Ankit Yadav

Explore the latest in AI research with 'Neural Interaction Energy for Multi-Agent Trajectory Prediction'. For a detailed summary, click the link: https://lnkd.in/gm2UyKhs #AI #TrajectoryPrediction #Research

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Qubits: Harnessing the Power of Quantum Machine Learning-An inter comparison with Classical Machine Learning From natural language processing to image recognition, Machine Learning has made a huge difference in many fields. Now what would happen if we added quantum computing into the weighing? In this blog, we will learn about, the difference between quantum machine learning (QML) and classical machine learning (CML), their strengths and limitations. Qubits: Harnessing the Power of Quantum Machine Learning-An inter comparison with Classical Machine Learning From natural language processing to image recognition, Machine Learning has made a huge difference in many fields. Now what would happen if we added quantum computing into the weighing? In this blog, we will learn about, the difference between quantum machine learning (QML) and classical machine learning (CML), their strengths and limitations. Or Classical Machine Learning (CML) Proven Track Record: Real world applications have tried, and tested CML It functions on the laws of classical physics in terms of binary bits (1s and 0s). Applications: CML is becoming more and widely used in finance, healthcare, recommendation systems etc. Which means the algorithms like linear regression, decision trees and neural networks belong to CML. Limitations: CML: Many tasks are slower than QML. This is a mitigation of the quantum advantage: problems that can only be efficiently solved on QC are not in CML. Conclusion In conclusion, QML has the potential to outperform classical ML algorithms, but it comes with limitations such as limited availability, high cost, and methodology complexity. CML, on the other hand, has been tried, tested, and proven to be useful across a wide range of applications. As quantum hardware improves, we’ll likely see more exciting developments in QML, bridging the gap between classical and quantum worlds. !Quantum Machine Learning #innovation #management #digitalmarketing #technology #creativity #futurism #startups #marketing #socialmedia #socialnetworking #motivation #personaldevelopment #jobinterviews #sustainability #personalbranding #education #productivity #travel #sales #socialentrepreneurship #fundraising #law #strategy #culture #fashion #business #networking #hiring #health #inspiration #flutter #dart #serverpod #devin_ai #AI #devin

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In this edition of Neural Notes, Australian industry experts weigh in on their 2025 AI predictions, from standards to the gender data gap.

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🌟 Explore the Cutting-Edge World of AI Innovations 🤖✨ Andrew Ng breaks down the game-changing opportunities in AI, shifting the focus to AI applications over just technology layers. Generative AI presents a transformative opportunity to enhance the development and performance of machine learning models, while simultaneously accelerating software development processes to align with the rapid advancements in artificial intelligence. Speed is key, but responsibility is crucial in AI progress. Discover the power of agentic AI workflows across diverse applications and delve into the four key design patterns for agentic reasoning workflows. Unveil the potential of the Vision Agent in processing video data and crafting metadata, opening doors to revolutionary visual AI applications. https://lnkd.in/enGz26FG #AI #MachineLearning #Innovation #VisualAI #FutureTech #ExploreNow Keep up to date with stories like this by subscribing to our newsletter - https://lnkd.in/eV7UWGs7

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【Prof Xie Haoran x MPhil student Rosie Ma: Lingnan AI teaching and learning🤖 】 How will AI develop in the next 5-10 years? Will it ultimately replace humans? 🧠How is Lingnan and the School of Data Science (SDS) nurturing students' AI literacy and fostering their research interests? What is the journey of Prof Xie Haoran, Acting Associate Dean of SDS, Director of LEO Dr David P. Chan Institute of Data Science, and Professor of the Division of Artificial Intelligence, in becoming an outstanding scholar in AI and big data? Check out the dialogue between Prof Xie and MPhil student Rosie Ma: bit.ly/3UgRgHS 【謝浩然教授 x MPhil 學生 Rosie：對談嶺大AI教與學🤖 】 AI在未來五至十年會如何發展？最終會否取代人類？🧠嶺南大學及數據科學學院如何培養學生的AI素養以及研究興趣？數據科學學院署理副院長、嶺南教育機構陳斌博士數據科學研究所總監、人工智能學部教授謝浩然教授如何成為AI和大數據的傑出學者？即看謝教授與MPhil學生Rosie的對話：bit.ly/3UgRgHS #LingnanUniversity #lingnan #liberalarts #AI #artificialintelligence #scientists #嶺南大學 #嶺大 #博雅教育 #人工智能 #人工智慧 #科學家

