Dr. Alessandra Sala

Worth a read. I like this quote: “Regulation helps us avoid mistakes and enables new benefits as we move into an uncertain future.”

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The AI Advisory Council for Ireland is looking for two more voluntary members with experience in AI economics and AI engineering. I'm mindful that these efforts are sometimes misguided. AI has been around for decades with few to none public advisory councils. Why now? Generative AI, ChatGPT, and frankly PR and lobbying by vested commercial interests. By raising the discussion to the level of nations vendors are co-opting people into legitimising generative AI. It can also entrench first movers and limit second movers. Vendors are stoking fears that generative AI may lead to AGI making us feel the need to be serious adults. This is a diversion from the rapacious business practices around LLMs which should be covered by existing common sense, regulation, and laws. https://lnkd.in/dDsYkqJP

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Like Rome’s mos maiorum, science relies on traditions of trust and scrutiny. But today, these pillars are under attack, threatening science and democracy. Forensic Scientometrics offers a way to safeguard integrity, ensuring that trust in research isn’t eroded.  As science faces growing global challenges, we must ask ourselves: What are we doing to uphold trust in science? Read more in my latest blog post. #TrustInScience Digital Science #ForensicScientometrics #FoSci #ResearchIntegrity

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From domain-specific models to mitigating data challenges using synthetic data and environmental cost of AI, our predictions keep you informed of and ahead of rapidly unfolding GenAI technology developments. Learn what’s behind these bold predictions — and how to prepare now. For an in-depth discussion on synthetic data market trends, feel free to schedule a Gartner inquiry with me to continue the conversation. #GenAI #DomainModels #SyntheticData #EmergingTechnology

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The latest report by BCG on AI implementations finds that majority (98%) of companies are experimenting with Gen AI, but only a minority (26%) have the capabilities to implement it at scale, and even a smaller subset (4%) have done so to achieve results. Which category is your organization in? Leaders in AI find value in both core business processes and support functions; they are more ambitious with AI, focusing on fewer, high-impact projects that promise higher returns. They invest strategically, integrating AI into both cost reduction and revenue-generating activities. The report stresses that success in AI relies more on investing in people and processes than on technology alone. Common obstacles include prioritizing AI initiatives, integrating AI with existing IT systems, and addressing workforce readiness. Source: https://lnkd.in/eRj4BuVf

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Emmet Dowling, Technology Consulting Partner and Head of Data and Artificial Intelligence, EY Ireland, discusses the many ways that AI can be used to power new discoveries through research. Find out more from Emmet in a special AI report in The Irish Times. Read more here - https://lnkd.in/eUqtX-42 #AI

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There is often an unspoken assumption by ML researchers that all we need to make progress and model every aspect of our world is data. We do not ascribe to this in my lab. In fact, we believe that data will never be enough. Our experiences with the nuances and complexities of scientific problems have informed us to the insufficiency of data to capture continuous physical processes, which afterall govern our biological and physical world. An example of this is this series of two papers led by my wonderful PhD student, Anowarul Kabir and advanced by a precious multi-year collaboration of my lab with Los Alamos National Lab: Anowarul Kabir, Manish Bhattarai, Kim Rasmussen, Amarda Shehu, Anny Usheva, Alan R Bishop, and Boian S Alexandrov. Examining DNA Breathing with pyDNA-EPBD. Bioinformatics 39(11):btad699, 2023. https://lnkd.in/gZaHE4hS Anowarul Kabir, Manish Bhattarai, Selma Peterson, Yonatan Najman-Licht, Kim Ø Rasmussen, Amarda Shehu, Alan R Bishop, Boian Alexandrov, Anny Usheva. DNA Breathing Integration with Deep learning Foundational Model Advances Genome-wide Binding Prediction of Human Transcription Factors. Nucleic Acids Research: gkae783, 2024. https://lnkd.in/gfTTqcPy Our goal: advance an exceptionally challenging problem in molecular biology, prediction of transcription factor binding sites. Our first step: capture the underlying physics that is missing in the data. Our second step: integrate that now with the data we have in a foundation model for predicting transcription factor binding sites. Performance improves. Most importantly, when we look at the sequence motifs that constitute "signatures" of what makes a transcription factor binding site, we obtain answers. All in the open, nothing opaque.

