Yorba is a web-based personal information management platform for finding, monitoring, or deleting online accounts and subscriptions. Yorba is a participating member of Consumer Reports’ Data Rights Protocol (DRP) consortium that develops open technical standards for exercising consumer data rights under laws including the California Consumer Privacy Act. == History == Yorba began as a research project around 2021. It was founded by Chris Zeunstrom (CEO), Nolan Cabeje (CDO) and David Schmudde (CTO). Zeunstrom says he began developing Yorba after growing frustrated with managing numerous email accounts, noting overloaded inboxes create distraction and potential security vulnerabilities. Yorba’s early development was also influenced by security issues he encountered at a previous company, which had been affected by data breaches at a time when such incidents were becoming increasingly common. In 2023, Yorba launched a private beta as a public benefit corporation funded through a give-back model operated by Zeunstrom's New York-based design firm, Ruca. In January 2024, Yorba entered public beta and reported over 1,000 users, including 160 premium subscribers. At the time of the public beta launch, Yorba integrated with Gmail and announced plans to expand compatibility to other online services and cloud storage providers. In September 2024, Yorba completed conformance testing under the Data Rights Protocol, an initiative developed by Consumer Reports, to establish a standard and open-source framework for securely transmitting consumer data rights requests under laws like the California Consumer Privacy Act. Yorba was named among twelve participating companies that implemented the protocol alongside OneTrust and Consumer Reports’ own Permission Slip app. Yorba was one of nine startups selected as 2025 finalist in the Santander X Global Awards international entrepreneurship competition. == Features == Yorba scans user inbox history data to identify online accounts, mailing lists, and possible data breaches. It uses natural language processing and machine learning to identify a user's accounts, services, and subscriptions. The platform prompts password resets for compromised accounts and locates unused accounts. The platform also supports mailing list management by identifying and helping users unsubscribe from newsletters. Paid subscribers can locate and cancel recurring charges. Yorba links with financial institutions in the U.S., Canada, and EU via Plaid Inc. to detect recurring charges and delete unwanted subscriptions. == Privacy and Ethics == Yorba's founder has openly criticized dark patterns that make canceling services difficult, citing personal frustration with inbox clutter as part of his inspiration for Yorba. Yorba offers privacy policy analysis in partnership with Amsterdam-based nonprofit Terms of Service; Didn’t Read, assigning grades based on invasiveness or ethical concerns. As of 2024, the company described its pricing as designed to cover operational costs and sustain the platform without outside investment.
Standard test image
A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class of processing techniques would need to deal with. Other test images are chosen because they present a range of challenges to image reconstruction algorithms, such as the reproduction of fine detail and textures, sharp transitions and edges, and uniform regions. == Historical origins == Test images as transmission system calibration material probably date back to the original Paris to Lyon pantelegraph link. Analogue fax equipment (and photographic equipment for the printing trade) were the largest user groups of the standardized image for calibration technology until the coming of television and digital image transmission systems. == Common test image resolutions == The standard resolution of the images is usually 512×512 or 720×576. Most of these images are available as TIFF files from the University of Southern California's Signal and Image Processing Institute. Kodak has released 768×512 images, available as PNGs, that was originally on Photo CD with higher resolution, that are widely used for comparing image compression techniques.
Defuzzification
Defuzzification is the process of producing a quantifiable result in crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems. These systems will have a number of rules that transform a number of variables into a fuzzy result, that is, the result is described in terms of membership in fuzzy sets. For example, rules designed to decide how much pressure to apply might result in "Decrease Pressure (15%), Maintain Pressure (34%), Increase Pressure (72%)". Defuzzification is interpreting the membership degrees of the fuzzy sets into a specific decision or real value. The simplest but least useful defuzzification method is to choose the set with the highest membership, in this case, "Increase Pressure" since it has a 72% membership, and ignore the others, and convert this 72% to some number. The problem with this approach is that it loses information. The rules that called for decreasing or maintaining pressure might as well have not been there in this case. A common and useful defuzzification technique is center of gravity. First, the results of the rules must be added together in some way. The most typical fuzzy set membership function has the graph of a triangle. Now, if this triangle were to be cut in a straight horizontal line somewhere between the top and the bottom, and the top portion were to be removed, the remaining portion forms a trapezoid. The first step of defuzzification typically "chops off" parts of the graphs to form trapezoids (or other shapes if the initial shapes were not triangles). For example, if the output has "Decrease Pressure (15%)", then this triangle will be cut 15% the way up from the bottom. In the most common technique, all of these trapezoids are then superimposed one upon another, forming a single geometric shape. Then, the centroid of this shape, called the fuzzy centroid, is calculated. The x coordinate of the centroid is the defuzzified value. == Methods == There are many different methods of defuzzification available, including the following: AI (adaptive integration) BADD (basic defuzzification distributions) BOA (bisector of area) CDD (constraint decision defuzzification) COA (center of area) COG (center of gravity) ECOA (extended center of area) EQM (extended quality method) FCD (fuzzy clustering defuzzification) FM (fuzzy mean) FOM (first of maximum) GLSD (generalized level set defuzzification) ICOG (indexed center of gravity) IV (influence value) LOM (last of maximum) MeOM (mean of maxima) MOM (middle of maximum) QM (quality method) RCOM (random choice of maximum) SLIDE (semi-linear defuzzification) WFM (weighted fuzzy mean) The maxima methods are good candidates for fuzzy reasoning systems. The distribution methods and the area methods exhibit the property of continuity that makes them suitable for fuzzy controllers.
