AI Detector Similar To Turnitin

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  • Rendering equation

    Rendering equation

    In computer graphics, the rendering equation is an integral equation that expresses the amount of light leaving a point on a surface as the sum of emitted light and reflected light. It was independently introduced into computer graphics by David Immel et al. and James Kajiya in 1986. The equation is important in the theory of physically based rendering, describing the relationships between the bidirectional reflectance distribution function (BRDF) and the radiometric quantities used in rendering. The rendering equation is defined at every point on every surface in the scene being rendered, including points hidden from the camera. The incoming light quantities on the right side of the equation usually come from the left (outgoing) side at other points in the scene (ray casting can be used to find these other points). The radiosity rendering method solves a discrete approximation of this system of equations. In distributed ray tracing, the integral on the right side of the equation may be evaluated using Monte Carlo integration by randomly sampling possible incoming light directions. Path tracing improves and simplifies this method. The rendering equation can be extended to handle effects such as fluorescence (in which some absorbed energy is re-emitted at different wavelengths) and can support transparent and translucent materials by using a bidirectional scattering distribution function (BSDF) in place of a BRDF. The theory of path tracing sometimes uses a path integral (integral over possible paths from a light source to a point) instead of the integral over possible incoming directions. == Equation form == The rendering equation may be written in the form L o ( x , ω o , λ , t ) = L e ( x , ω o , λ , t ) + L r ( x , ω o , λ , t ) {\displaystyle L_{\text{o}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)=L_{\text{e}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)+L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} L r ( x , ω o , λ , t ) = ∫ Ω f r ( x , ω i , ω o , λ , t ) L i ( x , ω i , λ , t ) ( ω i ⋅ n ) d ⁡ ω i {\displaystyle L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)=\int _{\Omega }f_{\text{r}}(\mathbf {x} ,\omega _{\text{i}},\omega _{\text{o}},\lambda ,t)L_{\text{i}}(\mathbf {x} ,\omega _{\text{i}},\lambda ,t)(\omega _{\text{i}}\cdot \mathbf {n} )\operatorname {d} \omega _{\text{i}}} where L o ( x , ω o , λ , t ) {\displaystyle L_{\text{o}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is the total spectral radiance of wavelength λ {\displaystyle \lambda } directed outward along direction ω o {\displaystyle \omega _{\text{o}}} at time t {\displaystyle t} , from a particular position x {\displaystyle \mathbf {x} } x {\displaystyle \mathbf {x} } is the location in space ω o {\displaystyle \omega _{\text{o}}} is the direction of the outgoing light λ {\displaystyle \lambda } is a particular wavelength of light t {\displaystyle t} is time L e ( x , ω o , λ , t ) {\displaystyle L_{\text{e}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is emitted spectral radiance L r ( x , ω o , λ , t ) {\displaystyle L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is reflected spectral radiance ∫ Ω … d ⁡ ω i {\displaystyle \int _{\Omega }\dots \operatorname {d} \omega _{\text{i}}} is an integral over Ω {\displaystyle \Omega } Ω {\displaystyle \Omega } is the unit hemisphere centered around n {\displaystyle \mathbf {n} } containing all possible values for ω i {\displaystyle \omega _{\text{i}}} where ω i ⋅ n > 0 {\displaystyle \omega _{\text{i}}\cdot \mathbf {n} >0} f r ( x , ω i , ω o , λ , t ) {\displaystyle f_{\text{r}}(\mathbf {x} ,\omega _{\text{i}},\omega _{\text{o}},\lambda ,t)} is the bidirectional reflectance distribution function, the proportion of light reflected from ω i {\displaystyle \omega _{\text{i}}} to ω o {\displaystyle \omega _{\text{o}}} at position x {\displaystyle \mathbf {x} } , time t {\displaystyle t} , and at wavelength λ {\displaystyle \lambda } ω i {\displaystyle \omega _{\text{i}}} is the negative direction of the incoming light L i ( x , ω i , λ , t ) {\displaystyle L_{\text{i}}(\mathbf {x} ,\omega _{\text{i}},\lambda ,t)} is spectral radiance of wavelength λ {\displaystyle \lambda } coming inward toward x {\displaystyle \mathbf {x} } from direction ω i {\displaystyle \omega _{\text{i}}} at time t {\displaystyle t} n {\displaystyle \mathbf {n} } is the surface normal at x {\displaystyle \mathbf {x} } ω i ⋅ n {\displaystyle \omega _{\text{i}}\cdot \mathbf {n} } is the weakening factor of outward irradiance due to incident angle, as the light flux is smeared across a surface whose area is larger than the projected area perpendicular to the ray. This is often written as cos ⁡ θ i {\displaystyle \cos \theta _{i}} . Two noteworthy features are: its linearity—it is composed only of multiplications and additions, and its spatial homogeneity—it is the same in all positions and orientations. These mean a wide range of factorings and rearrangements of the equation are possible. It is a Fredholm integral equation of the second kind, similar to those that arise in quantum field theory. Note this equation's spectral and time dependence — L o {\displaystyle L_{\text{o}}} may be sampled at or integrated over sections of the visible spectrum to obtain, for example, a trichromatic color sample. A pixel value for a single frame in an animation may be obtained by fixing t ; {\displaystyle t;} motion blur can be produced by averaging L o {\displaystyle L_{\text{o}}} over some given time interval (by integrating over the time interval and dividing by the length of the interval). Note that a solution to the rendering equation is the function L o {\displaystyle L_{\text{o}}} . The function L i {\displaystyle L_{\text{i}}} is related to L o {\displaystyle L_{\text{o}}} via a ray-tracing operation: The incoming radiance from some direction at one point is the outgoing radiance at some other point in the opposite direction. == Applications == Solving the rendering equation for any given scene is the primary challenge in realistic rendering. One approach to solving the equation is based on finite element methods, leading to the radiosity algorithm. Another approach using Monte Carlo methods has led to many different algorithms including path tracing, photon mapping, and Metropolis light transport, among others. == Limitations == Although the equation is very general, it does not capture every aspect of light reflection. Some missing aspects include the following: Transmission, which occurs when light is transmitted through the surface, such as when it hits a glass object or a water surface, Subsurface scattering, where the spatial locations for incoming and departing light are different. Surfaces rendered without accounting for subsurface scattering may appear unnaturally opaque — however, it is not necessary to account for this if transmission is included in the equation, since that will effectively include also light scattered under the surface, Polarization, where different light polarizations will sometimes have different reflection distributions, for example when light bounces at a water surface, Phosphorescence, which occurs when light or other electromagnetic radiation is absorbed at one moment and emitted at a later moment, usually with a longer wavelength (unless the absorbed electromagnetic radiation is very intense), Interference, where the wave properties of light are exhibited, Fluorescence, where the absorbed and emitted light have different wavelengths, Non-linear effects, where very intense light can increase the energy level of an electron with more energy than that of a single photon (this can occur if the electron is hit by two photons at the same time), and emission of light with higher frequency than the frequency of the light that hit the surface suddenly becomes possible, and Doppler effect, where light that bounces off an object moving at a very high speed will get its wavelength changed: if the light bounces off an object that is moving towards it, the light will be blueshifted and the photons will be packed more closely so the photon flux will be increased; if it bounces off an object moving away from it, it will be redshifted and the photon flux will be decreased. This effect becomes apparent only at speeds comparable to the speed of light, which is not the case for most rendering applications. For scenes that are either not composed of simple surfaces in a vacuum or for which the travel time for light is an important factor, researchers have generalized the rendering equation to produce a volume rendering equation suitable for volume rendering and a transient rendering equation for use with data from a time-of-flight camera.

