Computer Science and Engineering | University at Buffalo’s Post

NEW REPORT EXPLORES GLOBAL EFFORTS TO ACCELERATE AI ADOPTION IN SCIENCE AND RESEARCH Explore a groundbreaking report by the International Science Council's Center for Science Futures, titled 'Preparing National Research Ecosystems for AI: Strategies and Progress in 2024,' offering insights into the integration of artificial intelligence (AI) into science and research worldwide. Through comprehensive case studies and expert commentary, the report sheds light on the opportunities and challenges posed by AI adoption, emphasizing the importance of collective action and informed dialogue. https://lnkd.in/dJY3RGMy

Exploring the Opportunities of AI in Behavioral Science AI has transformed many fields while also opening up new opportunities. The intersection of AI with behavioral science was a matter of my profound curiosity, and I'm deeply appreciative of the research conducted by Stuart Mills, Samuel Costa, and Cass Sunstein in their paper "The Opportunities and Costs of AI in Behavioral Science." The Paper Identifies Three Key Opportunities: 1. Identifying Biases:  At the core of behavioral science are biases, predictable patterns or errors in human behavior. Modern AI, with its remarkable pattern detection capabilities, holds the promise of identifying biases, whether known cognitive biases in novel contexts or entirely new cognitive biases. AI has already been used to identify discriminatory biases, potentially bringing implicit biases in natural language to light. However, the potential of AI in identifying new pervasive errors in judgment and decision-making remains relatively uncharted, making it an area of compelling interest. 2. Integrating Heterogeneity: AI introduces the possibility of tailoring interventions to specific environments, individuals, and policy objectives. Its unique capacity to accommodate a wide spectrum of heterogeneity, both at the individual and contextual levels, allows for a holistic examination of each individual's unique personality profile. This includes considering individual-level variables and additional contextual factors like time and location. As a result, AI enables a departure from traditional one-size-fits-all strategies in behavioral interventions, paving the way for more personalized and effective approaches. 3. Handling Complexity AI provides us with the tools to comprehend complex and dynamic models of human behavior. This can be achieved through simulation models of human behavior in various settings or through data-intensive analyses that construct behavioral maps of decision landscapes. AI's ability to analyze vast datasets and dynamically detect patterns opens the door to mapping behavioral systems and identifying leverage points that enhance the effectiveness of behavioral interventions. As behavioral science evolves to address more complex behavioral challenges, the demand for strategies that can handle them is only growing. It's truly exciting to witness and potentially participate in these changes where AI emerges as a compelling approach, adding depth and complexity into this ever-evolving interdisciplinary field. #BehavioralScience #Innovation #ArtificialIntelligence

A comprehensive analysis of the integration of artificial intelligence in science and research across various countries addresses both the advancements made and the challenges faced in this field. While advancements in AI have huge implications for national R&D systems, very little is known about how governments plan to accelerate the uptake of AI by science and research institutions. In the report, "Preparing National Research Ecosystems for AI: Strategies and Progress in 2024," the International Science Council's Center for Science Futures addresses this knowledge gap by presenting a review of the existing literature on this topic, as well as a series of country case studies. This working paper provides new insights and resources from countries from all regions of the world, at various stages of integrating AI into their research ecosystems https://lnkd.in/d5KZuUJP

📝 Research is overwhelming. The sheer ammount of knowledge produced that needs to be filtered down and consumed can and will be very intense. Generative AI comes as a way to minimize this intensity, and not only improve a researcher efficiency, but make the research process more enjoyable. A few weeks back I presented a webinar on harnessing Generative AI Tools for Research. 120 people from more than 15 different nationalities tuned in, and with an average rating of 4.7 out of 5 (28 ratings), I can proudly say the webinar response was, and I quote: "overwhelmingly positive" 🎉 Below follows a slide where I mention a clear example where Generative AI is being employed to better understand the user and improve the search experience. 👇 You can watch the whole recording in Turacoz YouTube Channel: https://lnkd.in/dPtm_6DB Looking forward to more opportunities to share my opinion, knowledge and experience on how to use tools like askpaper.ai to succesfully employ Generative AI in fields of need, like research or education. Thank you the whole Turacoz Healthcare Solutions - A Medical Communications Company Team and Dr Namrata Namrata for letting this happen, always very professional and welcoming 🙌

Using AI to accelerate scientific progress is an absolutely extraordinay opportunity. Tom Hope is a global leader in the field, focusing all of his attention on the space. I have shared a couple of his fascinating papers here in the last months. I wanted to learn more, and share these insights with those who don't read many research papers. In this conversation we dive deep into the specific techniques he and his colleagues are developing and using across a range of aspects of scientific research. These include using analogiical reasoning to compare scientific domains, mapping problem spaces to identify possible solutions, and multi-agent research paper feedback. All of these are focused on augmenting the cognition of human scientists. These architectures can be applied across other complex Humans AI thinking processes. This is an episode who like diving deep, highly recommended!!

The team at Tsinghua University is thrilled to announce their latest publication titled "S3: Social-network Simulation System with Large Language Model-Empowered Agents" in arXiv. They have crafted an innovative system that integrates large language models to mimic human-like behaviour within social networks. Key Highlights of their research include: - The integration of AI to replicate emotions, attitudes, and interactions in social networks. - The use of real-world data to enable accurate simulation of social phenomena. - Advancements in the intersection of AI and social science research. This research represents a significant advancement in combining AI with social sciences, offering novel insights into complex social dynamics. The team is excited about the potential applications and the future developments this research may lead to. Paper: https://lnkd.in/ehQgfvGU #ComputationalSocialScience #SocialNetworkSimulation #SocialSciences

TRR 318 „Constructing Explainability“ will present a range of interesting results on the World Conference on eXplainable Artificial Intelligence (#XAI). Stay tuned!

The remarkable progress in artificial intelligence (AI) is transcending boundaries and finding invaluable applications across diverse academic disciplines. Recognizing the transformative potential of AI,  the Jantina Tammes School is proud to introduce the new theme, "Applied AI". More about the initiative here: https://lnkd.in/eAZauj_u

Tomorrow, the #NAMLeadershipConsortium will bring together health professionals, tech developers, and government stakeholders to discuss the transformative potential of generative AI in health and medicine and identify the key needs and policy implications to inform a collaborative framework. Register to learn more and attend the livestream: https://lnkd.in/d3VpM9ae

"A comprehensive analysis of the integration of artificial intelligence in science and research across various countries addresses both the advancements made and the challenges faced in this field. While advancements in AI have huge implications for national R&D systems, very little is known about how governments plan to accelerate the uptake of AI by science and research institutions. In the report, "Preparing National Research Ecosystems for AI: Strategies and Progress in 2024," the International Science Council's Center for Science Futures addresses this knowledge gap by presenting a review of the existing literature on this topic, as well as a series of country case studies." #ai #science #research