# Tag Info

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Commonsense knowledge is the collection of premises that everyone, in a certain context (hence common sense knowledge might be a function of the context), takes for granted. There would exist a lot of miscommunication between a human and an AI if the AI did not possess common sense knowledge. Therefore, commonsense knowledge is fundamental to human-AI ...

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We need this kind of common sense knowledge if we want to get computers to understand human language. It's easy for a computer program to analyse the grammatical structure of the example you give, but in order to understand its meaning we need to know the possible contexts, which is what you refer to as "common sense" here. This was emphasised a lot in ...

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I find it unlikely that you'll find a firm answer, so I will try my best to guide you towards information which may help you form an opinion either way. Turing had the controversial opinion (which remains controversial today) that: Digital computers have often been described as mechanical brains. Most scientists probably regard this description as a ...

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First of all, I would like to point out the main differences between knowledge base and (Deep) machine learning, specially when the main focus is on "AI" not "Data Science": NNs are like a black box; Even if they learn a dataset and gain the power of generalization over the problem domain, you'd never know how they are working. if you scrutinize the details ...

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Although it is common to represent a grid as two dimensional array in a computer program, this is not the only way to represent one. You could, for example, use a generalized graph structure made of linked nodes, with 4 links each. Many other representations are possible. Even if you use a 2d array, some languages would index the columns first rather than ...

3

First of all, sigmoid does not output 0 or 1, it outputs any real number in the range between 0 and 1. Furthermore, normal neural networks doesn't output binary values, unless the output layer uses the step function as activation function (which is rare). I'm not really sure if you want the NN to be a classifier or regressor, but it sounds like you want a ...

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One way of verifying whether two boolean expressions are equivalent is to assign all possibilities to all variables, and comparing all results. A B f1 f2 T T F F T F F T F T F T F F T T We can see (F, F, F, T) does not equal (F, T, T, T), for example for the assignment (A, B) = (T, F) we get result (f1, f2) = (F, T) , meaning f1 != f2.

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StarCraft II is a real time strategy game that combines fast paced micro actions with the need for high level planning and execution. StarCraft II being a popular game with millions of users it proceeds that defeating top players becomes a meaningful and measurable long term objective in AI research. Computer games provide a compelling solution to the ...

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I will first recapitulate the key concepts which you need to know in order to understand the answer to your question (which will be very simple, because I will just try to clarify what is given as a "definition"). In logic, a formula is e.g. $f$, $\lnot f$, $f \land g$, where $f$ can be e.g. the proposition (or variable) "today it will rain&...

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Although asked over 3 years ago, the question is still interesting and while I agree with the original answer, a lot can be added to it. First, I'd like to point out that the term "knowledge base" is very ambiguous and it means different things to different people. For example, there is no sharp distinction between knowledge base and neural network....

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To my knowledge, this is very much an open research issue. Here is a paper by Prof Leslie Smith, an acknowledged expert on neuromorphic perceptual coding, which explains the importance of the notion of perceptual time for Artificial General Intelligence and sketches an architecture from which a notion of 'now' might emerge: Perceptual Time.

3

Your question depends heavily on the method you are using for machine learning. It sounds like you want to extract certain features like "curves and straight lines" from your images and use them as training data. This step of extraction is usually not considered part of the training process but part of pre-processing. During pre-precessing you read your ...

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Rule-based systems cover a wide range of systems. Some make use of boolean if/then/else rules, others may use weighting or even probabilistic inference. Some operate on frames, some on java objects, some on propositions that can be formulated in predicate logic. An example of a popular rule system is Drools. Some rule systems can be expressed as a subset of ...

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Some of the work on descriptive logics and modal logics was done within the context of artificial intelligence from a research funding perspective. Some was part of the normal academic apparatus of mathematics departments. Furtherance of these fields in the AI context has been hindered by historically low return on investment. Although first order logic, ...

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Perhaps it would help to give an example of what can go wrong without common sense: At the start of the novel "The Two Faces of Tomorrow" by James Hogan, a construction supervisor on the Moon files a request with an automated system, asking that a particular large piece of construction equipment be delivered to his site as soon as possible. The system ...

