2.Now, a lot of new AI making great progress, as a result of using the ELF weight and Sgfs to train their AI. As the data sources, what do you think about it?
This is exactly the purpose of open sourcing. AI should benefit the community and the world. Moreover, we would like to have a reliable and reproducible implementation of AlphaGo Zero, such that we can use if for other research and provide a baseline for researchers over the world to work on and improve it.
3. 选择只用20block的神经网络而不是更深的网络，主要的考虑因素是？
3. Why you choose only 20 block neural network instead of a deeper network? what is the main consideration about this choice.
Our primary consideration is that our work should be accessible to a wide audience. By starting with an inexpensive model architecture, we enable those with mass-consumer hardware to take advantage of ELF. In the future, we might release additional deeper models.
4. 传说ELF为什么在训练时选择输入黑棋白棋获胜各半的棋谱，这样的“平衡性”考虑是什么？
4.It is said that ELF select and input Sgfs with black /white win 50-50 when training. If this is true, what is this "balance" considering about?
This is by design to avoid overfitting to a local solution in the early stage of training, in which one side always wins. Such overfitting will prevent the model from improving.
5. The latest weight of the ELF shows that black have a higher winning chance at the start, which is is different to other AI's judgment. This judgement is a great surprise to us, can you talk about the reason?
这跟上一条紧密联系。
We think #4 is the main reason for this.
6. 在退役棋坛之后，ELF的研究成果会在其他方面得到应用吗？
6. After the retirement, how can the ELF's research results be applied in other ways?
ELF is a general reinforcement learning platform which implements some common baselines. We will do more research with ELF on other competitive/strategy games.
7. ELF相比其他AI，胜率的波动似乎更为激烈，这样的理由是什么？
7. compared with other AI, ELF's win rate change seems to be more fierce, what is the reason for this?
This will make the AI more sensitive to the quality differences of the moves, and choose the best one. In theory, the better the model, the more difference between different moves from the same situation.
A ladder represents an exact, fairly long sequence of moves, and the presence of one is globally relevant across the game board. However, MCTS is a randomized algorithm. Before the ladder occurs, the AI will consider moves from the whole board and only a small fraction of rollouts will go to the ladder branch. If the ladder situation is complicated, the rollouts might not consider a sufficiently long sequence of moves to detect the ladder. This is more obvious if the rollout number is low.
9. Three years ago, facebook's dark forests came on the stage but did not reach the expectation, and now after three years with the ELF, the team must be experienced a lot. Please introduce something about the development to us.
First of all, Darkforest and ELF Opengo is not a continuous project. In terms of scientific novelty, Darkforest has a higher impact. We started the project on Go AI in May 2015. At that time, nobody thought this would work. The result was even a bit embarrassing – Lian Xiao would win against the top AI with 6 handicaps, and most people thought it would take a few decades for AI to beat the top human. However, FAIR's open research strategies allowed us to start working on Go, and actually Darkforest is beyond our expectations. We published the paper in Nov 2015, and the AI was as strong as Zen, who took 10 years of team effort. It wasn't until Jan 2016 that AlphaGo came out. Afterwards, we decided to put aside this project due to resource limitations and research interest changes.
Since 2016, we have been working on reinforcement learning. We have won 2016 Doom AI (FPS) championship, worked on natural language-based house navigation, designed ELF platform for reinforcement learning, trained real-time strategy AI, and worked on deep learning theories. In Oct 2017 AlphaGo Zero paper came out and we considered it interesting that such a complicated game could be learned from scratch. It would be impactful to reproduce it scientifically. We started Opengo project in Jan 2018. Of course, Opengo reused a lot of code from Darkforest to be more effiecient.
FAIR is a research lab, without a dedicated engineering team to maintain the Go project. Our primary tasks are always focusing on new scientific discoveries and research breakthroughs. We do not try to get the best AI possible. This is also the motivation for open sourcing the project.
10. The name "ELF"（Extensive，LightWeight，Flexible) sounds interesting and shows the design concept. Can you explain the implementation of this product concpets in details?
ELF is a paper published at NIPS 2017. It was originally applied to RTS games, but it is a sufficiently general platform to handle diverse usecases such as Go. Here is the link to the paper: https://arxiv.org/pdf/1707.01067.pdf
Extensive: the platform supports rich dynamics such as imperfect information, long-term rewards, concurrency, and can simulate the real world.
Lightweight: the platform is optimized so that it can collect large number of experiences in a short time.
Flexible: the platform is easily customizable with a rich choice of environments. Moreover, easy manipulation of parameters and model architecture accelerates RL research.