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  • IBM alignment models

    IBM alignment models

    The IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an alignment model, starting with lexical translation probabilities and moving to reordering and word duplication. They underpinned the majority of statistical machine translation systems for almost twenty years starting in the early 1990s, until neural machine translation began to dominate. These models offer principled probabilistic formulation and (mostly) tractable inference. The IBM alignment models were published in parts in 1988 and 1990, and the entire series is published in 1993. Every author of the 1993 paper subsequently went to the hedge fund Renaissance Technologies. The original work on statistical machine translation at IBM proposed five models, and a model 6 was proposed later. The sequence of the six models can be summarized as: Model 1: lexical translation Model 2: additional absolute alignment model Model 3: extra fertility model Model 4: added relative alignment model Model 5: fixed deficiency problem. Model 6: Model 4 combined with a HMM alignment model in a log linear way == Mathematical setup == The IBM alignment models translation as a conditional probability model. For each source-language ("foreign") sentence f {\displaystyle f} , we generate both a target-language ("English") sentence e {\displaystyle e} and an alignment a {\displaystyle a} . The problem then is to find a good statistical model for p ( e , a | f ) {\displaystyle p(e,a|f)} , the probability that we would generate English language sentence e {\displaystyle e} and an alignment a {\displaystyle a} given a foreign sentence f {\displaystyle f} . The meaning of an alignment grows increasingly complicated as the model version number grew. See Model 1 for the most simple and understandable version. == Model 1 == === Word alignment === Given any foreign-English sentence pair ( e , f ) {\displaystyle (e,f)} , an alignment for the sentence pair is a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . That is, we assume that the English word at location i {\displaystyle i} is "explained" by the foreign word at location a ( i ) {\displaystyle a(i)} . For example, consider the following pair of sentences It will surely rain tomorrow -- 明日 は きっと 雨 だWe can align some English words to corresponding Japanese words, but not everyone:it -> ? will -> ? surely -> きっと rain -> 雨 tomorrow -> 明日This in general happens due to the different grammar and conventions of speech in different languages. English sentences require a subject, and when there is no subject available, it uses a dummy pronoun it. Japanese verbs do not have different forms for future and present tense, and the future tense is implied by the noun 明日 (tomorrow). Conversely, the topic-marker は and the grammar word だ (roughly "to be") do not correspond to any word in the English sentence. So, we can write the alignment as 1-> 0; 2 -> 0; 3 -> 3; 4 -> 4; 5 -> 1where 0 means that there is no corresponding alignment. Thus, we see that the alignment function is in general a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Future models will allow one English world to be aligned with multiple foreign words. === Statistical model === Given the above definition of alignment, we can define the statistical model used by Model 1: Start with a "dictionary". Its entries are of form t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} , which can be interpreted as saying "the foreign word f j {\displaystyle f_{j}} is translated to the English word e i {\displaystyle e_{i}} with probability t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} ". After being given a foreign sentence f {\displaystyle f} with length l f {\displaystyle l_{f}} , we first generate an English sentence length l e {\displaystyle l_{e}} uniformly in a range U n i f o r m [ 1 , 2 , . . . , N ] {\displaystyle Uniform[1,2,...,N]} . In particular, it does not depend on f {\displaystyle f} or l f {\displaystyle l_{f}} . Then, we generate an alignment uniformly in the set of all possible alignment functions { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Finally, for each English word e 1 , e 2 , . . . e l e {\displaystyle e_{1},e_{2},...e_{l_{e}}} , generate each one independently of every other English word. For the word e i {\displaystyle e_{i}} , generate it according to t ( e i | f a ( i ) ) {\displaystyle t(e_{i}|f_{a(i)})} . Together, we have the probability p ( e , a | f ) = 1 / N ( 1 + l f ) l e ∏ i = 1 l e t ( e i | f a ( i ) ) {\displaystyle p(e,a|f)={\frac {1/N}{(1+l_{f})^{l_{e}}}}\prod _{i=1}^{l_{e}}t(e_{i}|f_{a(i)})} IBM Model 1 uses very simplistic assumptions on the statistical model, in order to allow the following algorithm to have closed-form solution. === Learning from a corpus === If a dictionary is not provided at the start, but we have a corpus of English-foreign language pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} (without alignment information), then the model can be cast into the following form: fixed parameters: the foreign sentences { f ( k ) } k {\displaystyle \{f^{(k)}\}_{k}} . learnable parameters: the entries of the dictionary t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} . observable variables: the English sentences { e ( k ) } k {\displaystyle \{e^{(k)}\}_{k}} . latent variables: the alignments { a ( k ) } k {\displaystyle \{a^{(k)}\}_{k}} In this form, this is exactly the kind of problem solved by expectation–maximization algorithm. Due to the simplistic assumptions, the algorithm has a closed-form, efficiently computable solution, which is the solution to the following equations: { max t ′ ∑ k ∑ i ∑ a ( k ) t ( a ( k ) | e ( k ) , f ( k ) ) ln ⁡ t ( e i ( k ) | f a ( k ) ( i ) ( k ) ) ∑ x t ′ ( e x | f y ) = 1 ∀ y {\displaystyle {\begin{cases}\max _{t'}\sum _{k}\sum _{i}\sum _{a^{(k)}}t(a^{(k)}|e^{(k)},f^{(k)})\ln t(e_{i}^{(k)}|f_{a^{(k)}(i)}^{(k)})\\\sum _{x}t'(e_{x}|f_{y})=1\quad \forall y\end{cases}}} This can be solved by Lagrangian multipliers, then simplified. For a detailed derivation of the algorithm, see chapter 4 and. In short, the EM algorithm goes as follows:INPUT. a corpus of English-foreign sentence pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} INITIALIZE. matrix of translations probabilities t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .This could either be uniform or random. It is only required that every entry is positive, and for each y {\displaystyle y} , the probability sums to one: ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} . LOOP. until t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} converges: t ( e x | f y ) ← t ( e x | f y ) λ y ∑ k , i , j δ ( e x , e i ( k ) ) δ ( f y , f j ( k ) ) ∑ j ′ t ( e i ( k ) | f j ′ ( k ) ) {\displaystyle t(e_{x}|f_{y})\leftarrow {\frac {t(e_{x}|f_{y})}{\lambda _{y}}}\sum _{k,i,j}{\frac {\delta (e_{x},e_{i}^{(k)})\delta (f_{y},f_{j}^{(k)})}{\sum _{j'}t(e_{i}^{(k)}|f_{j'}^{(k)})}}} where each λ y {\displaystyle \lambda _{y}} is a normalization constant that makes sure each ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} .RETURN. t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .In the above formula, δ {\displaystyle \delta } is the Dirac delta function -- it equals 1 if the two entries are equal, and 0 otherwise. The index notation is as follows: k {\displaystyle k} ranges over English-foreign sentence pairs in corpus; i {\displaystyle i} ranges over words in English sentences; j {\displaystyle j} ranges over words in foreign language sentences; x {\displaystyle x} ranges over the entire vocabulary of English words in the corpus; y {\displaystyle y} ranges over the entire vocabulary of foreign words in the corpus. === Limitations === There are several limitations to the IBM model 1. No fluency: Given any sentence pair ( e , f ) {\displaystyle (e,f)} , any permutation of the English sentence is equally likely: p ( e | f ) = p ( e ′ | f ) {\displaystyle p(e|f)=p(e'|f)} for any permutation of the English sentence e {\displaystyle e} into e ′ {\displaystyle e'} . No length preference: The probability of each length of translation is equal: ∑ e has length l p ( e | f ) = 1 N {\displaystyle \sum _{e{\text{ has length }}l}p(e|f)={\frac {1}{N}}} for any l ∈ { 1 , 2 , . . . , N } {\displaystyle l\in \{1,2,...,N\}} . Does not explicitly model fertility: some foreign words tend to produce a fixed number of English words. For example, for German-to-English translation, ja is usually omitted, and zum is usually translated to one of to the, for the, to a, for a. == Model 2 == Model 2 allows alignment to be conditional on sentence lengths. That is, we have a probability distribution p a ( j | i , l e , l f ) {\displaystyle

