Superb #AI #Keynote ! #reInvent @SwamiSivasubram – #data is genesis for modern #invention based on:
Data Foundation
#Connectivity #literacy & #culture to democratize #accessibility for #creativity #ML @awscloud #partner #NLP #STEAM #code #DevOps #100DaysOfCode @rwang0
RESEARCH
-
Data Foundation Drives Modern AI Innovation Through Connectivity Literacy
By
–
-
Make with H2O: Kaggle Strategies and Data Science Q&A Session
By
–
Join us at 11AM PT / 2PM ET for a Make with http://
H2O.ai! Hear winning #kaggle competition strategies and ask data science questions! Register for today's session https://
h2o.ai/events/make-wi
th-h2o/?utm_campaign=Make-With-H2O&utm_source=twitter&utm_medium=Organic-Social
… -
CICERO AI Diplomacy Champion Discusses Simultaneous Planning and Conversation
By
–
💬 Making plans, conversing simultaneously: 3x Diplomacy World Champ @thegoffy and @adamlerer talk about CICERO’s ability to make plans & converse w/ other players concurrently during gameplay.
— AI at Meta (@AIatMeta) 30 novembre 2022
Watch the full video: https://t.co/Lk4R4xW0jj
Would you play #CICERObyMetaAI? pic.twitter.com/mD5gPigQF8Making plans, conversing simultaneously: 3x Diplomacy World Champ @thegoffy and @adamlerer talk about CICERO’s ability to make plans & converse w/ other players concurrently during gameplay. Watch the full video: https://
youtu.be/kexYmcu1Zro Would you play #CICERObyMetaAI? -
Sparse GPT-3 Models Achieve 3x FLOP Reduction on CS-2
By
–
We trained unstructured sparse 1.3B GPT-3 models on CS-2 systems and demonstrated how we achieve competitive results at a fraction of the inference FLOPs – our 83.8% sparse model achieved a 3x reduction in FLOPs at matching performance Learn more here:
-
Data as Genesis for Modern Innovation in Machine Learning
By
–
“Data is the genesis for modern innovation” says Swami Sivasubramanian, Vice President of AWS Data and Machine Learning at #AWS #reInvent
-
Google Sycamore Simulates Quantum Wormhole Dynamics
By
–
As part of a collaboration with researchers at Caltech, Harvard, MIT, and Fermilab, learn how we simulated a quantum theory on the Google Sycamore processor to probe the dynamics of a quantum system equivalent to a wormhole in a model of gravity → https://
goo.gle/3gLymZ0 -
Deep Learning Models vs Complete Systems: The Real Challenge
By
–
Building a Deep Learning model is only a fraction of the work. Building an entire Deep Learning System is much more complex and takes time. Closing this gap is precisely what @abacusai does.
-
Google Booth: Functional View of Generative Models Discussion
By
–
Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions! pic.twitter.com/8cwm4rJhMv
— Google AI (@GoogleAI) 30 novembre 2022Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions!
-
Max-Pooling as Spatial ReLU: A Reframing of Network Operations
By
–
I started out with an instinctive dislike of max-pooling in ML networks, favoring avg-pooling based on image filtering concepts. I only later realized that it isn’t really doing image processing, but rather acting as a “spatial ReLU”.
-
BLOOM: 176B Parameter Open-Access Multilingual Language Model
By
–
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model Le Scao et al.: https://
arxiv.org/abs/2211.05100 #ArtificialIntelligence #DeepLearning #MachineLearning