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Section: Properties. Being algorithmically similar to A*, LPA* shares many of its properties. Each node is expanded (visited) at most twice for each run of LPA*. Locally overconsistent nodes are expanded at most once per LPA* run, thus its initial run (in which every node enters the overconsistent state) has similar pe... | Wikipedia - Lifelong Planning A* - Properties | 329 | 1,428 | null |
Article: Machine perception. Machine perception is the capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them. The basic method that the computers take in and respond to their environment is through the attached hardware. Until... | Wikipedia - Machine perception - Summary | 280 | 1,497 | null |
Section: Machine vision. Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and high-dimensional data from the real world to produce numerical or symbolic information, e.g., in the forms of decisions. Computer vision has many applications already in use today... | Wikipedia - Machine perception - Machine vision | 158 | 894 | null |
Section: Machine hearing. Machine hearing, also known as machine listening or computer audition is the ability of a computer or machine to take in and process sound data such as speech or music. This area has a wide range of application including music recording and compression, speech synthesis and speech recognition.... | Wikipedia - Machine perception - Machine hearing | 169 | 951 | null |
Section: Future. Other than those listed above, some of the future hurdles that the science of machine perception still has to overcome include, but are not limited to: - Embodied cognition - The theory that cognition is a full body experience, and therefore can only exist, and therefore be measure and analyzed, in ful... | Wikipedia - Machine perception - Future | 336 | 1,739 | null |
Section: Problem-solving as search. An important aspect of intelligent behavior as studied in AI is goal-based problem solving, a framework in which the solution to a problem can be described by finding a sequence of actions that lead to a desirable goal. A goal-seeking system is supposed to be connected to its outside... | Wikipedia - Means–ends analysis - Problem-solving as search | 244 | 1,259 | null |
Section: How means–ends analysis works. The MEA technique is a strategy to control search in problem-solving. Given a current state and a goal state, an action is chosen which will reduce the difference between the two. The action is performed on the current state to produce a new state, and the process is recursively ... | Wikipedia - Means–ends analysis - How means–ends analysis works | 247 | 1,264 | null |
Section: Some AI systems using MEA. The MEA technique as a problem-solving strategy was first introduced in 1961 by Allen Newell and Herbert A. Simon in their computer problem-solving program General Problem Solver (GPS). In that implementation, the correspondence between differences and actions, also called operators,... | Wikipedia - Means–ends analysis - Some AI systems using MEA | 239 | 1,243 | null |
Section: Uses > Military intelligence. In 2023, the United States Department of Defense tested generative AI based on large language models to digitize and integrate data across the military. In the Gaza war, Israel used two AI systems to generate targets to strike: Habsora (translated: "the gospel") was used to compil... | Wikipedia - Military applications of artificial intelligence - Uses > Military intelligence | 225 | 1,153 | null |
Section: Global trends. Various countries are researching and deploying AI military applications, in what has been termed the "artificial intelligence arms race". Ongoing research is focused on intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous v... | Wikipedia - Military applications of artificial intelligence - Global trends | 167 | 919 | null |
Section: Description. Participants in the project created one-line statements which aimed to be objectively true or false to 20 other anonymous participants. In order to submit their statement they had first to check the true/false validity of 20 such statements submitted by others. Participants whose replies were cons... | Wikipedia - Mindpixel - Description | 340 | 1,706 | null |
It was being rewritten by Chris McKinstry as Mindpixel 2 and was intended to appear on a new server in France. Chris McKinstry died of suicide on 23 January 2006 and the future of the project and the integrity of the data is uncertain. Some Mindpixel data have been utilized by Michael Spivey of Cornell University and R... | Wikipedia - Mindpixel - Description | 218 | 1,052 | null |
