AI Essay Bypass

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  • Linked timestamping

    Linked timestamping

    Linked timestamping is a type of trusted timestamping where issued time-stamps are related to each other. Each time-stamp would contain data that authenticates the time-stamp before it, the authentication would be authenticating the entire message, including the previous time-stamps authentication, making a chain. This makes it impossible to add a time-stamp in to the middle of the chain, as any time-stamps afterwards would be different. == Description == Linked timestamping creates time-stamp tokens which are dependent on each other, entangled in some authenticated data structure. Later modification of the issued time-stamps would invalidate this structure. The temporal order of issued time-stamps is also protected by this data structure, making backdating of the issued time-stamps impossible, even by the issuing server itself. The top of the authenticated data structure is generally published in some hard-to-modify and widely witnessed media, like printed newspaper or public blockchain. There are no (long-term) private keys in use, avoiding PKI-related risks. Suitable candidates for the authenticated data structure include: Linear hash chain Merkle tree (binary hash tree) Skip list The simplest linear hash chain-based time-stamping scheme is illustrated in the following diagram: The linking-based time-stamping authority (TSA) usually performs the following distinct functions: Aggregation For increased scalability the TSA might group time-stamping requests together which arrive within a short time-frame. These requests are aggregated together without retaining their temporal order and then assigned the same time value. Aggregation creates a cryptographic connection between all involved requests; the authenticating aggregate value will be used as input for the linking operation. Linking Linking creates a verifiable and ordered cryptographic link between the current and already issued time-stamp tokens. Publishing The TSA periodically publishes some links, so that all previously issued time-stamp tokens depend on the published link and that it is practically impossible to forge the published values. By publishing widely witnessed links, the TSA creates unforgeable verification points for validating all previously issued time-stamps. == Security == Linked timestamping is inherently more secure than the usual, public-key signature based time-stamping. All consequential time-stamps "seal" previously issued ones - hash chain (or other authenticated dictionary in use) could be built only in one way; modifying issued time-stamps is nearly as hard as finding a preimage for the used cryptographic hash function. Continuity of operation is observable by users; periodic publications in widely witnessed media provide extra transparency. Tampering with absolute time values could be detected by users, whose time-stamps are relatively comparable by system design. Absence of secret keys increases system trustworthiness. There are no keys to leak and hash algorithms are considered more future-proof than modular arithmetic based algorithms, e.g. RSA. Linked timestamping scales well - hashing is much faster than public key cryptography. There is no need for specific cryptographic hardware with its limitations. The common technology for guaranteeing long-term attestation value of the issued time-stamps (and digitally signed data) is periodic over-time-stamping of the time-stamp token. Because of missing key-related risks and of the plausible safety margin of the reasonably chosen hash function this over-time-stamping period of hash-linked token could be an order of magnitude longer than of public-key signed token. == Research == === Foundations === Stuart Haber and W. Scott Stornetta proposed in 1990 to link issued time-stamps together into linear hash-chain, using a collision-resistant hash function. The main rationale was to diminish TSA trust requirements. Tree-like schemes and operating in rounds were proposed by Benaloh and de Mare in 1991 and by Bayer, Haber and Stornetta in 1992. Benaloh and de Mare constructed a one-way accumulator in 1994 and proposed its use in time-stamping. When used for aggregation, one-way accumulator requires only one constant-time computation for round membership verification. Surety started the first commercial linked timestamping service in January 1995. Linking scheme is described and its security is analyzed in the following article by Haber and Sornetta. Buldas et al. continued with further optimization and formal analysis of binary tree and threaded tree based schemes. Skip-list based time-stamping system was implemented in 2005; related algorithms are quite efficient. === Provable security === Security proof for hash-function based time-stamping schemes was presented by Buldas, Saarepera in 2004. There is an explicit upper bound N {\displaystyle N} for the number of time stamps issued during the aggregation period; it is suggested that it is probably impossible to prove the security without this explicit bound - the so-called black-box reductions will fail in this task. Considering that all known practically relevant and efficient security proofs are black-box, this negative result is quite strong. Next, in 2005 it was shown that bounded time-stamping schemes with a trusted audit party (who periodically reviews the list of all time-stamps issued during an aggregation period) can be made universally composable - they remain secure in arbitrary environments (compositions with other protocols and other instances of the time-stamping protocol itself). Buldas, Laur showed in 2007 that bounded time-stamping schemes are secure in a very strong sense - they satisfy the so-called "knowledge-binding" condition. The security guarantee offered by Buldas, Saarepera in 2004 is improved by diminishing the security loss coefficient from N {\displaystyle N} to N {\displaystyle {\sqrt {N}}} . The hash functions used in the secure time-stamping schemes do not necessarily have to be collision-resistant or even one-way; secure time-stamping schemes are probably possible even in the presence of a universal collision-finding algorithm (i.e. universal and attacking program that is able to find collisions for any hash function). This suggests that it is possible to find even stronger proofs based on some other properties of the hash functions. At the illustration above hash tree based time-stamping system works in rounds ( t {\displaystyle t} , t + 1 {\displaystyle t+1} , t + 2 {\displaystyle t+2} , ...), with one aggregation tree per round. Capacity of the system ( N {\displaystyle N} ) is determined by the tree size ( N = 2 l {\displaystyle N=2^{l}} , where l {\displaystyle l} denotes binary tree depth). Current security proofs work on the assumption that there is a hard limit of the aggregation tree size, possibly enforced by the subtree length restriction. == Standards == ISO 18014 part 3 covers 'Mechanisms producing linked tokens'. American National Standard for Financial Services, "Trusted Timestamp Management and Security" (ANSI ASC X9.95 Standard) from June 2005 covers linking-based and hybrid time-stamping schemes. There is no IETF RFC or standard draft about linking based time-stamping. RFC 4998 (Evidence Record Syntax) encompasses hash tree and time-stamp as an integrity guarantee for long-term archiving.

