We're excited to announce that Xin Eric Wang has joined us on a full-time basis as Head of Research at Simular. ✨ As assistant professor of computer science and engineering at UC Santa Cruz, Eric has world-leading expertise spanning natural language processing, computer vision, and machine learning, with a focus on multimodal, generative, and embodied AI. Eric brings a wealth of experience from research positions at Google Research, Facebook AI Research(FAIR), Microsoft Research, and Adobe Research. He has also played significant roles in top-tier conferences, serving as Area Chair for ACL, NAACL, EMNLP, ICLR, and NeurIPS, and as a Senior Program Committee member for AAAI and IJCAI. Additionally, Eric has organized numerous workshops and tutorials on vision and language research at ACL, NAACL, CVPR, and ICCV. His contributions have been recognized with several prestigious awards, including the CVPR Best Student Paper Award, Google Research Faculty Award, and Amazon Alexa Prize Awards. We are thrilled to have Eric on board and look forward to his impactful research at Simular!
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Dear Researchers, We are pleased to announce that the Department of Applied Computational Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, India, is organising the 1st International Conference on Pervasive Computational Technologies (ICPCT-2025). This conference aims to bring together leading academic scientists, researchers, research scholars, and educators to exchange and share their experiences and research results in all areas of computing science, using deep learning and neural networks, natural language processing, machine learning algorithms, computer vision and image processing, artificial intelligence for autonomous systems, AI and emerging technologies, and the Internet of Things (IoT) with their theory, methodology, and applications. This conference will provide an interdisciplinary platform to present and discuss the most recent innovations, future trends, concerns, and the practical challenges encountered and solutions adopted in these technologies. Last date for Abstract Submission - 1st October 2024 Last date for full paper submission - 31st October 2024 Full Paper Acceptance Notification - 30th December 2024 Last Date for Registration- 20th January 2025 Registration and Submission of Camera Ready Paper: 25th January 2025 Conference Dates- 08th- 09th February 2025 Authors shall submit their full paper in the IEEE Proceedings format provided on the Conference website. Paper submission will be through Microsoft CMT. Link is given below: https://lnkd.in/gSTzXscA Conference Tracks: Track 1: Deep Learning and Neural Networks Track 2: Natural Language Processing (NLP) Track 3: Machine Learning Algorithms Track 4: Computer Vision and Image Processing Track 5: AI for Autonomous Systems Track 6: AI and Emerging Technologies Track 7: Internet of Things(IoT) Publications: Proceedings of ICPCT-2025 will be published in Scopus Indexed IEEE Proceedings after the peer review process. Please visit the conference website for more details: https://lnkd.in/gHxjK7cZ Copy of the Conference Brochure is also attached for your reference. For guidelines and further queries email at *icpct@glbitm.ac.in* We request you to circulate this call for papers to your research circle and academic community who may be benefited. Thanking You, Warm Regards Team ICPCT-2025
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We are very excited to be hosting our next guest speaker, Prof. Graham Neubig from CMU. His research focuses on natural language processing, with a particular interest in fundamentals, applications, and understanding of large language models. In this talk, he will discuss the progress towards automating machine learning engineering. Please tune in at 12:15 pm CT this coming Monday 01/22 to join us. See you there! #nlp #llm #llms #machinelearning #languagemodel Vanderbilt University Department of Computer Science Vanderbilt University Electrical and Computer Engineering Vanderbilt University School of Engineering Data Science Institute at Vanderbilt University Vanderbilt University
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The term artificial intelligence" was coined by John McCarthy, an American computer scientist, in 1955. He used the term in a paper proposal for a summer conference on the topic, which was held at Dartmouth College in 1956. The paper is considered to be the start of the field of artificial intelligence. McCarthy was one of the leading figures in the early days of AI research. He developed the Lisp programming language, which is still widely used in AI today. Over the following decades, AI research progressed in various domains, including problem-solving, natural language processing, and machine learning. Breakthroughs such as the development of expert systems, neural networks, and the availability of vast amounts of data fueled advancements in AI. Additionally, advancements in computing power and the rise of big data and cloud computing further accelerated AI's development. Today, AI has become an integral part of our lives, impacting various fields such as healthcare, finance, transportation, and more. McCarhthy’s legacy has just started to shape up our lives. Much more to come. #ai #artificialintelligence #McCarthy