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  • AI Marketing Tools: Free vs Paid (2026)

    AI Marketing Tools: Free vs Paid (2026)

    Shopping for the best AI marketing tool? An AI marketing tool 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 marketing tool 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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  • Robert Wilensky

    Robert Wilensky

    Robert Wilensky (26 March 1951 – 15 March 2013) was an American computer scientist and professor at the UC Berkeley School of Information, with his main focus of research in artificial intelligence. == Academic career == In 1971, Wilensky received his bachelor's degree in mathematics from Yale University, and in 1978, a Ph.D. in computer science from the same institution. After finishing his thesis, "Understanding Goal-Based Stories", Wilensky joined the faculty from the EECS Department of UC Berkeley. In 1986, he worked as the doctoral advisor of Peter Norvig, who then later published the standard textbook of the field: Artificial Intelligence: A Modern Approach. From 1993 to 1997, Wilensky was the Berkeley Computer Science Division Chair. During this time, he also served as director of the Berkeley Cognitive Science Program, director of the Berkeley Artificial Intelligence Research Project, and board member of the International Computer Science Institute. In 1997, he became a fellow of the Association for Computing Machinery "for research contributions to the areas of natural language processing and digital libraries as well as outstanding leadership in Computer Science." Furthermore, he also was a Fellow of the Association for the Advancement of Artificial Intelligence. He retired from faculty in 2007 and died on Friday, March 15, 2013, of a bacterial infection at the Alta Bates Summit Medical Center. Wilensky was married to Ann Danforth and he is survived by her and their two children, Avi and Eli Wilensky == Research == Throughout his career, Wilensky authored and co-authored over 60 scholarly articles and technical reports on AI, natural language processing, and information dissemination. In addition to his numerous technical publications, Wilensky also published two books on the programming language LISP, LISPcraft and Common LISPcraft, and had almost completed another book manuscript when he suffered a cardiac arrest and stopped writing. Among his publications are: R. Wilensky, (1986-09-17). Common LISPcraft. W. W. Norton & Company. ISBN 9780393955446. T. A. Phelps and R. Wilensky, "Toward active, extensible, networked documents: Multivalent architecture and applications," in Proc. 1st ACM Intl. Conf. on Digital Libraries, E. A. Fox and G. Marchionini, Eds., New York, NY: ACM Press, 1996, pp. 100–108. J. Traupman and R. Wilensky, "Experiments in Improving Unsupervised Word Sense Disambiguation," University of California, Berkeley, Department of EECS, Computer Science Division, Tech. Rep. 03–1227, Feb. 2003. R. Wilensky, Planning and Understanding: A Computational Approach to Human Reasoning, Advanced Book Program, Reading, MA: Addison-Wesley Publishing Co., 1983. R. Wilensky, "Understanding Goal-Based Stories," Yale University, Sep. 1978. B. Kahn and R. Wilensky, "A Framework for Distributed Digital Object Services", May 1995.