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Finding better entity embeddings, and capturing the most suited representation of an entity is an area of cutting-edge research. While working on improving the entity embeddings, here are the top areas/ideas one can think upon. Using a tremendous amount of data for training Using more sophisticated neural network architectures. Utilising the side information of the entity along with the context. A better Negative Sampling approach. Positive sample data is available but how does one create negative samples? And if negatives are too many how to better sample them? Utilising the available information on the sequence in which the entities interacted or were interacted with. And training sequence to sequence models around it. Here's a detailed video for Data Science enthusiasts I have created on the topic covering all the theory and a practical exercise training billions of rows to generate high-quality embeddings. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: youtu.be/DN4S96oHRhE YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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Finding better entity embeddings, and capturing the most suited representation of an entity is an area of cutting-edge research. While working on improving the entity embeddings, here are the top areas/ideas one can think upon. Using a tremendous amount of data for training Using more sophisticated neural network architectures. Utilising the side information of the entity along with the context. A better Negative Sampling approach. Positive sample data is available but how does one create negative samples? And if negatives are too many how to better sample them? Utilising the available information on the sequence in which the entities interacted or were interacted with. And training sequence to sequence models around it. Here's a detailed video for Data Science enthusiasts I have created on the topic covering all the theory and a practical exercise training billions of rows to generate high-quality embeddings. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: youtu.be/DN4S96oHRhE YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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We've got a hot take for you: LLMs are now more of a data engineering challenge than a data science one! In this talk from DataEngBytes Sydney 2023, Vinoaj (Vinny) Vijeyakumaar and Zak Sheikh dive into how data engineering teams can harness the power of LLMs using the Databricks Data+AI platform. Learn to leverage both fine-tuned and commercial LLMs for natural language querying and unstructured data. Watch the talk here: https://lnkd.in/gDQrdssh #deb2023 #dataengineering #learning

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Introduction to machine learning https://lnkd.in/g7kGJv4N

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This week in #TheBatch: 🎨 All about Claude’s Artifacts feature 🌱 AI growth hinders carbon emissions goals 🧠 GaLore, a memory-efficient method for fine-tuning and pretraining Plus: Andrew Ng shares his concerns about the proposed California regulation SB 1047. Read The Batch now: https://hubs.la/Q02G5YpM0

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This is the final profile for our 𝘞𝘰𝘮𝘦𝘯 𝘪𝘯 𝘈𝘐 series at DSI. Let’s hear from Fang Chen, Distinguished Professor, the visionary behind the 𝘞𝘰𝘮𝘦𝘯 𝘪𝘯 𝘈𝘐 LinkedIn activities, as she shares her inspiring journey in AI and groundbreaking contributions to AI. “As I wrap up the 𝘞𝘰𝘮𝘦𝘯 𝘧𝘰𝘳 𝘈𝘐 series at UTS Data Science Institute, I find myself reflecting on an incredible journey. My career began with astronomical and space research, leading to a PhD in AI, and since then, I’ve dedicated my life to advancing AI from blue-sky research to impactful products and solutions in industries and governments. Throughout this journey, I’ve collaborated with hundreds of industry and government partners across diverse sectors: water, transport, energy, agriculture, health, telecommunications, finance, retail, defence, real estate, and more. Together, we’ve crafted innovations and leveraged technology to improve the quality of life, safety, and security for citizens, while boosting companies' productivity and efficiency. My work has spanned the analysis of countless data types—from satellite imagery and IoT sensors to human physiological signals like EEG, ECG, and GSR; unstructured data such as speech and language; and environmental datasets like soil, weather, air quality, and transport data. This diversity has fueled my passion for curiosity-driven innovation, ensuring AI remains a tool for real, positive impact. From enhancing daily essentials like drinking water quality to predicting crop and livestock growth, resolving transport bottlenecks, and assessing emissions and climate risks, I have actively contributed to making our day-to-day life better, and also shaping digital transformation strategies and roadmaps for numerous companies and governments, driving both innovation and impactful change. From these achievements and alongside the many passionate individuals including all 𝘞𝘰𝘮𝘦𝘯 𝘪𝘯 𝘈𝘐 in our community, we are shaping a future where technologies truly transform the world for the better. Although this 𝘞𝘰𝘮𝘦𝘯 𝘪𝘯 𝘈𝘐 series is coming to a close, the journey of innovation continues. Let’s continue to support and celebrate the remarkable contributions of women who play a vital role in shaping the future.” #WomenInAI #ArtificialIntelligence #Innovation #TechnologyForGood

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Find List Of GPT Applications In - Theoretical Physics | Learn about the Impact of GPT and AI Technologies in Theoretical Physics (2024) go to https://lnkd.in/g9p8MMAc Find List Of GPT Applications In - Theoretical Physics What information this page has? Domain categories List of ChatGPTs Use cases FAQs Ethical challenges Future and more What Makes Comparesphere.com Unique? Comparesphere.com is not just another directory. It's a meticulously curated platform that tests thousands of GPT applications, ensuring quality and innovation across a wide range of categories. Our mission? To help you explore, find, and leverage the best GPT applications, all at your fingertips. comparesphere.com

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