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Dear Researchers, It gives us immense pleasure to announce that the *Department of Applied Computational Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, India*, is organising the 1st *International Conference on Pervasive Computational Technologies (ICPCT-2025)*. This conference aims to bring together leading academic scientists, researchers, research scholars, and educators to exchange and share their experiences and research results in all areas of computing science, using deep learning and neural networks, natural language processing, machine learning algorithms, computer vision and image processing, artificial intelligence for autonomous systems, AI and emerging technologies, and the Internet of Things (IoT) with their theory, methodology, and applications. This conference will provide an interdisciplinary platform to present and discuss the most recent innovations, future trends, and concerns, as well as the practical challenges encountered and solutions adopted in these technologies. *Last date for Abstract Submission - 1st October, 2024* *Last date for full paper submission - 31st October, 2024* *Full Paper Acceptance Notification - 30th December, 2024* *Last Date for Registration- 20th January, 2025* *Registration and Submission of Camera Ready Paper: 25th January, 2025* *Conference Dates- 08th- 09th February , 2025* Authors shall submit their full paper in the IEEE Proceedings format provided in the Conference website. Paper submission will be through Microsoft CMT. Link is given below: *https://lnkd.in/gbyRZ9Wk *Conference Tracks:* *Track 1: Deep Learning and Neural Networks* *Track 2: Natural Language Processing (NLP)* *Track 3: Machine Learning Algorithms* *Track 4: Computer Vision and Image Processing* *Track 5: AI for Autonomous Systems* *Track 6: AI and Emerging Technologies* *Track 7: Internet of Things(IoT)* Publications: *Proceedings of ICPCT-2025 will be published in Scopus Indexed IEEE Proceedings after the peer review process.* Please visit the conference website for more details: *https://lnkd.in/grj2-jZd Copy of Conference Brochure is also attached for your reference. For guidelines and further queries email at *icpct@glbitm.ac.in* We request you to circulate this call for papers to your research circle and academic community who may be benefited. Thanking You, Warm Regards Team ICPCT-2025
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Dear Researchers, It gives us immense pleasure to announce that the *Department of Applied Computational Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, India*, is organising the 1st *International Conference on Pervasive Computational Technologies (ICPCT-2025)*. This conference aims to bring together leading academic scientists, researchers, research scholars, and educators to exchange and share their experiences and research results in all areas of computing science, using deep learning and neural networks, natural language processing, machine learning algorithms, computer vision and image processing, artificial intelligence for autonomous systems, AI and emerging technologies, and the Internet of Things (IoT) with their theory, methodology, and applications. This conference will provide an interdisciplinary platform to present and discuss the most recent innovations, future trends, and concerns, as well as the practical challenges encountered and solutions adopted in these technologies. *Last date for Abstract Submission - 1st October, 2024* *Last date for full paper submission - 31st October, 2024* *Full Paper Acceptance Notification - 30th December, 2024* *Last Date for Registration- 20th January, 2025* *Registration and Submission of Camera Ready Paper: 25th January, 2025* *Conference Dates- 08th- 09th February , 2025* Authors shall submit their full paper in the IEEE Proceedings format provided in the Conference website. Paper submission will be through Microsoft CMT. Link is given below: *https://lnkd.in/gW9rmbk2 *Conference Tracks:* *Track 1: Deep Learning and Neural Networks* *Track 2: Natural Language Processing (NLP)* *Track 3: Machine Learning Algorithms* *Track 4: Computer Vision and Image Processing* *Track 5: AI for Autonomous Systems* *Track 6: AI and Emerging Technologies* *Track 7: Internet of Things(IoT)* Publications: *Proceedings of ICPCT-2025 will be published in Scopus Indexed IEEE Proceedings after the peer review process.* Please visit the conference website for more details: *https://lnkd.in/gCtN8iax We request you to circulate this call for papers to your research circle and academic community who may be benefited. Thanking You, Warm Regards Team ICPCT-2025
