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People in these jobs are most afraid of a robot takeover

#artificialintelligence

Sometimes, it seems like robots are completely taking over the world. Every year, thousands of machines are deployed into the workforce, taking jobs that humans used to do. And, workers are rightly worried. A new survey from CNBC and Survey Monkey found that almost four in 10 workers between the ages of 18 and 24 are concerned about new technology – like robots and artificial intelligence systems, taking over their jobs. Dan Schawbel, research director of Future Workplace, told CNBC that one reason why the younger generation is more concerned about a robot takeover is that artificial intelligence has rapidly become normalized throughout our society, and the length remaining in young people's careers will likely be impacted by AI. "They are starting to see the value of [AI] and how it's impacting their personal and professional lives," he said.


The Fear of Artificial Intelligence in Job Loss

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With all the hype over Artificial Intelligence, there is additionally a lot of disturbing buzz about the negative results of AI. More than one-quarter (27%) of all employees state they are stressed that the work they have now will be disposed of within the next five years because of new innovation, robots or artificial intelligence, as indicated by the quarterly CNBC/SurveyMonkey Workplace Happiness review. In certain industries where technology already has played a profoundly disruptive role, employees fear of automation likewise run higher than the normal: Workers in automotives, business logistics and support, marketing and advertising, and retail are proportionately more stressed over new technology replacing their jobs than those in different industries. The dread stems from the fact that the business is already witnessing it. Self-driving trucks already are compromising the jobs of truck drivers, and it is causing a huge frenzy in this job line.


Global Big Data Conference

#artificialintelligence

With all the hype over Artificial Intelligence, there is additionally a lot of disturbing buzz about the negative results of AI. More than one-quarter (27%) of all employees state they are stressed that the work they have now will be disposed of within the next five years because of new innovation, robots or artificial intelligence, as indicated by the quarterly CNBC/SurveyMonkey Workplace Happiness review. In certain industries where technology already has played a profoundly disruptive role, employees fear of automation likewise run higher than the normal: Workers in automotives, business logistics and support, marketing and advertising, and retail are proportionately more stressed over new technology replacing their jobs than those in different industries. The dread stems from the fact that the business is already witnessing it. Self-driving trucks already are compromising the jobs of truck drivers, and it is causing a huge frenzy in this job line.


Explainable Artificial Intelligence (XAI): An Engineering Perspective

arXiv.org Artificial Intelligence

The remarkable advancements in Deep Learning (DL) algorithms have fueled enthusiasm for using Artificial Intelligence (AI) technologies in almost every domain; however, the opaqueness of these algorithms put a question mark on their applications in safety-critical systems. In this regard, the `explainability' dimension is not only essential to both explain the inner workings of black-box algorithms, but it also adds accountability and transparency dimensions that are of prime importance for regulators, consumers, and service providers. eXplainable Artificial Intelligence (XAI) is the set of techniques and methods to convert the so-called black-box AI algorithms to white-box algorithms, where the results achieved by these algorithms and the variables, parameters, and steps taken by the algorithm to reach the obtained results, are transparent and explainable. To complement the existing literature on XAI, in this paper, we take an `engineering' approach to illustrate the concepts of XAI. We discuss the stakeholders in XAI and describe the mathematical contours of XAI from engineering perspective. Then we take the autonomous car as a use-case and discuss the applications of XAI for its different components such as object detection, perception, control, action decision, and so on. This work is an exploratory study to identify new avenues of research in the field of XAI.


Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

arXiv.org Artificial Intelligence

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.


From fantasy to reality: Misunderstanding the impact of AI - AI News

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The prominence of artificial intelligence (AI) has significantly grown in pop culture and science fiction over the years. It has speculated on how AI can change people's lives, the places we live and our day-to-day activities. However, despite the increase of AI in popular films such as I, Robot, Star Trek and WALL-E, it's continued depiction and futuristic tendencies throughout the years have altered individual perceptions about the true meaning of AI and how it is already playing a vital part in our everyday lives. A recent survey conducted by O'Reilly paints this exact picture. It gives AI-creators an in-depth look at how consumers identify and use AI technology, showcasing the heightened misunderstanding that consumers have of AI and its use.


Don't Fear Artificial General Intelligence

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AI has blasted its way into the public consciousness and our everyday lives. It is powering advances in medicine, weather prediction, factory automation, and self-driving cars. Even golf club manufacturers report that AI is now designing their clubs. Google Translate helps us understand foreign language webpages and talk to Uber drivers in foreign countries. Vendors have built speech recognition into many apps.


Man vs. machine? How Automated Machine Learning will Evolve Actuaries

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But this form of software is so smart that it can teach itself to code, and it could enable insurers to automate tasks in fields as diverse as fraud investigation, valuation, and lapse detection. For some, this outcome sounds terrifying. But for many, it simply confuses. Many past attempts to apply AI have foundered against the natural limits of data availability, storage capacity, and computing power, but those barriers are now falling. As advances in chip design have delivered exponential increases in processing power and information storage, the results are visible across multiple branches of this discipline from rapid advances in the most basic forms of AI voice, speech, and object recognition to more advanced forms of machine learning. AutoML sits near the top of this technological food chain.


Neural's market outlook for artificial intelligence in 2021 and beyond

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The year is coming to a close and it's safe to say Elon Musk's prediction that his company would field one million "robotaxis" by the end of 2020 isn't going to come true. In fact, so far, Tesla's managed to produce exactly zero self-driving vehicles. And we can probably call off the singularity too. GPT-3 has been impressive, but the closer machines get to aping human language the easier it is to see just how far away from us they really are. So where does that leave us, ultimately, when it comes to the future of AI?


The Ethics of AI and Autonomous Vehicles

#artificialintelligence

In the most diverse sectors of our society, artificial intelligence ( AI) is assuming a significant role. We have no return point, and artificial intelligence will be incorporated into our daily life, professionally or socially, into our future. But together with the crescent adoption of the technology, some ethical concerns are posed by the notion of "thinking computers" being able to making decisions like humans. A practical approach to AI adoption must be researched and examined, and this article starts to explore ethical guidelines for the use of intelligent and autonomous systems. Artificial Intelligence ( AI) has been applied widely among us, with potentially great benefits to humanity but at the same time, several concerns regarding AI's unethical use are growing.