Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts

Grace, Katja; Salvatier, John; Dafoe, Allan; Zhang, Baobao; Evans, Owain

doi:10.1613/jair.1.11222

Cited by 459 publications

(329 citation statements)

References 9 publications

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“…Deep learning methods have been able to defeat humans in the strategy board game of Go, an achievement that was previously thought to be decades away given the highly complex game space and massive number of potential moves 6 . Following the trend towards a human-level general AI, researchers predict that AI will automate many tasks, including translating languages, writing best-selling books and performing surgery — all within the coming decades 7 .…”

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confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…With automated systems showing up in every sector, Elon further predicts a universal wage, to be paid by government since humans will be left with almost nothing to do. Though not everyone will agree with this position, current progress in AI and robotics supports such a future which was further confirmed in a recent study that evaluated when robots can be expected to take over current human jobs completely (Grace et al 2017) based on a survey of topmost experts in AI and machine learning. The findings, shown in Figure 2 as milestones of achievement in AI, presents an interesting discussion; the experts predicted 45 years from 2016 (i.e.…”

Section: The Future Of Robots In Human Life and Societymentioning

confidence: 86%

Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

Edwards¹,

Cheok²

2017

Preprint

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Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labour inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this paper, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.

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“…There are many studies that try to predict when artificial general intelligence will emerge (e.g., [10][11][12][13]). However, I will not rely on these predictions here in order to not be involved in the controversy regarding the very possibility of thinking machines.…”

Section: Timeline Extrapolationmentioning

confidence: 99%

Technological Singularity: What Do We Really Know?

Potapov

2018

Information

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Abstract:The concept of the technological singularity is frequently reified. Futurist forecasts inferred from this imprecise reification are then criticized, and the reified ideas are incorporated in the core concept. In this paper, I try to disentangle the facts related to the technological singularity from more speculative beliefs about the possibility of creating artificial general intelligence. I use the theory of metasystem transitions and the concept of universal evolution to analyze some misconceptions about the technological singularity. While it may be neither purely technological, nor truly singular, we can predict that the next transition will take place, and that the emerged metasystem will demonstrate exponential growth in complexity with a doubling time of less than half a year, exceeding the complexity of the existing cybernetic systems in few decades.

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Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts

Cited by 459 publications

References 9 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

Technological Singularity: What Do We Really Know?

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