Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

Hao Luo Xiang Li Yuchen Jiang + 2 lainnya

7 Maret 2022

The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.

David Donoho

2 Oktober 2023

A purported `AI Singularity' has been in the public eye recently. Mass media and US national political attention focused on `AI Doom' narratives hawked by social media influencers. The European Commission is announcing initiatives to forestall `AI Extinction'. In my opinion, `AI Singularity' is the wrong narrative for what's happening now; recent happenings signal something else entirely. Something fundamental to computation-based research really changed in the last ten years. In certain fields, progress is dramatically more rapid than previously, as the fields undergo a transition to frictionless reproducibility (FR). This transition markedly changes the rate of spread of ideas and practices, affects mindsets, and erases memories of much that came before. The emergence of frictionless reproducibility follows from the maturation of 3 data science principles in the last decade. Those principles involve data sharing, code sharing, and competitive challenges, however implemented in the particularly strong form of frictionless open services. Empirical Machine Learning (EML) is todays leading adherent field, and its consequent rapid changes are responsible for the AI progress we see. Still, other fields can and do benefit when they adhere to the same principles. Many rapid changes from this maturation are misidentified. The advent of FR in EML generates a steady flow of innovations; this flow stimulates outsider intuitions that there's an emergent superpower somewhere in AI. This opens the way for PR to push worrying narratives: not only `AI Extinction', but also the supposed monopoly of big tech on AI research. The helpful narrative observes that the superpower of EML is adherence to frictionless reproducibility practices; these practices are responsible for the striking progress in AI that we see everywhere.

Terah Lyons Nestor Maslej Russell Wald + 11 lainnya

5 Oktober 2023

Welcome to the sixth edition of the AI Index Report. This year, the report introduces more original data than any previous edition, including a new chapter on AI public opinion, a more thorough technical performance chapter, original analysis about large language and multimodal models, detailed trends in global AI legislation records, a study of the environmental impact of AI systems, and more. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI.

D. Zha Fan Yang Xia Hu + 4 lainnya

17 Maret 2023

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI

Shivaram Kalyanakrishnan A. Saxenian W. Press + 14 lainnya

31 Oktober 2022

In September 2016, Stanford's"One Hundred Year Study on Artificial Intelligence"project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled"Artificial Intelligence and Life in 2030,"examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.