Where is AI research going
May 14, 2023 ◦ 2 min ◦
What I'm not currently not interested in doing
- renting GPUs
- training my own models
- collecting and cleaning training data
- trying to create data pipelines
- spending hours trying to build something, and not using the built thing
- doing the work of a data engineer
- creating a "data flywheel" or a self-maintaining system that can learns continually
Instead, I'm more interested in the below.
The concepts behind the models
- Watch Why Neural Networks can learn (almost) anything
- Watch But what is a neural network? | Chapter 1, Deep learning
- Watch From RNNs to GPT 4 - 10 years of NLP research explained in 50 concepts | Neural Breakdown
- Watch How ChatGPT Works Technically | ChatGPT Architecture
- Watch A Taxonomy of the Neural Network Zoo - Stefan Leijnan
- Watch Neural Network Architectures & Deep Learning
Where things in the field are going
- Watch CHATGPT + WOLFRAM - THE FUTURE OF AI!
- Watch Sparks of AGI: early experiments with GPT-4
- Watch videos on the @WeightsBiases channel
- Watch Filling the Gap in Large Language Models | Yann LeCun | Eye on AI #116
- Watch videos on the Eye on AI channel
- Read archived TheSequence newsletter posts
- Read State of AI Report Compute Index
How can I be a better user of AI, as opposed to how to become a creator or maintainer of AI systems
-
Try using prompts from
:ChatGPTActAs
What are expected near future business applications of machine learning models?
- Read about H20 AI Use cases
- Read or try writing emails generated by Flowrite
After watching the videos above, don't forget to write some notes to summarize the key ideas and specific topics/threads to look into more