Blended artificial intelligence
Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms, and technologies to leverage their respective strengths and compensate for their weaknesses. == Background == In the context of machine learning, blended AI can involve using different types of models, such as generative AI, decision trees, neural networks, and support vector machines. By combining their results, predictions are more accurate and reliable. This blending of models can be done through techniques like ensemble learning, where multiple models are trained independently and their predictions are combined to make a final decision. Blended AI can also involve combining different AI techniques or technologies, such as natural language processing, computer vision, and expert systems, to tackle complex problems that require a multi-dimensional approach. For example, in a sales scenario AI could be used for lead generation and gathering information from social media such as LinkedIn posts, or understanding a prospect's hobbies and interests. Another blended AI could achieve customer profiling including past interactions and purchasing habits, by them, their industry and growth areas. Blended AI could be used to do predictive analytics to look at historical sales data, market trends, and external factors to generate accurate sales forecasts. This method is critical to gauge and increase "efficiency, revenue, and productivity". Lastly, another could integrate all the information into the CRM to build and maintain better prospect and customer profiles. Blended AI aims to leverage the strengths of different AI techniques and technologies, allowing them to complement each other and create more powerful and comprehensive AI solutions. By combining multiple approaches, blended AI aims to achieve better performance, higher accuracy, improved robustness, and enhanced capabilities in solving diverse and challenging problems.
AI@50
AI@50, formally known as the "Dartmouth Artificial Intelligence Conference: The Next Fifty Years" (July 13–15, 2006), was a conference organized by James H. Moor, commemorating the 50th anniversary of the Dartmouth workshop which effectively inaugurated the history of artificial intelligence. Five of the original ten attendees were present: Marvin Minsky, Ray Solomonoff, Oliver Selfridge, Trenchard More, and John McCarthy. While sponsored by Dartmouth College, General Electric, and the Frederick Whittemore Foundation, a $200,000 grant from the Defense Advanced Research Projects Agency (DARPA) called for a report of the proceedings that would: Analyze progress on AI's original challenges during the first 50 years, and assess whether the challenges were "easier" or "harder" than originally thought and why Document what the AI@50 participants believe are the major research and development challenges facing this field over the next 50 years, and identify what breakthroughs will be needed to meet those challenges Relate those challenges and breakthroughs against developments and trends in other areas such as control theory, signal processing, information theory, statistics, and optimization theory. A summary report by the conference director, James H. Moor, was published in AI Magazine. == Conference Program and links to published papers == James H. Moor, conference Director, Introduction Carol Folt and Barry Scherr, Welcome Carey Heckman, Tonypandy and the Origins of Science === AI: Past, Present, Future === John McCarthy, What Was Expected, What We Did, and AI Today Marvin Minsky, The Emotion Machine === The Future Model of Thinking === Ron Brachman and Hector Levesque, A Large Part of Human Thought David Mumford, What is the Right Model for 'Thought'? Stuart Russell, The Approach of Modern AI === The Future of Network Models === Geoffrey Hinton & Simon Osindero, From Pandemonium to Graphical Models and Back Again Rick Granger, From Brain Circuits to Mind Manufacture === The Future of Learning & Search === Oliver Selfridge, Learning and Education for Software: New Approaches in Machine Learning Ray Solomonoff, Machine Learning — Past and Future Leslie Pack Kaelbling, Learning to be Intelligent Peter Norvig, Web Search as a Product of and Catalyst for AI === The Future of AI === Rod Brooks, Intelligence and Bodies Nils Nilsson, Routes to the Summit Eric Horvitz, In Pursuit of Artificial Intelligence: Reflections on Challenges and Trajectories === The Future of Vision === Eric Grimson, Intelligent Medical Image Analysis: Computer Assisted Surgery and Disease Monitoring Takeo Kanade, Artificial Intelligence Vision: Progress and Non-Progress Terry Sejnowski, A Critique of Pure Vision === The Future of Reasoning === Alan Bundy, Constructing, Selecting and Repairing Representations of Knowledge Edwina Rissland, The Exquisite Centrality of Examples Bart Selman, The Challenge and Promise of Automated Reasoning === The Future of Language and Cognition === Trenchard More The Birth of Array Theory and Nial Eugene Charniak, Why Natural Language Processing is Now Statistical Natural Language Processing Pat Langley, Intelligent Behavior in Humans and Machines === The Future of the Future === Ray Kurzweil, Why We Can Be Confident of Turing Test Capability Within a Quarter Century George Cybenko, The Future Trajectory of AI Charles J. Holland, DARPA's Perspective === AI and Games === Jonathan Schaeffer, Games as a Test-bed for Artificial Intelligence Research Danny Kopec, Chess and AI Shay Bushinsky, Principle Positions in Deep Junior's Development === Future Interactions with Intelligent Machines === Daniela Rus, Making Bodies Smart Sherry Turkle, From Building Intelligences to Nurturing Sensibilities === Selected Submitted Papers: Future Strategies for AI === J. Storrs Hall, Self-improving AI: An Analysis Selmer Bringsjord, The Logicist Manifesto Vincent C. Müller, Is There a Future for AI Without Representation? Kristinn R. Thórisson, Integrated A.I. Systems === Selected Submitted Papers: Future Possibilities for AI === Eric Steinhart, Survival as a Digital Ghost Colin T. A. Schmidt, Did You Leave That 'Contraption' Alone With Your Little Sister? Michael Anderson & Susan Leigh Anderson, The Status of Machine Ethics Marcello Guarini, Computation, Coherence, and Ethical Reasoning
Distributed file system for cloud
A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations (create, delete, modify, read, write) on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system. Users can share computing resources through the Internet thanks to cloud computing which is typically characterized by scalable and elastic resources – such as physical servers, applications and any services that are virtualized and allocated dynamically. Synchronization is required to make sure that all devices are up-to-date. Distributed file systems enable many big, medium, and small enterprises to store and access their remote data as they do local data, facilitating the use of variable resources. == Overview == === History === Today, there are many implementations of distributed file systems. The first file servers were developed by researchers in the 1970s. Sun Microsystem's Network File System became available in the 1980s. Before that, people who wanted to share files used the sneakernet method, physically transporting files on storage media from place to place. Once computer networks started to proliferate, it became obvious that the existing file systems had many limitations and were unsuitable for multi-user environments. Users initially used FTP to share files. FTP first ran on the PDP-10 at the end of 1973. Even with FTP, files needed to be copied from the source computer onto a server and then from the server onto the destination computer. Users were required to know the physical addresses of all computers involved with the file sharing. === Supporting techniques === Modern data centers must support large, heterogenous environments, consisting of large numbers of computers of varying capacities. Cloud computing coordinates the operation of all such systems, with techniques such as data center networking (DCN), the MapReduce framework, which supports data-intensive computing applications in parallel and distributed systems, and virtualization techniques that provide dynamic resource allocation, allowing multiple operating systems to coexist on the same physical server. === Applications === Cloud computing provides large-scale computing thanks to its ability to provide the needed CPU and storage resources to the user with complete transparency. This makes cloud computing particularly suited to support different types of applications that require large-scale distributed processing. This data-intensive computing needs a high performance file system that can share data between virtual machines (VM). Cloud computing dynamically allocates the needed resources, releasing them once a task is finished, requiring users to pay only for needed services, often via a service-level agreement. Cloud computing and cluster computing paradigms are becoming increasingly important to industrial data processing and scientific applications such as astronomy and physics, which frequently require the availability of large numbers of computers to carry out experiments. == Architectures == Most distributed file systems are built on the client-server architecture, but other, decentralized, solutions exist as well. === Client-server architecture === Network File System (NFS) uses a client-server architecture, which allows sharing of files between a number of machines on a network as if they were located locally, providing a standardized view. The NFS protocol allows heterogeneous clients' processes, probably running on different machines and under different operating systems, to access files on a distant server, ignoring the actual location of files. Relying on a single server results in the NFS protocol suffering from potentially low availability and poor scalability. Using multiple servers does not solve the availability problem since each server is working independently. The model of NFS is a remote file service. This model is also called the remote access model, which is in contrast with the upload/download model: Remote access model: Provides transparency, the client has access to a file. He sends requests to the remote file (while the file remains on the server). Upload/download model: The client can access the file only locally. It means that the client has to download the file, make modifications, and upload it again, to be used by others' clients. The file system used by NFS is almost the same as the one used by Unix systems. Files are hierarchically organized into a naming graph in