Deep learning techniques have achieved great success in recent years, but still lack general intelligence. Several approaches may lead to artificial general intelligence (AGI): 1. Scaling up current deep learning methods like supervised learning using vast amounts of labeled data, or unsupervised learning with huge generative models. However, it is unclear if these narrow techniques can develop general intelligence without other changes. 2. Formal approaches like AIXI aim to mathematically define optimal intelligent behavior, but suffer from computational intractability in practice. 3. Brain simulation attempts to reverse engineer the human brain, but faces challenges in modeling its high-dimensional state and dynamics at an appropriate level of abstraction. 4. Artificial life

1. Where will AGI
come from?
Y Conf, June 10, 2017
andrej @karpathy

2. “Deep Learning” search popularity
2012
2012+ image recognition,
2010+ speech recognition,
2014+ machine translation,
etc.

4. (from @ML_Hipster)

6. CS231n: Convolutional Neural Networks for Visual Recognition
(Stanford Class)
2015: 150 students
2016: 330 students
2017: 750 students
2018: ??? (max students per class is capped at 999)

7. The Current State of Machine Intelligence 3.0 [Shivon Zilis]

8. In popular media...

9. 1. AI today is still very narrow*.
*2. but thanks to Deep Learning, we can
repurpose solution components faster.
Two comments:

10. Example: AlphaGo
(see my Medium post “AlphaGo, in context”)

11. Convenient properties of Go:
1. Deterministic. No noise in the game.
2. Fully observed. Each player has complete information.
3. Discrete action space. Finite number of actions possible.
4. Perfect simulator. The effect of any action is know exactly.
5. Short episodes. ~200 actions per game.
6. Clear + fast evaluation. According to Go rules.
7. Huge dataset available. Human vs human games.

12. Q: “Can we run AlphaGo on a robot for the Amazon
Picking Challenge”?

13. Q: “Can we run AlphaGo on a robot for the Amazon
Picking Challenge”?
A:

14. 1. Deterministic. No noise in the game.
2. Fully observed. Each player has complete information.
3. Discrete action space. Finite number of actions possible.
4. Perfect simulator. The effect of any action is know exactly.
5. Short episodes. ~200 actions per game.
6. Clear + fast evaluation. According to Go rules.
7. Huge dataset available. Human vs human games.

15. 1. Deterministic. No noise in the game.
2. Fully observed. Each player has complete information.
3. Discrete action space. Finite number of actions possible.
4. Perfect simulator. The effect of any action is know exactly.
5. Short episodes. ~200 actions per game.
6. Clear + fast evaluation. According to Go rules.
7. Huge dataset available. Human vs human games.
OK
OKish
OK
TROUBLE.
challenge
challenge
not good

16. Summary so far:
1. in interest in AI
2. AI is still
3. AI tech works in some cases and can
be repurposed much
(narrow)

17. “What if we succeed in making it not narrow?”
Nick Bostrom
Stephen Hawking
Bill Gates
Elon Musk
Sam Altman
Stuart Russell
Eliezer Yudkowsky
...
~2014+

18. Normal hype cycle

19. AI is different.

20. “AGI imminent.”
“Oh no, AI winter imminent.
My funding is about to dry
up again.”
Meanwhile, in Academia...

21. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

22. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

23. Supervised Learning:
Collect lots of labeled data, train a neural network on it.

24. How do we get labels of
intelligent behavior?

25. Short Story on AI: A Cognitive Discontinuity.
Nov 14, 2015
see:
link

26. Amazon Mechanical Turk
CORE IDEA: collect data from
humans, then train a big Neural
Net to mimic what humans do.

27. Amazon Mechanical Turk ++
SSH
lots of
train
data

28. Big
Neural
Network
STATE:
vision
audio
joint positions/velocities
TASK
description
ACTION:
joint torques, etc.
LABEL:
ACTION taken by
the human
OBJECTIVE:
Make these equal

29. Amazon Mechanical Turk ++
Step 2:
autonomy
Big
Neural
Network
STATE:
vision
audio
joint positions/velocities
TASK
description
ACTION:
joint torques, etc.

30. What would this AI look like?

31. Possible hint: char-rnn
The cat sat on a ma_?

32. Big
Neural
Network
STATE:
previous
characters
TASK
none
ACTION:
next character
LABEL:
next character by
human
OBJECTIVE:
Make these equal
Possible hint:
char-rnn

34. at first:
Generate text from the model

35. train for a bit
at first:

36. train more
train more
at first:
train for a bit

38. open source textbook on algebraic geometry
Latex source

40. The low-level gestalt is right, but the high-level,
long-term structure is missing. This is mitigated
with more data / larger models.

41. AIs in this approach…
- Imitate/generate human-like actions
- Can these AIs be creative?
- Can they assemble a room of chairs/tables?
- Can they make human domination schemes?

42. AIs in this approach…
- Imitate/generate human-like actions
- Can these AIs be creative?
- Can they assemble a room of chairs/tables?
- Can they make human domination schemes?
(Kind of)
(Yes)
(No.)

43. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

44. Unsupervised Learning: Big generative models.
1. Initialize a Big Neural Network
2. Train it to compress a huge amount of
data on the internet
3. ???
4. Profit

45. Example2: (variational) autoencoders
Also see:
Autoregressive models,
Generative Adversarial Networks,
etcetc.
identity function
Information bottleneck:
30 numbers.
(must compress the data to 30
numbers to reconstruct later)

46. Example2: (variational) autoencoders
Meddle with the code, then
“decode” to the image

47. Work at OpenAI: “Unsupervised Sentiment Neuron”
(Alec Radford et al.)
Another example:
1. Train a large char-rnn on a large corpus of unlabeled reviews from Amazon
2. One of the neurons automagically “discovers” a small sentiment classifier (this
high-level feature must help predict the next character)
(char-rnn also optimizes compression of data; prediction and compression are closely linked.)