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A semantic network is a way to implement an ontology. An ontology is just a generalised way of representing knowledge in a particular domain, and there are multiple ways of doing so. The key that distinguishes an ontology from, say, Wikipedia, is that it is formally defined, so that the knowledge represented can be used in programs to reason with. Semantic ...

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I'll answer this question in several parts: Why do AGI systems need to have common sense? Humans in the wild reason and communicate using common sense more than they do with strict logic, you can see this by noting that it is easier to appeal to someone's emotion than logic. So any system that seeks to replicate human cognition (as in AGI) should also ...

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I think the first question you should answer is: "What questions should the AI be able to answer?" If the intend was that the AI should be able to answer any questions, then that is simply not doable (or at least currently it is not doable). Currently this is similar to asking for a program that can do anything. Currently the AI field is split between ...

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This is (even though it doesn't look like it at first glance) a deeply philosophical question about the nature of 'meaning'. This answer is necessarily limited in scope. There are many ways of representing knowledge, and countless formalisms have been developed since the early days of AI. Many of them are based on some kind of predicate calculus, ontologies,...

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The impossibility is referring how to learn the disentangled representations from the observed distribution or to know whether you have a disentangled representation in the first place. Basically, an unsupervised learning agent tasked with learning a disentangled transformation of some features $\mathbf{z}$ needs to infer a set of features from the data ...

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Is this common sense, or is this natural language understanding? It's been said that natural language understanding is one of the hardest AI tasks. This is one of the examples showing why. The first part of the sentence is related to the second part, that how sentences work. Now the relevant question is how the two parts are related. There are a few ...

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A recent research example is the "Grind" system. Take a look at the paper Computing FO-Rewritings in $\mathcal{E} \mathcal{L}$ in Practice: from Atomic to Conjunctive Queries (2018) by Peter Hansen and Carsten Lutz. Here's the abstract. A prominent approach to implementing ontology-mediated queries (OMQs) is to rewrite into a first-order query, ...

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I think Demento has answered it well, but probably below can add some more understanding to you. [1] Usually the data stored for image processing relates to the Image Pixel Densities(as per the many courses i visited online), which can be very well maintained using a matrix of pixel density values, corresponding to the resolution of each image. Then it ...

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It seems that they are stating that a knowledge base is consistent if and only if it never asserts the truth of both the truth and the negation of a particular P. In other words, a knowledge base is consistent if it never contradicts itself. Their definition allows incomplete knowledge bases to be considered consistent; by their definition, an empty ...

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A very large one, the world wide web with highly scaled and optimized indexing by Google.com is the most distributed and robust schema-agnostic database known today. Without the schema-awareness Google brought to the table by applying more rigorous information science to the table, it was almost useless to those that did not know the URL of the target ...

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Methodology for a social enabled AI. Regarding social interactions, I believe that trying to build a copy of human behaviour based on technologies we understand, might not be effective . Instead, I would start from the roots of how human grows and learn, or better, by what is each human trying to solve from childhood to the end of their life. In that way ...

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Theory of mind Relationships and normal social behavior require a human to possess a reasonable "theory of mind", a skill in understanding and modeling the thought processes that happen in the minds of others, and making reasonably accurate predictions on how particular actions will be understood by others. In general, this might be treated as any other ...

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In general, what you are describing implies a hierarchical sequence model, in which mannerisms adapt to the regime or paradigm in effect. Expressive modalities are how we recognize the operative context from the behaviors of other agents. For an artificial agent, avoiding un-canniness would involve clustering the factors underlying the classification of ...

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That depends on how broadly you define "machine learning techniques". You could construct a definition so that, by definition, all learning falls under that rubric. OTOH, there is such a broad array of machine learning techniques that doing so wouldn't not gain one much. It probably makes more sense to talk about the different kinds of learning we use ...

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Well, we are talking about a system (a machine) which develops knowledge (learns), so it is kind of difficult for such a technique to not fall within machine learning. But you could argue that inference engines which work on a graph based knowledge database to derive new propositions or probabilities are not part of machine learning. Of course in that case ...

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