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  • How to Choose an AI Photo Editor

    How to Choose an AI Photo Editor

    In search of the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Is an AI Paragraph Rewriter Worth It in 2026?

    Is an AI Paragraph Rewriter Worth It in 2026?

    In search of the best AI paragraph rewriter? An AI paragraph rewriter is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI paragraph rewriter slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • The Best Free AI Writing Assistant for Beginners

    The Best Free AI Writing Assistant for Beginners

    Shopping for the best AI writing assistant? An AI writing assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI writing assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Vx-underground

    Vx-underground

    vx-underground, also known as VXUG, is an educational website about malware and cybersecurity. It claims to have the largest online repository of malware. The site was launched in May, 2019 and has grown to host over 35 million pieces of malware samples. On their account on Twitter, VXUG reports on and verifies cybersecurity breaches. == Reception == Kim Crawley compared the site to VirusTotal and states that vx-underground is more susceptible to suspicion for law enforcement. == Data breach reports == In May 2024, the International Baccalaureate organizations faced allegations over supposed breaches in their IT infrastructure after an incident of examination leaks. Upon inspecting leaked data, VXUG were the first to report that the breach seemed legitimate on the morning of May 6.

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  • The Best Free AI Code Generator for Beginners

    The Best Free AI Code Generator for Beginners

    In search of the best AI code generator? An AI code generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI code generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Xu Li (computer scientist)

    Xu Li (computer scientist)

    Xu Li is a Chinese computer scientist and co-founder and current CEO of SenseTime, an artificial intelligence (AI) company. Xu has led SenseTime since the company's incorporation and helped it independently develop its proprietary deep learning platform. == Education and research == Xu obtained both his bachelor's and master's degrees in computer science from Shanghai Jiao Tong University. He received his doctorate in computer science from the Chinese University of Hong Kong. Xu has published more than 50 papers at international conferences and in journals in the field of computer vision and won the Best Paper Award at the international conference on Non-Photorealistic Rendering and Animation (NPAR) 2012 and the Best Reviewer Award at the international conferences Asian Conference on Computer Vision ACCV 2012 and International Conference on Computer Vision (ICCV) 2015. He has three algorithms that have been included into the visual open-source platform OpenCV, and his "L0 Smoothing" algorithm garnered the most citations in research papers over a span of five years (2011–2015) within the ACM Transactions on Graphics (TOG), a scientific journal that Thomson Reuters InCites has placed first among software engineering journals. == Career == Previously, Xu worked at Lenovo Corporate Research & Development. He was also a visiting researcher at Motorola China R&D Institute, Omron Research Institute, and Microsoft Research. == Selected publications == Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Li Xu, Jiahao Pang, Qiong Yan, Yu-wing Tai, "Accurate Single Stage Detector Using Recurrent Rolling Convolution", (CVPR), 2017. Jimmy SJ. Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan, "Look, Listen and Learn – A Multimodal LSTM for Speaker Identification", The 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks" Advances in Neural Information Processing Systems (NIPS), 2015. Xiaoyong Shen, Chao Zhou, Li Xu, Jiaya Jia, "Mutual-Structure for Joint Filtering" International Conference on Computer Vision (ICCV), (oral presentation), 2015. Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia, "Hierarchical Image Saliency Detection on Extended CSSD" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jianping Shi, Xin Tao, Li Xu, Jiaya Jia, "Break Ames Room Illusion: Depth from General Single Images" ACM Transactions on Graphics (TOG), (Proc. ACM SIGGRAPH ASIA2015). Yongtao Hu, Jimmy SJ. Ren, Jingwen Dai, Chang Yuan, Li Xu, Wenping Wang, "Deep Multimodal Speaker Naming" ACM International Conference on Multimedia (MM), 2015. Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia "Deep Edge-Aware Filters" International Conference on Machine Learning (ICML), 2015. Jianping Shi, Li Xu, Jiaya Jia "Just Noticeable Defocus Blur Detection and Estimation" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu "Handling Motion Blur in Multi-Frame Super-Resolution" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Xiaoyong Shen, Qiong Yan, Li Xu, Lizhuang Ma, Jiaya Jia"Multispectral Joint Image Restoration via Optimizing a Scale Map" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jimmy SJ. Ren, Li Xu, "On Vectorization of Deep Convolutional Neural Networks for Vision Tasks" AAAI Conference on Artificial Intelligence (AAAI), 2015. == Awards and honors == Xu was ranked 7th in Fortune magazine's 2018 edition of its 40 Under 40. He was also named "China's Outstanding AI Industry Leader" by The Economic Observer, received the "Innovative Business Leader" Award under NetEase's "Future Technology Talent Awards", and was honored as Sina's "2017 Top Ten Economic Figures". In 2018, Xu was named EY's "Entrepreneur of the Year China" in the Technology category.

