Too Good To Go is a service with a mobile application that connects customers to restaurants and stores that have surplus unsold food. The service covers major European cities, and in October 2020 started operations in North America. As part of the initiatives taken on the International Day of Awareness of Food Loss and Waste to reduce food loss and waste, the app is suggested alongside OLIO among many others. In 2023 Too Good To Go was the fastest-growing sustainable food app startup by number of downloads. As of August 2023, it claimed 164,000 businesses, serving 62 million users, have saved 155 million bags of food. As of March 2023, it claimed to have saved over 200 million meals. == History == The company was created in 2015 in Denmark by Thomas Bjørn Momsen, Klaus Bagge Pedersen, Adam Sigbrand and Brian Christensen. In 2017, Mette Lykke (co-founder of Endomondo) joined as CEO. In February 2019, the company raised an additional 6 million euros in a new round of investment. In August 2019, Too Good To Go was re-launched in Austria. In September 2019, Too Good To Go acquired the Spanish startup weSAVEeat and merged it into its own brand. In November 2019, the offer of Too Good To Go extended to plants through a partnership with the French retail plants company Jardiland. In December 2019, Too Good To Go partnered with the French grocery retail stores Intermarché, and donated 60K euros to the French charity Restaurants du Cœur. In October 2021, Bonnie Wright teamed up with Too Good To Go to drive the initiative to reduce food waste. == Corporate affairs == The key trends for the Danish entity Too Good To Go ApS are (as of the financial year ending December 31): == International expansion == As of March 2026 the company serves the European countries Austria, Belgium, Czechia, Denmark, the Faroe Islands, France, Germany, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, the United Kingdom. Outside of Europe the service is available in Australia, Canada, Japan, New Zealand and the United States. == Purpose == The purpose of Too Good To Go is to reduce food waste worldwide. It developed a mobile application that connects restaurants and stores that have unsold, surplus food, with customers who can then buy whatever food the outlet considers surplus to requirements—without being able to choose—at a much lower price than normal. The food on the app is priced at one-third its original price. The company claims this reduces the waste of food that would otherwise be discarded; food waste is a global problem that affects the environment. In three years active, the app reached more than 9.5 million users. As of 2022, more than 57.7 million users and 154,000 establishments have signed up, and 139 million meals have been collected. In 2019, the company had 350 employees in Europe. As of June 2023 the company was estimated to have 1,289 employees. == Use == Food outlets must notify the TGTG company about what they have available on each day, stating what sort of food they have (baked foods, meals, produce, vegan food), and the price for a 'surprise bag', whose contents they determine; the user cannot choose, but the original prices will be three or more times the TGTG price. Notification is made early based upon the quantity predicted to be left over, not at the end of a selling period. Users must register to use the service. A mobile phone with an Internet connection running Android or iOS is needed. The user runs the TGTG app, which lists outlets available within a chosen distance and time range. The customer can then order and pay for a 'surprise bag'. The supplier can cancel an order at any time if the expected surplus is not available—the purchaser is notified by text message—and the purchaser can cancel with two hours' notice. The phone must be taken to the food supplier in a specified pickup time window, often 30 or 60 minutes long, and the transaction is finalised by swiping the app—connected to the Internet—to confirm collection.