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Challenges and Ethical Considerations of Implementing Generative AI in Manufacturing

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Another demonstration of ScienceCast.org #AI enhancements of arXiv postings - free, simple interface https://lnkd.in/e_gXsF_9

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If you’re into LLMs, here’s a quick repost: Must-read papers for understanding LLMs. To add one more to the list: Attention Is All You Need deserves a spot for laying the foundation of modern transformers. https://lnkd.in/ggzr8s_K

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The Art Newspaper writes about the dilemma of museums how to work with artificial intelligence industry giants - on the one side generative AI is moving forward affecting the world of museums while institutions have to think through relevant risks https://lnkd.in/eBURwjSg. #artnewspaper #artificialintelligence #bigtech #generativeai #museums

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The more we know, the better. Please read, share, and activate. The trade offs are never in favor of those already in the margins including our planet. “We scrutinize the current scaling trends and trade-offs across multiple axes and refute two common assumptions underlying the 'bigger-is-better' AI paradigm: 1) that improved performance is a product of increased scale, and 2) that all interesting problems addressed by AI require large-scale models. Rather, we argue that this approach is not only fragile scientifically, but comes with undesirable consequences. First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint. Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate. Finally, it exacerbates a concentration of power, which centralizes decision-making in the hands of a few actors while threatening to disempower others”

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Amazon is making massive investments in Amazon Web Services (AWS) European Sovereign Cloud and data-center infrastructure across Germany, Spain and Italy. By investing in infrastructure and technology, the e-commerce giant is positioning itself as a key player in Europe’s digital transformation, potentially influencing market dynamics and competitive landscapes. These investments are not only strategic moves for Amazon but also have substantial economic implications, including job creation and regional development. Going forward, we expect continued advancements in AI and robotics to trigger a wave of investments from tech giants aiming to enhance their offerings and meet demand for advanced cloud services. #amazon #amazonwebservices #aws

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Today’s kick-off call with the EU AI Office for the drawing-up of the first General-Purpose AI Code of Practice highlighted the significant work that has already gone into, and still remains, to ensure we’re building #trustworthyAI and #responsibleAI. Cross-sector stakeholders coming together will be an important part of success here, and I’m honored to be included in the expert group assisting in this effort. https://lnkd.in/eK5fegz5

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Microsoft has announced the launch of new adapted AI models tailored to meet the unique needs of various industries. #datascience #AI #artificialintelligence https://hubs.li/Q02ZmqF10 If you enjoyed this post, follow me for more insights on AI and data science!

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This blog post by Linda Raftree outlines the basics and nuances of AI/Evaluation and the implications of generative AI on MERL (monitoring, evaluation, research, and learning) in a helpful way. https://lnkd.in/e6QeicMx

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#Performance of #LLMs. The adoption of #LLMs is rapidly growing, but the difficulty arises when trying to apply them beyond experimental settings. Deploying LLMs in an actual environment is complex, and one of the significant challenges is effectively evaluating their performance. Evaluation of LLMs can be based on different factors like toxicity, helpfulness, correctness and more. This article by Surya Prakash Pathak from Red Hat explores various evaluation techniques and metrics employed in assessing the performance of LLMs, with a particular emphasis on retrieval-augmented generation (#RAG) use cases. RAG, a pivotal use case for different enterprises, focuses on combining retrieval methods with the generation capabilities of LLMs. This unique approach involves not only generating content based on learned patterns but also leveraging pre-existing information through retrieval mechanisms. Scott Creeley :: Stephen Flaherty https://lnkd.in/gJTBagjv

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A Primer on the Inner Workings of Transformer-based Language Models Javier Ferrando, Gabriele Sarti, Arianna Bisazza, Marta R. Costa-jussà Abstract The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of Transformer-based language models, focusing on the generative decoder-only architecture. We conclude by presenting a comprehensive overview of the known internal mechanisms implemented by these models, uncovering connections across popular approaches and active research directions in this area. 👉 https://lnkd.in/dZz8f7xj #machinelearning

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