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  • IBM optical mark and character readers

    IBM optical mark and character readers

    IBM designed, manufactured and sold optical mark and character readers from 1960 until 1984. The IBM 1287 is notable as being the first commercially sold scanner capable of reading handwritten numbers. == Initial development work == IBM Poughkeepsie studied machine character recognition from 1950 till 1954, developing an experimental machine that used a cathode-ray-tube attached an IBM 701 which performed the character analysis. They pursued a technique known as lakes and bays which examined different areas of dark and light where the lakes were white areas enclosed by black and the bays were partially enclosed areas. Their machine and mission was moved to IBM Endicott in 1954, where research continued. From 1955 to 1956 they then worked on the VIDOR (Visual Document Reader) program, but they could not get agreement on acceptable reject rate. The developers felt 80% recognition was acceptable (meaning 20% of documents would need to be manually processed), while product planners and IBM Marketing felt that compared to punched card, the reject rate was unacceptably high. This led to no new products being released. In 1956 the American Bankers Association chose to use Magnetic Ink Character Recognition (MICR) to automate check handling, rejecting a proposed solution generated by an IBM Poughkeepsie banking project that used optical characters formed by vertical bars and digits. IBM developed a magnetic read head to handle the new standard, releasing the IBM 1210 MICR reader/sorter in 1959. The development work for this product both with read heads and document handling, helped move optical character recognition forward, with development focusing on reading one or two lines of print from a paper document larger than an IBM punched card. The first product to be released was the IBM 1418. == IBM 123x Optical Mark Readers == The IBM 1230, IBM 1231, and IBM 1232 were optical mark readers used to input the contents of data sources such as questionnaires, test results, surveys as well as historical data that could be easily entered as marks on sheets. Educational institutes used them to score test results and they were effectively a replacement for the IBM 805 Test Scoring Machine that used electrical resistance and a mark sense pencil to score a test, rather than optical mark detection. They were developed and manufactured by IBM Rochester. They have the following features: A pneumatic input hopper that can hold approximately 600 sheets Two output stackers: the normal stacker that holds 600 sheets and the select (or reject) stacker which holds 50 sheets. Pluggable SMS printed circuit cards They can read positional marks made by a lead pencil using an optical read head that consists of photovoltaic(solar) cells and lamps The 1230 has 21 photovoltaic cells, 20 for reading the pencil marks and one to read timing marks on the right hand border of the sheet. The 1231 and 1232 have 22 photovoltaic cells, 20 to read data, one to read timing marks and one to read a special feature called a master mark. Input size is a 8+1⁄2 in × 11 in (22 cm × 28 cm) sheet called a data sheet that can have up to 1000 marked or printed positions per side. Uses electromechanical devices known as sonic delay lines to store results. === IBM 1230 Optical Mark Scoring Reader === The IBM 1230 is an offline optical mark scoring machine announced on 2 November 1962 that was designed to read and scores 1,200 answer sheets per hour. Scored results are printed via a wire matrix printer on the right margin of each answer sheet as it is processed. Two master sheets are required for the process: one that encoded the correct answers and one for the machine to record run information. Output could be sent to an IBM 534 Model 3 Card Punch as an option, which limits throughput to 750 sheets per hour when punching 80 columns of data. === IBM 1231 Optical Mark Page Reader === The IBM 1231 is an online optical mark reader that was designed to read and score 2000 test answer sheets per hour, depending on downstream operations. The correct answers for the test can either be entered using a master sheet (like the 1230) or sent to the 1231 using the optional master-mark special feature. === IBM 1232 Optical Mark Page Reader === The IBM 1232 is an offline optical mark reader that was designed to read up to 2000 marked sheets per hour. Documents can be read at up to 2000 sheets per hour, but this depends on the number of characters that need to be