Section: History. MindsDB was founded in 2017 by Jorge Torres and Adam Carrigan. The idea was incubated in early 2018 at Skydeck from UC Berkeley during the first funded batch, this led to the MindsDB Open Source project which started in August 2018. On April 16, 2020, MindsDB raised $3 million. Among the investors wer... | Wikipedia - MindsDB - History | 300 | 1,393 | null |
Article: Mode collapse. In machine learning, mode collapse is a failure mode observed in generative models, originally noted in Generative Adversarial Networks (GANs). It occurs when the model produces outputs that are less diverse than expected, effectively "collapsing" to generate only a few modes of the data distrib... | Wikipedia - Mode collapse - Summary | 161 | 841 | null |
Section: Distinctions. Mode collapse is distinct from overfitting, where a model learns detailed patterns in the training data that does not generalize to the test data, and underfitting, where it fails to learn patterns. Memorization is where a model learns to reproduce data from the training data. Memorization is oft... | Wikipedia - Mode collapse - Distinctions | 197 | 947 | null |
Section: In GANs. Training-time mode collapse was originally noted and studied in GANs, where it arises primarily due to imbalances in the training dynamics between the generator and discriminator in GANs. In the original GAN paper, it was also called the "Helvetica scenario". Common causes include: If the discriminato... | Wikipedia - Mode collapse - In GANs | 329 | 1,583 | null |
Section: Finetuning. The large language models are usually trained in two steps. In the first step ("pretraining"), the model is trained to simply generate text sampled from a large dataset. In the second step ("finetuning"), the model is trained to perform specific tasks by training it on a small dataset containing ju... | Wikipedia - Mode collapse - Finetuning | 298 | 1,385 | null |
Section: Definition. The term "moral outsourcing" was first coined by Dr. Rumman Chowdhury, a data scientist concerned with the overlap between artificial intelligence and social issues. Chowdhury used the term to describe looming fears of a so-called “Fourth Industrial Revolution” following the rise of artificial inte... | Wikipedia - Moral outsourcing - Definition | 323 | 1,639 | null |
Section: Real-World Applications. In the medical field, AI is increasingly involved in decision-making processes about which patients to treat, and how to treat them. The responsibility of the doctor to make informed decisions about what is best for their patients is outsourced to an algorithm. Sympathy is also noted t... | Wikipedia - Moral outsourcing - Real-World Applications | 311 | 1,611 | null |
Article: Neural computation. Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition known as Computational theory of mind, also referred to as computationalism, which advances the thesis that neural computation explains cognit... | Wikipedia - Neural computation - Summary | 329 | 1,936 | null |
A rule in this sense is simply an instruction for executing a manipulation on the current state of the variable, in order to produce a specified output. In other words, a rule dictates which output to produce given a certain input to the computing system. A computing system is a mechanism whose components must be funct... | Wikipedia - Neural computation - Summary | 329 | 1,848 | null |
Section: Introduction > Size of the training dataset. The size of the training dataset is usually quantified by the number of data points within it. Larger training datasets are typically preferred, as they provide a richer and more diverse source of information from which the model can learn. This can lead to improved... | Wikipedia - Neural scaling law - Introduction > Size of the training dataset | 201 | 972 | null |
Section: Introduction > Cost of training. Training cost is typically measured in terms of time (how long it takes to train the model) and computational resources (how much processing power and memory are required). It is important to note that the cost of training can be significantly reduced with efficient training al... | Wikipedia - Neural scaling law - Introduction > Cost of training | 156 | 826 | null |
Section: Introduction > Performance. The performance of a neural network model is evaluated based on its ability to accurately predict the output given some input data. Common metrics for evaluating model performance include: Negative log-likelihood per token (logarithm of perplexity) for language modeling; Accuracy, p... | Wikipedia - Neural scaling law - Introduction > Performance | 197 | 936 | null |