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  • Artificial brain

    Artificial brain

    An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science: An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience. A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being. A long-term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI. An example of the first objective is the project reported by Aston University in Birmingham, England where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, motor neurone and Parkinson's disease. The second objective is a reply to arguments such as John Searle's Chinese room argument, Hubert Dreyfus's critique of AI or Roger Penrose's argument in The Emperor's New Mind. These critics argued that there are aspects of human consciousness or expertise that can not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper "Computing Machinery and Intelligence". The third objective is generally called artificial general intelligence by researchers. However, Ray Kurzweil prefers the term "strong AI". In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009. == Approaches to brain simulation == W. Ross Ashby's pioneering work in cybernetics provided an early mathematical framework for understanding adaptive brain-like systems. In his 1952 book Design for a Brain, Ashby proposed that the brain could be modeled as an ultrastable system that maintains equilibrium through continuous adaptation to environmental perturbations. His approach used differential equations and state-space models to describe how neural systems could exhibit purposeful behavior through feedback mechanisms. Ashby's homeostat, a physical machine built in 1948, demonstrated these principles through an electromechanical device with four interconnected units that automatically adjusted their parameters to maintain stability when disturbed. The homeostat represented one of the first attempts to build an artificial system exhibiting brain-like adaptive behavior, influencing subsequent work in adaptive systems, neural networks, and artificial intelligence. Although direct human brain emulation using artificial neural networks on a high-performance computing engine is a commonly discussed approach, there are other approaches. An alternative artificial brain implementation could be based on Holographic Neural Technology (HNeT) non linear phase coherence/decoherence principles. The analogy has been made to quantum processes through the core synaptic algorithm which has strong similarities to the quantum mechanical wave equation. EvBrain is a form of evolutionary software that can evolve "brainlike" neural networks, such as the network immediately behind the retina. In November 2008, IBM received a US$4.9 million grant from the Pentagon for research into creating intelligent computers. The Blue Brain project is being conducted with the assistance of IBM in Lausanne. The project is based on the premise that it is possible to artificially link the neurons "in the computer" by placing thirty million synapses in their proper three-dimensional position. Some proponents of strong AI speculated in 2009 that computers in connection with Blue Brain and Soul Catcher may exceed human intellectual capacity by around 2015, and that it is likely that we will be able to download the human brain at some time around 2050. While Blue Brain is able to represent complex neural connections on the large scale, the project does not achieve the link between brain activity and behaviors executed by the brain. In 2012, project Spaun (Semantic Pointer Architecture Unified Network) attempted to model multiple parts of the human brain through large-scale representations of neural connections that generate complex behaviors in addition to mapping. Spaun's design recreates elements of human brain anatomy. The model, consisting of approximately 2.5 million neurons, includes features of the visual and motor cortices, GABAergic and dopaminergic connections, the ventral tegmental area (VTA), substantia nigra, and others. The design allows for several functions in response to eight tasks, using visual inputs of typed or handwritten characters and outputs carried out by a mechanical arm. Spaun's functions include copying a drawing, recognizing images, and counting. There are good reasons to believe that, regardless of implementation strategy, the predictions of realising artificial brains in the near future are optimistic. In particular brains (including the human brain) and cognition are not currently well understood, and the scale of computation required is unknown. Another near term limitation is that all current approaches for brain simulation require orders of magnitude larger power consumption compared with a human brain. The human brain consumes about 20 W of power, whereas current supercomputers may use as much as 1 MW—i.e., an order of 100,000 more. == Artificial brain thought experiment == Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds.

