Read Along, formerly known as Bolo, is an Android language-learning app for children developed by Google for the Android operating system. The application was released on the Play Store on March 7, 2019. It features a character named Diya helping children learn to read through illustrated stories. It has the facility to learn English and Indian major languages i.e. Hindi, Bengali, Tamil, Telugu, Marathi and Urdu, as well as Spanish, Portuguese and Arabic. == Technology == The app uses text-to-speech technology, through which the character named Dia reads the story, as well as speech-to-text technology, which mechanically identifies the matches between the text and the reading of the user. The story of Chhota Bheem and Katha Kids was added in September 2019. In April 2020, a new version of the application was released. In September 2020, it added Arabic language to its language option. A web version was launched in August 2022.
Separable filter
A separable filter in image processing can be written as product of two more simple filters. Typically a 2-dimensional convolution operation is separated into two 1-dimensional filters. This reduces the computational costs on an N × M {\displaystyle N\times M} image with a m × n {\displaystyle m\times n} filter from O ( M ⋅ N ⋅ m ⋅ n ) {\displaystyle {\mathcal {O}}(M\cdot N\cdot m\cdot n)} down to O ( M ⋅ N ⋅ ( m + n ) ) {\displaystyle {\mathcal {O}}(M\cdot N\cdot (m+n))} . == Examples == 1. A two-dimensional smoothing filter: 1 3 [ 1 1 1 ] ∗ 1 3 [ 1 1 1 ] = 1 9 [ 1 1 1 1 1 1 1 1 1 ] {\displaystyle {\frac {1}{3}}{\begin{bmatrix}1\\1\\1\end{bmatrix}}{\frac {1}{3}}{\begin{bmatrix}1&1&1\end{bmatrix}}={\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}}} 2. Another two-dimensional smoothing filter with stronger weight in the middle: 1 4 [ 1 2 1 ] ∗ 1 4 [ 1 2 1 ] = 1 16 [ 1 2 1 2 4 2 1 2 1 ] {\displaystyle {\frac {1}{4}}{\begin{bmatrix}1\\2\\1\end{bmatrix}}{\frac {1}{4}}{\begin{bmatrix}1&2&1\end{bmatrix}}={\frac {1}{16}}{\begin{bmatrix}1&2&1\\2&4&2\\1&2&1\end{bmatrix}}} 3. The Sobel operator, used commonly for edge detection: [ 1 2 1 ] ∗ [ 1 0 − 1 ] = [ 1 0 − 1 2 0 − 2 1 0 − 1 ] {\displaystyle {\begin{bmatrix}1\\2\\1\end{bmatrix}}{\begin{bmatrix}1&0&-1\end{bmatrix}}={\begin{bmatrix}1&0&-1\\2&0&-2\\1&0&-1\end{bmatrix}}} This works also for the Prewitt operator. In the examples, there is a cost of 3 multiply–accumulate operations for each vector which gives six total (horizontal and vertical). This is compared to the nine operations for the full 3x3 matrix. Another notable example of a separable filter is the Gaussian blur whose performance can be greatly improved the bigger the convolution window becomes.
