Suggest edit — Machine Intelligence Research Frontier

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title: "Machine Intelligence Research Frontier"
visibility: public
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# Machine Intelligence Research Frontier

Category: [[machine-intelligence|Machine Intelligence]]

[Read the original document](https://docs.google.com/document/d/1G-ppYPhrAm82PqJWidx75EjvMDz8SRLvbC9QXuIq17k/edit?usp=drivesdk&sa=D&ust=1596495076461000&usg=AOvVaw2K-di8EbvAvT4bFLRQ2mPu)

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Let’s start with the intersection of the Brain, Deepmind, and OpenAI research frontiers, and follow up with the workshops at ICML, NeurIPS, ICLR, EMNLP and ACL for the last 2 years.
Later, I can add CVPR, AAAI.

* ICML 2019, ICML 2018
* NeurIPS 2019, NIPS 2018
* ICLR 2019, ICLR 2018
* EMNLP 2018
* ACL 2018
* ICML 2020
* Deepmind Research Overview
* Google Brain Research Overview
* Open AI Research Overview

1. Transfer Learning / Domain Adaptation
2. Tools, Environment & Datasets
3. Reinforcement Learning
   1. Model-based RL[a][b]
   2. Exploration in RL[c][d]
   3. Multi-Task Learning
   4. Imitation Learning +1
4. Safety[e]
5. Deep Learning
   1. Convolutional Neural Networks
   2. Sequence 39.Modeling
      1. Recurrent Neural Networks
      2. Attention
   3. Scalability and Speed
   4. Parallel and Distributed Learning
   5. Distillation / Compactness
6. Natural Language Processing & Understanding[f]
   1. Word, Phrase, Paragraph, Document Representation
   2. Semantics
   3. Multilingual Methods
   4. Information Extraction
7. Regularization
8. Multi-Modal[g]
9. Generative Models
   1. GANs
   2. VAEs
   3. Normalizing Flows
10. Variational Inference
11. Linear Models
12. Unsupervised Learning
   1. Clustering
   2. Dimensionality Reduction
   3. Autoencoders
13. Representation Learning
14. Memory
15. Multi-Agent Systems[h]
16. Metalearning
   1. Neural Programming[i]
   2. Hyperparameter Optimization
   3. Loss Function Learning[j]
17. Evolution
18. Game Theory
19. General Machine Learning
20. Theory
   1. Mean Field Theory
   2. Infinite Width NNs
   3. Probably Approximate Correctness
   4. VC Dimension
21. Neuroscience
22. Interpretability[k]
23. Adversarial Examples
24. Kernel Machines
25. Collaborative Filtering
26. Graphical / Relational Learning
27. Optimization
   1. Convex
   2. Non-Convex
28. Bandits
   1. Multi-Armed Bandit
29. Topological Data Analysis
30. Semi-Supervised Learning
31. Self-Supervised Learning
32. Learning to Rank & Structured Prediction
33. Feature Selection
34. Statistical Machine Learning
   1. Bayesian Machine Learning[l]
      1. Bayesian Deep Learning
      2. Bayesian Nonparametrics
   2. Statistical Processes
      1. Gaussian Processes
      2. Poisson Processes
   3. MCMC
   4. Uncertainty Estimation
   5. Distributional Shift Robustness[m][n]
35. Reasoning
36. Causal Inference[o]
37. Online Learning
38. Active Learning[p]
39. Continual Learning / Life-Long Learning
40. Time Series
41. Information Theory
42. Intuitive Physics
43. Privacy, Anonymity
44. Security[q]
45. Miscellaneous
46. Applications
   1. Speech Recognition
   2. Image Categorization
   3. Image Captioning
   4. Natural Language Understanding
      1. Machine Translation[r]
      2. Language Modeling / Generation
      3. Question Answering
      4. Summarization
      5. Search
      6. Parsing
   5. Pedestrian Detection
   6. Grasp Detection
   7. Go
   8. Video
   9. Dialogue
   10. 3D Object Reconstruction
   11. Speaker Verification
   12. Health Care
   13. Theorem Proving
   14. Music
   15. Pose Estimation
   16. Social Media
   17. Speech Generation
   18. System Design / Device Placement
   19. Fairness
   20. Super Resolution
   21. Chemistry
      1. Molecules and Materials
   22. Robotics
      1. Autonomous Vehicles
   23. Physics
   24. Games
   25. Art
48. New types of hardware: neuromorphic computing or other types could SIGNIFICANTLY speed up some types of AI, with unpredictable consequences

Application:
1. Question for each subfield:
2. “If I want to do _____, what concepts do I need to understand, what facts do I need to memorize, what procedures do I need to practice?”
3. Ranking subfields by a standard
   1. Impact on safety
   2. Impact on general intelligence

[a]+ Models of the environment and its dynamics leads to interpretability
[b]+1
[c]AIXI theoretical concerns on exploration: "to explore or not to explore" is a safety-related decision, and it's a fundamental uncertainty
[d]+1
[e]Of course
[f]+ Important for interfacing with the complexity of human goals, values
[g]Making ConvNets explain their decisions using natural language and RL agents speak about decision would improve their interpretability (->safety) and how all of them work
[h]Important for safety because many "human values" are derived from the need for cooperation in our evolutionary environment. Seems plausible that an AI trained in a multi-agent way would be easier to "control" or at least do positive-sum bargains with.
[i]- Program Induction / Source Code Rewrites are likely to lead to unpredictable systems
[j]+ Loss function learning / learned objective (for alignment with Human values)
[k]+1
[l]Will help with uncertainty
[m]Helpful for both inner optimizers, and using a NN to estimate a reward function
[n]+1, Uncertainty Estimation is very important for behaving in the real world safely
[o]+ Causal graphs of the environment, at the right level of abstraction, gives strong interpretability and controllability of the system
[p]Critical for safety - plausibly amount of supervision is the bottleneck for alignment, and active learning seems the most promising way to use supervision more effectively.
[q]Very important in short / medium term . Senior people I've talked to say this defending against adversaries (broader definition of security) is actually the #1 problem in ensuring recommender systems are good for users.
[r]+ Translation of decisions / models of the world from neuralese into human language

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*Source: [Original Google Doc](https://docs.google.com/document/d/1G-ppYPhrAm82PqJWidx75EjvMDz8SRLvbC9QXuIq17k/edit?usp=drivesdk&sa=D&ust=1596495076461000&usg=AOvVaw2K-di8EbvAvT4bFLRQ2mPu)*