RIP. If it's any consolation, it sounds like the list is at least three years old by now. Which is a long time considering that 2016 is generally regarded as the date of the deep learning revolution.
I would've hoped he'd be exploring weirder alternatives off the beaten path. I mean, neural networks might not even be necessary for AGI, but no one at OpenAI is going to tell Carmack that.
AlexNet (2012) VGGNet (2014) ResNet (2015) GoogleNet (2015) Transformer (2017) Reinforcement Learning:
Q-Learning (Watkins & Dayan, 1992) SARSA (R. S. Sutton & Barto, 1998) DQN (Mnih et al., 2013) A3C (Mnih et al., 2016) PPO (Schulman et al., 2017) Natural Language Processing:
Word2Vec (Mikolov et al., 2013) GLUE (Wang et al., 2018) ELMo (Peters et al., 2018) GPT (Radford et al., 2018) BERT (Devlin et al., 2019)
But AGI is one of those very ambiguous terms. For many people it's either an exact digital replica of human behavior that is alive, or something like a God. I think it should also apply to general purpose AI that can do most human tasks in a strictly guided way, although not have other characteristics of humans or animals. For that I think it can be built on advanced multimodal transformer-based architectures.
For the other stuff, it's worth giving a passing glance to the fairly extensive amount of research that has been labeled AGI over the last decade or so. It's not really mainstream except maybe the last couple of years because really forward looking people tend to be marginalized including in academia.
Looking forward, my expectation is that things like memristors or other compute-in-memory will become very popular within say 2-5 years (obviously total speculation since there are no products yet that I know of) and they will be vastly more efficient and powerful especially for AI. And there will be algorithms for general purpose AI possibly inspired by transformers or AGI research but tailored to the new particular compute-in-memory systems.
Strikes me as the kind of thing where that last 10% will need 400 papers
On models: Obviously, almost everything is Transformer nowadays (Attention is all you need paper). However, I think to get into the field, to get a good overview, you should also look a bit beyond the Transformer. E.g. RNNs/LSTMs are still a must learn, even though Transformers might be better in many tasks. And then all those memory-augmented models, e.g. Neural Turing Machine and follow-ups, are important too.
It also helps to know different architectures, such as just language models (GPT), attention-based encoder-decoder (e.g. original Transformer), but then also CTC, hybrid HMM-NN, transducers (RNN-T).
Some self-promotion: I think my Phd thesis does a good job on giving an overview on this: https://www-i6.informatik.rwth-aachen.de/publications/downlo...
Diffusion models is also another recent different kind of model.
Then, a separate topic is the training aspect. Most papers do supervised training, using cross entropy loss to the ground-truth target. However, there are many others:
There is CLIP to combine text and image modalities.
There is the whole field on unsupervised or self-supervised training methods. Language model training (next label prediction) is one example, but there are others.
And then there is the big field on reinforcement learning, which is probably also quite relevant for AGI.
Unlocking the Secrets of AI: A Journey through the Foundational Papers by @vrungta (2023)
1. "Attention is All You Need" (2017) - https://arxiv.org/abs/1706.03762 (Google Brain) 2. "Generative Adversarial Networks" (2014) - https://arxiv.org/abs/1406.2661 (University of Montreal) 3. "Dynamic Routing Between Capsules" (2017) - https://arxiv.org/abs/1710.09829 (Google Brain) 4. "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" (2016) - https://arxiv.org/abs/1511.06434 (University of Montreal) 5. "ImageNet Classification with Deep Convolutional Neural Networks" (2012) - https://papers.nips.cc/paper/4824-imagenet-classification-wi... (University of Toronto) 6. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (2018) - https://arxiv.org/abs/1810.04805 (Google) 7. "RoBERTa: A Robustly Optimized BERT Pretraining Approach" (2019) - https://arxiv.org/abs/1907.11692 (Facebook AI) 8. "ELMo: Deep contextualized word representations" (2018) - https://arxiv.org/abs/1802.05365 (Allen Institute for Artificial Intelligence) 9. "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" (2019) - https://arxiv.org/abs/1901.02860 (Google AI Language) 10. "XLNet: Generalized Autoregressive Pretraining for Language Understanding" (2019) - https://arxiv.org/abs/1906.08237 (Google AI Language) 11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research) 12. "Language Models are Few-Shot Learners" (2021) - https://arxiv.org/abs/2005.14165 (OpenAI)
John Carmack’s ‘Different Path’ to Artificial General Intelligence - https://news.ycombinator.com/item?id=34637650 - Feb 2023 (402 comments)