Humans Need Not Apply

2021 ◽  
pp. 60-85
Author(s):  
Mike Berrell
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Social Relations ◽  
New World ◽  
Legal Services ◽  
Occupational Groups ◽  
Job Advertisement ◽  
The Social ◽  
Smart Machines

Advanced technologies including artificial intelligence, robotics, and machine learning (smart machines) impact understandings about the nature of work. For professionals, semi-professionals, and ancillary workers supplying healthcare and legal services, for example, smart machines change the social relations of work and subvert notions of status and hierarchy that come with occupational groups such as doctors or lawyers. As smart machines continue to disrupt employment, job advertisement might soon carry the warning that humans need not apply. Under the prospect of a new world of work, people require additional knowledge, skills, and attitudes to cope with a future where smart machines radically alter the nature of work in settings where some people work anywhere and anytime while others work nowhere. In any future, people require skills and attitudes to cope with uncertainty. Ideas about multiple intelligences, emotional intelligence, critical thinking, creativity, and problem-solving will help employees cope with any of the futures of work predicted in the literature.

scholarly journals Robots at Work: Where Do We Fit?

2016 ◽  
Vol 138 (04) ◽  
pp. 32-37
Author(s):  
Alan S. Brown
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Problem Solving ◽  
Common Sense ◽  
Emerging Technologies ◽  
Cloud Robotics ◽  
Controlled Environments ◽  
Smart Machines ◽  
New Generation

This article presents a dilemma related to increasing use of robots at work. Artificial intelligence could erase jobs or create them, but economists agree that a new generation of smart machines will alter the rules of employment. Two emerging technologies that will help robots learn even faster are cloud robotics and deep learning, an advanced type of machine learning that allows robots to learn things that humans understand tacitly. However, robots require controlled environments, while humans, who are more flexible, can cope with unstructured tasks. That same adaptability is essential for medical technicians, plumbers, electricians, and many other middle-skill jobs. The experts expect pressures on middle-skill jobs to eventually reverse because these jobs combine not only knowledge, but also adaptability, problem solving, common sense, and the ability to communicate with other people. Businesses are already pairing human flexibility with mechanical precision.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

A Pragmatic Analytics of Covid Data Patterns for Predictive Mining of Patients

2020 ◽  
Vol 17 (11) ◽  
pp. 5105-5108
Author(s):  
Rubika Walia ◽  
Neelam Oberoi ◽  
Sakshi Sachdeva
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Research Work ◽  
Human Being ◽  
Global Perspectives ◽  
Integration Pattern ◽  
The Social

The year 2020 has emerged as a menace and threat for the human being whereby the social as well as professional livings getting affected. In the global perspectives, the human lives are affecting and huge demise occurring. In this research work, the effectual implementation towards the usage of Artificial Intelligence is done with the machine learning so that the overall outcomes and predictive mining can be done with higher degree of performance. The work is having the integration pattern of COVID datasets of patients with benchmark characteristics and thereby to have the predictions for the upcoming tests and by this way overall prediction can be done.

scholarly journals Reports on the 2017 AAAI Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 99-106
Author(s):  
Jeannette Bohg ◽  
Xavier Boix ◽  
Nancy Chang ◽  
Elizabeth F. Churchill ◽  
Vivian Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
User Experience ◽  
Object Perception ◽  
Learning Systems ◽  
Language Understanding ◽  
Social Good ◽  
Symposium Series ◽  
The Social

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place.

Conversational Interface as a mediating technology of organization

2019 ◽  
pp. 148-159
Author(s):  
Mercedes Bunz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Organization ◽  
Social Fabric ◽  
Conversational Interfaces ◽  
The Social ◽  
Conversational Interface

Conversational interfaces such as Apple’s Siri or Amazon’s Alexa allow technology companies to reach deeper into the social fabric of our societies than they already had. For centuries we used media to speak with each other; now to speak directly to a device has become normal. To understand this reorganisation further, this chapter explores the technology that drives it—the new Artificial Intelligence driven by machine learning—and links it back to social organization such as to the bias conversational interfaces learn from the data they are trained with, and to bots that start to converse in their own human-like language.

Robots, Replicants, and Surrogates

2021 ◽  
pp. 84-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science Fiction ◽  
Intelligent Systems ◽  
New World ◽  
Attitudes And Perceptions ◽  
The World ◽  
Recent Developments ◽  
Brave New World ◽  
And Robotics

The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. This has given rise to machine learning, intelligent systems, and robotics. Robots and AI might right now still seem the reserve of blockbuster science fiction movies and documentaries, but it's no doubt the world is changing. This chapter explores the origins, attitudes, and perceptions of robotics and the multiple types of robots that exist today. Perhaps most importantly, it focuses on ethical and societal concerns over the question: Are we heading for a brave new world or a science fiction horror-show where AI and robots displace or, perhaps more worryingly, replace humans?

Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

2021 ◽  
Vol 5 (2) ◽  
pp. 113
Author(s):  
Youngseok Lee ◽  
Jungwon Cho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Model Concept ◽  
Core Technology ◽  
Machine Learning Model ◽  
The Social ◽  
Education Model ◽  
Intelligence Education ◽  
Near Future

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.

A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving

2020 ◽  
Vol 17 (9) ◽  
pp. 4336-4339
Author(s):  
D. S. V. Suma Priya ◽  
D. Esther Rani ◽  
A. Pavan Shankar Sai ◽  
A. Konda Babu ◽  
Durgesh Nandan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Concept Learning ◽  
Real Life ◽  
Learning System ◽  
Level System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Life Problem

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.

Automation, machine learning, and artificial intelligence in echocardiography: A brave new world

2018 ◽  
Vol 35 (9) ◽  
pp. 1402-1418 ◽  
Author(s):  
Sumeet Gandhi ◽  
Wassim Mosleh ◽  
Joshua Shen ◽  
Chi-Ming Chow
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New World ◽  
Brave New World

Working beyond the confines of academic discipline to resolve a real-world problem: A community of scientists discussing long-tail data in the cloud

First Monday ◽  
2016 ◽  
Author(s):  
Catherine F. Brooks ◽  
P. Bryan Heidorn ◽  
Gretchen R. Stahlman ◽  
Steven S. Chong
Keyword(s):  
Problem Solving ◽  
Data Collection ◽  
Social Relations ◽  
Academic Discipline ◽  
Collaborative Work ◽  
Collaborative Problem Solving ◽  
Long Tail ◽  
Collaborative Problem ◽  
Real World Problem ◽  
The Social

This project interrogates a workshop leader and whole-meeting talk among a group of scientists gathered at a workshop to discuss cyberinfrastructure and the sharing of both ‘light’ and ‘dark’ data in the sciences. This project analyzes discourses working through the workshop talk to interrogate the social relations, interdisciplinary identities, concerns, and commonalities in the sciences and in relation to emerging opportunities for computing and data sharing in the cloud. The findings point to the efficacy of arranging scientists around data collection processes for collaborative work as opposed to groupings around data type, discipline, work sectors, or collection location. This research provides an opportunity to consider the democratization of data, academic boundaries in the sciences, as well as interdisciplinary and collaborative problem-solving processes that happen in groups across academic and applied contexts.

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