The 28-page strategy, called "Winning the Race: America's AI Action Plan," centers on three "pillars": accelerating AI innovation, building AI infrastructure in the U.S., and establishing the U.S. as a leader in AI globally. It recommends dozens of actions for the federal government to take across those pillars over the next few months, including reducing the number of environmental regulations imposed on data centers and only contracting with AI developers deemed free from "ideological bias." Trump has previously rolled back guardrails that President Biden had put on AI and made efforts to accelerate development of the technology since returning to the White House. Just hours after his inauguration, he rescinded an Executive Order Biden issued in 2023 aimed at establishing safety standards for AI's development and use. Days later, he signed an Executive Order focused on revoking "certain existing AI policies and directives that act as barriers to American AI innovation, clearing a path for the United States to act decisively to retain global leadership in artificial intelligence."

On Wednesday, the Trump Administration unveiled its new artificial intelligence action plan geared at keeping US efforts competitive with China. With over 90 policies recommended, it's a wide-ranging document that, if followed, would give Silicon Valley's most powerful companies even more leeway to grow. "We believe we're in an AI race," White House AI czar David Sacks said on a call ahead of the action plan's release. "We want the United States to win that race." The Office of Science and Technology Policy drafted the plan, which focuses on three key "pillars" for AI strategy: accelerating AI innovation, building infrastructure, and leading international diplomacy and security.

The current paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022, incorporating the ARDL methodology. The outcomes observe that AI innovation, renewable energy usage, and the digital economy reduce CO2 emissions, while GDP expansion and industrialization intensify ecosystem damage. Unit root tests (ADF, PP, and DF-GLS) reveal heterogeneous integration levels amongst components, ensuring robustness in the ARDL analysis. Complementary methods (FMOLS, DOLS, and CCR) validate the results, enhancing their reliability. Pairwise Granger causality assessments identify strong unidirectional connections within CO2 emissions and AI innovation, as well as the digital economy, underscoring their significant roles in ecological sustainability. This research highlights the requirement for strategic actions to nurture equitable growth, including advancements in AI technology, green energy adoption, and environmentally conscious industrial development, to improve environmental quality in the United States.

In the rapidly evolving field of artificial intelligence (AI), mapping innovation patterns and understanding effective technology transfer from research to applications are essential for economic growth. However, existing data infrastructures suffer from fragmentation, incomplete coverage, and insufficient evaluative capacity. Here, we present DeepInnovationAI, a comprehensive global dataset containing three structured files. DeepPatentAI.csv: Contains 2,356,204 patent records with 8 field-specific attributes. DeepDiveAI.csv: Encompasses 3,511,929 academic publications with 13 metadata fields. These two datasets leverage large language models, multilingual text analysis and dual-layer BERT classifiers to accurately identify AI-related content, while utilizing hypergraph analysis to create robust innovation metrics. Additionally, DeepCosineAI.csv: By applying semantic vector proximity analysis, this file presents approximately one hundred million calculated paper-patent similarity pairs to enhance understanding of how theoretical advancements translate into commercial technologies. DeepInnovationAI enables researchers, policymakers, and industry leaders to anticipate trends and identify collaboration opportunities. With extensive temporal and geographical scope, it supports detailed analysis of technological development patterns and international competition dynamics, establishing a foundation for modeling AI innovation and technology transfer processes.

This article examines how unequal access to AI innovation creates systemic challenges for developing countries. While developing nations contribute significantly to AI development through data annotation labor, they face limited access to advanced AI technologies and are increasingly caught between divergent regulatory approaches from democratic and authoritarian tendencies. I argue this challenge entails new institutional mechanisms for technology transfer and regulatory cooperation, while carefully balancing universal standards with local needs. In turn, good practices could help developing countries close the deepening gap of global technological divides, while ensuring responsible AI development in developing countries. However, instead of reasoning about this puzzle, current debates on AI development reflect an alarmist attitude, ranging from national security concerns to domestic commercial competition among billion-dollar tech startups. This stems from a race among political and commercial actors to be the first in the AI market. However, such acute competition can lead to critical unintended spillovers for developing countries, which lag behind in AI innovation. With their growing populations and economies, developing countries will need AI-enhanced tools in many sectors for their social infrastructure and services.

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.

Academia's greatest strength lies in its ability to pursue long-term research projects and fundamental studies that push the boundaries of knowledge. The freedom to explore and experiment with bold, cutting-edge theories will lead to discoveries and innovations that serve as the foundation for future innovation. While tools enabled by LFMs are in everybody's pocket, there are many questions that need to be answered about them, since they remain a "black box" in many ways. For example, we know AI models have a propensity to hallucinate, but we still don't fully understand why. Because they are insulated from market forces, universities can chart a future where AI truly benefits the many.

The US government issued new rules Thursday requiring more caution and transparency from federal agencies using artificial intelligence, saying they are needed to protect the public as AI rapidly advances. But the new policy also has provisions to encourage AI innovation in government agencies when the technology can be used for public good. The US hopes to emerge as an international leader with its new regime for government AI. Vice President Kamala Harris said during a news briefing ahead of the announcement that the administration plans for the policies to "serve as a model for global action." She said that the US "will continue to call on all nations to follow our lead and put the public interest first when it comes to government use of AI."

Microsoft recently unveiled yet another round of AI services that can help businesses accelerate AI production, whether by adding intelligence to existing applications and processes or creating new ones from scratch. Microsoft is also reimagining every aspect of their data centers to deliver the agility, power, scalability, and efficiencies AI workloads demand. Microsoft's pioneering performance for AI has ranked them as the number-one cloud in the Top500 List of the world's supercomputers and powered innovations like a new battery material. AI trailblazers are building and training the most sophisticated models in the world on Microsoft Azure AI infrastructure. Here are some of Microsoft's latest infrastructure advancements: Companies can experience Microsoft's latest AI services and technologies and learn how to power their AI transformation at the NVIDIA GTC AI Conference March 18 to 21 in San Jose, California (and virtually).

AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents. As the creator of PDF, the world's most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.