Over the past four decades, artificial intelligence (AI) research has flourished at the nexus of academia and industry. However, Big Tech companies have increasingly acquired the edge in computational resources, big data, and talent. So far, it has been largely unclear how many papers the industry funds, how their citation impact compares to non-funded papers, and what drives industry interest. This study fills that gap by quantifying the number of industry-funded papers at 10 top AI conferences (e.g., ICLR, CVPR, AAAI, ACL) and their citation influence. We analyze about 49.8K papers, about 1.8M citations from AI papers to other papers, and about 2.3M citations from other papers to AI papers from 1998-2022 in Scopus. Through seven research questions, we examine the volume and evolution of industry funding in AI research, the citation impact of funded papers, the diversity and temporal range of their citations, and the subfields in which industry predominantly acts. Our findings reveal that industry presence has grown markedly since 2015, from less than 2 percent to more than 11 percent in 2020. Between 2018 and 2022, 12 percent of industry-funded papers achieved high citation rates as measured by the h5-index, compared to 4 percent of non-industry-funded papers and 2 percent of non-funded papers. Top AI conferences engage more with industry-funded research than non-funded research, as measured by our newly proposed metric, the Citation Preference Ratio (CPR). We show that industry-funded research is increasingly insular, citing predominantly other industry-funded papers while referencing fewer non-funded papers. These findings reveal new trends in AI research funding, including a shift towards more industry-funded papers and their growing citation impact, greater insularity of industry-funded work than non-funded work, and a preference of industry-funded research to cite recent work.

Artificial intelligence (AI) has seen tremendous development in industry and academia. However, striking recent advances by industry have stunned the world, inviting a fresh perspective on the role of academic research in this field. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations struggle to replicate the novelty of academic teams and tend to look similar to industry teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the healthy progress of AI.

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to the nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.'s position as the global leader in this field. Given AI's massive potential, as well as the fierce geopolitical tensions between the two nations, a number of policies have been put in place that discourage AI scientists from migrating to, or collaborating with, the other country. However, the extents of such brain drain and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that, since the year 2000, China and the U.S. have been leading the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who migrate to China come from the U.S., and most who migrate to the U.S. come from China, highlighting a notable brain drain in both directions. Upon migrating from one country to the other, scientists continue to collaborate frequently with the origin country. Although the number of collaborations between the two countries has been increasing since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each of them works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the field could benefit from promoting such activities.

The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous efforts devoted to understanding AI's impact on labor and the economy and its recent success in accelerating scientific discovery and progress, we lack a systematic understanding of how advances in AI may benefit scientific research across disciplines and fields. Here we develop a measurement framework to estimate both the direct use of AI and the potential benefit of AI in scientific research by applying natural language processing techniques to 87.6 million publications and 7.1 million patents. We find that the use of AI in research appears widespread throughout the sciences, growing especially rapidly since 2015, and papers that use AI exhibit an impact premium, more likely to be highly cited both within and outside their disciplines. While almost every discipline contains some subfields that benefit substantially from AI, analyzing 4.6 million course syllabi across various educational disciplines, we find a systematic misalignment between the education of AI and its impact on research, suggesting the supply of AI talents in scientific disciplines is not commensurate with AI research demands. Lastly, examining who benefits from AI within the scientific workforce, we find that disciplines with a higher proportion of women or black scientists tend to be associated with less benefit, suggesting that AI's growing impact on research may further exacerbate existing inequalities in science. As the connection between AI and scientific research deepens, our findings may have an increasing value, with important implications for the equity and sustainability of the research enterprise.

You may have heard the news of ChatGPT fooling professors. Recently, it bamboozled scientists with convincing AI papers. The reports came from a preprint from the scientific bioRxiv server in December 2022. Researchers asked ChatGPT to create 50 abstracts based on several scientific sources. They found that medical researchers struggled to distinguish the fakes from the originals.

I explain Artificial Intelligence terms and news to non-experts. Two years ago, I saw my first research paper ever. I remember how old it looked and how discouraging the mathematics inside was. It really did look like what the researchers worked on in movies. To be fair, the paper was from the 1950s, but it hasn't changed much since then.