Brill tagger
The Brill tagger is an inductive method for part-of-speech tagging. It was described and invented by Eric Brill in his 1993 PhD thesis. It can be summarized as an "error-driven transformation-based tagger". It is: a form of supervised learning, which aims to minimize error; and, a transformation-based process, in the sense that a tag is assigned to each word and changed using a set of predefined rules. In the transformation process, if the word is known, it first assigns the most frequent tag, or if the word is unknown, it naively assigns the tag "noun" to it. High accuracy is eventually achieved by applying these rules iteratively and changing the incorrect tags. This approach ensures that valuable information such as the morphosyntactic construction of words is employed in an automatic tagging process. == Algorithm == The algorithm starts with initialization, which is the assignment of tags based on their probability for each word (for example, "dog" is more often a noun than a verb). Then "patches" are determined via rules that correct (probable) tagging errors made in the initialization phase: Initialization: Known words (in vocabulary): assigning the most frequent tag associated to a form of the word Unknown word == Rules and processing == The input text is first tokenized, or broken into words. Typically in natural language processing, contractions such as "'s", "n't", and the like are considered separate word tokens, as are punctuation marks. A dictionary and some morphological rules then provide an initial tag for each word token. For example, a simple lookup would reveal that "dog" may be a noun or a verb (the most frequent tag is simply chosen), while an unknown word will be assigned some tag(s) based on capitalization, various prefix or suffix strings, etc. (such morphological analyses, which Brill calls Lexical Rules, may vary between implementations). After all word tokens have (provisional) tags, contextual rules apply iteratively, to correct the tags by examining small amounts of context. This is where the Brill method differs from other part of speech tagging methods such as those using Hidden Markov Models. Rules are reapplied repeatedly, until a threshold is reached, or no more rules can apply. Brill rules are of the general form: tag1 → tag2 IF Condition where the Condition tests the preceding and/or following word tokens, or their tags (the notation for such rules differs between implementations). For example, in Brill's notation: IN NN WDPREVTAG DT while would change the tag of a word from IN (preposition) to NN (common noun), if the preceding word's tag is DT (determiner) and the word itself is "while". This covers cases like "all the while" or "in a while", where "while" should be tagged as a noun rather than its more common use as a conjunction (many rules are more general). Rules should only operate if the tag being changed is also known to be permissible, for the word in question or in principle (for example, most adjectives in English can also be used as nouns). Rules of this kind can be implemented by simple Finite-state machines. See Part of speech tagging for more general information including descriptions of the Penn Treebank and other sets of tags. Typical Brill taggers use a few hundred rules, which may be developed by linguistic intuition or by machine learning on a pre-tagged corpus. == Code == Brill's code pages at Johns Hopkins University are no longer on the web. An archived version of a mirror of the Brill tagger at its latest version as it was available at Plymouth Tech can be found on Archive.org. The software uses the MIT License.
Terminator (franchise)
Terminator is an American media franchise created by James Cameron and Gale Anne Hurd. It is considered to be of the cyberpunk subgenre of science fiction. The franchise primarily focuses on the events leading to a future post-apocalyptic war between a synthetic intelligence known as Skynet, and a surviving resistance of humans led by John Connor. In this future, Skynet uses an arsenal of cyborgs known as Terminators, designed to mimic humans and infiltrate the resistance. Much of the franchise takes place in time periods prior to the Skynet takeover, with both humans and Terminators using time travel to attempt to alter the past and change the outcome of the future. A prominent Terminator model throughout the films is the T-800, commonly known as "the Terminator", with instances of this model portrayed by Arnold Schwarzenegger. The franchise began with the 1984 film The Terminator, written and directed by Cameron, with Hurd as producer. They would return for the 1991 sequel Terminator 2: Judgment Day (or T2). Both films were critical and commercial successes. Terminator 3: Rise of the Machines (or T3) was released in 2003 to positive reviews, followed by Terminator Salvation in 2009 to more negative reviews. Salvation was intended as the first in a new trilogy, which was later scrapped after the film rights were sold. Cameron was consulted for the 2015 film Terminator Genisys, a reboot branching off from the timeline of the original film. It was negatively received and performed poorly at the box-office. Cameron had a larger role as a producer of the 2019 film Terminator: Dark Fate, a direct sequel to T2 that ignores the three preceding films. As with Salvation, both Genisys and Dark Fate were planned as first