The tumultuous rise of artificial intelligence in the last few years, especially with the advent of fine-tuned LLM-based generative tools, is rapidly changing many of our daily activities. Some experts describe its potential impact on world economy as second only to the Industrial Revolution. Scientific research has already been leveraging AI's in many ways to help the researchers in their pursuit of knowledge. AI was used in genomics to analyze large-scale DNA sequencing data, to predict protein structures for drug discovery and biological research, or in climate science to foresee climate change patterns more accurately. But it would be naive to think that this is the only way in which AI is being used in scientific research. Some researchers are already using these tools to write abstracts (possibly even this one), or to help them write long repetitive introductions to their work. Is this behavior ethically acceptable? The debate is open: some argue that using AI to help in writing the more mundane parts of a paper is just "one step forward" with respect to using automated grammar-correction tools; others are more critical and think that using generative AI's is not appropriate for writing research papers. Of course, even assuming that the latter viewpoint is correct, making it actionable is difficult or impossible. We all know of the infamous case of a paper (published by Elsevier, and then retracted) starting as "Certainly, here is a possible introduction for your topic" [link to paper]. This example raises a second, even more critical, issue: not only is AI impacting on how research is being produced, but also on its evaluation mechanisms.

• Will AI put the final nail in the coffin of peer-reviewing?
• what alternatives do we have to ensure that the scientific community does not fall apart?
• How can we ensure that the results of scientific research are sound if we cannot rely anymore on peer reviewing?
• These questions are of course intertwined with the exponential increase in the production of scientific papers. Will science survive to AI?

Recent advances in data, computing, and communication technologies are showing unique opportunities for unprecedented societal transformation, while also exposing significant potential vulnerabilities. The CHIPS and Science Act laid out various priorities in science and technology areas, to draw attention to high priority R&D areas in a timely manner to address increasing challenges related to: ensuring prosperous and equitable society, national security and global leadership of US in science and technology. Several National or Federal Strategies (e.g., related to AI, Cybersecurity and Privacy, Microeconomics, etc.) or Executive Orders (e.g., related to AI, Digital Assets, etc.) indicate the US government’s recent focus on driving R&D in high priority and emerging areas. Various federal agencies have been playing the critical role of promoting and accelerating innovations in these areas and providing essential funding to support foundational, use-inspired and translational R&D, and workforce development efforts. This panel will explore and debate about the R&D priorities in data, computing and communications areas including AI, Cybersecurity and Privacy, NextG/6G, Quantum Computing, and other emerging technologies. The panelists will also share their thoughts on R&D directions various agencies are pursuing and/or helping promote/accelerate, and the funding landscape/ecosystem within the public sector. The panelists will also share their thoughts on how the public and private sectors should think ahead for future in terms of accelerating technological innovations while considering their societal impact. Here are some panel questions for your consideration:

• What are the key R&D priorities for your agency or program/programs, and how are you strategically addressing/promoting the related R&D efforts?
• The R&D efforts and funding in public and private sectors can vary significantly; for instance, academia may not have the resources to conduct research related to GAI/Large Language Models, or R&D that requires big datasets or infrastructures. How should the public sector address these issues and how should the public and private sectors work together to strengthen the R&D ecosystem to advance science and technology for good?
• The rise of LLMs/GAI has attracted unprecedented interests. What are your thoughts for research in next 5 or 10 years?
• What are the funding or collaboration opportunities in R&D that your agency provides for researchers and practitioners?
• How does your agency/program look at public-private as well as cross-border partnerships to promote R&D efforts including funding?

AI is significantly transforming our global society. While AI technologies have provided significant potential for increasing productivity, and to extract deep insights from the humungous amounts of data available to accelerate scientific discoveries and innovations, they can and have also been used for malicious purposes (e.g., generating malicious code, and deepfakes; or create and spread misinformation). The World Economic Forum 2024 Global Risks report lists “Misinformation and Disinformation”, “Cybersecurity” in top five Global Risks in the next two years, and the same two plus “Adverse Outcomes of AI Technologies” as top 10 Global Risks over the next 10 years. Further, while there are many unsolved security and privacy issues related to AI technologies, AI techniques themselves also form an increasingly popular set of approaches to address security and privacy issues in many areas (e.g., for detecting vulnerabilities in all components in a computing stack; intrusion detection, etc.). It is inevitable that we are in a cyber era or will be in one soon where AI techniques are being used by attackers with various malicious intents (e.g., from simple hackers to nation-state bent on influencing national level activities of targeted nations) capable of inflicting consequential societal harms, and the only way to defend against such AI-assisted attackers would likely be the AI-assisted defenders! In this panel, the panelists will debate and discuss about the future cyberspace where AI vs. AI is a key battle in terms of ensuring a safe, trustworthy and equitable society founded on the principles of freedom, human dignity and collective well-being. Some of the key panel questions are:

• How do you envision the [possibility of the] emerging AI vs. AI scenario? What are some destructive/doomsday scenarios that worries you, if any?
• What are the challenges AI as defenders face and how do we minimize the effectiveness of the AI use for malicious activities?
• What R&D priorities should be set to ensure the more positive result of the AI vs. AI battle? Are we doing enough – as a R&D community, as producers/developers/consumers of AI technologies, and at a national or global level?
• How do you see the private and public sectors contributing to alleviating or exacerbating the situation, if any? Private sector may be driven by the economic/business baseline, and intense competitions in its pursuit of AI technologies. How do you see such AI race in industry affecting the AI vs. AI landscape?
• How should governments pursue this issue? How do you see current efforts? What about public-private partnerships and multi-national partnerships, and what is their importance towards AI vs. AI future?

The Industrial Internet of Things (IIoT) is transforming systems in various domains leading to the fourth industrial revolution. This is manifested in cyber manufacturing incorporating virtual factories, smart energy grids and connected advanced metering infrastructure, and a new era for space technologies with large-scale satellite constellations. The increased reliance on digitization and connectivity is a cornerstone of IIoT creating both unique opportunities and unprecedent challenges. This cross-sector panel brings experts from different domains to discuss cybersecurity challenges in IIoT with special focus on manufacturing, supply chain and space systems.