In the philosophy of mind, the China brain thought experiment (also known as the Chinese Nation, Chinese Gym, or China-body) considers what would happen if each person in the entire population of China were asked to simulate the action of one neuron in the brain, using telephones or walkie-talkies to simulate the axons and dendrites that connect neurons. The question this thought experiment attempts to answer is whether this arrangement would have a mind or consciousness in the same way that the human brain exhibits. Early versions of this scenario were put forward in 1961 by Anatoly Dneprov, in 1974 by Lawrence Davis, and again in 1978 by Ned Block. Block argues that the China brain would not have a mind, whereas Daniel Dennett argues that it would. The China brain problem is a special case of the more general problem of whether minds could exist within other, larger minds. The Chinese room scenario analyzed by John Searle is a similar thought experiment in philosophy of mind that relates to artificial intelligence. Instead of people who each model a single neuron of the brain, in the Chinese room, clerks who do not speak Chinese accept notes in Chinese and return an answer in Chinese according to a set of rules, without the people in the room ever understanding what those notes mean. In fact, the original short story The Game (1961) by Dneprov contains both the China brain and the Chinese room scenarios. == Background == Many theories of mental states are materialist, that is, they describe the mind as the behavior of a physical object like the brain. One formerly prominent example is the identity theory, which says that mental states are brain states. One criticism is the problem of multiple realizability. The physicalist theory that responds to this is functionalism, which states that a mental state can be whatever functions as a mental state. That is, the mind can be composed of neurons, or it could be composed of wood, rocks or toilet paper, as long as it provides mental functionality. == Description == Suppose that the whole nation of China were reordered to simulate the workings of a single brain (that is, to act as a mind according to functionalism). Each Chinese person acts as (say) a neuron, and communicates by special two-way radio in corresponding way to the other people. The current mental state of the China brain is displayed on satellites that may be seen from anywhere in China. The China brain would then be connected via radio to a body, one that provides the sensory inputs and behavioral outputs of the China brain. Thus, the China brain possesses all the elements of a functional description of mind: sensory inputs, behavioral outputs, and internal mental states causally connected to other mental states. If the nation of China can be made to act in this way, then, according to functionalism, this system would have a mind. Block's goal is to show how unintuitive it is to think that such an arrangement could create a mind capable of thoughts and feelings. == Consciousness == The China brain argues that consciousness is a problem for functionalism. Block's Chinese nation presents a version of what is known as the absent qualia objection to functionalism because it purports to show that it is possible for something to be functionally equivalent to a human being and yet have no conscious experience. A creature that functions like a human being but does not feel anything is known as a "philosophical zombie". So the absent qualia objection to functionalism could also be called the "zombie objection". == Criticisms == Some philosophers, like Daniel Dennett, have concluded that the China brain does create a mental state. Functionalist philosophers of mind endorse the idea that something like the China brain can realise a mind, and that neurons are, in principle, not the only material that can create a mental state.
Perusall
Perusall is a social web annotation tool intended for use by students at schools and universities. It allows users to annotate the margins of a text in a virtual group setting that is similar to social media—with upvoting, emojis, chat functionality, and notification. It also includes automatic AI grading. == History == Perusall began as a research project at Harvard University. It later became an educational product for students and teachers. As of 2024, Perusall states more than 5 million students have used the tool at over 5,000 educational institutions in 112 countries." == Functionality == Perusall integrates with learning management systems such as Moodle, Canvas and Blackboard to aid with collaborative annotation. The tool supports annotation of a range of media including text, images, equations, videos, PDFs and snapshots of webpages.