which directories and files are represented by nodes. === Cluster-based architectures === A cluster-based architecture ameliorates some of the issues in client-server architectures, improving the execution of applications in parallel. The technique used here is file-striping: a file is split into multiple chunks, which are "striped" across several storage servers. The goal is to allow access to different parts of a file in parallel. If the application does not benefit from this technique, then it would be more convenient to store different files on different servers. However, when it comes to organizing a distributed file system for large data centers, such as Amazon and Google, that offer services to web clients allowing multiple operations (reading, updating, deleting,...) to a large number of files distributed among a large number of computers, then cluster-based solutions become more beneficial. Note that having a large number of computers may mean more hardware failures. Two of the most widely used distributed file systems (DFS) of this type are the Google File System (GFS) and the Hadoop Distributed File System (HDFS). The file systems of both are implemented by user level processes running on top of a standard operating system (Linux in the case of GFS). ==== Design principles ==== ===== Goals ===== Google File System (GFS) and Hadoop Distributed File System (HDFS) are specifically built for handling batch processing on very large data sets. For that, the following hypotheses must be taken into account: High availability: the cluster can contain thousands of file servers and some of them can be down at any time A server belongs to a rack, a room, a data center, a country, and a continent, in order to precisely identify its geographical location The size of a file can vary from many gigabytes to many terabytes. The file system should be able to support a massive number of files The need to support append operations and allow file contents to be visible even while a file is being written Communication is reliable among working machines: TCP/IP is used with a remote procedure call RPC communication abstraction. TCP allows the client to know almost immediately when there is a problem and a need to make a new connection. ===== Load balancing ===== Load balancing is essential for efficient operation in distributed environments. It means distributing work among different servers, fairly, in order to get more work done in the same amount of time and to serve clients faster. In a system containing N chunkservers in a cloud (N being 1000, 10000, or more), where a certain number of files are stored, each file is split into several parts or chunks of fixed size (for example, 64 megabytes), the load of each chunkserver being proportional to the number of chunks hosted by the server. In a load-balanced cloud, resources can be efficiently used while maximizing the performance of MapReduce-based applications. ===== Load rebalancing ===== In a cloud computing environment, failure is the norm, and chunkservers may be upgraded, replaced, and added to the system. Files can also be dynamically created, deleted, and appended. That leads to load imbalance in a distributed file system, meaning that the file chunks are not distributed equitably between the servers. Distributed file systems in clouds such as GFS and HDFS rely on central or master servers or nodes (Master for GFS and NameNode for HDFS) to manage the metadata and the load balancing. The master rebalances replicas periodically: data must be moved from one DataNode/chunkserver to another if free space on the first server falls below a certain threshold. However, this centralized approach can become a bottleneck for those master servers, if they become unable to manage a large number of file accesses, as it increases their already heavy loads. The load rebalance problem is NP-hard. In order to get a large number of chunkservers to work in collaboration, and to
Global Artificial Intelligence Summit & Awards
The Global Artificial Intelligence Summit & Awards (GAISA) is an international conference on Artificial Intelligence organized annually by AICRA. Since its inception in 2019, GAISA has been held at various locations each year. The 5th Edition of GAISA will be Scheduled on April 11-12, 2024, at Bharat Mandapam. GAISA 2025 features a distinguished lineup of speakers, including leading experts, researchers, and executives from top global tech companies. These thought leaders are at the forefront of AI innovation, with deep expertise in areas such as machine learning, robotics, and ethical AI. Their diverse backgrounds span academia, industry, and entrepreneurship, offering unique insights into how AI is reshaping sectors like healthcare, finance, transportation, and more. Attendees can expect thought-provoking discussions on the future of AI, its societal impact, and the transformative potential of emerging technologies in solving complex global challenges Few Speakers are listed below:- Shri Nitin Gadkari, Rao Inderjit Singh, Piyush Goyal, Admiral R Hari Kumar PVSM, AVSM, ADC, Samir V Kamat, Narayan Tatu Rane, Prof. K. Vijay Raghavan and many others. == History == The conference was launched first in 2019 as Vigyan Bhawan New Delhi by AICRA with an objective of discussion and exploring artificial intelligence in engrossed sectors.