Pavankumar Mulgund’s Post

The fusion of artificial intelligence (AI) with scientific inquiry is not just a trend, but a paradigm shift, driven by the remarkable advancements in technology that characterize our era. The AI sector is experiencing exponential growth, transforming the landscape of research and development across various industries. This integration has become a cornerstone for innovation, enabling the exploration of uncharted territories in science and technology. Today, leading corporations and research institutions recognize AI's pivotal role in enhancing the efficiency and scope of scientific applications. The digitization of science, underpinned by vast data resources, has made AI an indispensable tool for navigating and exploiting this wealth of information. With AI, we can extract, analyze, and synthesize data on an unprecedented scale, uncovering new insights and opportunities for advancement. This transformative power of AI extends beyond mere data analysis. It is reshaping how we approach problem-solving, fostering a new era of discovery where data-driven insights lead to novel solutions and breakthroughs. As we stand on the brink of this new frontier, it's clear that leveraging AI is not an option but a necessity for those who wish to pioneer the future of scientific exploration and technological innovation.

Embark on an enlightening expedition with Kevin Dalmeijer, the virtuoso of AI Optimization. As the Senior Research Associate at Georgia Tech and Managing Director of the NSF AI4OPT - AI Institute for Advances in Optimization, Kevin unfolds the captivating world where artificial intelligence meets the art of efficiency. Imagine algorithms as brushes, and optimization as the canvas—Kevin's workshop paints a picture of innovation. AI for Optimization is more than just reducing costs or improving processes; it's a journey into the realm of intelligent decision-making. Join us as Kevin unveils the transformative potential of leveraging AI to streamline operations, enhance productivity, and unlock new possibilities. This workshop isn't just about understanding algorithms; it's about mastering the symphony of data, predicting patterns, and orchestrating solutions. Kevin's insights will empower you to not only grasp the essence of optimization but to wield it as a powerful tool in shaping a smarter, more efficient future. Ready to decode the language of AI-driven efficiency? Workshop Date: January 24, 2024 |11:30 AM EST| 10:30 PM BDT 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄: https://lnkd.in/gGne2NAZ

Goldman Sachs Research - Gen AI: Too Much Spend, Too Little Benefit? This was an interesting read. It explores the growing tension between massive investments in AI infrastructure and the limited tangible benefits observed so far. The document's key concerns are: The potential for an AI bubble The risk of overestimating current AI capabilities Challenges in scaling power infrastructure to meet AI's demands I've prepared a summary; the full report is in the link below.

I have been fortunate to be on the forefront of the fundamentally transformative technology - Generative AI with enterprises across industries . I could not think of a better way to sharpen my knowledge and skills than learning directly from one of the most prestigious universities - UC Berkeley - which has produced world-renowned AI leaders, pioneering AI research, and innovations.

What research are AI companies doing into safe AI development? What research might they do in the future? A new Institute for AI Policy and Strategy (IAPS) paper by Oscar Delaney 🔸 Oliver Guest and Zoe Williams addresses this. They find that enhancing human feedback, mechanistic interpretability, and robustness are key focuses of recently published research. Several topics have little published research, potentially because AI companies have weak incentives to research. These include 'model organisms of misalignment' and ‘safe by design’ approaches. Reach out to them if you are keen to discuss how to make the follow-up research more useful!

It’s likely that artificial intelligence has the potential to drive greater efficiency, but what long-term impact could AI have on global productivity? Here, https://lnkd.in/eAhtYkU2 , my colleague Irina Tytell examines the potential for AI to eventually drive higher productivity and higher profits within and beyond the technology ecosystem, including what we can learn from the impact of past tech advances, factors impacting AI adoption, and how AI may reshape the economic landscape for asset allocators and investors.

If you care about the implications of #GenerativeAI and its responsible use, then this is a must read academic paper - https://lnkd.in/eMZSece8) "The proliferation of #AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less." Related article - https://lnkd.in/efuScMMJ - "Scientists’ visions of AI often portray these tools as ‘superhuman’ partners capable of overcoming human limitations, especially with regard to objectivity and quantitative analysis. This focus on AI’s ability to provide simple, quantifiable explanations echoes our natural cognitive preferences, making these tools seem exceptionally trustworthy and masking the potential for illusions of understanding. Such illusions can erode the nuanced, qualitative assessments essential to scientific rigor, as AI’s drive towards reductive, quantitative outputs risks replacing genuine understanding of complex phenomena." #GenAI #EntArch #Trust #StochasticParrots

Leveraging AI in Industrial Research: Opportunities and Challenges At Harrison SPARC, we are committed to advancing industrial research with cutting edge technologies to create a better world. AI-driven solutions are reshaping the landscape and revolutionising how Harrison SPARC approach chemical research and innovation, leading to significant breakthroughs and efficiencies. AI’s potential in our research efforts is immense, enabling us to analyse complex data, predict outcomes, and accelerate discovery and technology transfer processes. By harnessing machine learning algorithms and advanced analytics, we can uncover new chemical compounds and optimise processes more rapidly than ever before. However, we must tread carefully, and it is essential to acknowledge the challenges that come with integrating AI into chemical research. Issues such as data quality, model interpretability, and ethical considerations must be carefully managed to ensure accurate and responsible use of AI technologies. As we continue to push the boundaries of innovation, our commitment to addressing these pitfalls remains a priority. Stay tuned as we navigate these exciting developments and strive to make a positive impact through our research. Discover more about our innovative approach https://bit.ly/4bxe7p0 #IndustrialResearch #Innovation #AI #MachineLearning #ChemicalResearch #TechForGood #Innovation

#AI #machinelearning for #industrial #research can uplift the future tech in various sectors. Harrison SPARC Pty Ltd is advancing the future tech in #watertreatment #energy #construction and #sustainability

https://lnkd.in/gWu43zZa. A recent Gartner report reveals a staggering 30% of generative AI (GenAI) projects are expected to be abandoned by 2025. This high rate of failure often stems from challenges like unclear objectives, inadequate data, and lack of integration with existing systems. Enter Zapata AI. With its cutting-edge AI solutions, Zapata AI offers a lifeline for these struggling projects. Our expertise in AI strategy, data management, and seamless integration can help organizations navigate the complexities of AI adoption and unlock the full potential of their investments. By addressing the core issues that lead to project abandonment, Zapata AI empowers businesses to transform stalled initiatives into successful, value-driven solutions. Let’s turn those abandoned AI projects into triumphs! Connect with Zapata AI to see how they can help drive your AI initiatives forward.