installments of new trilogies, with the plans scrapped each time due to the films' poor box-office performances. Outside of the theatrical films, Cameron co-directed T2-3D: Battle Across Time, a 1996 theme park film-based attraction. It was produced as the original sequel to T2 and reunited its main cast. A television series, Terminator: The Sarah Connor Chronicles, was developed without Cameron's involvement and aired for two seasons in 2008 and 2009. It was also produced as a T2 sequel, taking place in an alternate timeline that ignores the third film and subsequent events. Terminator Zero, an anime series, premiered in August 2024. The franchise has also inspired several lines of comic books since 1988, and numerous video games since 1991. By 2010, the franchise had generated $3 billion in revenue. == Themes and setting == The central theme of the franchise is the battle for survival between the nearly-extinct human race and the world-spanning, synthetic intelligence that is Skynet. Skynet is positioned in the first film, The Terminator (1984), as a U.S. strategic "Global Digital Defense Network" computer system by Cyberdyne Systems which becomes self-aware. Shortly after activation, Skynet seemingly perceives all humans as a threat to its existence and formulates a plan to systematically wipe out humanity itself. The system initiates a nuclear first strike against Russia, thereby ensuring a devastating second strike and a nuclear holocaust which wipes out much of humanity in the resulting nuclear war. In the post-apocalyptic aftermath, Skynet later builds up its own autonomous machine-based military capability which includes the Terminators used against individual human targets and thereafter proceeds to wage a persistent total war against the surviving elements of humanity, some of whom have militarily organized themselves into a Resistance. At some point in this future, Skynet develops the capability of time travel and both it and the Resistance seek to use this technology in order to win the war; either by altering or accelerating past events or by preventing the apocalyptic timeline. === Judgment Day === In the franchise, Judgment Day (a reference to the biblical Day of Judgment) is the date on which Skynet becomes self-aware, in which case its creators panic and attempt to deactivate the network. As a result, Skynet perceives humanity as a threat and attempts to exterminate them. Skynet launches an all-out nuclear attack on Russia in order to provoke a nuclear counter-strike against the United States, knowing this will eliminate its human enemies. Due to time travel and the consequent ability to change the future, several differing dates are given for Judgment Day. In Terminator 2: Judgment Day (1991), Sarah Connor states that Judgment Day will occur on August 29, 1997. However, this date is delayed following the attack on Cyberdyne Systems in the same film. Judgment Day has various different dates in different timelines of the subsequent films, as well as the television series, creating a multiverse of temporal phenomena. In Terminator 3: Rise of the Machines (2003) and Terminator Salvation (2009), Judgment Day was postponed to July 2003. In Terminator: The Sarah Connor Chronicles (2008–2009), the attack on Cyberdyne Systems in the second film delayed Judgment Day to April 21, 2011. In Terminator Genisys (2015), the fifth film in the franchise, Judgment Day was postponed to an unspecified day in October 2017, attributed to altered events in both the future and the past. Sarah and Kyle Reese travel through time to the year 2017 and seemingly defeat Skynet, but the system core, contained inside a subterranean blast shelter, survives unknown to them, thus further delaying, rather than preventing, Judgment Day. In Terminator: Dark Fate (2019), the direct sequel to Terminator 2: Judgment Day, a date is not given for the new Judgment Day though it is named as such by Grace. Since Grace is a ten-year-old in 2020 and shown as a teenager in the post-Judgment Day world in flash-forwards throughout the film, Judgment Day occurs sometime in the early 2020s in this timeline. == Franchise rights == Before the first film was created, director James Cameron sold the rights for $1 to Gale Anne Hurd, his future wife, who produced the film, under the strict provision that he be allowed to direct it. Hemdale Film Corporation also became a 50-percent owner of the franchise rights, until its share was sold in 1990 to Carolco Pictures, a company founded by Andrew G. Vajna and Mario Kassar. Terminator 2: Judgment Day was released a year later. Carolco filed for bankruptcy in 1995 and its library was subsequently acquired by StudioCanal, which continues to own the franchise today. However, the rights to future Terminator films were ultimately put up for auction. By that time, Cameron had become interested in making a Terminator 3 film. The rights were ultimately auctioned to Vajna in 1997, for $8 million. Vajna and Kassar spent another $8 million to purchase Hurd's half of the rights in 1998, becoming the full owners of the franchise. Hurd was initially opposed to the sale of the rights, while Cameron had lost interest in the franchise and a third film. After the 2003 release of Terminator 3: Rise of the Machines, the franchise rights were sold in 2007 for about $25 million to The Halcyon Company, which produced Terminator Salvation in 