Script theory
Script theory is a psychological theory which posits that human behaviour largely falls into patterns called scripts because they function the way a written script does, by providing a program for action. Silvan Tomkins created script theory as a further development of his affect theory, which regards human beings' emotional responses to stimuli as falling into categories called affects: he noticed that the purely biological response of affect may be followed by awareness and by what we cognitively do in terms of acting on that affect, so that more was needed to produce a complete explanation of what he called human being theory. These scripts fall under the larger cognitive concept called schemas, which are organized chunks of information. A schema is a script that has the potential to lack the specificity of the sequence of events. A schema becomes a script is when there is an ordering to it that requires action, such as the process of starting a car (get in, put on the seatbelt, turn the car on, release the emergency brake, etc.). In script theory, the basic unit of analysis is called a scene, defined as a sequence of events linked by the affects triggered during the experience of those events. Tomkins recognized that affective experiences fall into patterns that we may group together according to criteria, such as the types of persons and places involved and the degree of intensity of the effect experienced—the patterns of which constitute scripts that inform behavior in an effort to maximize positive affect and to minimize negative affect. == In artificial intelligence == Roger Schank, Robert P. Abelson and their research group extended Tomkins' scripts and used them in early artificial intelligence work as a method of representing procedural knowledge. In their work, scripts are very much like frames, except the values that fill the slots must be ordered. A script is a structured representation describing a stereotyped sequence of events in a particular context. Scripts are used in natural-language understanding systems to organize a knowledge base in terms of the situations that the system should understand. The classic example of a script involves the typical sequence of events that occur when a person drinks in a restaurant: finding a seat, reading the menu, ordering drinks from the waitstaff, etc. In the script form, these would be decomposed into conceptual transitions, such as MTRANS and PTRANS, which refer to mental transitions [of information] and physical transitions [of things]. Schank, Abelson and their colleagues tackled some of the most difficult problems in artificial intelligence (i.e., story understanding), but ultimately their line of work ended without tangible success. This type of work received little attention after the 1980s, but became very influential in later knowledge representation techniques, such as case-based reasoning. Scripts can be inflexible. To deal with inflexibility, smaller modules called memory organization packets (MOP) can be combined in a way that is appropriate for the situation.
Executive Order 14179
Executive Order 14179, titled "Removing Barriers to American Leadership in Artificial Intelligence", is an executive order signed by Donald Trump, the 47th President of the United States, on January 23, 2025. The executive order aims to initiate the process of strengthening U.S. leadership in artificial intelligence, promote AI development free from ideological bias or social agendas, establish an action plan to maintain global AI dominance, and to revise or rescind policies that conflict with these goals. == Background == === Joe Biden === This executive order comes in response to the Executive Order 14110 titled Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (sometimes referred to as "Executive Order on Artificial Intelligence") signed by Joe Biden on October 30, 2023. === Donald Trump === Donald Trump rescinded Executive Order 14110 on his first day in office with the Initial Rescissions of Harmful Executive Orders and Actions executive order. On January 23, 2025, Trump signed the Removing Barriers to American Leadership in Artificial Intelligence executive order as the replacement executive order covering the development of artificial intelligence technologies. == Provisions == It revokes existing AI policies and directives that are seen as barriers to U.S. AI innovation. It mandates the creation of an action plan within 180 days to sustain U.S. AI leadership, focusing on human flourishing, economic competitiveness, and national security. It requires the review of policies, directives, and regulations related to Executive Order 14110 (from October 2023) to identify actions that may conflict with the new policy goals. Agencies are instructed to suspend, revise, or rescind actions from the previous executive order that may be inconsistent with the new policy. The Office of Management and Budget (OMB) must revise certain memoranda (M-24-10 and M-24-18) within 60 days to align with the new policy. The order specifies that it does not create new enforceable rights or benefits and should be implemented within the boundaries of existing law and appropriations. == Implementation == The NITRD program, on behalf of the Office of Science and Technology Policy (OSTP), requested public input on the development of an AI Action Plan by March 15. == Reactions == Over 10,000 public comments were submitted in response to the OSTP request for public input. OpenAI submitted comments proposing a five-point strategy focused on regulatory preemption, export controls, copyright protections, infrastructure investment, and government adoption to ensure AI innovation, promote democratic AI globally, and protect national security. They emphasized the ability to learn from copyrighted material to maintain America's lead against China's state-controlled AI efforts like DeepSeek. Google submitted comments advocating for a three-pronged plan that invests in domestic AI development through energy infrastructure reform, balanced export controls, continued research funding, and coherent federal policies, while modernizing government AI adoption and promoting innovation-friendly approaches internationally. Both OpenAI and Google urged White House opposition to foreign copyright and transparency obligations, for example in the UK Government's preferred option in their Copyright and AI consultation.
