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Problem Solving


Python Data Structures Tutorial

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Also explains sequence and string functions, slicing, concatenating, iterating, sorting, etc. with code examples. Also explains sequence and string functions, slicing, concatenating, iterating, sorting, etc. with code examples. This course combines conceptual lectures to explain how a data structure works, and code lectures that walk through how to implement a data structure in Python code. All the code lectures are based on Python 3 code in a Jupyter notebook. Data structures covered in this course include native Python data structures String, List, Tuple, Set, and Dictionary, as well as Stacks, Queues, Heaps, Linked Lists, Binary Search Trees, and Graphs.


Applications of AI in CAD Technology

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A new feature to be found in modern CAD software releases is KBE (Knowledge Based Engineering) to support diagnosis, selection, and monitoring of tasks. KBE relies on capturing and storing experiential knowledge which includes proprietary design and manufacturing practices exercised during a product development cycle. KBE helps engineering companies to retain and preserve in-house knowledge and intellectual information. A related technology which could significantly augment problem solving capabilities in CAD software is AI (Artificial Intelligence), which was introduced in the mid-1980s. The purpose of AI is to learn and replicate human problem solving capabilities.


Hash Tables in Data Structure and Algorithm

#artificialintelligence

The above data structures all of these operations can be guaranteed to be in O(Logn) time. So can we perform it with O(1) time? this is why the hash table comes in. The simplest method to build Hash function is each key, we can perform sum of each key by add all character and then we can use Modulo for M. M is typically a prime number and it is the size of Hash array. I just suppose in a simple case of password but in real life, we must encode password (this is not the purpose of this article and apply a ton of algorithm for encoding password).


Global Artificial Intelligence in Aviation Market : Industry Analysis and Forecast (2019-2026) _ by Offering (Hardware, Software), by Technology (Natural Language Processing (NLP), Context Awareness Computing, and Others), by Application, and by Geography - re:Jerusalem

#artificialintelligence

Artificial Intelligence in Aviation Market has been segmented on the basis of technology, offering, application, and geography. Based on offering market is split into Hardware & Software. Technology is divided into Natural Language Processing (NLP), Context, Awareness Computing, Machine Learning, and Computer Vision. Application of the market is Flight Operations, Smart Maintenance, Training, Virtual Assistants, Surveillance, Dynamic Pricing, and Manufacturing. Artificial intelligence in aviation sort the information and provide the pilot with the best possible options for operation, which is impossible for human being to perform.


Problem Solvers Caucus leader says COVID stimulus deal is 'within inches'

FOX News

Moody's Analytics chief markets economist John Lonski on whether or not the U.S. needs another stimulus. A leader of the bipartisan Problem Solvers Caucus lamented the White House calling off coronavirus stimulus talks and urged leaders to get back to the negotiating table because a deal is within reach. Rep. Tom Reed, R-N.Y., urged President Trump and congressional leaders to continue to fight for a broad relief package, rather than a piecemeal deal. "We are within inches of getting this done," Reed, R-N.Y., said Wednesday. "Let's not walk away now."


A Representation System User Interface for Knowledge Base Designers

AI Magazine

A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.


Towards a Taxonomy of Problem Solving Types

AI Magazine

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver.


Knowledge Representation in Sanskrit and Artificial Intelligence

AI Magazine

In the past twenty years, much time, effort, and money has been expended on designing an unambiguous representation of natural language to make them accessible to computer processing, These efforts have centered around creating schemata designed to parallel logical relations with relations expressed by the syntax and semantics of natural languages, which are clearly cumbersome and ambiguous in their function as vehicles for the transmission of logical data. Understandably, there is a widespread belief that natural languages are unsuitable for the transmission of many ideas that artificial languages can render with great precision and mathematical rigor. But this dichotomy, which has served as a premise underlying much work in the areas of linguistics and artificial intelligence, is a false one. There is at least one language, Sanskrit, which for the duration of almost 1000 years was a living spoken language with a considerable literature of its own. Besides works of literary value, there was a long philosophical and grammatical tradition that has continued to exist with undiminished vigor until the present century.


Artificial Intelligence Research at General Electric

AI Magazine

General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments.


A Framework for the Development of Personalized, Distributed Web-Based Configuration Systems

AI Magazine

For the last two decades, configuration systems relying on AI techniques have successfully been applied in industrial environments. These systems support the configuration of complex products and services in shorter time with fewer errors and, therefore, reduce the costs of a mass-customization business model. The European Union-funded project entitled CUSTOMER-ADAPTIVE WEB INTERFACE FOR THE CONFIGURATION OF PRODUCTS AND SERVICES WITH MULTIPLE SUPPLIERS (CAWICOMS) aims at the next generation of web-based configuration applications that cope with two challenges of today's open, networked economy: (1) the support for heterogeneous user groups in an open-market environment and (2) the integration of configurable subproducts provided by specialized suppliers. This article describes the CAWICOMS WORKBENCH for the development of configuration services, offering personalized user interaction as well as distributed configuration of products and services in a supply chain. The developed tools and techniques rely on a harmonized knowledge representation and knowledge-acquisition mechanism, open XMLbased protocols, and advanced personalization and distributed reasoning techniques.