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Dear Researchers, It gives us immense pleasure to announce that the *Department of Applied Computational Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, India*, is organising the 1st *International Conference on Pervasive Computational Technologies (ICPCT-2025)*. This conference aims to bring together leading academic scientists, researchers, research scholars, and educators to exchange and share their experiences and research results in all areas of computing science, using deep learning and neural networks, natural language processing, machine learning algorithms, computer vision and image processing, artificial intelligence for autonomous systems, AI and emerging technologies, and the Internet of Things (IoT) with their theory, methodology, and applications. This conference will provide an interdisciplinary platform to present and discuss the most recent innovations, future trends, and concerns, as well as the practical challenges encountered and solutions adopted in these technologies. *Last date for Abstract Submission - 1st October, 2024* *Last date for full paper submission - 31st October, 2024* *Full Paper Acceptance Notification - 30th December, 2024* *Last Date for Registration- 20th January, 2025* *Registration and Submission of Camera Ready Paper: 25th January, 2025* *Conference Dates- 08th- 09th February , 2025* Authors shall submit their full paper in the IEEE Proceedings format provided in the Conference website. Paper submission will be through Microsoft CMT. Link is given below: *https://lnkd.in/gSTzXscA *Conference Tracks:* *Track 1: Deep Learning and Neural Networks* *Track 2: Natural Language Processing (NLP)* *Track 3: Machine Learning Algorithms* *Track 4: Computer Vision and Image Processing* *Track 5: AI for Autonomous Systems* *Track 6: AI and Emerging Technologies* *Track 7: Internet of Things(IoT)* Publications: *Proceedings of ICPCT-2025 will be published in Scopus Indexed IEEE Proceedings after the peer review process.* Please visit the conference website for more details: *https://lnkd.in/gHxjK7cZ Copy of Conference Brochure is also attached for your reference. For guidelines and further queries email at *icpct@glbitm.ac.in* We request you to circulate this call for papers to your research circle and academic community who may be benefited. Thanking You, Warm Regards Team ICPCT-2025
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Come and join us at our info events in Winterthur and Zürich to find out what MSc Studies could look like for you in the field of AI with us at the CAI! Registration is open until 27. November 2023. #AI #appliedai #mastersdegree #appliedscience
🎇 Interested in world-class MSc studies on Applied AI in the Zurich region? 🎇 Come to our info-lunch and discover the opportunities offered by a MSc of Engineering at the ZHAW Centre for Artificial Intelligence ! 🚀 When: Dec 7th 12:30 Zurich and Winterthur 🔭 Where: Zurich and Winterthur More info and registration: https://shorturl.at/afzCP #MSE #Master #Engineering #AI #AIethics #NLP #ComputerVision #MLOps #MSE #trustworthyAI #ExplainableAI #AIinnovation Thilo Stadelmann Mark Cieliebak Jasmina Bogojeska Frank-Peter Schilling Alisa Rupenyan Ricardo Chavarriaga Claude Lehmann Stefan Huschauer Daniel Neururer Benjamin Meier Gabriel Eyyi Pascal Sager Pius von Däniken @Francisco Ribera Laszkowski
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"In recent years, artificial intelligence technologies, especially machine learning algorithms, have made great strides. These technologies have enabled unprecedented efficiency in tasks such as image recognition, natural language generation and processing, and object detection, but such outstanding functionality requires substantial computational power as a foundation. Current computational resources are approaching their limit, so effectively reducing the training cost of machine learning models and improving their training efficiency is an important issue in the research field." #opticalneuralnetworks
Classical optical neural network exhibits 'quantum speedup'
phys.org
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I know Stanislav Fedotov and his team. The are one of the best teams working in the ML EdTech space, so check their new course, if you want to know more about generative AI.