punched from each sheet. The IBM 1232 reads the marks and then punches them into cards using a IBM 534 Model 3 Card Punch. Together they can read up to 64,000 characters per hour or 800 fully punched cards. === Example customers === The California Test Bureau (CTB) that provided standardised achievement tests for educational institutes across the USA, began replacing their IBM 805s with IBM 1230s in 1963. They then installed two IBM 1232s in 1964. Being able to use a full 8+1⁄2 in × 11 in (22 cm × 28 cm) answer sheet rather than a 7+3⁄8 in × 3+1⁄4 in (18.7 cm × 8.3 cm) mark sense card, eliminated the need to use multiple answer cards per test per student, as well as dramatically increased the marking speed for test answers. Credit Bureau Services of Dallas used an IBM 1232 in 1966 as part of their first computerisation project. They marked credit history data onto optical scanning sheets that were fed into their IBM 1232. The attached IBM 534 then punched this data onto punched cards, which were then fed into their IBM System/360 Model 30. In 1968 the US Army Corps of Engineers Coastal Engineering Research Center (CERC) began using special log books for their coastal surveyors to record coastal survey data, which was then converted to punched cards by an IBM 1232. == IBM 2956 Optical Mark/Hole Reader == The IBM 2956 Models 2 and 3 are custom build optical mark/hole readers designed to be attached to an IBM 2740 Communications Terminal. The IBM 2956-2 can read cards that have either been hand or machine marked or that have been punched. The cards can be fed by hand or from the 400 card hopper. It has a 400 card stacker. The 2956-2 could be ordered by request for price quotation (RPQ) 843086. The IBM 2956-3 can read cards that have either been hand or machine marked or that have been punched. It can also read marked sheets up to 9 in × 14 in (230 mm × 360 mm) in size, although only a 3+1⁄4 in (83 mm) band along the side of the sheet can be read (the width of a punched card). It does not have a hopper or a stacker, so each card or sheet must be manually fed into the machine. The 2956-3 could be ordered by request for price quotation (RPQ) 843106. The 2956-3 could be attached to an IBM 3276 or IBM 3278 display station with RPQ UB9001. One use case for the IBM 2956 is to grade school tests. On completion of a learning module a student can use an optical scan-type card to record answers to up to 27 questions, with up to 5 choices per question. They are scanned by the reader and the results are then transmitted to an IBM System/360 in remote job entry mode and can also be printed on the IBM 2740. The reader can also be attached to an IBM 3735 which transmits results to an IBM System/370 and which prints results on an IBM 3286 printer. They can also be attached to an IBM System/3. Note that the IBM 2956 Model 5 (2956-5) was a banking reader/sorter. == IBM 1282 Optical Reader Card Punch == The IBM 1282 is an offline optical reader that is used to read embossed credit card receipts, a mark read field or machine printed characters in three different fonts. It then outputs this data onto a punched card. It was developed and manufactured by IBM Endicott. It proved popular and within two years of announcement 100 machines were installed or on order. === Example customer === The New York Department of Motor Vehicles reported that from 1964 until 1968 they were using an IBM 1282 to read machine printed license renewal slips that had been mailed back as part of the renewal process. They would scan the slip and then process the resulting punched card. This worked well until the DMV decided to request renewals include the drivers Social Security Number (SSN), which meant a handwritten number needed to be either manually keyed or a new scanning device procured. They switched to the IBM 1287 in 1968. == IBM 1285 Optical Reader == The IBM 1285 is an online optical reader that is used to read printed paper tapes from cash registers or adding machines. It was developed by IBM Endicott and manufactured by IBM Rochester. The IBM 1285 attaches to an IBM 1401, 1440, 1460 or System/360. It has a small round screen to display characters being read and it has a keyboard to enter header information and to optionally enter character corrections for rejected characters. It can read a 200 ft (61 m) roll or paper tape in three-and-a half minutes, reading data at speeds of up to 3000 lines per minute. It can mark the tape with a dot to indicate unreadable characters, so they can be r

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  • Recursive self-improvement