Section: Examples > (Hestness, Narang, et al, 2017). The 2017 paper is a common reference point for neural scaling laws fitted by statistical analysis on experimental data. Previous works before the 2000s, as cited in the paper, were either theoretical or orders of magnitude smaller in scale. Whereas previous works gen... | Wikipedia - Neural scaling law - Examples > (Hestness, Narang, et al, 2017) | 339 | 1,217 | null |
They also found that for a given architecture, the number of parameters necessary to reach lowest levels of loss, given a fixed dataset size, grows like N ∝ D β {\displaystyle N\propto D^{\beta }} for another exponent β {\displaystyle \beta } . They studied machine translation with LSTM ( α ∼ 0.13 {\displaystyle \alpha... | Wikipedia - Neural scaling law - Examples > (Hestness, Narang, et al, 2017) | 240 | 751 | null |
Section: Examples > (Henighan, Kaplan, et al, 2020). A 2020 analysis studied statistical relations between C , N , D , L {\displaystyle C,N,D,L} over a wide range of values and found similar scaling laws, over the range of N ∈ [ 10 3 , 10 9 ] {\displaystyle N\in [10^{3},10^{9}]} , C ∈ [ 10 12 , 10 21 ] {\displaystyle C... | Wikipedia - Neural scaling law - Examples > (Henighan, Kaplan, et al, 2020) | 341 | 1,002 | null |
The parameter α {\displaystyle \alpha } is the most important one. When N {\displaystyle N} is the varied variable, α {\displaystyle \alpha } ranges from 0.037 {\displaystyle 0.037} to 0.24 {\displaystyle 0.24} depending on the model modality. This corresponds to the α = 0.34 {\displaystyle \alpha =0.34} from the Chinc... | Wikipedia - Neural scaling law - Examples > (Henighan, Kaplan, et al, 2020) | 346 | 1,008 | null |
Given fixed computing budget, optimal model parameter count is consistently around N o p t ( C ) = ( C 5 × 10 − 12 petaFLOP-day ) 0.7 = 9.0 × 10 − 7 C 0.7 {\displaystyle N_{opt}(C)=\left({\frac {C}{5\times 10^{-12}{\text{petaFLOP-day}}}}\right)^{0.7}=9.0\times 10^{-7}C^{0.7}} The parameter 9.0 × 10 − 7 {\displaystyle 9... | Wikipedia - Neural scaling law - Examples > (Henighan, Kaplan, et al, 2020) | 345 | 913 | null |
Section: Examples > Chinchilla scaling (Hoffmann, et al, 2022). One particular scaling law ("Chinchilla scaling") states that, for a large language model (LLM) autoregressively trained for one epoch, with a cosine learning rate schedule, we have: { C = C 0 N D L = A N α + B D β + L 0 {\displaystyle {\begin{cases}C=C_{0... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 246 | 747 | null |
L {\displaystyle L} is the average negative log-likelihood loss per token (nats/token), achieved by the trained LLM on the test dataset. L 0 {\displaystyle L_{0}} represents the loss of an ideal generative process on the test data A N α {\displaystyle {\frac {A}{N^{\alpha }}}} captures the fact that a Transformer langu... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 322 | 1,080 | null |
Although Besiroglu et al. claims that the statistical estimation is slightly off, and should be α = 0.35 , β = 0.37 , A = 482.01 , B = 2085.43 , L 0 = 1.82 {\displaystyle \alpha =0.35,\beta =0.37,A=482.01,B=2085.43,L_{0}=1.82} . The statistical laws were fitted over experimental data with N ∈ [ 7 × 10 7 , 1.6 × 10 10 ]... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 274 | 792 | null |
In particular, for any fixed C {\displaystyle C} , we can uniquely solve for all 4 variables that minimizes L {\displaystyle L} . This provides us with the optimal D o p t ( C ) , N o p t ( C ) {\displaystyle D_{opt}(C),N_{opt}(C)} for any fixed C {\displaystyle C} : N o p t ( C ) = G ( C 6 ) a , D o p t ( C ) = G − 1 ... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 325 | 707 | null |
{\displaystyle N_{opt}(C)=G\left({\frac {C}{6}}\right)^{a},\quad D_{opt}(C)=G^{-1}\left({\frac {C}{6}}\right)^{b},\quad {\text{ where }}\quad G=\left({\frac {\alpha A}{\beta B}}\right)^{\frac {1}{\alpha +\beta }},\quad a={\frac {\beta }{\alpha +\beta }}{\text{, and }}b={\frac {\alpha }{\alpha +\beta }}{\text{. }}} Plug... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 379 | 812 | null |
}}} Plugging in the numerical values, we obtain the "Chinchilla efficient" model size and training dataset size, as well as the test loss achievable: { N o p t ( C ) = 0.6 C 0.45 D o p t ( C ) = 0.3 C 0.55 L o p t ( C ) = 1070 C − 0.154 + 1.7 {\displaystyle {\begin{cases}N_{opt}(C)=0.6\;C^{0.45}\\D_{opt}(C)=0.3\;C^{0.5... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 289 | 735 | null |
The above is based on a statistical model of L = A N α + B D β + L 0 {\displaystyle L={\frac {A}{N^{\alpha }}}+{\frac {B}{D^{\beta }}}+L_{0}} . One can also directly fit a statistical law for D o p t ( C ) , N o p t ( C ) {\displaystyle D_{opt}(C),N_{opt}(C)} without going through the detour, for which one obtains: { N... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) | 224 | 478 | null |