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  • Isolation forest

    Isolation forest

    Isolation forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies, instead of the most common techniques of profiling normal points. In statistics, an anomaly (a.k.a. outlier) is an observation or event that deviates so much from other events to arouse suspicion it was generated by a different mean. For example, the graph in Fig.1 represents ingress traffic to a web server, expressed as the number of requests in 3-hours intervals, for a period of one month. It is quite evident by simply looking at the picture that some points (marked with a red circle) are unusually high, to the point of inducing suspect that the web server might have been under attack at that time. On the other hand, the flat segment indicated by the red arrow also seems unusual and might possibly be a sign that the server was down during that time period. Anomalies in a big dataset may follow very complicated patterns, which are difficult to detect "by eye" in the great majority of cases. This is the reason why the field of anomaly detection is well suited for the application of machine learning techniques. The most common techniques employed for anomaly detection are based on the construction of a profile of what is "normal": anomalies are reported as those instances in the dataset that do not conform to the normal profile. Isolation Forest uses a different approach: instead of trying to build a model of normal instances, it explicitly isolates anomalous points in the dataset. The main advantage of this approach is the possibility of exploiting sampling techniques to an extent that is not allowed to the profile-based methods, creating a very fast algorithm with a low memory demand. == History == The Isolation Forest (iForest) algorithm was initially proposed by Fei Tony Liu, Kai Ming Ting and Zhi-Hua Zhou in 2008. The authors took advantage of two quantitative properties of anomalous data points in a sample, that is: they are the minority consisting of fewer instances and they have attribute-values that are very different from those of normal instances Since anomalies are typically few and very different from the other points in the sample, they must be easier to "isolate" compared to normal points. On the basis of this principle, Isolation Forest builds an ensemble of "Isolation Trees" (iTrees) for the data set and marks as anomalies the points that have short average path lengths on the iTrees. In a later paper, published in 2012 the same authors described a set of experiments to prove that iForest: has a low linear time complexity and a small memory requirement is able to deal with high dimensional data with irrelevant attributes can be trained with or without anomalies in the training set can provide detection results with different levels of granularity without re-training In 2013 Zhiguo Ding and Minrui Fei proposed a framework based on iForest to resolve the problem of detecting anomalies in streaming data. More application of iForest to streaming data are described in papers by Swee Chuan Tan et al., G. A. Susto et al. and Yu Weng et al. One of the main problems of the application of iForest to anomaly detection was not with the model itself, but rather in the way the "anomaly score" was computed. This problem was highlighted by Sahand Hariri, Matias Carrasco Kind and Robert J. Brunner in a 2018 paper, wherein they proposed an improved iForest model named Extended Isolation Forest (EIF). In the same paper the authors describe the improvements made to the original model and how they are able to enhance the consistency and reliability of the anomaly score produced for a given data point. == Algorithm == At the basis of the Isolation Forest algorithm there is the tendency of anomalous instances in a dataset to be easier to separate from the rest of the sample (isolate), compared to normal points. In order to isolate a data point the algorithm recursively generates partitions on the sample by randomly selecting an attribute and then randomly selecting a split value for the attribute, between the minimum and maximum values allowed for that attribute. An example of random partitioning in a 2D dataset of normally distributed points is given in Fig. 2 for a non-anomalous point and Fig. 3 for a point that's more likely to be an anomaly. It is apparent from the pictures how anomalies require fewer random partitions to be isolated, compared to normal points. From a mathematical point of view, recursive partitioning can be represented by a tree structure named Isolation Tree, while the number of partitions required to isolate a point can be interpreted as the length of the path, within the tree, to reach a terminating node starting from the root. For example, the path length of point xi in Fig. 2 is greater than the path length of xj in Fig. 3. More formally, let X = { x1, ..., xn } be a set of d-dimensional points and X' ⊂ X a subset of X. An Isolation Tree (iTree) is defined as a data structure with the following properties: for each node T in the Tree, T is either an external-node with no child, or an internal-node with one "test" and exactly two daughter nodes (Tl, Tr) a test at node T consists of an attribute q and a split value p such that the test q < p determines the traversal of a data point to either Tl or Tr. In order to build an iTree, the algorithm recursively divides X' by randomly selecting an attribute q and a split value p, until either (i) the node has only one instance or (ii) all data at the node have the same values. When the iTree is fully grown, each point in X is isolated at one of the external nodes. Intuitively, the anomalous points are those (easier to isolate, hence) with the smaller path length in the tree, where the path length h(xi) of point x i ∈ X {\displaystyle x_{i}\in X} is defined as the number of edges xi traverses from the root node to get to an external node. A probabilistic explanation of iTree is provided in the iForest original paper. == Properties of Isolation Forest == Sub-sampling: since iForest does not need to isolate all of normal instances, it can frequently ignore the big majority of the training sample. As a consequence, iForest works very well when the sampling size is kept small, a property that is in contrast with the great majority of existing methods, where large sampling size is usually desirable. Swamping: when normal instances are too close to anomalies, the number of partitions required to separate anomalies increases, a phenomena known as swamping, which makes it more difficult for iForest to discriminate between anomalies and normal points. One of the main reasons for swamping is the presence of too many data for the purpose of anomaly detection, which implies one possible solution to the problem is sub-sampling. Since iForest respond very well to sub-sampling in terms of performance, the reduction of the number of points in the sample is also a good way to reduce the effect of swamping. Masking: when the number of anomalies is high it is possible that some of those aggregate in a dense and large cluster, making it more difficult to separate the single anomalies and, in turn, to detect such points as anomalous. Similarly to swamping, this phenomena (known as "masking") is also more likely when the number of points in the sample is big, and can be alleviated through sub-sampling. High Dimensional Data: one of the main limitation to standard, distance-based methods is their inefficiency in dealing with high dimensional datasets:. The main reason for that is, in a high dimensional space every point is equally sparse, so using a distance-based measure of separation is pretty ineffective. Unfortunately, high-dimensional data also affects the detection performance of iForest, but the performance can be vastly improved by adding a features selection test like Kurtosis to reduce the dimensionality of the sample space. Normal Instances Only: iForest performs well even if the training set does not contain any anomalous point, the reason being that iForest describes data distributions in such a way that high values of the path length h(xi) correspond to the presence of data points. As a consequence, the presence of anomalies is pretty irrelevant to iForest's detection performance. == Anomaly Detection with Isolation Forest == Anomaly detection with Isolation Forest is a process composed of two main stages: in the first stage, a training dataset is used to build iTrees as described in previous sections. in the second stage, each instance in test set is passed through the iTrees build in the previous stage, and a proper "anomaly score" is assigned to the instance using the algorithm described below Once all the instances in the test set have been assigned an anomaly score, it is possible to mark as "anomaly" any point whose score is greater than a predefined threshold, which depends on the domain the analysis is being applied to. === Anomaly Score === Th

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  • Devi Parikh

    Devi Parikh

    Devi Parikh is an American computer scientist. == Career == Parikh earned her PhD in Electrical and Computer Engineering at Carnegie Mellon University. She has served as a professor at Virginia Tech and Georgia Tech, and as of 2022 she is a research director at Meta. == Research == Parikh's research focuses on computer vision and natural language processing. In 2015, Parikh and her students at Virginia Tech worked on AI for Visual Question Answering (VQA). This technology allows users to ask questions about pictures, e.g. "Is this a vegetarian pizza?" Parikh's VQA dataset has been used to evaluate over 30 AI models. In 2017, Parikh published a conversational agent called ParlAI. In 2020, she developed an AI system that generates dance moves in sync with songs. In 2022, Parikh and a team at Meta developed Make-a-Video, a text-to-video AI model that is based on the diffusion algorithm. == Awards == 2017 IJCAI Computers and Thought Award 2011 ICCV Best-Paper Award ("Marr Prize")