Hi everyone! Last week, I shared some exciting news with you about my new job. This week, I'd like to delve deeper into the product I'm developing with our outstanding team, which boasts experts from Meta, AI21 Labs and Snapchat, as well as researchers from Technion, Weizmann Institute of Science, among others. Together, we've created a Generative AI program tailored for ML professionals, where you can delve into such topics as: – The internals of transformers and LLMs; – Practical applications of LLMs such as database interfaces; – AI Ethics and AI Safety basics, risks and limitations of generative models; – Techniques for generated data detection; – Generative model architectures for pictures and their applications in image enhancement and editing such as creating avatars from a handful of photos; – Model training and inference on Multi GPU servers; – And many more :) Some may find the theoretical aspects less captivating, so we've divided the program into two modules. In the first module we will focus on integrating generative models into applications making special emphasis on the questions of AI Ethics and Safety. Module two will deepen the understanding of anatomy of generative models including transformers, GANs, and diffusion models. We'll also cover diverse applications, like text-to-image transformations or code generation, and discuss techniques for efficient training and deployment of large neural networks. The entire program will be accessible online. Although the lectures are pre-recorded, students will engage directly with experts: both hometask graders and mentors guiding them through assignments and hands-on projects. We designed the program to accommodate those with full-time jobs, requiring within 10 hours of your time each week (and maybe less, depending on your experience with Python and Deep Learning). To participate in our program, you will need to undergo an online test. This will help both us and you determine if the program aligns with your skill-set. Our first cohort starts on October 23rd. If you are interested in Generative AI, you can apply to the program via our website: https://lnkd.in/dA3eEBrv.
Practical Generative AI with LLM | Online Course
ai-dt.school
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Facts, Data, Analyses, Philosophy, Semiologist, prof. Education. Threads expert -- NASA Frequent Flyer || This Account, my account, is PERSONAL and Exclusive
-The major break that occurred time before Sam Altman entered OpenAI. 'Alex #Krizhevsky didn’t get into the #AI business to change the course of history. Krizhevsky, born in #Ukraine but raised in #Canada, was just looking to delay getting a coding job when he reached out to Geoff #Hinton about doing a computer-science #PhD program in AI at the University of Toronto. The fateful moment was when, as a #graduate student, Krizhevsky and a fellow student named Ilya #Sutskever, decided to enter the ImageNet competition, a test for AI consisting of a huge database of online images. The competition, open to anyone in the world, was to evaluate algorithms designed for large-scale object detection and image classification. The point wasn’t just to crown a winner, but to test a #hypothesis: with the right algorithm, the massive amount data in the ImageNet database could be the key to unlocking AI’s potential. The two grad students, working with Hinton as an advisor, decided to enter the 2012 competition using a fringe idea: an artificial neural network #designed #by #Krizhevsky. The approach dominated the contest, beating every other research lab by a [huge?] 10.8% margin Thus, the current AI boom was born. #Google hired the #three researchers to seed a new, major projects using #neural #nets; the technology’s decision-making prowess soon put the words “deep learning” on the lips of every founder and Silicon Valley executive. Other tech companies like Facebook, Amazon, and Microsoft started positioning their businesses around the tech. A highly non-obvious solution Back in his grad school years, Krizhevsky was reading papers on an earlier algorithm invented by his advisor, #Hinton, called the “#restricted #Boltzmann #machine.” He had seen #graphics processing units (#GPUs) used with restricted Boltzmann machines, instead of central process units (CPU). He thought that if could use those GPUs on other kinds of neural networks with more layers (or, “deep neural networks”) he could ratchet up processing speeds of deep neural networks and create a better algorithm. The result was a neural network design to quickly beat other state-of-the-art benchmarks in algorithm #accuracy. Shortly #after that discovery, #in2011, Sutskever, another of Hinton’s grad students, learned about the ImageNet dataset. ... “[Krizhevsky] has an extremely deep understanding of [machine learning], and unlike many other researchers, he’s an engineer at heart,” says Sutskever, who is now director of research at OpenAI. “He has the ability to keep at a problem #until #it’s #solved.” Krizhevsky, who is soft-spoken and has never talked to the media before now, chuckles when recalling the weeks after the 2012 ImageNet results came out. “It became kind of surreal,” he says. “We started getting acquisition offers very quickly. Lots of emails.” 11/20/2023 (2)
The inside story of how AI got good enough to dominate Silicon Valley
qz.com
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