Section: Examples > Chinchilla scaling (Hoffmann, et al, 2022) > Discrepancy. The Chinchilla scaling law analysis for training transformer language models suggests that for a given training compute budget ( C {\displaystyle C} ), to achieve the minimal pretraining loss for that budget, the number of model parameters ( ... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) > Discrepancy | 296 | 951 | null |
Section: Examples > Chinchilla scaling (Hoffmann, et al, 2022) > Beyond Chinchilla scaling. As Chinchilla scaling has been the reference point for many large-scaling training runs, there had been a concurrent effort to go "beyond Chinchilla scaling", meaning to modify some of the training pipeline in order to obtain th... | Wikipedia - Neural scaling law - Examples > Chinchilla scaling (Hoffmann, et al, 2022) > Beyond Chinchilla scaling | 332 | 1,634 | null |
Section: Examples > Broken neural scaling laws (BNSL). A 2022 analysis found that many scaling behaviors of artificial neural networks follow a smoothly broken power law functional form: y = a + ( b x − c 0 ) ∏ i = 1 n ( 1 + ( x d i ) 1 / f i ) − c i ∗ f i {\displaystyle y=a+{\bigg (}bx^{-c_{0}}{\bigg )}\prod _{i=1}^{n... | Wikipedia - Neural scaling law - Examples > Broken neural scaling laws (BNSL) | 335 | 972 | null |
. . f n {\displaystyle a,b,c_{0},c_{1}...c_{n},d_{1}...d_{n},f_{1}...f_{n}} are found by statistical fitting. On a log–log plot, when f i {\displaystyle f_{i}} is not too large and a {\displaystyle a} is subtracted out from the y-axis, this functional form looks like a series of linear segments connected by arcs; the n... | Wikipedia - Neural scaling law - Examples > Broken neural scaling laws (BNSL) | 310 | 1,196 | null |
The scenarios in which the scaling behaviors of artificial neural networks were found to follow this functional form include large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, AI capabilities, robotics, out-of-distribution (OOD) generaliz... | Wikipedia - Neural scaling law - Examples > Broken neural scaling laws (BNSL) | 276 | 1,222 | null |
Section: Examples > Inference scaling. Other than scaling up training compute, one can also scale up inference compute (or "test-time compute"). As an example, the Elo rating of AlphaGo improves steadily as it is allowed to spend more time on its Monte Carlo Tree Search per play.: Fig 4 For AlphaGo Zero, increasing Elo... | Wikipedia - Neural scaling law - Examples > Inference scaling | 308 | 1,432 | null |
Section: Examples > Other examples > Vision transformers. Vision transformers, similar to language transformers, exhibit scaling laws. A 2022 research trained vision transformers, with parameter counts N ∈ [ 5 × 10 6 , 2 × 10 9 ] {\displaystyle N\in [5\times 10^{6},2\times 10^{9}]} , on image sets of sizes D ∈ [ 3 × 10... | Wikipedia - Neural scaling law - Examples > Other examples > Vision transformers | 276 | 782 | null |
Section: Examples > Other examples > Neural machine translation. Ghorbani, Behrooz et al. studied scaling laws for neural machine translation (specifically, English as source, and German as target) in encoder-decoder Transformer models, trained until convergence on the same datasets (thus they did not fit scaling laws ... | Wikipedia - Neural scaling law - Examples > Other examples > Neural machine translation | 242 | 781 | null |
It is not simply a function of total parameter count N = N E + N D {\displaystyle N=N_{E}+N_{D}} . The function has form L ( N e , N d ) = α ( N ¯ e N e ) p e ( N ¯ d N d ) p d + L ∞ {\displaystyle L\left(N_{e},N_{d}\right)=\alpha \left({\frac {{\bar {N}}_{e}}{N_{e}}}\right)^{p_{e}}\left({\frac {{\bar {N}}_{d}}{N_{d}}}... | Wikipedia - Neural scaling law - Examples > Other examples > Neural machine translation | 303 | 613 | null |
They found that N d / N ≈ 0.55 {\displaystyle N_{d}/N\approx 0.55} minimizes loss if N {\displaystyle N} is held fixed. L {\displaystyle L} "saturates" (that is, it reaches L ∞ {\displaystyle L_{\infty }} ) for smaller models when the training and testing datasets are "source-natural" than "target-natural". A "source-n... | Wikipedia - Neural scaling law - Examples > Other examples > Neural machine translation | 281 | 1,182 | null |
The authors hypothesize that source-natural datasets have uniform and dull target sentences, and so a model that is trained to predict the target sentences would quickly overfit. trained Transformers for machine translations with sizes N ∈ [ 4 × 10 5 , 5.6 × 10 7 ] {\displaystyle N\in [4\times 10^{5},5.6\times 10^{7}]}... | Wikipedia - Neural scaling law - Examples > Other examples > Neural machine translation | 301 | 787 | null |