2009. Later that year, the company faced legal issues and filed for bankruptcy, putting the franchise rights up for sale. The rights were valued at about $70 million. In 2010, the rights were sold for $29.5 million to Pacificor, a hedge fund that was Halcyon's largest creditor. In 2012, the rights were sold to Megan Ellison and her production company Annapurna Pictures for less than $20 million, a lower price than what was previously offered. The low price was because of the possibility of Cameron regaining the rights in 2019, as a result of new North American copyright laws. Megan's brother David Ellison and Skydance Productions produced Terminator Genisys in 2015. Cameron worked together with David Ellison to produce the 2019 film Terminator: Dark Fate. As the film neared its release, Hurd filed to terminate a copyright grant made 35 years earlier. Under this move, Hurd would again become a 50-percent owner of the rights with Cameron and Skydance could lose the rights to make any additional Terminator films beginning in November 2020, unless a new deal is worked out. Skydance responded that it had a deal in place with Cameron and that it "controls the rights to the Terminator franchise for the foreseeable future". == Films == === The Terminator (1984) === The Terminator is a 1984 science fiction action film released by Orion Pictures, co-written and directed by James Cameron and starring Arnold Schwarzenegger, Linda Hamilton and Michael Biehn. It is the first work in the Terminator franchise. In the film, robots take over the world in the near future, directed by the artificial intelligence Skynet. With its sole mission to completely annihilate humanity, it develops android assassins called Terminators that outwardly appear human. A man named John Connor starts the Tech-Com resistance to fight the machi
Oasis (Minecraft clone)
Oasis is a 2024 video game that attempts to replicate the 2011 sandbox game Minecraft, run entirely using generative artificial intelligence. The project, which began development in 2022 between the AI company Decart and the computer hardware startup Etched, was released by Decart to the public on October 31, 2024. The AI-driven simulation uses "next-frame prediction" to anticipate player actions based on keyboard and mouse inputs, trained on millions of hours of gameplay footage. Without memory or code, the game often outputs unpredictable changes in scenery and inventory, limiting its functionality as a traditional video game. Critics noted its lack of sound, low frame rate, and "dream-like" appearance, though some praised its unpredictability as entertaining. The project is seen as a potential proof of concept for AI-driven video games. == Creation and gameplay == The demo "proof of concept" version of the game was developed by Israeli San Francisco–based AI company Decart and Silicon Valley hardware startup Etched. The idea originated in 2022 when Robert Wachen, a Harvard graduate and co-founder of Etched, met Dean Leitersdorf, an Israel Institute of Technology graduate and co-founder of Decart. Sharing an interest in OpenAI's GPT-3, they collaborated to create the game, naming it after the setting of the novel and film Ready Player One. It was funded by a $21 million grant from Israeli-American billionaire Oren Zeev and New York–based Sequoia Capital. Decart released the game to the public for free on October 31, 2024. The AI replicates Minecraft's gameplay without code using "next-frame prediction", in which the AI tries to predict what the player will see after each keyboard and mouse input, which it was trained to do on millions of hours of Minecraft footage. The game used Nvidia graphics processing units or GPUs for its demo but plans to transition to more energy-efficient Sohu GPUs, under development by Etched, capable of supporting up to 4K graphics. Etched has also suggested the possibility of making the game open source in the future. Alongside Oasis, the company is co-developing AI-generated video and educational content. == Reception == Upon its launch, many players posted videos of their experience with the game online, which often showed Oasis could not maintain coherent logic in its actions or setting. The game also presented low-quality graphics, running between 360p and 720p consistently at 20 FPS, no in-game sound, and could only be played for five minutes at a time before restarting. These issues led some news outlets to refer to the game as a "nightmarish hallucination", and drawing comparisons to dementia and dreams. Despite the negative reviews, Leitersdorf, as well as a number of commentators, have commented that while the game may have fallen short of replicating Minecraft in its demo launch, it was the first step towards something more advanced, which could one day resemble Minecraft or any other game. Online publication The Backdash commented the game could be a "glimpse at the future of game development", while others like Tom's Hardware expressed doubts a game without code could ever look as good as one with, arguing they fail to capture "the point of what makes games fun—or even coherent". In terms of legality, Decart and Etched did not receive permission from Microsoft to create a copy of their game using generative artificial intelligence. No legal actions have been taken by the latter, however, as artificial intelligence and copyright remains largely vague legally.