Algorithmic learning theory
Algorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory. == Distinguishing characteristics == Unlike statistical learning theory and most statistical theory in general, algorithmic learning theory does not assume that data are random samples, that is, that data points are independent of each other. This makes the theory suitable for domains where observations are (relatively) noise-free but not random, such as language learning and automated scientific discovery. The fundamental concept of algorithmic learning theory is learning in the limit: as the number of data points increases, a learning algorithm should converge to a correct hypothesis on every possible data sequence consistent with the problem space. This is a non-probabilistic version of statistical consistency, which also requires convergence to a correct model in the limit, but allows a learner to fail on data sequences with probability measure 0 . Algorithmic learning theory investigates the learning power of Turing machines. Other frameworks consider a much more restricted class of learning algorithms than Turing machines, for example, learners that compute hypotheses more quickly, for instance in polynomial time. An example of such a framework is probably approximately correct learning . == Learning in the limit == The concept was introduced in E. Mark Gold's seminal paper "Language identification in the limit". The objective of language identification is for a machine running one program to be capable of developing another program by which any given sentence can be tested to determine whether it is "grammatical" or "ungrammatical". The language being learned need not be English or any other natural language - in fact the definition of "grammatical" can be absolutely anything known to the tester. In Gold's learning model, the tester gives the learner an example sentence at each step, and the learner responds with a hypothesis, which is a suggested program to determine grammatical correctness. It is required of the tester that every possible sentence (grammatical or not) appears in the list eventually, but no particular order is required. It is required of the learner that at each step the hypothesis must be correct for all the sentences so far. A particular learner is said to be able to "learn a language in the limit" if there is a certain number of steps beyond which its hypothesis no longer changes. At this point it has indeed learned the language, because every possible sentence appears somewhere in the sequence of inputs (past or future), and the hypothesis is correct for all inputs (past or future), so the hypothesis is correct for every sentence. The learner is not required to be able to tell when it has reached a correct hypothesis, all that is required is that it be true. Gold showed that any language which is defined by a Turing machine program can be learned in the limit by another Turing-complete machine using enumeration. This is done by the learner testing all possible Turing machine programs in turn until one is found which is correct so far - this forms the hypothesis for the current step. Eventually, the correct program will be reached, after which the hypothesis will never change again (but note that the learner does not know that it won't need to change). Gold also showed that if the learner is given only positive examples (that is, only grammatical sentences appear in the input, not ungrammatical sentences), then the language can only be guaranteed to be learned in the limit if there are only a finite number of possible sentences in the language (this is possible if, for example, sentences are known to be of limited length). Language identification in the limit is a highly abstract model. It does not allow for limits of runtime or computer memory which can occur in practice, and the enumeration method may fail if there are errors in the input. However the framework is very powerful, because if these strict conditions are maintained, it allows the learning of any program known to be computable. This is because a Turing machine program can be written to mimic any program in any conventional programming language. See Church-Turing thesis. == Other identification criteria == Learning theorists have investigated other learning criteria, such as the following. Efficiency: minimizing the number of data points required before convergence to a correct hypothesis. Mind Changes: minimizing the number of hypothesis changes that occur before convergence. Mind change bounds are closely related to mistake bounds that are studied in statistical learning theory. Kevin Kelly has suggested that minimizing mind changes is closely related to choosing maximally simple hypotheses in the sense of Occam’s Razor. == Annual conference == Since 1990, there is an International Conference on Algorithmic Learning Theory (ALT), called Workshop in its first years (1990–1997). Between 1992 and 2016, proceedings were published in the LNCS series. Starting from 2017, they are published by the Proceedings of Machine Learning Research. The 34th conference will be held in Singapore in Feb 2023. The topics of the conference cover all of theoretical machine learning, including statistical and computational learning theory, online learning, active learning, reinforcement learning, and deep learning.