Section: Examples > Other examples > Transfer learning. Hernandez, Danny et al. studied scaling laws for transfer learning in language models. They trained a family of Transformers in three ways: pretraining on English, finetuning on Python pretraining on an equal mix of English and Python, finetuning on Python trainin... | Wikipedia - Neural scaling law - Examples > Other examples > Transfer learning | 216 | 847 | null |
We say that its "transferred token count" is D T {\displaystyle D_{T}} , if another model with the same N {\displaystyle N} achieves the same L {\displaystyle L} after training on D F + D T {\displaystyle D_{F}+D_{T}} Python tokens. They found D T = 1.9 e 4 ( D F ) .18 ( N ) .38 {\displaystyle D_{T}=1.9e4\left(D_{F}\ri... | Wikipedia - Neural scaling law - Examples > Other examples > Transfer learning | 218 | 524 | null |
Section: Examples > Other examples > Precision. Kumar et al. study scaling laws for numerical precision in the training of language models. They train a family of language models with weights, activations, and KV cache in varying numerical precision in both integer and floating-point type to measure the effects on loss... | Wikipedia - Neural scaling law - Examples > Other examples > Precision | 328 | 1,497 | null |
Section: Examples > Other examples > Densing laws. Xiao et al. considered the parameter efficiency ("density") of models over time. The idea is that over time, researchers would discover models that use their parameters more efficiently, in that models with the same performance can have fewer parameters. A model can ha... | Wikipedia - Neural scaling law - Examples > Other examples > Densing laws | 311 | 1,214 | null |
Article: Neuro-symbolic AI. Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of r... | Wikipedia - Neuro-symbolic AI - Summary | 334 | 1,746 | null |
Section: Approaches. Approaches for integration are diverse. Henry Kautz's taxonomy of neuro-symbolic architectures follows, along with some examples: Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of... | Wikipedia - Neuro-symbolic AI - Approaches | 328 | 1,635 | null |
Section: Implementations. Implementations of neuro-symbolic approaches include: AllegroGraph: an integrated Knowledge Graph based platform for neuro-symbolic application development. Scallop: a language based on Datalog that supports differentiable logical and relational reasoning. Scallop can be integrated in Python a... | Wikipedia - Neuro-symbolic AI - Implementations | 156 | 816 | null |
Article: Neurorobotics. Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (... | Wikipedia - Neurorobotics - Summary | 257 | 1,265 | null |
Section: Major classes of models > Locomotion and motor control. Neurorobots are often used to study motor feedback and control systems, and have proved their merit in developing controllers for robots. Locomotion is modeled by a number of neurologically inspired theories on the action of motor systems. Locomotion cont... | Wikipedia - Neurorobotics - Major classes of models > Locomotion and motor control | 277 | 1,500 | null |
Section: Major classes of models > Learning and memory systems. Robots designed to test theories of animal memory systems. Many studies examine the memory system of rats, particularly the rat hippocampus, dealing with place cells, which fire for a specific location that has been learned. Systems modeled after the rat h... | Wikipedia - Neurorobotics - Major classes of models > Learning and memory systems | 301 | 1,563 | null |
Section: Major classes of models > Action selection and value systems. Action selection studies deal with negative or positive weighting to an action and its outcome. Neurorobots can and have been used to study simple ethical interactions, such as the classical thought experiment where there are more people than a life... | Wikipedia - Neurorobotics - Major classes of models > Action selection and value systems | 299 | 1,495 | null |
Section: Biological robots. Biological robots are not officially neurorobots in that they are not neurologically inspired AI systems, but actual neuron tissue wired to a robot. This employs the use of cultured neural networks to study brain development or neural interactions. These typically consist of a neural culture... | Wikipedia - Neurorobotics - Biological robots | 206 | 1,091 | null |
Section: Implications for neuroscience. Neuroscientists benefit from neurorobotics because it provides a blank slate to test various possible methods of brain function in a controlled and testable environment. While robots are more simplified versions of the systems they emulate, they are more specific, allowing more d... | Wikipedia - Neurorobotics - Implications for neuroscience | 182 | 991 | null |