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  • AI Code-review Tools Reviews: What Actually Works in 2026

    AI Code-review Tools Reviews: What Actually Works in 2026

    Shopping for the best AI code-review tool? An AI code-review tool 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 code-review tool 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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  • Dr. Sbaitso

    Dr. Sbaitso

    Dr. Sbaitso ( SPAYT-soh) is an artificial intelligence speech synthesis program released late in 1991 by Creative Labs in Singapore for MS-DOS-based personal computers. The name is an acronym for "SoundBlaster Acting Intelligent Text-to-Speech Operator." == History == Dr. Sbaitso was distributed with various sound cards manufactured by Creative Technology in the early 1990s. The text-to-speech engine used is a version of Monologue, which was developed by First Byte Software. Monologue is a later release of First Byte's "SmoothTalker" software from 1984. The program "conversed" with the user as if it were a psychologist, though most of its responses were along the lines of "WHY DO YOU FEEL THAT WAY?" rather than any sort of complicated interaction. When confronted with a phrase it could not understand, it would often reply with something such as "THAT'S NOT MY PROBLEM." Dr. Sbaitso repeated text out loud that was typed after the word "SAY." Repeated swearing or abusive behavior on the part of the user caused Dr. Sbaitso to "break down" in a "PARITY ERROR" before resetting itself. The same would happen, if the user types "SAY PARITY." The program introduced itself with the following lines: HELLO [UserName], MY NAME IS DOCTOR SBAITSO. I AM HERE TO HELP YOU. SAY WHATEVER IS IN YOUR MIND FREELY, OUR CONVERSATION WILL BE KEPT IN STRICT CONFIDENCE. MEMORY CONTENTS WILL BE WIPED OFF AFTER YOU LEAVE, SO, TELL ME ABOUT YOUR PROBLEMS. The program was designed to showcase the digitized voices the cards were able to produce, though the quality was far from lifelike. Additionally, there was a version of this program for Microsoft Windows through the use of a program called Prody Parrot; this version of the software featured a more detailed graphical user interface. The text-to-speech was also used as the voice of 1st Prize from the Baldi's Basics series, albeit slowed down. == Commands == If the user submits "HELP", a list of commands will appear. If the user then submits "M", more commands will appear. There are three pages of commands in total, with guidance on how to use each of the features.

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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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  • Forrest N. Iandola

    Forrest N. Iandola

    Forrest N. Iandola is an American computer scientist specializing in efficient AI. == Career == Iandola earned a PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016, advised by Kurt Keutzer. As part of his dissertation, he co-authored SqueezeNet, a deep neural network for image classification optimized for smartphones and other mobile devices. Iandola and Keutzer went on to co-found DeepScale. The firm squeezes deep neural networks onto low-cost automotive-grade processors for use in driver assistance systems. Tesla acquired DeepScale in 2019. In 2020, he co-authored SqueezeBERT, an efficient neural network for natural language processing. In 2022, he joined Meta as an AI research scientist. His research at Meta includes developing efficient AI models, such as EfficientSAM and MobileLLM.

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  • Indic OCR

    Indic OCR

    Indic OCR refers to the process of converting text images written in Indic scripts into e-text using Optical character recognition (OCR) techniques. Broadly, it can also refer to the OCR systems of Brahmic scripts for languages of South Asia and Southeast Asia, not just the scripts of the Indian subcontinent, which are all written in an abugida-based writing system. OCR for Latin characters is still not 100% accurate but a relatively high degree of accuracy in conversion has been able to be achieved. Such accuracy has not yet been able to be achieved for Indic scripts using OCR. This is due in part to the writing systems of Indic languages as well as a lack of standard representation, encoding, and support among operating systems and keyboards. The Centre for Development of Advanced Computing (C-DAC) and Technology Development for Indian Languages, the premier R&D organisation of the Ministry of Electronics and Information Technology (also known as MeitY) of India have carried out many projects relating to OCR. Their projects include OCR for Malayalam, Odia, Punjabi, Telugu and Devanagari script. == Properties of Indian writing systems == There are 22 officially recognised languages in India. Of these, Hindi, Bengali and Punjabi are the most widely spoken Indo-Aryan languages and are also the fourth, seventh and tenth most widely spoken languages in the world respectively. Two or more languages can be written with same script. For example, Devanagari is used to write Hindi, Marathi, Rajasthani, Sanskrit, Bhojpuri and others, while Eastern Nagari is used to write Bengali, Assamese, Manipuri and others. Apart from basic characters as consonants and vowels, most Indic languages combine 2 or more basic characters to form compound characters. The shape of a compound character is more complex than the constituent basic characters. Some Indo-Aryan languages (including Hindi and Punjabi) have a horizontal line over the characters, while other languages (including Gujarati) and Dravidian languages (Malayalam, Kannada, Tamil, and Telugu) do not. These are some of the main challenges for creating a single OCR for all Indic languages. Indic OCR also generally includes support for recently invented scripts in India like Ol Chiki, Warang Citi, Mundari Bani, etc. which are mainly created for writing Munda languages of Austroasiatic family. The concept of upper/lower case is absent in Indic scripts. Apart from Urdu, Sindhi, Kashmiri and Thaana, all other Indic languages are written from left to right. == Examples == SanskritOCR - OCR software for Sanskrit, Hindi and other Indo-Aryan languages based on the Devanagari script. Sanskrit OCR is developed by a Sanskrit scholar from Germany - Dr. Oliver Hellwig of Department for Languages and Cultures of Southern Asia, Freie Universität Berlin. The official website is in German. The interface of earlier versions of the software was also in German, but later versions have an English interface too. E-aksharayan - Optical character recognition engine for Indian languages Chitrankan - This technology was developed by ISI, Kolkata, and transferred to C-DAC. It processes printed Hindi text from a scanner or from an image. Indic OCR models for Tesseract (software) == OCR in use == OCR has been used for Wikisource and other projects.