Constellation model
The constellation model is a probabilistic, generative model for category-level object recognition in computer vision. Like other part-based models, the constellation model attempts to represent an object class by a set of N parts under mutual geometric constraints. Because it considers the geometric relationship between different parts, the constellation model differs significantly from appearance-only, or "bag-of-words" representation models, which explicitly disregard the location of image features. The problem of defining a generative model for object recognition is difficult. The task becomes significantly complicated by factors such as background clutter, occlusion, and variations in viewpoint, illumination, and scale. Ideally, we would like the particular representation we choose to be robust to as many of these factors as possible. In category-level recognition, the problem is even more challenging because of the fundamental problem of intra-class variation. Even if two objects belong to the same visual category, their appearances may be significantly different. However, for structured objects such as cars, bicycles, and people, separate instances of objects from the same category are subject to similar geometric constraints. For this reason, particular parts of an object such as the headlights or tires of a car still have consistent appearances and relative positions. The Constellation Model takes advantage of this fact by explicitly modeling the relative location, relative scale, and appearance of these parts for a particular object category. Model parameters are estimated using an unsupervised learning algorithm, meaning that the visual concept of an object class can be extracted from an unlabeled set of training images, even if that set contains "junk" images or instances of objects from multiple categories. It can also account for the absence of model parts due to appearance variability, occlusion, clutter, or detector error. == History == The idea for a "parts and structure" model was originally introduced by Fischler and Elschlager in 1973. This model has since been built upon and extended in many directions. The Constellation Model, as introduced by Dr. Perona and his colleagues, was a probabilistic adaptation of this approach. In the late '90s, Burl et al. revisited the Fischler and Elschlager model for the purpose of face recognition. In their work, Burl et al. used manual selection of constellation parts in training images to construct a statistical model for a set of detectors and the relative locations at which they should be applied. In 2000, Weber et al. made the significant step of training the model using a more unsupervised learning process, which precluded the necessity for tedious hand-labeling of parts. Their algorithm was particularly remarkable because it performed well even on cluttered and occluded image data. Fergus et al. then improved upon this model by making the learning step fully unsupervised, having both shape and appearance learned simultaneously, and accounting explicitly for the relative scale of parts. == The method of Weber and Welling et al. == In the first step, a standard interest point detection method, such as Harris corner detection, is used to generate interest points. Image features generated from the vicinity of these points are then clustered using k-means or another appropriate algorithm. In this process of vector quantization, one can think of the centroids of these clusters as being representative of the appearance of distinctive object parts. Appropriate feature detectors are then trained using these clusters, which can be used to obtain a set of candidate parts from images. As a result of this process, each image can now be represented as a set of parts. Each part has a type, corresponding to one of the aforementioned appearance clusters, as well as a location in the image space. === Basic generative model === Weber & Welling here introduce the concept of foreground and background. Foreground parts correspond to an instance of a target object class, whereas background parts correspond to background clutter or false detections. Let T be the number of different types of parts. The positions of all parts extracted from an image can then be represented in the following "matrix," X o = ( x 11 , x 12 , ⋯ , x 1 N 1 x 21 , x 22 , ⋯ , x 2 N 2 ⋮ x T 1 , x T 2 , ⋯ , x T N T ) {\displaystyle X^{o}={\begin{pmatrix}x_{11},x_{12},{\cdots },x_{1N_{1}}\\x_{21},x_{22},{\cdots },x_{2N_{2}}\\\vdots \\x_{T1},x_{T2},{\cdots },x_{TN_{T}}\end{pmatrix}}} where N i {\displaystyle N_{i}\,} represents the number of parts of type i ∈ { 1 , … , T } {\displaystyle i\in \{1,\dots ,T\}} observed in the image. The superscript o indicates that these positions are observable, as opposed to missing. The positions of unobserved object parts can be represented by the vector x m {\displaystyle x^{m}\,} . Suppose that the object will be composed of F {\displaystyle F\,} distinct foreground