Section: Natural > Animal rights and personhood. In the animal rights movement, it is common to distinguish between "human animals" and "non-human animals". Participants in the animal rights movement generally recognize that non-human animals have some similar characteristics to those of human persons. For example, var... | Wikipedia - Non-human - Natural > Animal rights and personhood | 168 | 855 | null |
Section: Motivation. The differences between nouvelle AI and symbolic AI are apparent in early robots Shakey and Freddy. These robots contained an internal model (or "representation") of their micro-worlds consisting of symbolic descriptions. As a result, this structure of symbols had to be renewed as the robot moved o... | Wikipedia - Nouvelle AI - Motivation | 270 | 1,443 | null |
Article: Operation Serenata de Amor. Operation Serenata de Amor is an artificial intelligence project designed to analyze public spending in Brazil. The project has been funded by a recurrent financing campaign since September 7, 2016, and came in the wake of major scandals of misappropriation of public funds in Brazil... | Wikipedia - Operation Serenata de Amor - Summary | 167 | 836 | null |
Section: Modules. Throughout development of the project, new modules have been newly introduced in addition to the main repository: The main repository, serenata-de-amor, serves as the starting point for investigative work. Rosie is the robot programmed to identify public funds expenses with discrepancies, starting wit... | Wikipedia - Operation Serenata de Amor - Modules | 188 | 918 | null |
Section: History. Operation Serenata de Amor is an Artificial intelligence project for analysis of public expenditures. It was conceived in March 2016 by data scientist Irio Musskopf, sociologist Eduardo Cuducos and entrepreneur Felipe Cabral. The project was financed collectively in the Catarse platform, where it reac... | Wikipedia - Operation Serenata de Amor - History | 240 | 1,125 | null |
Section: Results. In January 2017, concluding the period financed by the initial campaign, the group carried out an investigation into the suspicious activities found by the data analysis system. 629 complaints were made to the Ombudsman's Office of the Chamber of Deputies, questioning expenses of 216 federal deputies.... | Wikipedia - Operation Serenata de Amor - Results | 347 | 1,774 | null |
It was also presented both by Irio and Yaso at the Digital Harvard Kennedy School, over a lunch seminar, where the transparency of the platform and the main solutions found were discussed, so that the code and data are always available to verify its suitability. This infographic provides information about the first res... | Wikipedia - Operation Serenata de Amor - Results | 225 | 1,154 | null |
Section: Definitions. According to a white paper by software company Tupl Inc, continuous machine learning model training and results extraction in the telecom industry requires a large number of automation utilities in order to "facilitate the development and deployment of a multitude of use cases, the collection and ... | Wikipedia - Operational artificial intelligence - Definitions | 163 | 923 | null |
Section: Differences with non-organic computing. As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware." Scientists hope that such organoids can provide faster, more efficient, and more powerful computing power than regular silicon-... | Wikipedia - Organoid intelligence - Differences with non-organic computing | 344 | 1,822 | null |
Section: Intended functions. Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain functio... | Wikipedia - Organoid intelligence - Intended functions | 164 | 912 | null |
Article: Pattern theory. Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it p... | Wikipedia - Pattern theory - Summary | 266 | 1,396 | null |
Article: Pedagogical agent. A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogi... | Wikipedia - Pedagogical agent - Summary | 173 | 808 | null |
Section: History. The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent w... | Wikipedia - Pedagogical agent - History | 225 | 1,119 | null |
Section: Effectiveness. It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to a... | Wikipedia - Pedagogical agent - Effectiveness | 327 | 1,664 | null |
Section: Design > Affective Response. Pedagogical agents have reached a point where they can convey and elicit emotion, but also reason about and respond to it. These agents are often designed to elicit and respond to affective actions from users through various modalities such as speech, facial expressions, and body g... | Wikipedia - Pedagogical agent - Design > Affective Response | 162 | 841 | null |
Section: Design > Digital Representation. The design of a pedagogical agent often begins with its digital representation, whether it will be 2D or 3D and static or animated. Several studies have developed pedagogical agents that were both static and animated, then evaluated the relative benefits. Similar to other desig... | Wikipedia - Pedagogical agent - Design > Digital Representation | 177 | 940 | null |