Conference on Neural Information Processing Systems
The Conference on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. The conference includes three days of invited talks along with oral and poster presentations of refereed papers, followed by two days of workshops and competitions. == History == The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in 1987 with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa as program chairman. Research presented in the early NeurIPS meetings included a wide range of topics from efforts to solve purely engineering problems to the use of computer models as a tool for understanding biological nervous systems. Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning, artificial intelligence and statistics. From 1987 until 2000 NeurIPS was held in Denver, United States. Since then, the conference was held in Vancouver, Canada (2001–2010), Granada, Spain (2011), and Lake Tahoe, United States (2012–2013). In 2014 and 2015, the conference was held in Montreal, Canada, in Barcelona, Spain in 2016, in Long Beach, United States in 2017, in Montreal, Canada in 2018 and Vancouver, Canada in 2019. Reflecting its origins at Snowbird, Utah, the meeting was accompanied by workshops organized at a nearby ski resort up until 2013, when it outgrew ski resorts. The first NeurIPS Conference was sponsored by the IEEE. The following NeurIPS Conferences have been organized by the NeurIPS Foundation, established by Ed Posner. Terrence Sejnowski has been the president of the NeurIPS Foundation since Posner's death in 1993. The board of trustees consists of previous general chairs of the NeurIPS Conference. The first proceedings was published in book form by the American Institute of Physics in 1987, and was entitled Neural Information Processing Systems, then the proceedings from the following conferences have been published by Morgan Kaufmann (1988–1993), MIT Press (1994–2004) and Curran Associates (2005–present) under the name Advances in Neural Information Processing Systems. The conference was originally abbreviated as "NIPS". By 2018 a few commentators were criticizing the abbreviation as encouraging sexism due to its association with the word nipples, and as being a slur against Japanese. The board changed the abbreviation to "NeurIPS" in November 2018. == Topics == Along with machine learning and neuroscience, other fields represented at NeurIPS include cognitive science, psychology, computer vision, statistical linguistics, and information theory. Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition, object recognition in images, image captioning, language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex (ConvNet) and reinforcement learning inspired by the basal ganglia (Temporal difference learning). Notable affinity groups have emerged from the NeurIPS conference and displayed diversity, including Black in AI (in 2017), Queer in AI (in 2016), and others. === Named lectures === In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. The NeurIPS Board introduced the Posner Lectureship in honor of NeurIPS founder Ed Posner; two Posner Lectures were given each year up to 2015. Past lecturers have included: 2010 – Josh Tenenbaum and Michael I. Jordan 2011 – Rich Sutton and Bernhard Schölkopf 2012 – Thomas Dietterich and Terry Sejnowski 2013 – Daphne Koller and Peter Dayan 2014 – Michael Kearns and John Hopfield 2015 – Zoubin Ghahramani and Vladimir Vapnik 2016 – Yann LeCun 2017 – John Platt 2018 – Joëlle Pineau 2019 – Yoshua Bengio 2020 – Christopher Bishop 2021 – Peter Bartlett In 2015, the NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman, who served on the NeurIPS Board from 1994 to 2005. Past lecturers have included: 2015 – Robert Tibshirani 2016 – Susan Holmes 2017 – Yee Whye Teh 2018 – David Spiegelhalter 2019 – Bin Yu 2020 – Marloes Maathuis 2021 – Gabor Lugosi 2022 – Emmanuel Candes 2023 – Susan Murphy 2024 – Arnaud Doucet == NeurIPS consistency experiment == In NIPS 2014, the program chairs duplicated 10% of all submissions and sent them through separate reviewers to evaluate randomness in the reviewing process. Several researchers interpreted the result. Regarding whether the decision in NIPS is completely random or not, John Langford writes: "Clearly not—a purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%." He concludes that the result of the reviewing process is mostly arbitrary. In NeurIPS 2021, the program chairs repeated the 2014 experiment and found similar levels of review inconsistency; 23% of duplicated submissions received different accept/reject decisions, and 50.6% of accepted papers would have been rejected under re-review. == Locations == 1987–2000: Denver, Colorado, United States 2001–2010: Vancouver, British Columbia, Canada 2011: Granada, Spain 2012 & 2013: Stateline, Nevada, United States 2014 & 2015: Montréal, Quebec, Canada 2016: Barcelona, Spain 2017: Long Beach, California, United States 2018: Montréal, Quebec, Canada 2019: Vancouver, British Columbia, Canada 2020: Vancouver, British Columbia, Canada (virtual conference) 2021: Virtual conference 2022 & 2023: New Orleans, Louisiana, United States 2024: Vancouver, British Columbia, Canada 2025: San Diego, California, United States and Mexico City, Mexico 2026: Sydney, New South Wales, Australia, with satellite events in Atlanta and Paris
Outline of deep learning
