In ml model, there is an actual value in the dependent variable and the machine predicts a value according to the data. So bias is the distance/gap/difference between the actual value and predicted value. High bias means the gap is very much and low bias means the distance or gap is less or can be no gap or distance. Low bias model makes fewer assumptions about the form of the target function. A high bias makes more assumptions and for this reason it's unable to capture the important features of the dataset.

Mobile robots have been widely used in various aspects of human life. When a robot moves between different positions in the working area to perform the task, controlling motion to follow a pre-defined path is the primary task of a mobile robot. Furthermore, the robot must remain at its desired speed to cooperate with other agents. This paper presents a development of a motion controller, in which the fuzzy logic method is combined with a kinematic model of a differential drive robot. The simulation results are compared well with experimental results indicate that the method is effective and applicable for actual mobile robots.

Automated planning focuses on strategies, building domain models and synthesizing plans to transit initial states to goals. Natural language processing concerns with the interactions between agents and human language, especially processing and analyzing large amounts of natural language data. These two fields have abilities to generate explicit knowledge, e.g., preconditions and effects of action models, and learn from tacit knowledge, e.g., neural models, respectively. Integrating AI planning and natural language processing effectively improves the communication between human and intelligent agents. This paper outlines the commons and relations between AI planning and natural language processing, argues that each of them can effectively impact on the other one by four areas: (1) planning-based text understanding, (2) planning-based text generation, (3) text-based human-robot interaction, and (4) text-based explainable planning. We also explore some potential future issues between AI planning and natural language processing.

Mixing the perfect drink could soon be as easy as pushing a few buttons. A Los Angeles-based startup has launched the Somabar, a $430 robot bartender that can create simple mixed drinks selected by the user via the device's accompanying app. It could one day replace minibars in hotel rooms, help busy bartenders in restaurants or be used to mix drinks for airplane passengers. An LA-based startup has launched the Somabar (pictured), a robot bartender that can create mixed drinks selected by the user via the device's touchscreen or an accompanying app The Somabar is a futuristic robot bar that can mix drinks in 10 seconds or less. It's made of hardwood and plastic and can store up to six different Soma Pods, where users manually fill and remove air-tight containers of alcohol.

Why do 97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation? Thought leaders in PPC, social and mobile marketing explain in this in-depth survey. Are disciplines such as search engine marketing, social and mobile marketing all trending towards a fully automated world where artificial intelligence (AI) robots take over our jobs? In a survey of top influencers in online marketing with expertise in paid search, social and mobile, 97% of respondents suggested that the future of marketing will actually be smart marketers working hand-in-hand with machine learning-based automation solutions. To help us understand the growing role of machine learning in marketing, we spoke with some of the top influencers in the space, including Michael Brenner (@brennermichael) of Marketing Insider Group, Serena Ehrlich (@serena) of BusinessWire, Adelyn Zhou (@adelynzhou) of TOPBOTS and Chris Messina (@chrismessina), creator of the hashtag - among others.

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

Language processing has a large practical potential in intelligent interfaces if we take into account multiple modalities of communication. Multimodality refers to the perception of different coordinated media used in delivering a message as well as the combination of various attitudes in relation to communication. In particular, the integration of natural language processing and hypermedia allows each modality to overcome the constraints of the other, resulting in a novel class of integrated environments for complex exploration and information access. Information presentation is a key element of such environments; generation techniques can contribute to their quality by producing texts ex novo or flexibly adapting existing material to the current situation. A great opportunity arises for intelligent interfaces and language technology of this kind to play an important role for individual-oriented cultural tourism.

The first substantial interactive, display-based implementation was the SMALLTALK language (Goldberg & Robson, 1983). The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS (Lieberman, 1981), and multiple inheritance as in FLAVORS (Weinreb & Moon, 1981). It is also related to a line of work in AI on the theory of frames (Minsky, 1975) and their implementation in knowledge representation languages such as KRL (Bobrow & Winograd, 1977), KEE (Fikes & Kehler, 1985), FRL (Goldstein & Roberts, 1977) and UNITS (Stefik, 1979). One might expect from this long history that by now there would be agreement on the fundamental principles of object-oriented programming.

Li is not small compared to that of A. However, To understand how this rule works, let us return to the submarine example and assume that there are two groups of experts El,..., As is pointed out in Zadeh (1979a), the Dempster rule P*(notA) 1. This, in a nutshell, is the basic idea underly-of combination of evidence may lead to counterintuitive coning the Dempster-Shafer theory. The An important observation is in order at this juncture. P(A), that S is in A, the answer would be (after the object under consideration does not exist. P*(A) are the degrees of belief and plausibility associated of evidence, consider the following situation.