Just happened across this video: towards the end of 2017 – this clip from the recording of http://
fast.ai lesson 4 was the first time the effectiveness of a fine-tuned universal language model was demonstrated. (Our IMDB dataset result shown at the top.)
MACHINE LEARNING
-
First Fine-Tuned Universal Language Model Demonstrated 2017
By
–
-
TPUs Integration Challenges with PyTorch Framework
By
–
TPUs are still a bit of a pain (to say the least!) to get working with PyTorch, and PyTorch is the easiest way still to get stuff done, on the whole.
-
Deep Learning Revolution: Insider Perspective on AI Development
By
–
our intertwined scientific journeys towards the dream of building an intelligent machine and unleashing the deep learning revolution in 2012, the ten years between 2012 and 2022 where we saw the unfolding of the powerful AI technologies as insiders, and… 2/
-
Geoffrey Hinton discusses ImageNet AlexNet history at University of Toronto
By
–
My public conversation with @geoffreyhinton earlier today at @UofT covered a wide range of topics, moderated by our wonderful friend @JordanJacobs10 . We discussed the early history of ImageNet and AlexNet moment, 1/
-
Criticism of AI hype: preferring technical analysis over AGI claims
By
–
I don’t even mean result. I mean analysis. Interesting like the way it was interesting to see this stuff on word2vec or others. That’s all. Nothing about AGI, world models or whatever. But that they coat it in the latter makes me very much uninterested.
-
Nvidia’s AI Supremacy: Why It Won’t Last
By
–
Why Nvidia’s AI Supremacy is Only Temporary https://
bit.ly/44WvZFY #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Spatial Entity Embeddings: Historical Context Since 2017
By
–
The spatial locations thing has been around since at least the entity embeddings paper. IIRC that was around 2017 or so.
-
AI Startups ARR Milestones: TrustVanta, Census, Databricks, Gusto
By
–
→ @TrustVanta
: $500k ARR (9th employee)
→ @Census
: “mid-six figures” (4th employee)
→ @databricks and @GustoHQ
: "under $1m ARR" -
Spherical Deep Learning Outperforms Flat Space Models
By
–
Some well-rounded results: @GoogleResearch work shows that deep learning on a sphere — instead of flat space — is superior for things like prediction of weather & molecular properties.
— Jeff Dean (@JeffDean) 4 octobre 2023
Consider spherical surfaces (much better than pretending the world is flat!). See JAX code! https://t.co/8Q0msyJDcC pic.twitter.com/uGzb4piHYQSome well-rounded results: @GoogleResearch work shows that deep learning on a sphere — instead of flat space — is superior for things like prediction of weather & molecular properties. Consider spherical surfaces (much better than pretending the world is flat!). See JAX code!