The following outline is provided as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language processing, speech recognition, recommender systems, robotics, and generative artificial intelligence. == Ways to categorize deep learning == A field of study A branch of artificial intelligence A subfield of machine learning A subfield of computer science A form of representation learning A class of methods based on artificial neural networks An approach used in computational statistics == History == === Precursors === Cybernetics Perceptron Connectionism Neocognitron Backpropagation === Milestones === LeNet Long short-term memory Deep belief network AlexNet Sequence to sequence learning Generative adversarial network Residual neural network Transformer BERT Generative pre-trained transformer Diffusion model === Related histories === History of artificial intelligence History of machine learning Timeline of machine learning == Core concepts == == Learning settings == Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask learning Multimodal learning Online machine learning Continual learning == Common tasks == Image classification Object detection Image segmentation Automatic speech recognition Neural machine translation Question answering Automatic summarization Text-to-image model Protein structure prediction == Architectures == === Feedforward and convolutional architectures === Feedforward neural network Multilayer perceptron Convolutional neural network Radial basis function network Residual neural network U-Net === Recurrent and sequence architectures === Recurrent neural network Long short-term memory Gated recurrent unit Sequence to sequence learning Recursive neural network === Representation-learning architectures === Autoencoder Denoising autoencoder Sparse autoencoder Variational autoencoder Restricted Boltzmann machine Deep belief network === Attention and transformer architectures === Attention (machine learning) Transformer BERT Generative pre-trained transformer Vision transformer === Generative and probabilistic architectures === Autoregressive model Diffusion model Energy-based model Generative adversarial network Mixture of experts === Graph and memory architectures === Graph neural network Graph convolutional network Siamese network Neural Turing machine Memory network Echo state network Capsule neural network == Neural network components and techniques == Artificial neuron Activation function Rectified linear unit Sigmoid function Softmax function Embedding Convolution Pooling layer Attention Batch normalization Layer normalization Residual connections == Training and optimization == Backpropagation Gradient descent Stochastic gradient descent Adam optimization Learning rate Loss function Cross-entropy Mean squared error Regularization Dropout Early stopping Batch normalization Data augmentation Transfer learning Knowledge distillation Ensemble learning Curriculum learning == Datasets and benchmarks == CIFAR-10 ImageNet MNIST database Common Objects in Context (COCO) General Language Understanding Evaluation (GLUE) benchmark LibriSpeech SQuAD == Applications == === Computer vision === Computer vision Facial recognition system Image classification Image segmentation Medical imaging Object detection Optical character recognition === Natural language processing === Automatic summarization Chatbot Information retrieval Large language model Natural language processing Neural machine translation Question answering Sentiment analysis === Speech and audio === Automatic speech recognition Music information retrieval Speaker recognition Speech synthesis === Science and medicine === Bioinformatics Computational biology Drug discovery Medical diagnosis Protein structure prediction === Robotics and control === Autonomous car Computer game bot Control theory Robotics === Recommendation, search, and forecasting === Anomaly detection Forecasting Fraud detection Recommender system Search engine === Generative artificial intelligence === Deepfake Generative artificial intelligence Large language model Speech synthesis Text-to-image model === Computer graphics and video games === Deep Learning Anti-Aliasing (DLAA) Deep Learning Super Sampling (DLSS) == Hardware == AMD Instinct AMD XDNA Application-specific integrated circuit Deep learning processor, Neural processing unit (NPU), or Neural Engine Field-programmable gate array General-purpose computing on graphics processing units (GPGPU) Graphics processing unit NVIDIA Deep Learning Accelerator (NVDLA) Tensor processing unit Vision processing unit Wafer-scale integration === Supporting software platforms === CUDA Metal ROCm == Software == === Open-source frameworks and libraries === === Neural network software === EDLUT Emergent Encog JOONE Neuroph NeuroSolutions OpenNN Peltarion Synapse SNNS === Platforms, tools, and deployment === Amazon SageMaker Google Colab Hugging Face Kaggle Kubeflow MLflow ONNX OpenVINO TensorFlow Hub == Algorithms for deep learning and neural networks == Backpropagation Conjugate gradient method Generalized Hebbian algorithm Gradient descent Levenberg–Marquardt algorithm Perceptron Quasi-Newton method Wake-sleep algorithm == Methods and related topics == === Representation and metric learning === Contrastive learning Embedding Feature learning Manifold learning Metric learning === Generative modeling === Autoregressive model Diffusion model Generative adversarial network Generative model Variational inference === Efficient and scalable deep learning === Knowledge distillation Low-rank approximation Mixture of experts Quantization Sparsity === Reliability, safety, and interpretability === Adversarial machine learning AI alignment Algorithmic bias Catastrophic forgetting Differential privacy Explainable artificial intelligence Federated learning Hallucination (artificial intelligence) == Conferences and