John McCarthy, the father of Artificial Intelligence, describes it as "the science and engineering of making intelligent machines, especially intelligent computer programs". Artificial Intelligence is a way of making a computer and software related to computer which can think intelligently and autonomously, kind of similar to a human mind. In general understanding artificial intelligence is accomplished by studying how a human brain works while solving a problem and in what manner it learns and makes decisions, where outcomes of such kind of study are used as the basis of developing intelligent software and systems. Till now this field was dominated by quasi-artificial intelligent systems called "expert systems," which mainly used a rules-based decision-making process.1 In other words, we can interpret that these systems were not fully autonomous and, therefore, not truly intelligent, because they lacked the ability to learn and produce unpredictable results, and mostly they acted in a manner predetermined by their programming.2

We all know that feeling when the solution to a problem we have invested significant time into suddenly and unexpectedly reveals itself in a wondrous "a-ha" moment. Such an epiphany can be described as an enlightening realization that allows a problem or situation to be understood from a new and deeper perspective. Epiphanies, once deemed as insight from the divine, are relatively rare occurrences, but what if today's artificial intelligence (AI) tools can inspire and increase the frequency of epiphanies about the nature of very complex problems? BuildingIQ has set out to do exactly that --to move epiphanies out of the realm of the miraculous and into our everyday experience. We recently launched our powerful AI-driven inference engine, named Epiphany, which pulls together disparate data points within a given system; creates a virtualized network of that holistic system; and then learns how each point is connected and influenced by the other points in the network.

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.

What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T'. We define interesting in this way: The "harder to learn" relation is reversed when comparing human intelligence (HI) to AI. While humans seems to be able to understand problems by formulating rules, ML using neural networks does not rely on constructing rules. We discuss a novel approach where the challenge is to "perform well under rules that have been created by human beings." We suggest that this provides a rigorous and precise pathway for understanding the difference between the two kinds of learning. Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules. With these tasks, both the AI and HI will be studied with rigor and precision. The immediate goal is to find interesting groundtruth rule pairs. In the long term, the goal will be to understand, in a generalizable way, what distinguishes interesting pairs from ordinary pairs, and to define saliency behind interesting pairs. This may open new ways of thinking about AI, and provide unexpected insights into human learning.

This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that explains the sensory sequence and satisfies a set of unity conditions. This model was inspired by Kant's discussion of the synthetic unity of apperception in the Critique of Pure Reason. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the Kantian unity constraints. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and "impute" (fill in the blanks of) missing sensory readings, in any combination. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction IQ tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction IQ tasks, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve IQ tasks, but a general purpose apperception system that was designed to make sense of any sensory sequence.

With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our approach is capable of identifying a single relation as important explanatory construct. Afterwards, we present the more complex relational concept of towers. Finally, we show how the generated relational rules can be explicitly related with the input image, resulting in richer explanations.

Find powerful new insights in your data; discover machine learning with R.

This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.

I am writing this guide to cover all OSCP topics as well as other infosec knowledge in details, I will also provide a cheat-sheet in each section so that you can use the commands directly once you understand the topics/tools. But I think to become a good pentester you should know how things work. Pentesting is a very wide field, like if you are interested in webapp pentesting then you should know how application interacts with databases, basics of databses, webapp languages. Similarly if you want to become system/platform(OS) pentester, you should know how to setup the OS, how to do system configuration, and what people can miss when they do configuration. This can guide you to think in the direction of finding vulnerability.

Answer set programming (ASP) is a declarative problem solvi ng paradigm for nonmonotonic reasoning. ASP allows intuitiive represe ntation of combinatorial search and optimization problems and is widely use d for knowledge representation and reasoning in various applications like plan generation, natural language processing etc [14, 15]. But ASP can not dea l with fuzzy information, where attributes and truth degrees lie in a con tinuous range of values. Fuzzy Answer Set Programming (F ASP) is proposed as a n extension of ASP that allows graded truth values from the interval [0,1 ]. Theoretical advancement of F ASP is remarkable [18, 32, 9, 22, 23]. Howeve r, this approach performs reasoning with absolutely certain but vagu e information and doesn't involve reasoning with uncertain information.