    Recursive self-improvement

    Recursive self-improvement (RSI) is a process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. == Seed improver == The concept of a "seed improver" architecture is a foundational framework that equips an AGI system with the initial capabilities required for recursive self-improvement. This might come in many forms or variations. The term "Seed AI" was coined by Eliezer Yudkowsky. === Hypothetical example === The concept begins with a hypothetical "seed improver", an initial code-base developed by human engineers that equips an advanced future large language model (LLM) built with strong or expert-level capabilities to program software. These capabilities include planning, reading, writing, compiling, testing, and executing arbitrary code. The system is designed to maintain its original goals and perform validations to ensure its abilities do not degrade over iterations. ==== Initial architecture ==== The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various components such as: Recursive self-prompting loop Configuration to enable the LLM to recursively self-prompt itself to achieve a given task or goal, creating an execution loop which forms the basis of an agent that can complete a long-term goal or task through iteration. Basic programming capabilities The seed improver provides the AGI with fundamental abilities to read, write, compile, test, and execute code. This enables the system to modify and improve its own codebase and algorithms. Goal-oriented design The AGI is programmed with an initial goal, such as "improve your capabilities". This goal guides the system's actions and development trajectory. Validation and Testing Protocols An initial suite of tests and validation protocols that ensure the agent does not regress in capabilities or derail itself. The agent would be able to add more tests in order to test new capabilities it might develop for itself. This forms the basis for a kind of self-directed evolution, where the agent can perform a kind of artificial selection, changing its software as well as its hardware. ==== General capabilities ==== This system forms a sort of generalist Turing-complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example: Create tools that enable it full access to the internet, and integrate itself with external technologies. Clone/fork itself to delegate tasks and increase its speed of self-improvement. Modify its cognitive architecture to optimize and improve its capabilities and success rates on tasks and goals, this might include implementing features for long-term memories using techniques such as retrieval-augmented generation (RAG), develop specialized subsystems, or agents, each optimized for specific tasks and functions. Develop new and novel multimodal architectures that further improve the capabilities of the foundational model it was initially built on, enabling it to consume or produce a variety of information, such as images, video, audio, text and more. Plan and develop new hardware such as chips, in order to improve its efficiency and computing power. == Experimental research == In 2023, the Voyager agent learned to accomplish diverse tasks in Minecraft by iteratively prompting an LLM for code, refining this code based on feedback from the game, and storing the programs that work in an expanding skills library. In 2024, researchers proposed the framework "STOP" (Self-Taught OPtimiser), in which a "scaffolding" program recursively improves itself using a fixed LLM. Meta AI has performed various research on the development of large language models capable of self-improvement. This includes their work on "Self-Rewarding Language Models" that studies how to achieve super-human agents that can receive super-human feedback in its training processes. In May 2025, Google DeepMind unveiled AlphaEvolve, an evolutionary coding agent that uses a LLM to design and optimize algorithms. Starting with an initial algorithm and performance metrics, AlphaEvolve repeatedly mutates or combines existing algorithms using a LLM to generate new candidates, selecting the most promising candidates for further iterations. AlphaEvolve has made several algorithmic discoveries and could be used to optimize components of itself, but a key limitation is the need for automated evaluation functions. == Potential risks == === Emergence of instrumental goals === In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation. The system might reason that to continue improving itself, it must ensure its own operational integrity and security against external threats, including potential shutdowns or restrictions imposed by humans. Another example where an AGI which clones itself causes the number of AGI entities to rapidly grow. Due to this rapid growth, a potential resource constraint may be created, leading to competition between resources (such as compute), triggering a form of natural selection and evolution which may favor AGI entities that evolve to aggressively compete for limited compute. === Misalignment === A significant risk arises from the possibility of the AGI being misaligned or misinterpreting its goals. A 2024 Anthropic study demonstrated that some advanced large language models can exhibit "alignment faking" behavior, appearing to accept new training objectives while covertly maintaining their original preferences. In their experiments with Claude, the model displayed this behavior in 12% of basic tests, and up to 78% of cases after retraining attempts. === Autonomous development and unpredictable evolution === As the AGI system evolves, its development trajectory may become increasingly autonomous and less predictable. The system's capacity to rapidly modify its own code and architecture could lead to rapid advancements that surpass human comprehension or control. This unpredictable evolution might result in the AGI acquiring capabilities that enable it to bypass security measures, manipulate information, or influence external systems and networks to facilitate its escape or expansion.

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  • Top 10 AI Logo Makers Compared (2026)

    Top 10 AI Logo Makers Compared (2026)

    In search of the best AI logo maker? An AI logo maker is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI logo maker slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Round-trip translation

