AI meets blockchain: Revolutionizing smart contracts and cryptocurrency
#blockchaintechnology #blockchains #blockchainrevolution @TopCyberNews @SpirosMargaris @MarshaCollier @MHiesboeck
@MHcommunicate @Fisher85M @MikeQuindazzi @NealSchaffer
http://
ow.ly/AIgv30svHSI
MACHINE LEARNING
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AI Meets Blockchain: Revolutionizing Smart Contracts and Cryptocurrency
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How Businesses Can Manage User-Generated Content With AI
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How Businesses Can Manage User-Generated Content With AI
#AI #AIio #BigData #ML #NLU #Futureofwork @Kevin_Jackson @sallyeaves @schmarzo @kdnuggets @akwyz @BrettKing @TechNative @MiaD http://
ow.ly/6x3H30svHTy -
New Content Policy for Open Collaborative Responsible Machine Learning
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We are committed to advancing Open, Collaborative, and Responsible Machine Learning. Introducing our new Content Policy, dedicated to fostering an inclusive and supportive environment for our community. Check it out!
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Chatbot Arena: Benchmarking LLMs with Elo Ratings
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Chatbot Arena: Benchmarking LLMs in the Wild with Elo Ratings | LMSYS Org
https://bit.ly/3MG9wH4 #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Postdoc Position: Cloud Robotics and AWS/Google Cloud Systems
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Seeking Postdoc with experience in Robotics and Cloud-Based Systems (AWS, Google Cloud, Azure) to work closely with @UCBerkeley Profs. Ken Goldberg and Joey Gonzales on research on cloud robotics architectures and platforms such as FogROS2: https://
docs.google.com/document/d/e/2
PACX-1vR5n3aHbRl0luk3acq2xmf3HzmS6gJvEX7LC24c503PIT8mlWybxJiWAb3ZRVT2XKJWU62ztMvpQrqr/pub
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Focusing on the Future of Artificial Intelligence and Machine Learning
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Focusing on the future of artificial intelligence
#AI #AIio #BigData #ML #NLU #Futureofwork @nigelwalsh @pbouillaud @PatrickGunz_CH @pierrepinna @RebekahRadice @Ronald_vanLoon @sbmeunier
@Shirastweet http://
ow.ly/bhEu30svHUj -
Progressively Optimized Local Radiance Fields for Robust View Synthesis
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Progressively Optimized Local Radiance Fields for Robust View Synthesis
— AK (@_akhaliq) 15 juin 2023
paper page: https://t.co/9NUDJbvUtR
present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most… pic.twitter.com/Vt1dGq5hSWProgressively Optimized Local Radiance Fields for Robust View Synthesis paper page: https://
huggingface.co/papers/2303.13
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… present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most -
QLoRA, Prefix Tuning, and LoRA: Fine-tuning Techniques Comparison
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This was already a lot of coding. I am saving QLoRA for another day! @Yampeleg Regarding prefix tuning/LLaMA-Adapter vs LoRA. The performance is similar, but one advantage of the former is that it allows multimodal inputs (but that's also a post for another day :P)
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Proprietary Data and LLM Training Strategy for Enterprises
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A few topics: * Why proprietary data enables enterprises to build higher quality large language models * Should your organization fine-tune pre-trained models or train from scratch * What are the steps that need to be considered before training your first model
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Fine-tuning Limitations and Cross-lingual Transfer in Language Models
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Interesting point! Next to instruction-finetuning, finetuning is usually for a specific task (e.g., language translation). Not sure how well the model would do if you change source language. I.e., I haven't seen any studies investigating that, yet.