parts. For notational simplicity, we assume here that F = T {\displaystyle F=T\,} , though the model can be generalized to F > T {\displaystyle F>T\,} . A hypothesis h {\displaystyle h\,} is then defined as a set of indices, with h i = j {\displaystyle h_{i}=j\,} , indicating that point x i j {\displaystyle x_{ij}\,} is a foreground point in X o {\displaystyle X^{o}\,} . The generative probabilistic model is defined through the joint probability density p ( X o , x m , h ) {\displaystyle p(X^{o},x^{m},h)\,} . === Model details === The rest of this section summarizes the details of Weber & Welling's model for a single component model. The formulas for multiple component models are extensions of those described here. To parametrize the joint probability density, Weber & Welling introduce the auxiliary variables b {\displaystyle b\,} and n {\displaystyle n\,} , where b {\displaystyle b\,} is a binary vector encoding the presence/absence of parts in detection ( b i = 1 {\displaystyle b_{i}=1\,} if h i > 0 {\displaystyle h_{i}>0\,} , otherwise b i = 0 {\displaystyle b_{i}=0\,} ), and n {\displaystyle n\,} is a vector where n i {\displaystyle n_{i}\,} denotes the number of background candidates included in the i t h {\displaystyle i^{th}} row of X o {\displaystyle X^{o}\,} . Since b {\displaystyle b\,} and n {\displaystyle n\,} are completely determined by h {\displaystyle h\,} and the size of X o {\displaystyle X^{o}\,} , we have p ( X o , x m , h ) = p ( X o , x m , h , n , b ) {\displaystyle p(X^{o},x^{m},h)=p(X^{o},x^{m},h,n,b)\,} . By decomposition, p ( X o , x m , h , n , b ) = p ( X o , x m | h , n , b ) p ( h | n , b ) p ( n ) p ( b ) {\displaystyle p(X^{o},x^{m},h,n,b)=p(X^{o},x^{m}|h,n,b)p(h|n,b)p(n)p(b)\,} The probability density over the number of background detections can be modeled by a Poisson distribution, p ( n ) = ∏ i = 1 T 1 n i ! ( M i ) n i e − M i {\displaystyle p(n)=\prod _{i=1}^{T}{\frac {1}{n_{i}!}}(M_{i})^{n_{i}}e^{-M_{i}}} where M i {\displaystyle M_{i}\,} is the average number of background detections of type i {\displaystyle i\,} per image. Depending on the number of parts F {\displaystyle F\,} , the probability p ( b ) {\displaystyle p(b)\,} can be modeled either as an explicit table of length 2 F {\displaystyle 2^{F}\,} , or, if F {\displaystyle F\,} is large, as F {\displaystyle F\,} independent probabilities, each governing the presence of an individual part. The density p ( h | n , b ) {\displaystyle p(h|n,b)\,} is modeled by p ( h | n , b ) = { 1 ∏ f = 1 F N f b f , if h ∈ H ( b , n ) 0 , for other h {\displaystyle p(h|n,b)={\begin{cases}{\frac {1}{\textstyle \prod _{f=1}^{F}N_{f}^{b_{f}}}},&{\mbox{if }}h\in H(b,n)\\0,&{\mbox{for other }}h\end{cases}}} where H ( b , n ) {\displaystyle H(b,n)\,} denotes the set of all hypotheses consistent with b {\displaystyle b\,} and n {\displaystyle n\,} , and N f {\displaystyle N_{f}\,} denotes the total number of detections of parts of type f {\displaystyle f\,} . This expresses the fact that all consistent hypotheses, of which there are ∏ f = 1 F N f b f {\displaystyle \textstyle \prod _{f=1}^{F}N_{f}^{b_{f}}} , are equally likely in the absence of information on part locations. And finally, p ( X o , x m | h , n ) = p f g ( z ) p b g ( x b g ) {\displaystyle p(X^{o},x^{m}|h,n)=p_{fg}(z)p_{bg}(x_{bg})\,} where z = ( x o x m ) {\displaystyle z=(x^{o}x^{m})\,} are the coordinates of all foreground detections, observed and missing, and x b g {\displaystyle x_{bg}\,} represents the coordinates of the background detections. Note that foreground detections are assumed to be independent of the background. p f g ( z ) {\displaystyle p_{fg}(z)\,} is modeled as a joint Gaussian with mean μ {\displaystyle \mu \,} and covariance Σ {\displaystyle \Sigma \,} . === Classification === The ultimate objective of this model is to classify images into classes "object present" (class C 1 {\displaystyle C_{1}\,} ) and "object absent" (class C 0 {\displaystyle C_{0}\,} ) given t
Variable-order Bayesian network
Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models. VOBN models are used in machine learning in general and have shown great potential in bioinformatics applications. These models extend the widely used position weight matrix (PWM) models, Markov models, and Bayesian network (BN) models. In contrast to the BN models, where each random variable depends on a fixed subset of random variables, in VOBN models these subsets may vary based on the specific realization of observed variables. The observed realizations are often called the context and, hence, VOBN models are also known as context-specific Bayesian networks. The flexibility in the definition of conditioning subsets of variables turns out to be a real advantage in classification and analysis applications, as the statistical dependencies between random variables in a sequence of variables (not necessarily adjacent) may be taken into account efficiently, and in a position-specific and context-specific manner.