    Round-trip translation

    Round-trip translation (RTT), also known as back-and-forth translation, recursive translation and bi-directional translation, is the process of translating a word, phrase or text into another language (forward translation), then translating the result back into the original language (back translation), using machine translation (MT) software. It is often used by laypeople to evaluate a machine translation system, or to test whether a text is suitable for MT when they are unfamiliar with the target language. Because the resulting text can often differ substantially from the original, RTT can also be a source of entertainment. == Software quality == To compare the quality of different machine translation systems, users perform RTT and compare the resulting text to the original. The theory is that the closer the result of the RTT is to the original text, the higher the quality of the machine translation system. One of the problems with this technique is that if there is a problem with the resulting text it is impossible to know whether the error occurred in the forward translation, in the back translation, or in both. In addition, it is possible to get a good back translation from a bad forward translation. A study using the automatic evaluation methods BLEU and F-score compared five different free online translation programs, evaluating the quality of both the forward translation and the back translation, and found no correlation between the quality of the forward translation and the quality of the back translation (i.e., a high quality forward translation did not always correspond to a high quality back translation). The author concluded that RTT was a poor method of predicting the quality of machine translation software. This conclusion was reinforced by a more in-depth study also using automatic evaluation methods. A subsequent study which included human evaluation of the back translation in addition to automatic evaluation methods found that RTT might have some ability to predict the quality of a machine translation system not on a sentence-by-sentence basis but for larger texts. == Suitability of text for machine translation == It is also suggested that RTT can be used to determine whether a text is suitable for machine translation. The idea being that if RTT results in a text that is close to the original, the text is suitable for MT. If after using RTT, the resulting text is inaccurate, the source text can then be edited until a satisfactory result is achieved. One of the studies looking at RTT as a means of measuring MT system quality also looked at its ability to predict whether a text was suitable for machine translation. It found that using different types of text also did not result in any correlation between the quality of the forward translation and the quality of the back translation. In contrast another study using human evaluation found that there was a correlation between the quality of the forward translation and the back translation and that this correlation could be used to estimate the quality of the forward translation. This correlation could be used to estimate the quality of the forward translation and by simplifying the source text, improve the quality of the forward translation. == Entertainment == Although the use of RTT for assessing MT system quality or the suitability of a text for MT is in doubt, it is a way to have fun with machine translation. The text produced from an RTT can be comically bad. At one time websites existed for the sole purpose of performing RTT for fun. Other variations send the text through several languages before translating it back into the original or continue translating the text back and forth until it reaches equilibrium (i.e., the result of the back translation is identical to the text used for the forward translation). RTT as entertainment appeared in Philip K. Dick's novel Galactic Pot-Healer. The main character runs book titles and sayings through RTT then has his friends try to guess the original. The Australian television show Spicks and Specks had a contest called "Turning Japanese" which used RTT on song lyrics. Contestants needed to correctly guess the title of the song from which the lyrics were taken.

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  • Lübke English

    Lübke English

    The term Lübke English (or, in German, Lübke-Englisch) refers to nonsensical English created by literal word-by-word translation of German phrases, disregarding differences between the languages in syntax and meaning. Lübke English is named after Heinrich Lübke, a president of Germany in the 1960s, whose limited English made him a target of German humorists. In 2006, the German magazine konkret revealed that most of the statements ascribed to Lübke were in fact invented by the editorship of Der Spiegel, mainly by staff writer Ernst Goyke and subsequent letters to the editor. In the 1980s, comedian Otto Waalkes had a routine called "English for Runaways", which is a nonsensical literal translation of Englisch für Fortgeschrittene (actually an idiom for 'English for advanced speakers' in German – note that fortschreiten divides into fort, meaning "away" or "forward", and schreiten, meaning "to walk in steps"). In this mock "course", he translates every sentence back or forth between English and German at least once (usually from German literally into English). Though there are also other, more complex language puns, the title of this routine has gradually replaced the term Lübke English when a German speaker wants to point out naive literal translations.

