Artificial Intelligence : The Supermind - A Review

Artificial intelligence (AI) is one of the most important technologies in the world today. In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial Intelligence is impacting the future of virtually every industry and every human being. AI has acted as the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the foreseeable future. AI is simply the study of how to make computer do things which at the moment people do the better. There are many ways to define AI, but one simple definition is “intelligence demonstrated by machines”. Primary goal of AI is to improve computer behaviour so that it can be called intelligent. AI is ubiquitous and is not only limited to computer science but has evolved to include other areas like health, security, education, music, art, and business application. This paper gives an overview of how the AI actually works, its scopes , the different applications of AI, its advantages and disadvantages and many more topics which will give a clear understanding inspite of the boundlessness of AI.

Affective Computing in Marketing: Practical Implications and Research Opportunities Afforded by Emotionally Intelligent Machines

After years of using AI to perform cognitive tasks, marketing practitioners can now use it to perform tasks that require emotional intelligence. This advancement is made possible by the rise of affective computing, which develops AI and machines capable of detecting and responding to human emotions. From market research, to customer service, to product innovation, the practice of marketing will likely be transformed by the rise of affective computing, as preliminary evidence from the field suggests. In this Idea Corner, we discuss this transformation and identify the research opportunities that it offers.

Past, Present, and Future of Artificial Intelligence in Library Services

In the past, human imagination about intelligent machines was only found in the science fiction of storybooks and films. Today, artificial intelligence (AI) can be found in people's daily lives. Various professions should prepare to face the automation era in the future. Libraries may be one of the slowest institutions to develop AI. Gradually, the institution adopts it for their services. Many papers focus on AI development in libraries, but the opportunities and challenges for librarians to face the era of automation are essential to discuss. This chapter provides insights into the professions that librarians can offer. First, this chapter provides information on the history and development of AI in library services. Then, based on bibliometric analysis, this chapter discusses AI trends in library services. Next, this chapter conducts a systematic review and presents the types of AI developed over time for library services. Finally, this chapter discusses the types of jobs, expertise, and skills that librarians can develop in the robotics era in the future.

The Digitalization of Pakistan's Universities?

Higher educational institutes today need to focus on identifying the requirements of industry as well as the market, so that they can help students develop the necessary skills and enable them to work with intelligent machines in today's era of the 4th industrial revolution which is also termed digitalization. Digitalization has increased pressure on educational institutions to update their existing curricula and course contents. It is important to note that, while industry as well as educational institutions in the developed world are rather quick on embracing such trends, developing economies often lag behind. Universities in developed countries are mostly on the path towards a hybrid way of teaching, while those in developing countries, such as Pakistan, frequently struggle to make these changes. This chapter seeks to provide suggestions and recommendations for the higher education sector, including universities and policymakers. It identifies the role that the higher education sector must play in preparing and upskilling future employees for Pakistan's digital future.

Digital TV Network Management System Based on WEB

With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people’s lives. This paper studies the control method of snake robot based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.

John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

Future of Us – Transforming Healthcare With Intelligent Machines (Vol. 25, No. 12, Full Issue)

For the month of December 2021, APBN looks the applications of artificial intelligence and robotics in the healthcare space. In Features, we hear from Christopher Khang, President and CEO of GE Healthcare ASEAN, on how innovative technologies such as AI and automation are shaping a bright future for radiologists and their patients. Then, we have David Irecki from Boomi, who proposes that a unified view of patient data can enable holistic, coordinated services that will improve people'soverall quality of life, and an article contribution by Lewis Ho, Chief Executive Officer at Avalon SteriTech on innovative healthcare solutions and the future of smart cleaning. Shifting away from intelligent machines, we speak to Dr. Goh Choo Beng, Head of Medical Affairs at Takeda APAC, on unmet disease areas and learn more about the different treatments and trials currently in the pipeline at Takeda, and finally, we wrap up the year 2021 with Lu-Ching Lau, Director for External Affairs, Policy and Communications, Singapore and Malaysia, MSD, as she shares with us how we may navigate through current and future health challenges.

DETERMINING LEARNING SUCCESS of kNN ALGORITHM on ZOO DATASET

People learn by examining, observing and researching their environment. They actually gains experience from what they have learned. By using the experience they have gained, they can adapt to the new situation they encounter and make decisions. People always make decisions by comparing their previous knowledge while describing objects and classifying them. Similarities and differences to previously learned objects are very effective in decision making. It has been shown in the studies that the experiential learning method can also be used on machines. Intelligent machines and devices that use machine learning methods in their structure are widely used in many areas. Machine learning can be performed using different algorithms. These algorithms use the attributes of the objects in the data set when making decisions. Similarities and differences in the attributes of objects are obtained by comparing them with previous experiences. As a result of the comparison, a decision is made and predictions are made about the classes of the objects. In this study, kNN machine learning algorithm, which is a supervised learning method, was used on the Zoo dataset. In this data set, there are attributes of common living things. By using these attributes, the classes of living things in the data set are determined. The “k” neighbor value and weight parameter selected in the kNN algorithm affect the learning success. In this study, the effect of two parameters used in the kNN algorithm on learning success is shown. According to the results obtained, the "k=1" neighbor value and the "Distance Weight" parameter were selected and the highest success result was obtained.