Continuous Exposure Management
Continuous Exposure Management (CEM) is a cybersecurity approach that provides continuous, real-time monitoring, assessment, and prioritization of an organization’s security vulnerabilities and exposures. CEM focuses on identifying and mitigating risks by analyzing attack paths and providing recommendations, ensuring organizations maintain a resilient cybersecurity posture. == Overview == CEM platforms enable organizations to detect and remediate cybersecurity exposures, such as vulnerabilities, misconfigurations and weak credentials, across their entire ecosystem, including on-premises, cloud environments, and hybrid infrastructures. By simulating potential attack scenarios and mapping attack paths, these platforms help organizations understand how exposures could be exploited and which ones pose the greatest risk to critical assets. The XM Cyber Continuous Exposure Management platform, for example, integrates automated attack path mapping and contextual risk analysis, allowing security teams to prioritize remediation efforts effectively. In 2023, the platform uncovered over 40 million exposures affecting 11.5 million critical business entities. As cyber threats evolve, CEM platforms are becoming indispensable for modern enterprises. According to Gartner, organizations implementing continuous exposure management are three times less likely to experience a breach by 2026. In addition to risk mapping and simulation, some CEM approaches incorporate automated security validation to verify the exploitability of identified vulnerabilities. Platforms such as Pentera utilize automated security testing to emulate real-world adversary behavior across the network, identifying how security gaps could be leveraged to gain access to critical assets. This process aims to move beyond theoretical risk assessments by providing empirical evidence of exposure, allowing security teams to focus remediation efforts on validated attack vectors. By integrating this validation phase into the broader exposure management lifecycle, organizations can refine their prioritization strategies based on the actual effectiveness of their existing security controls and the proven reachability of their most sensitive data. == Key features == CEM platforms are designed to address the dynamic nature of cybersecurity risks through the following features: Attack Path Simulation: Continuously maps attack paths to critical assets, highlighting exploitable exposures and chokepoints. Risk Prioritization: Focuses on exposures with the highest impact on critical assets, ensuring efficient allocation of resources. Remediation Guidance: Provides clear, actionable recommendations to resolve exposures and strengthen defenses. Integration with Existing Tools: Seamlessly works with Security Information and Event Management (SIEM), ticketing, and Security Orchestration, Automation, and Response (SOAR) systems. Real-time Monitoring: Offers continuous visibility into exposures, ensuring that new ones are quickly identified and addressed.
Nearest centroid classifier
In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation. When applied to text classification using word vectors containing tfidf weights to represent documents, the nearest centroid classifier is known as the Rocchio classifier because of its similarity to the Rocchio algorithm for relevance feedback. An extended version of the nearest centroid classifier has found applications in the medical domain, specifically classification of tumors. == Algorithm == === Training === Given labeled training samples { ( x → 1 , y 1 ) , … , ( x → n , y n ) } {\displaystyle \textstyle \{({\vec {x}}_{1},y_{1}),\dots ,({\vec {x}}_{n},y_{n})\}} with class labels y i ∈ Y {\displaystyle y_{i}\in \mathbf {Y} } , compute the per-class centroids μ → ℓ = 1 | C ℓ | ∑ i ∈ C ℓ x → i {\displaystyle \textstyle {\vec {\mu }}_{\ell }={\frac {1}{|C_{\ell }|}}{\underset {i\in C_{\ell }}{\sum }}{\vec {x}}_{i}} where C ℓ {\displaystyle C_{\ell }} is the set of indices of samples belonging to class ℓ ∈ Y {\displaystyle \ell \in \mathbf {Y} } . === Prediction === The class assigned to an observation x → {\displaystyle {\vec {x}}} is y ^ = arg min ℓ ∈ Y ‖ μ → ℓ − x → ‖ {\displaystyle {\hat {y}}={\arg \min }_{\ell \in \mathbf {Y} }\|{\vec {\mu }}_{\ell }-{\vec {x}}\|} .