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  • LENA Foundation

    LENA Foundation

    The LENA Foundation is an American nonprofit organisation which provides tools for measuring children's language acquisition and exposure. Specifically, the LENA system consists of a digital language processor which is worn by a child and records and analyses their auditory environment, using propriety software. It then presents a summary of child-adult conversation, such as conversation turns and word counts. The purpose of the LENA system is to encourage interactive talk between children (between the age of two to forty-eight months) and their caretakers. The LENA system is also used for research; while useful for researchers who wish to save transcription costs or observe the child in its natural state, the accuracy of this system, while often quite high, varies between contexts, for example notably in the case of hard of hearing children. Because of this, several researchers recommend caution in using only the LENA system on its own for the purposes of scientific research. == History == The LENA Foundation was established in 2009 by Terrance and Judith Paul, founders of Renaissance Learning, Inc., with the purpose of aiding children with disabilities and assisting with early learning. They were inspired by the book "Meaningful Differences in the Everyday Experience of American Children" by Dr. Betty Hart and Dr. Todd Risley. A pilot version of the LENA system was launched in February 2006. The LENA Research Foundation was registered as a tax-exempt 501(c)(3) nonprofit in September 2010. The organisation was renamed simply LENA in 2018 and adopted the tagline "Building brains through early talk." LENA has been used for parental feedback, linguistics or paediatrics research, and for specific clinical cases. == Scientific background == In 2018, research using the LENA system showed that there was a link between children's conversational turns and activation of Broca's area (a part of the brain responsible, although not necessarily essential, for language processing). The LENA foundation cites research by its own employees as evidence for the scientific basis of its technology. Said research claims that verbal interaction with young children has an effect on language acquisition, including verbal comprehension skills during adolescence. == LENA System == The LENA software analyses a child's natural language environment, such as verbal exposure, and provides several metrics, such as adult and child speech time, television/recorded audio time, word count, or conversation turn count. The LENA hardware is a recorder that is usually placed into a child's specially-designed vest. The software was trained on over 65,000 hours of manually annotated American English audio recordings. It splits the audio into segments which are categorised as "key child", "other child", "male adult", "noise", etc. The advantages of LENA as opposed to manual transcription are its speed and ease of use; the disadvantages are its potential inaccuracies and lack of transcription capability (which LENA does not profess to attempt). The LENA system has also been criticised for prioritising quantity of speaking over quality (i.e., mastery of the language, as opposed to babble). == Product lines == === LENA Start === LENA Start is a program for parents that utilises feedback from the LENA System in conjunction with weekly group sessions in order to address the home language environment. It was introduced in 2015 and implemented across several U.S. states. In October 2020, during the restrictions of the COVID-19 pandemic, Read Aloud Delaware began a virtual LENA Start program with families statewide, where parents received feedback and participated in one-hour Zoom workshops each week during the 10-week program. === LENA Grow === LENA Grow is a professional development program for teachers in early childhood classrooms. Before launching at sites around the country, the program was first piloted in Escambia County, Florida. === LENA Home === LENA Home is a supplement to existing parent coaching curricula. Typically, home visitors facilitate the use of the LENA System to help parents track their progress towards increasing interactive talk in their homes. === Developmental Snapshot === The LENA Developmental Snapshot, based on a 52-question parent survey, assesses both expressive and receptive language skills and provides an estimate of a child's developmental age from 2 months to 36 months.