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  • Cloud manufacturing

    Cloud manufacturing

    Cloud manufacturing (CMfg) is a new manufacturing paradigm developed from existing advanced manufacturing models (e.g., ASP, AM, NM, MGrid) and enterprise information technologies under the support of cloud computing, Internet of Things (IoT), virtualization and service-oriented technologies, and advanced computing technologies. It transforms manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and circulating of manufacturing resources and manufacturing capabilities. CMfg can provide safe and reliable, high quality, cheap and on-demand manufacturing services for the whole lifecycle of manufacturing. The concept of manufacturing here refers to big manufacturing that includes the whole lifecycle of a product (e.g. design, simulation, production, test, maintenance). The concept of Cloud manufacturing was initially proposed by the research group led by Prof. Bo Hu Li and Prof. Lin Zhang in China in 2010. Related discussions and research were conducted hereafter, and some similar definitions (e.g. Cloud-Based Design and Manufacturing (CBDM). ) to cloud manufacturing were introduced. Cloud manufacturing is a type of parallel, networked, and distributed system consisting of an integrated and inter-connected virtualized service pool (manufacturing cloud) of manufacturing resources and capabilities as well as capabilities of intelligent management and on-demand use of services to provide solutions for all kinds of users involved in the whole lifecycle of manufacturing. == Types == Cloud Manufacturing can be divided into two categories. The first category concerns deploying manufacturing software on the Cloud, i.e. a “manufacturing version” of Computing. CAx software can be supplied as a service on the Manufacturing Cloud (MCloud). The second category has a broader scope, cutting across production, management, design and engineering abilities in a manufacturing business. Unlike with computing and data storage, manufacturing involves physical equipment, monitors, materials and so on. In this kind of Cloud Manufacturing system, both material and non-material facilities are implemented on the Manufacturing Cloud to support the whole supply chain. Costly resources are shared on the network. This means that the utilisation rate of rarely used equipment rises and the cost of expensive equipment is reduced. According to the concept of Cloud technology, there will not be direct interaction between Cloud Users and Service Providers. The Cloud User should neither manage nor control the infrastructure and manufacturing applications. As a matter of fact, the former can be considered part of the latter. In CMfg system, various manufacturing resources and abilities can be intelligently sensed and connected into wider Internet, and automatically managed and controlled using IoT technologies (e.g., RFID, wired and wireless sensor network, embedded system). Then the manufacturing resources and abilities are virtualized and encapsulated into different manufacturing cloud services (MCSs), that can be accessed, invoked, and deployed based on knowledge by using virtualization technologies, service-oriented technologies, and cloud computing technologies. The MCSs are classified and aggregated according to specific rules and algorithms, and different kinds of manufacturing clouds are constructed. Different users can search and invoke the qualified MCSs from related manufacturing cloud according to their needs, and assemble them to be a virtual manufacturing environment or solution to complete their manufacturing task involved in the whole life cycle of manufacturing processes under the support of cloud computing, service-oriented technologies, and advanced computing technologies. Four types of cloud deployment modes (public, private, community and hybrid clouds) are ubiquitous as a single point of access. Private cloud refers to a centralized management effort in which manufacturing services are shared within one company or its subsidiaries. Enterprises' mission-critical and core-business applications are often kept in a private cloud. Community cloud is a collaborative effort in which manufacturing services are shared between several organizations from a specific community with common concerns. Public cloud realizes the key concept of sharing services with the general public in a multi-tenant environment. Hybrid cloud is a composition of two or more clouds (private, community or public) that remain distinct entities but are also bound together, offering the benefits of multiple deployment modes. == Resources == From the resource’s perspective, each kind of manufacturing capability requires support from the related manufacturing resource. For each type of manufacturing capability, its related manufacturing resource comes in two forms, soft resources and hard resources. === Soft resources === Software: software applications throughout the product lifecycle including design, analysis, simulation, process planning, and are only beginning to be embraced by the electronics manufacturing industry. Knowledge: experience and know-how needed to complete a production task, i.e. engineering knowledge, product models, standards, evaluation procedures and results, customer feedback, and manufacturing in the cloud provides just as many solutions as the number of questions it also raises for manufacturing executives wanting to make the best possible decision. Skill: expertise in performing a specific manufacturing task. Personnel: human resource engaged in the manufacturing process, i.e. designers, operators, managers, technicians, project teams, customer service, etc. Experience: performance, quality, client evaluation, etc. Business Network: business relationships and business opportunity networks that exist in an enterprise. === Hard resources === Manufacturing Equipment: facilities needed for completing a manufacturing task, e.g. machine tools, cutters, test and monitoring equipment and other fabrication tools. Monitoring/Control Resource: devices used to identify and control other manufacturing resource, for instance, RFID (Radio-Frequency IDentification), WSN (Wireless Sensor Network), virtual managers and remote controllers. Computational Resource: computing devices to support production process, e.g. servers, computers, storage media, control devices, etc. Materials: inputs and outputs in a production system, e.g. raw material, product-in-progress, finished product, power, water, lubricants, etc. Storage: automated storage and retrieval systems, logic controllers, location of warehouses, volume capacity and schedule/optimization methods. Transportation: movement of manufacturing inputs/outputs from one location to another. It includes the modes of transport, e.g. air, rail, road, water, cable, pipeline and space, and the related price, and time taken.