workshops == Annual Meeting of the Association for Computational Linguistics Conference on Computer Vision and Pattern Recognition Conference on Neural Information Processing Systems International Conference on Computer Vision International Conference on Learning Representations International Conference on Machine Learning == Organizations == === Research laboratories and institutions === Allen Institute for AI Alberta Machine Intelligence Institute European Laboratory for Learning and Intelligent Systems Google DeepMind Meta AI Mila Microsoft Research Vector Institute === Companies === Anthropic Cerebras Cohere DeepSeek Mistral AI OpenAI Stability AI xAI == Publications == === Books === Deep Learning – Ian Goodfellow and Yoshua Bengio Neural Networks and Deep Learning – Michael Nielsen Perceptrons – Marvin Minsky and Seymour Papert === Journals === IEEE Transactions on Neural Networks and Learning Systems Neural Networks Neural Computation == Influential persons ==
Fuzzy pay-off method for real option valuation
The fuzzy pay-off method for real option valuation (FPOM or pay-off method) is a method for valuing real options, developed by Mikael Collan, Robert Fullér, and József Mezei; and published in 2009. It is based on the use of fuzzy logic and fuzzy numbers for the creation of the possible pay-off distribution of a project (real option). The structure of the method is similar to the probability theory based Datar–Mathews method for real option valuation, but the method is not based on probability theory and uses fuzzy numbers and possibility theory in framing the real option valuation problem. == Method == The Fuzzy pay-off method derives the real option value from a pay-off distribution that is created by using three or four cash-flow scenarios (most often created by an expert or a group of experts). The pay-off distribution is created simply by assigning each of the three cash-flow scenarios a corresponding definition with regards to a fuzzy number (triangular fuzzy number for three scenarios and a trapezoidal fuzzy number for four scenarios). This means that the pay-off distribution is created without any simulation whatsoever. This makes the procedure easy and transparent. The scenarios used are a minimum possible scenario (the lowest possible outcome), the maximum possible scenario (the highest possible outcome) and a best estimate (most likely to happen scenario) that is mapped as a fully possible scenario with a full degree of membership in the set of possible outcomes, or in the case of four scenarios used - two best estimate scenarios that are the upper and lower limit of the interval that is assigned a full degree of membership in the set of possible outcomes. The main observations that lie behind the model for deriving the real option value are the following: The fuzzy NPV of a project is (equal to) the pay-off distribution of a project value that is calculated with fuzzy numbers. The mean value of the positive values of the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values. Real option value, ROV, calculated from the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values multiplied with the positive area of the fuzzy NPV over the total area of the fuzzy NPV. The real option formula can then be written simply as: R O V = A ( P o s ) A ( P o s ) + A ( N e g ) × E [ A + ] {\displaystyle \mathrm {ROV} ={\frac {A(\mathrm {Pos} )}{A(\mathrm {Pos} )+A(\mathrm {Neg} )}}\times E[A_{+}]} where A(Pos) is the area of the positive part of the fuzzy distribution, A(Neg) is the area of the negative part of the fuzzy distribution, and E[A+] is the mean value of the positive part of the distribution. It can be seen that when the distribution is totally positive, the real options value reduces to the expected (mean) value, E[A+]. As can be seen, the real option value can be derived directly from the fuzzy NPV, without simulation. At the same time, simulation is not an absolutely necessary step in the Datar–Mathews method, so the two methods are not very different in that respect. But what is totally different is that the Datar–Mathews method is based on probability theory and as such has a very different foundation from the pay-off method that is based on possibility theory: the way that the two models treat uncertainty is fundamentally different. == Use of the method == The pay-off method for real option valuation is very easy to use compared to the other real option valuation methods and it can be used with the most commonly used spreadsheet software without any add-ins. The method is useful in analyses for decision making regarding investments that have an uncertain future, and especially so if the underlying data is in the form of cash-flow scenarios. The method is less useful if optimal timing is the objective. The method is flexible and accommodates easily both one-stage investments and multi-stage investments (compound real options). The method has been taken into use in some large international industrial companies for the valuation of research and development projects and portfolios. In these analyses triangular fuzzy numbers are used. Other uses of the method so far are, for example, R&D project valuation IPR valuation, valuation of M&A targets and expected synergies, valuation and optimization of M&A strategies, valuation of area development (construction) projects, valuation of large industrial real investments. The use of the pay-off method is lately taught within the larger framework of real options, for example at the Lappeenranta University of Technology and at the Tampere University of Technology in Finland.