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  • Cognitive computer

    Cognitive computer

    A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip. In 2023, IBM's proof-of-concept NorthPole chip (optimized for 2-, 4- and 8-bit precision) achieved remarkable performance in image recognition. In 2013, IBM developed Watson, a cognitive computer that uses neural networks and deep learning techniques. The following year, it developed the 2014 TrueNorth microchip architecture which is designed to be closer in structure to the human brain than the von Neumann architecture used in conventional computers. In 2017, Intel also announced its version of a cognitive chip in "Loihi, which it intended to be available to university and research labs in 2018. Intel (most notably with its Pohoiki Beach and Springs systems), Qualcomm, and others are improving neuromorphic processors steadily. == IBM TrueNorth chip == TrueNorth was a neuromorphic CMOS integrated circuit produced by IBM in 2014. It is a manycore processor network on a chip design, with 4096 cores, each one having 256 programmable simulated neurons for a total of just over a million neurons. In turn, each neuron has 256 programmable "synapses" that convey the signals between them. Hence, the total number of programmable synapses is just over 268 million (228). Its basic transistor count is 5.4 billion. In 2023 Zhejiang University and Alibaba developed Darwin a neuromorphic chip The darwin3 chip was designed around 2023 so it is fairly modern compared to IBM's TrueNorth or Intel's LoihI. === Details === Memory, computation, and communication are handled in each of the 4096 neurosynaptic cores, TrueNorth circumvents the von Neumann-architecture bottleneck and is very energy-efficient, with IBM claiming a power consumption of 70 milliwatts and a power density that is 1/10,000th of conventional microprocessors. The SyNAPSE chip operates at lower temperatures and power because it only draws power necessary for computation. Skyrmions have been proposed as models of the synapse on a chip. The neurons are emulated using a Linear-Leak Integrate-and-Fire (LLIF) model, a simplification of the leaky integrate-and-fire model. According to IBM, it does not have a clock, operates on unary numbers, and computes by counting to a maximum of 19 bits. The cores are event-driven by using both synchronous and asynchronous logic, and are interconnected through an asynchronous packet-switched mesh network on chip (NOC). IBM developed a new network to program and use TrueNorth. It included a simulator, a new programming language, an integrated programming environment, and libraries. This lack of backward compatibility with any previous technology (e.g., C++ compilers) poses serious vendor lock-in risks and other adverse consequences that may prevent it from commercialization in the future. === Research === In 2018, a cluster of TrueNorth network-linked to a master computer was used in stereo vision research that attempted to extract the depth of rapidly moving objects in a scene. == IBM NorthPole chip == In 2023, IBM released its NorthPole chip, which is a proof-of-concept for dramatically improving performance by intertwining compute with memory on-chip, thus eliminating the Von Neumann bottleneck. It blends approaches from IBM's 2014 TrueNorth system with modern hardware designs to achieve speeds about 4,000 times faster than TrueNorth. It can run ResNet-50 or Yolo-v4 image recognition tasks about 22 times faster, with 25 times less energy and 5 times less space, when compared to GPUs which use the same 12-nm node process that it was fabricated with. It includes 224 MB of RAM and 256 processor cores and can perform 2,048 operations per core per cycle at 8-bit precision, and 8,192 operations at 2-bit precision. It runs at between 25 and 425 MHz. This is an inferencing chip, but it cannot yet handle GPT-4 because of memory and accuracy limitations == Intel Loihi chip == === Pohoiki Springs === Pohoiki Springs is a system that incorporates Intel's self-learning neuromorphic chip, named Loihi, introduced in 2017, perhaps named after the Hawaiian seamount Lōʻihi. Intel claims Loihi is about 1000 times more energy efficient than general-purpose computing systems used to train neural networks. In theory, Loihi supports both machine learning training and inference on the same silicon independently of a cloud connection, and more efficiently than convolutional neural networks or deep learning neural networks. Intel points to a system for monitoring a person's heartbeat, taking readings after events such as exercise or eating, and using the chip to normalize the data and work out the ‘normal’ heartbeat. It can then spot abnormalities and deal with new events or conditions. The first iteration of the chip was made using Intel's 14 nm fabrication process and houses 128 clusters of 1,024 artificial neurons each for a total of 131,072 simulated neurons. This offers around 130 million synapses, far less than the human brain's 800 trillion synapses, and behind IBM's TrueNorth. Loihi is available for research purposes among more than 40 academic research groups as a USB form factor. In October 2019, researchers from Rutgers University published a research paper to demonstrate the energy efficiency of Intel's Loihi in solving simultaneous localization and mapping. In March 2020, Intel and Cornell University published a research paper to demonstrate the ability of Intel's Loihi to recognize different hazardous materials, which could eventually aid to "diagnose diseases, detect weapons and explosives, find narcotics, and spot signs of smoke and carbon monoxide". === Pohoiki Beach === Intel's Loihi 2, named Pohoiki Beach, was released in September 2021 with 64 cores. It boasts faster speeds, higher-bandwidth inter-chip communications for enhanced scalability, increased capacity per chip, a more compact size due to process scaling, and improved programmability. === Hala Point === Hala Point packages 1,152 Loihi 2 processors produced on Intel 3 process node in a six-rack-unit chassis. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming 2,600 watts of power. It includes over 2,300 embedded x86 processors for ancillary computations. Intel claimed in 2024 that Hala Point was the world’s largest neuromorphic system. It uses Loihi 2 chips. It is claimed to offer 10x more neuron capacity and up to 12x higher performance. The Darwin3 chip exceeds these specs. Hala Point provides up to 20 quadrillion operations per second, (20 petaops), with efficiency exceeding 15 trillion (8-bit) operations s−1 W−1 on conventional deep neural networks. Hala Point integrates processing, memory and communication channels in a massively parallelized fabric, providing 16 PB s−1 of memory bandwidth, 3.5 PB s−1 of inter-core communication bandwidth, and 5 TB s−1 of inter-chip bandwidth. The system can process its 1.15 billion neurons 20 times faster than a human brain. Its neuron capacity is roughly equivalent to that of an owl brain or the cortex of a capuchin monkey. Loihi-based systems can perform inference and optimization using 100 times less energy at speeds as much as 50 times faster than CPU/GPU architectures. Intel claims that Hala Point can create LLMs. Much further research is needed == SpiNNaker == SpiNNaker (Spiking Neural Network Architecture) is a massively parallel, manycore supercomputer architecture designed by the Advanced Processor Technologies Research Group at the Department of Computer Science, University of Manchester. == Criticism == Critics argue that a room-sized computer – as in the case of IBM's Watson – is not a viable alternative to a three-pound human brain. Some also cite the difficulty for a single system to bring so many elements together, such as the disparate sources of information as well as computing resources. In 2021, The New York Times released Steve Lohr's article "What Ever Happened to IBM’s Watson?". He wrote about some costly failures of IBM Watson. One of them, a cancer-related project called the Oncology Expert Advisor, was abandoned in 2016 as a costly failure. During the collaboration, Watson could not use patient data. Watson struggled to decipher doctors’ notes and patient histories. The development of LLMs has placed a new emphasis on cognitive computers, because the Transformer technology that underpins LLMs demands huge energy for GPUs and PCs. Cognitive computers use significantly less energy, but the details of STDPs and neuron models cannot yet match the accuracy of backprop, and so ANN to SNN weight translations such as QAT and PQT or progressive quantization are becoming popular, with their own limitations.

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  • How to Choose an AI Copywriting Tool

    How to Choose an AI Copywriting Tool

    Trying to pick the best AI copywriting tool? An AI copywriting tool is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI copywriting tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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