Group to establish standards for AI in papers Else, Holly
Science (American Association for the Advancement of Science),
2024-Apr-19, 2024-04-19, 20240419, Letnik:
384, Številka:
6693
Journal Article
Recenzirano
Researchers may be using generative artificial intelligence to help write 1%-5% of manuscripts.
Reward is enough Silver, David; Singh, Satinder; Precup, Doina ...
Artificial intelligence,
October 2021, 2021-10-00, 20211001, Letnik:
299
Journal Article
Recenzirano
Odprti dostop
In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that ...exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is ...focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.
Why aren't the most powerful new technologies being used to solve the world's most important problems: hunger, poverty, conflict, employment, disease? In Link, Dr. Lorien Pratt answers these ...questions by exploring the solution that is emerging worldwide to take Artificial Intelligence to the next level: Decision Intelligence.
The ability to convey relevant and diverse information is critical in multi-document summarization and yet remains elusive for neural seq-to-seq models whose outputs are often redundant and fail to ...correctly cover important details. In this work, we propose an attention mechanism which encourages greater focus on relevance and diversity. Attention weights are computed based on (proportional) probabilities given by Determinantal Point Processes (DPPs) defined on the set of content units to be summarized. DPPs have been successfully used in extractive summarisation, here we use them to select relevant and diverse content for neural abstractive summarisation. We integrate DPP-based attention with various seq-to-seq architectures ranging from CNNs to LSTMs, and Transformers. Experimental evaluation shows that our attention mechanism consistently improves summarization and delivers performance comparable with the state-of-the-art on the MultiNews dataset
Can We Trust AI? Chellappa, Rama
Johns Hopkins University Press eBooks,
2022
eBook
Odprti dostop
Artificial intelligence is part of our daily lives. How can we address its limitations and guide its use for the benefit of communities worldwide?Artificial intelligence (AI) has evolved from an ...experimental computer algorithm used by academic researchers to a commercially reliable method of sifting through large sets of data that detect patterns not readily apparent through more rudimentary search tools. As a result, AI-based programs are helping doctors make more informed decisions about patient care, city planners align roads and highways to reduce traffic congestion with better efficiency, and merchants scan financial transactions to quickly flag suspicious purchases. But as AI applications grow, concerns have increased, too, including worries about applications that amplify existing biases in business practices and about the safety of self-driving vehicles. In Can We Trust AI?, Dr. Rama Chellappa, a researcher and innovator with 40 years in the field, recounts the evolution of AI, its current uses, and how it will drive industries and shape lives in the future. Leading AI researchers, thought leaders, and entrepreneurs contribute their expertise as well on how AI works, what we can expect from it, and how it can be harnessed to make our lives not only safer and more convenient but also more equitable. Can We Trust AI? is essential reading for anyone who wants to understand the potential—and pitfalls—of artificial intelligence. The book features:• an exploration of AI's origins during the post–World War II era through the computer revolution of the 1960s and 1970s, and its explosion among technology firms since 2012;• highlights of innovative ways that AI can diagnose medical conditions more quickly and accurately;• explanations of how the combination of AI and robotics is changing how we drive; and• interviews with leading AI researchers who are pushing the boundaries of AI for the world's benefit and working to make its applications safer and more just. Johns Hopkins WavelengthsIn classrooms, field stations, and laboratories in Baltimore and around the world, the Bloomberg Distinguished Professors of Johns Hopkins University are opening the boundaries of our understanding of many of the world's most complex challenges. The Johns Hopkins Wavelengths book series brings readers inside their stories, illustrating how their pioneering discoveries and innovations benefit people in their neighborhoods and across the globe in artificial intelligence, cancer research, food systems' environmental impacts, health equity, planetary science, science diplomacy, and other critical arenas of study. Through these compelling narratives, their insights will spark conversations from dorm rooms to dining rooms to boardrooms.
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