Celebrating the contributions of Joy Buolamwini, a leading computer scientist and #AI ethicist. Joy's work on using #AI to detect bias & promote accountability has been groundbreaking & is a shining example of what can be achieved with technology for good. #BlackHistoryMonth
RESEARCH
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SpeechT5 Deep Dive Workshop on Hugging Face Discord
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We're going to be taking SpeechT5 apart with @juancopi81 in the Hugging Face discord at 17:00CET. Come be a part of the fun! o/ https://
discord.com/events/8795489
62464493619/1074631980232228885
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AI Leaders Share Insights on ChatGPT and NLP Transformation
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Insights from 7 AI leaders reveal the tremendous potential and challenges of this cutting-edge technology. Learn how ChatGPT is transforming natural language processing and paving the way for exciting new applications: http://
bit.ly/3Eohz6W @karpathy @AndrewYNg @GaryMarcus -
Few Organizations Can Train 100B+ Parameter Models Estimate
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Definitely way less than 200. A wide spectrum on what it means to "train 100B+ parameter models". But I would estimate this number to be <50 optimistically.
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Instruction Tuning as a Form of Data Improvement
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Yeah, instruction tuning is kinda like "better data"
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Finetuning vs Emergence: Different Paths to Model Abilities
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Finetuning is a great way to bring an ability to a smaller model. Emergence often occurs for an ability that is *not* explicitly trained for, just by increasing scale (compute/ model size).
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Inductive Bias and Data Shape Emergence of AI Abilities
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Inductive bias, data and other changes does influence the point where emergent abilities emerge. We showed this in UL2R paper: https://
arxiv.org/abs/2210.11399 I also wrote a blogpost: -
AI’s potential to transform our understanding and experience of death
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Recent developments in #artificial #intelligence (#AI) suggest that it may be possible to change the way we think about and experience #death. Here, we explore some of the options that may be available to those who wish to avoid death.
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Programming Language Journey: Assembly to Python and Compiler Development
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1. Assembly (8080, later 68000, x86, …)
2. Basic
3. Pascal
4. Modula-2
5. C
6. Lisp/Scheme
7. Ada
8. Perl
9. Miranda
10. C++
11. Self
12. Cecil
13. Modula-3
14. Java
15. Javascript
16. Python
17. Go
Fun! (Written/worked on compilers or interpreters for 6, 10, 11, 12, 13, 14)