Article: Percept (artificial intelligence). A percept is the input that an intelligent agent is perceiving at any given moment. It is essentially the same concept as a percept in psychology, except that it is being perceived not by the brain but by the agent. A percept is detected by a sensor, often a camera, processed... | Wikipedia - Percept (artificial intelligence) - Summary | 233 | 1,031 | null |
Section: Overview. Personality computing addresses three main problems involving personality: automatic personality recognition, perception, and synthesis. Automatic personality recognition is the inference of the personality type of target individuals from their digital footprint. Automatic personality perception is t... | Wikipedia - Personality computing - Overview | 221 | 1,349 | null |
Section: History. Personality computing began around 2005 with the pioneering research in personality recognition by Shlomo Argamon and later by François Mairesse. These works showed that personality traits could be inferred with reasonable accuracy from text, such as blogs, self-presentations, and email addresses. In ... | Wikipedia - Personality computing - History | 227 | 1,292 | null |
Article: Personoid. Personoid is the concept coined by Stanisław Lem, a Polish science-fiction writer, in Non Serviam, from his book A Perfect Vacuum (1971). His personoids are an abstraction of functions of human mind and they live in computers; they do not need any human-like physical body. In cognitive and software ... | Wikipedia - Personoid - Summary | 176 | 880 | null |
Article: POP-11. POP-11 is a reflective, incrementally compiled programming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of B... | Wikipedia - POP-11 - Summary | 349 | 1,737 | null |
This made it possible for Poplog to be used by teachers, researchers, and developers who were interested in only one of the languages. The most successful product developed in POP-11 was the Clementine data mining system, developed by ISL. After SPSS bought ISL, they renamed Clementine to SPSS Modeler and decided to po... | Wikipedia - POP-11 - Summary | 214 | 1,037 | null |
Section: Simple code examples. Here is an example of a simple POP-11 program: define Double(Source) -> Result; Source*2 -> Result; enddefine; Double(123) => That prints out: ** 246 This one includes some list processing: define RemoveElementsMatching(Element, Source) -> Result; lvars Index; [[% for Index in Source do u... | Wikipedia - POP-11 - Simple code examples | 348 | 1,274 | null |
Section: Popper. Popper called for social science to be grounded in what he called situational analysis or situational logic. This requires building models of social situations which include individual actors and their relationship to social institutions, e.g. markets, legal codes, bureaucracies, etc. These models attr... | Wikipedia - Principle of rationality - Popper | 264 | 1,276 | null |
Article: Problem solving. Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields. The former is an example of simp... | Wikipedia - Problem solving - Summary | 345 | 1,894 | null |
Section: Definition. The term problem solving has a slightly different meaning depending on the discipline. For instance, it is a mental process in psychology and a computerized process in computer science. There are two different types of problems: ill-defined and well-defined; different approaches are used for each. ... | Wikipedia - Problem solving - Definition | 192 | 1,055 | null |
Section: Definition > Psychology. Problem solving in psychology refers to the process of finding solutions to problems encountered in life. Solutions to these problems are usually situation- or context-specific. The process starts with problem finding and problem shaping, in which the problem is discovered and simplifi... | Wikipedia - Problem solving - Definition > Psychology | 332 | 1,947 | null |
Section: Definition > Cognitive sciences. Among the first experimental psychologists to study problem solving were the Gestaltists in Germany, such as Karl Duncker in The Psychology of Productive Thinking (1935). Perhaps best known is the work of Allen Newell and Herbert A. Simon. Experiments in the 1960s and early 197... | Wikipedia - Problem solving - Definition > Cognitive sciences | 152 | 851 | null |
Section: Definition > Logic. Formal logic concerns issues like validity, truth, inference, argumentation, and proof. In a problem-solving context, it can be used to formally represent a problem as a theorem to be proved, and to represent the knowledge needed to solve the problem as the premises to be used in a proof th... | Wikipedia - Problem solving - Definition > Logic | 317 | 1,693 | null |