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  • Rob Fergus

    Rob Fergus

    Rob Fergus is a British-American computer scientist working primarily in the fields of machine learning, deep learning, representational learning, and generative models. He is a professor of computer science at Courant Institute of Mathematical Sciences at New York University (NYU) and a research scientist at DeepMind. Fergus developed ZFNet in 2013 together with M.D. Zeiler, his PhD student in NYU. Fergus co-founded Meta AI (then known as Facebook Artificial Intelligence Research (FAIR)) along with Yann Le Cun in September 2013. In 2009, Rob Fergus co-founded the Computational Intelligence, Learning, Vision, and Robotics (CILVR) Lab at NYU along with Yann Le Cun. == Awards and recognition == Rob Fergus has been recognized in academia and received the following awards: NSF Faculty Early Career Development Program (CAREER) Sloan Research Fellowship Test-of-time awards at ECCV, CVPR and ICLR == Notable PhD students == Matt Zeiler (Clarifai founder) Wojciech Zaremba (OpenAI co-founder) Denis Yarats (Perplexity co-founder) Alex Rives (EvolutionaryScale co-founder; faculty at MIT)

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  • Katia Sycara

    Katia Sycara

    Ekaterini Panagiotou Sycara (Greek: Κάτια Συκαρά) is a Greek computer scientist. She is an Edward Fredkin Research Professor of Robotics in the Robotics Institute, School of Computer Science at Carnegie Mellon University internationally known for her research in artificial intelligence, particularly in the fields of negotiation, autonomous agents and multi-agent systems. She directs the Advanced Agent-Robotics Technology Lab at Robotics Institute, Carnegie Mellon University. She also serves as academic advisor for PhD students at both Robotics Institute and Tepper School of Business. == Education and early life == Born in Greece, she went to the United States to pursue advanced education through various scholarships, including a Fulbright (1965-1969). She received a B.S. in applied mathematics from Brown University, M.S. in electrical engineering from the University of Wisconsin–Milwaukee, and PhD in computer science from Georgia Institute of Technology. == Research and career == Sycara is a pioneer in the field of semantic web, case-based reasoning, autonomous agents and multi-agent systems. She has authored or co-authored more than 700 technical papers dealing with multi-agent systems, software agents, web services, semantic web, human–computer interaction, human-robot interaction, negotiation, case-based reasoning and the application of these techniques to crisis action planning, scheduling, manufacturing, healthcare management, financial planning and e-commerce.[1] She has led multimillion-dollar research effort funded by DARPA, NASA, AFOSR, ONR, AFRL, NSF and industry. Through an ONR MURI program and though the COABS DARPA program, Prof. Sycara's group has developed the RETSINA multiagent infrastructure, a toolkit that enables the development of heterogeneous software agents that can dynamically coordinate in open information environments (e.g. the Internet). RETSINA has been used in multiple applications including supporting human joint mission teams for crisis response; creating autonomous agents for situation awareness and information fusion; financial portfolio management, negotiations and coalition formation for e-commerce, and coordinating robots for Urban Search and Rescue. Sycara is one of the contributors to the development of OWL-S, the Darpa-sponsored language for Semantic Web services, as well as matchmaking and brokering software for agent discovery, service integration and semantic interoperation. === Academic service === Sycara is the founding Editor-in-Chief of the journal Autonomous Agents and Multi-Agent Systems; Editor-in-Chief, of the Springer Series on Agents; and Area Editor of AI and Management Science, the journal "Group Decision and Negotiation." She is a member of the Editorial Board, the Kluwer book series on "Multiagent Systems, Artificial Societies and Simulated Organizations"; member of the editorial board, the journals "Agent Oriented Software Engineering", "Web Intelligence and Agent Technologies", "Journal of Infonomics", "Fundamenda Informaticae", and "Concurrent Engineering: Research and Applications"; and member of the editorial board of the "ETAI journal on the Semantic Web" (1998–2001). She was on the Editorial Board of "IEEE Intelligent Systems and their Applications" (1992–1996), and "AI in Engineering" (1990–1996). She is a member of the Scientific Advisory Board of France Telecom, 2003-2009; member of the Scientific Advisory Board of the Institute of Informatics and Telecommunications of the Greek National Research Center Demokritos, 2004-2012; member of the AAAI Executive Council (1996–99); member of the OASIS Technical committee on the development of UDDI (Universal Description and Discovery for Interoperability) software which is an industry standard; and an invited expert for W3C (the World Wide Web Consortium) Working Group on Web Services Architecture. She was a founding member of the Board of Directors of the International Foundation of Multiagent Systems (IFMAS), and founding member of the Semantic Web Science Association. Sycara served as the program chair of the Second International Semantic Web Conference (ISWC 2003); general chair, of the Second International Conference on Autonomous Agents (Agents 98); chair of the Steering Committee of the Agents Conference (1999–2001); scholarship chair of AAAI (1993–1999); and the US co-chair for the US-Europe Semantic Web Services Initiative. === Awards and honors === Sycara is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of American Association for Artificial Intelligence (AAAI). Sycara is the recipient of the 2002 ACM/SIGART Agents Research Award. She is also the recipient of the 2015 Group Decision and Negotiation (GDN) Award of the Institute for Operations Research and the Management Sciences (INFORMS) GDN Section for her outstanding contributions to the field of group decision and negotiation. According to the citation of the award: Katia Sycara is widely acknowledged as one of the leading researchers in the field of autonomous software agents and in particular on problems related to joint decision making and negotiations of such agents. Her work is characterized by a unique combination of methods from Artificial Intelligence and research on human negotiations, and thus has contributed to significant advances in both fields. Sycara's robot teams have won multiple international awards. In the 2005 Robocup Urban Search and Rescue (US Open) held in Atlanta, her team won the First-in-Class Award for Autonomy, and the First-in-Class Award for Mobility. Two years later, again in Atlanta, she led another team that became a world champions in the 2007 International Robocup Search and Rescue Simulation League Competition. In 2008, her robotic team placed third in the Worldwide Robocup Championship Competition in the Urban Search and Rescue Virtual robots League held in Beijing, China. In 2005, she received the Outstanding Alumnus Award from the University of Wisconsin–Milwaukee. She was awarded an Honorary Doctorate from the University of the Aegean in 2004.

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