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Artificial intelligence is becoming a true industry, with all the pluses and minuses that entails, according to a sweeping new report.Why it matters: AI is now in nearly every area of business, with the pandemic pushing even more investment in drug design and medicine. But as the technology matures, challenges around ethics and diversity grow.Stay on top of the latest market trends and economic insights with Axios Markets. Subscribe for freeDriving the news: This morning, the Stanford Institute for Human-Centered Artificial Intelligence (HAI) released its annual AI Index, a top overview of the current state of the field.A majority of North American AI Ph.D.s — 65% — now go into industry, up from 44% in 2010, a sign of the growing role that large companies are playing both in AI research and implementation."The striking thing to me is that AI is moving from a research phase to much more of an industrial practice," says Erik Brynjolfsson, a senior fellow at HAI and director of the Stanford Digital Economy Lab.By the numbers: Even with the pandemic, private AI investment grew by 9.3% in 2020, a bigger increase than in 2019.For the third year in a row, however, the number of newly funded companies decreased, a sign that "we're moving from pure research and exploratory small startups to industrial-stage companies," says Brynjolfsson.While academia remains the single-biggest source worldwide for peer-reviewed AI papers, corporate-affiliated research now represents nearly a fifth of all papers in the U.S., making it the second-biggest source.The drug and medical industries took in by far the biggest share of overall AI private investment in 2020, absorbing more than $13.8 billion — 4.5 times greater than in 2019 and nearly three times more than the next category of autonomous vehicles.The catch: While the field has experienced sudden busts in the past — the "AI winters" that vaporized funding — there's little indication such a collapse is on the horizon. But industrialization comes with its own growing pains.Cutting-edge AI increasingly requires huge amounts of computing and data, which puts more power in the hands of fewer big players.Conversely, the commoditization of AI technologies like facial recognition means more players in the field, both domestically and internationally, which makes it more difficult to regulate their use. As AI grows, the ethical challenges embedded in the field — and the fact that 45% of new AI Ph.D.s are white, compared to just about 2% who are Black — will mean "there's a new frontier of potential privacy violations and other abuses," says Brynjolfsson.The AI Index found that while the field of AI ethics is growing, the interest level of big companies is still "disappointingly small," says Brynjolfsson.Details: Those growing pains are at play in one of the most exciting applications in AI today: massive text-generating models. Systems like OpenAI's GPT-3, released last year, swallow hundreds of billions of words along the way to producing original text that can be eerily human-like in its execution.Text-generating AI models could help polish human-written resumes for job search, but could also potentially be used to spam corporate competitors with realistic computer-generated applicants, not to mention warp our shared reality."What we increasingly have with these models is a double-edged sword," says Kristin Tynski, a co-founder and senior VP at Fractl, a data-driven marketing company.What to watch: The growing geopolitical AI competition between the U.S. and China.The National Security Commission on Artificial Intelligence warned in a major report this week that "China possesses the might, talent, and ambition to surpass the United States as the world’s leader in AI in the next decade if current trends do not change.""We don’t have to go to war with China," former Google CEO Eric Schmidt, who chaired the committee that authored the report, told my Axios colleague Ina Fried. "We do need to be competitive."Yes, but: While researchers in China publish the most AI papers, the U.S. still leads on quality, according to the Stanford survey.And while a majority of AI Ph.D.s in the U.S. are from abroad, more than 80% remain in the country when they take jobs — a sign of the lasting attraction of the U.S. tech sector.The bottom line: AI still has a long way to go, but the challenges the field faces are shifting from what it can do to what it should do.Like this article? Get more from Axios and subscribe to Axios Markets for free.

Are you an AI enthusiast who wants to keep abreast of the latest developments in space but do not know where to begin? This Mumbai-based computer science engineer may have an answer for you. With the number of papers and publications that are published each week growing exponentially, one of the biggest challenges for the AI and machine learning enthusiasts is to pick the papers that are trending in the space. There are very few dedicated platforms that host the archives of the technical papers and even fewer websites that surface and suggest top trending papers in AI, ML, computer vision and related domains. In fact, 42papers is one of a kind initiative that lets tech enthusiasts pick from the top trending papers.

Artificial Intelligence is one of the most rapidly growing fields in science and is one of the most sought skills of the past few years, commonly labeled as Data Science. The area has far-reaching applications, being usually divided by input type: text, audio, image, video, or graph; or by problem formulation: supervised, unsupervised, and reinforcement learning. Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. In this spirit, I present some reading suggestions to keep you updated on the latest and classic breakthroughs in AI and Data Science. Although most papers I listed deal with image and text, many of their concepts are fairly input agnostic and provide insight far beyond vision and language tasks. Alongside each suggestion, I listed some of the reasons I believe you should read (or re-read) the paper and added some further readings, in case you want to dive a bit deeper into a given subject. Before we begin, I would like to apologize to the Audio and Reinforcement Learning communities for not adding these subjects to the list, as I have only limited experience with both.