National Library of Medicine classification
The National Library of Medicine (NLM) classification system is a library indexing system covering the fields of medicine and preclinical basic sciences. Operated and maintained by the U.S. National Library of Medicine, the NLM classification is patterned after the Library of Congress (LC) Classification system: alphabetical letters denote broad subject categories which are subdivided by numbers. For example, QW 279 would indicate a book on an aspect of microbiology or immunology. The one- or two-letter alphabetical codes in the NLM classification use a limited range of letters: only QS–QZ and W–WZ. This allows the NLM system to co-exist with the larger LC coding scheme as neither of these ranges are used in the LC system. There are, however, three pre-existing codes in the LC system which overlap with the NLM: Human Anatomy (QM), Microbiology (QR), and Medicine (R). To avoid further confusion, these three codes are not used in the NLM. The headings for the individual schedules (letters or letter pairs) are given in brief form (e.g., QW - Microbiology and Immunology; WG - Cardiovascular System) and together they provide an outline of the subjects covered by the NLM classification. Headings are interpreted broadly and include the physiological system, the specialties connected with them, the regions of the body chiefly concerned and subordinate related fields. The NLM system is hierarchical, and within each schedule, division by organ usually has priority. Each main schedule, as well as some sub-sections, begins with a group of form numbers ranging generally from 1–49 which classify materials by publication type, e.g., dictionaries, atlases, laboratory manuals, etc. The main schedules QS-QZ, W-WY, and WZ (excluding the range WZ 220–270) classify works published after 1913; the 19th century schedule is used for works published 1801–1913; and WZ 220-270 is used to provide century groupings for works published before 1801. == Classification categories == === Preclinical Sciences === QS Human Anatomy QT Physiology QU Biochemistry QV Pharmacology QW Microbiology & Immunology QX Parasitology QY Clinical Pathology QZ Pathology === Medicine and Related Subjects === W Health Professions WA Public Health WB Practice of Medicine WC Communicable Diseases WD Disorders of Systemic, Metabolic, or Environmental Origin, etc. WE Musculoskeletal System WF Respiratory System WG Cardiovascular System WH Hemic and Lymphatic Systems WI Digestive System WJ Urogenital System WK Endocrine System WL Nervous System WM Psychiatry WN Radiology. Diagnostic Imaging WO Surgery WP Gynecology WQ Obstetrics WR Dermatology WS Pediatrics WT Geriatrics. Chronic Disease WU Dentistry. Oral Surgery WV Otolaryngology WW Ophthalmology WX Hospitals & Other Health Facilities WY Nursing WZ History of Medicine 19th Century Schedule
RFPolicy
The RFPolicy outlines a method for contacting vendors about security vulnerabilities found in their products. It was initially written in 2000 by hacker and security consultant Rain Forest Puppy. It was perhaps the second disclosure policy, following Simple Nomad's. The policy gives the vendor five working days to respond to the reporter of the bug. If the vendor fails to contact the reporter within those five days, the issue is recommended to be disclosed to the general community. The reporter should help the vendor reproduce the bug and work out a fix. The reporter should delay notifying the general community about the bug if the vendor provides feasible reasons for requiring so. If the vendor fails to respond or shuts down communication with the reporter of the problem within five working days, the reporter should disclose the issue to the general community. When issuing an alert or fix, the vendor should give the reporter proper credit for reporting the bug. Context for the history of vulnerability disclosure is available in a history article.
Revoscalepy
revoscalepy is a machine learning package in Python created by Microsoft. It is available as part of Machine Learning Services in Microsoft SQL Server 2017 and Machine Learning Server 9.2.0 and later. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, in addition to some summary functions for inspecting data. Other machine learning algorithms such as neural network are provided in microsoftml, a separate package that is the Python version of MicrosoftML. revoscalepy also contains functions designed to run machine learning algorithms in different compute contexts, including SQL Server, Apache Spark, and Hadoop. In June 2021, Microsoft announced to open source the revoscalepy and RevoScaleR packages, making them freely available under the MIT License.