognitive Technologies develops business application software and self-driving vehicle artificial intelligence. The main products are: C-pilot, AI-based ADAS E1 Evfrat – electronic workflow system CognitiveLot – e-purchasing systems == Cooperation with global companies == Under the contract signed between Cognitive Technologies and Hewlett-Packard, all scanners sold in Russia had text recognition software developed by Cognitive Technologies. It was the first contract with HP for an Eastern European company. Afterwards, Cognitive Technologies signed OEM contracts and business agreements with several global IT-companies, including IBM, Canon, Corel, Samsung, Xerox, Brother, Epson, and Olivetti. In 1998 Cognitive Technologies became the first company in Eastern Europe to get the Oracle Complementary Software Provider status. In 2001 Cognitive Technologies sold its Russian language speech corpus to Intel. In 2010 Cognitive Technologies sold its text parsing module to Yandex. The company also signed an agreement with NVIDIA join efforts in the development of intelligent document recognition technologies. == Self-driving car project == The system developed by Cognitive Technologies does not require building smart cities and smart roads equipped with multiple sensors – it works the opposite way, trying to understand the situation on the road like humans do. The system uses a video camera like a driver who uses his eyes, analyzing the information and focusing on the relevant data. For this purpose the system uses a special type of computer vision – foveal computer vision. Only 5–7% of the data gathered by the video cameras and sensors is processed by the system as relevant. The prototype is being tested in Russia on rough roads, on roads without marking, with the goal to prepare the system for work in difficult situations and on bad roads all around the world. == C-Pilot ADAS project == In August 2016 Cognitive Technologies started its own ADAS development project C-Pilot for ground transport control automation. == Self-driving tractors and harvesters project == The experts from Cognitive Technologies claim that the system will track stones, poles, and other obstacles that might be dangerous for the vehicles. This data will enable the engineers to develop an interactive field map, with GPS coordinates for stones and other obstacles. Eventually, this will result in an alteration of the harvester's movement pattern preventing it from running into stones or other objects that may inflict damage. Harvesters will work autonomously on the field, on the territory that is narrowed by radio beacons. == Present international activities == In 2016 Cognitive Technologies has joined the international community OpenPower Foundation, a consortium of open source solutions to developers based on POWER technology from IBM, which includes the world's leading IT map of Google, NVidia, Mellanox, etc. Within the consortium Cognitive Technologies is the initiator of forming of an international working group to develop a single software standard for the self-driving vehicle control. == Awards == In 2016, the leading Russian business newspaper Kommersant, announced that Cognitive Technologies is the TOP-2 Russian software company. TOP-6 Russian software company in 2015 according to Russoft TOP-500 biggest Russian companies according to RBC TOP-2 company of the Russian EDMS market in 2014 according to IDC TOP-20 Russian biggest IT-companies in 2013 according to Cnews Analytics
Pachinko allocation
In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive power than latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. The model is named for pachinko machines—a game popular in Japan, in which metal balls bounce down around a complex collection of pins until they land in various bins at the bottom. == History == Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006. The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007. In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP). The algorithm has been implemented in the MALLET software package published by McCallum's group at the University of Massachusetts Amherst. == Model == PAM connects words in V and topics in T with an arbitrary directed acyclic graph (DAG), where topic nodes occupy the interior levels and the leaves are words. The probability of generating a whole corpus is the product of the probabilities for every document: P ( D | α ) = ∏ d P ( d | α ) {\displaystyle P(\mathbf {D} |\alpha )=\prod _{d}P(d|\alpha )}
Best AI Image Generators in 2026
Comparing the best AI image generator? An AI image generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI image generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Attack path management
Attack path management is a cybersecurity technique that involves the continuous discovery, mapping, and risk assessment of identity-based attack paths. Attack path management is distinct from other computer security mitigation strategies in that it does not rely on finding individual attack paths through vulnerabilities, exploits, or offensive testing. Rather, attack path management techniques analyze all attack paths present in an environment based on active identity management policies, authentication configurations, and active authenticated "sessions" between objects. == Overview == Attack path management relies on concepts such as mapping and removing attack paths, identifying attack path choke points, and remediation of attack paths. Identity-based attacks are present in most publicly disclosed breaches, whether through social engineering to gain initial access to Active Directories or lateral movement for privilege escalation. Attackers require privileges to attack an environment’s most sensitive segments. Attack path management often involves removing out-of-date privileges and privilege assignments given to overly large groups. In attack path management, attack graphs are used to represent how a network of machines’ security is vulnerable to attack. The nodes in an attack graph represent principals and other objects such as machines, accounts, and security groups. The edges in an attack graph represent the links and relationships between nodes. Some nodes are easy to penetrate due to short paths from regular users to domain admins, resulting in focal points of concentrated network traffic, which are known as attack path choke points. Attack graphs are often analyzed using algorithms and visualization. Attack path management also identifies tier 0 assets, which are considered the most vulnerable because they have direct or indirect control of an Active Directory or Microsoft Entra ID environment.
Distributional–relational database
A distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantage of distributional–relational models over purely logical or semantic web models is the fact that the core semantic associations can be automatically captured from corpora, in contrast to the definition of manually curated ontologies and rule knowledge bases. == Distributional–relational models == Distributional–relational models were first formalized as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the vocabulary of the designer). In this model, the database symbols (entities and relations) are embedded into a distributional semantic space and have a geometric interpretation under a latent or explicit semantic space. The geometric aspect supports the semantic approximation between entities from different databases, or between a query term and a database entity. The distributional relational model then becomes a double layered model where the semantics of the structured data provides the fine-grained semantics intended by the database designer, which is extended by the distributional semantic model which contains the semantic associations expressed at a broader use. These models support the generalization from a closed communication scenario (in which database designers and users live in the same context, e.g. the same organization) to an open communication scenario (e.g. different organizations, the Web), creating an abstraction layer between users and the specific representation of the conceptual model.