Section: Definition > Engineering. When products or processes fail, problem solving techniques can be used to develop corrective actions that can be taken to prevent further failures. Such techniques can also be applied to a product or process prior to an actual failure event—to predict, analyze, and mitigate a potenti... | Wikipedia - Problem solving - Definition > Engineering | 309 | 1,608 | null |
Section: Problem-solving strategies. Problem-solving strategies are steps to overcoming the obstacles to achieving a goal. The iteration of such strategies over the course of solving a problem is the "problem-solving cycle". Common steps in this cycle include recognizing the problem, defining it, developing a strategy ... | Wikipedia - Problem solving - Problem-solving strategies | 285 | 1,547 | null |
Some problem-solving strategies include: Abstraction solving the problem in a tractable model system to gain insight into the real system Analogy adapting the solution to a previous problem which has similar features or mechanisms Brainstorming (especially among groups of people) suggesting a large number of solutions ... | Wikipedia - Problem solving - Problem-solving strategies | 327 | 1,894 | null |
Section: Problem-solving methods. A3 problem solving – Structured problem improvement approach Design thinking – Processes by which design concepts are developed Eight Disciplines Problem Solving – Eight disciplines of team-oriented problem solving methodPages displaying short descriptions of redirect targets GROW mode... | Wikipedia - Problem solving - Problem-solving methods | 197 | 1,103 | null |
Section: Common barriers > Confirmation bias. Confirmation bias is an unintentional tendency to collect and use data which favors preconceived notions. Such notions may be incidental rather than motivated by important personal beliefs: the desire to be right may be sufficient motivation. Scientific and technical profes... | Wikipedia - Problem solving - Common barriers > Confirmation bias | 281 | 1,564 | null |
Section: Common barriers > Mental set. Mental set is the inclination to re-use a previously successful solution, rather than search for new and better solutions. It is a reliance on habit. It was first articulated by Abraham S. Luchins in the 1940s with his well-known water jug experiments. Participants were asked to f... | Wikipedia - Problem solving - Common barriers > Mental set | 316 | 1,588 | null |
Section: Common barriers > Functional fixedness. Functional fixedness is the tendency to view an object as having only one function, and to be unable to conceive of any novel use, as in the Maier pliers experiment described above. Functional fixedness is a specific form of mental set, and is one of the most common form... | Wikipedia - Problem solving - Common barriers > Functional fixedness | 270 | 1,337 | null |
Section: Common barriers > Unnecessary constraints. Unnecessary constraints are arbitrary boundaries imposed unconsciously on the task at hand, which foreclose a productive avenue of solution. The solver may become fixated on only one type of solution, as if it were an inevitable requirement of the problem. Typically, ... | Wikipedia - Problem solving - Common barriers > Unnecessary constraints | 267 | 1,438 | null |
Section: Common barriers > Irrelevant information. Irrelevant information is a specification or data presented in a problem that is unrelated to the solution. If the solver assumes that all information presented needs to be used, this often derails the problem solving process, making relatively simple problems much har... | Wikipedia - Problem solving - Common barriers > Irrelevant information | 179 | 924 | null |
Section: Common barriers > Avoiding barriers by changing problem representation. The disruption caused by the above cognitive biases can depend on how the information is represented: visually, verbally, or mathematically. A classic example is the Buddhist monk problem: A Buddhist monk begins at dawn one day walking up ... | Wikipedia - Problem solving - Common barriers > Avoiding barriers by changing problem representation | 259 | 1,309 | null |
Section: Dreaming: problem solving without waking consciousness. People can also solve problems while they are asleep. There are many reports of scientists and engineers who solved problems in their dreams. For example, Elias Howe, inventor of the sewing machine, figured out the structure of the bobbin from a dream. Th... | Wikipedia - Problem solving - Dreaming: problem solving without waking consciousness | 329 | 1,590 | null |
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