48. Basic idea:
all of
internet
Big Neural Network
+compression
objective

49. What would this AI look like?
- The neural network has a powerful “brain state”:
- Given any input data, could get e.g. 10,000
numbers of the networks “thoughts” about
the data.
- Given any vector of 10,000 numbers, we
could maybe ask the network to generate
samples of data that correspond.
- Does it want to take over the world? (no; has no
agency, no planning, etc.)

50. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

51. AIXI
- Algorithmic information theory applied to general artificial
intelligence. (Marcus Hutter)
- Allows for a formal definition of “Universal Intelligence”
(Shane Legg)
- Bayesian Reinforcement Learning agent over the
hypothesis space of all Turing machines.

52. Turing machines
Prior probability:
“Simpler worlds” are more likely
P
Turing machines
Likelihood probability:
Which TMs are consistent with my
experience so far?
P
System identification: which Turing machine am I in? If I knew, I could plan perfectly.
Multiply vertically to get a posterior

53. We can write down the optimal agent’s action at time t:
(from http://www.vetta.org/documents/Machine_Super_Intelligence.pdf)
where

54. Complete history of
interactions up to this point
time t
time m
all possible future
action-state
sequences
Weighted average of the
total discounted reward,
across all possible
Turing Machines.
The weights are
[prior] x [likelihood] for
each Turing machine.
(description length of the
TM, number of bits)

56. There’s just a few problems...
!!! !!!
!!!!!!!!!!!!11

57. Attempts have been made...
I like “A Monte-Carlo AIXI Approximation” from Veness et al. 2011,
https://www.aaai.org/Papers/JAIR/Vol40/JAIR-4004.pdf

58. What would this agent look like?
- We need to feed it a reward signal. Might be very hard to write
down. Might lead to “perverse instantiations” (e.g. paper clip
maximizers etc.)
- Or maybe humans have a dial that gives the reward. But its
actions might not be fully observable to humans.
- Very computationally intractable. Also, people are really not
good at writing complex code. (e.g. for “AIXI approximation”).
- This agent could be quite scary. Definitely has agency.

59. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

60. Brain simulation
BRAIN initiative, Human Brain Project, optogenetics,
multi-electrode arrays, connectomics, NeuraLink, ...

61. Brain simulation
- How to measure a complete brain state?
- At what level of abstraction?
- How to model the dynamics?
- How do you simulate the “environment” to
feed into senses?
- Various ethical dilemmas
- Timescale-bearish neuroscientists.

62. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

63. How did intelligence arise in nature?

64. We don’t have to redo 4B years of evolution.
- Work at a higher level of abstraction. We don’t have to
simulate chemistry etc. to get intelligent networks.
- Intelligent design. We can meddle with the system and
initialize with RL agents, etc.

65. Intelligence is the ability to win, in the face of world dynamics
and a changing population of other intelligent agents with
similar goals.

66. ● attention. The at-will ability to selectively "filter out" parts of the input that is judged not to be relevant for a current top-down
goal. e.g. the "cocktail party effect".
● working memory: some structures/processes that temporarily store and manipulate information (7 +/- 2). Related to this,
phonological loop: a special part of working memory dedicated to storing a few seconds of sound (e.g. when you repeat a 7-digit
phone number in your mind to keep it in memory). also: the visuospatial sketchpad and an episodic buffer.
● long-term memory of quite a few suspected different types: procedural memory (e.g. driving a car), semantic memory (e.g. the
name of the current President), episodic memory (for autobiographical sequences of events, e.g. where one was during 9/11)
● knowledge representation; the ability to rapidly learn and incorporate facts into some "world model" that can be inferred over in
what looks to be approximately bayesian ways. the ability to detect and resolve contradictions, or propose experiments that
disambiguate cases. the ability to keep track of what source provided a piece of information and later down-weigh its confidence
if the source is suddenly judged not trust-worthy.
● spatial reasoning, some crude "game engine" model of a scene and its objects and attributes. All the complex biases we have
built in that only get properly revealed with optical illusions. Spatial memory: cells in the brain that keep track of the connectivity
of the world and do something like an automatic "SLAM", putting together a lot of information from different senses to position
the brain in the world.
● reasoning by analogy, eg applying a proverb such as "that’s locking the barn door after the horse has gone" to a current situation.
● emotions; heuristics that make our genes more likely to spread - e.g. frustration.
● a forward simulator, which lets us roll forward and consider abstract events and situations.
● various skill acquisition heuristics; practicing something repeatedly, including the abstract idea of "resetting" an experiment, or
deciding when an experiment is finished, or what its outcomes were. The heuristic inclination for "fun", experimentation, and
curiosity. The heuristic of empowerment, or the idea that it is better to take actions that leave more options available in the
future.
● consciousness / theory of mind: the understanding that other agents are like me but also slightly different in unknown ways.
Empathy (e.g. the cringy feeling when seeing someone else get hurt). Imitation learning, or the heuristic of paying attention to
and then later repeating what the other agents are doing.
Intelligence “Cognitive toolkit” includes but is not limited to:

67. Conclusion: we need to create environments that
incentivize the emergence of a cognitive toolkit.

68. Conclusion: we need to create environments that
incentivize the emergence of cognitive toolkit.
Incentives a lookup table of correct moves.
Doing it wrong:

69. Conclusion: we need to create environments that
incentivize the emergence of cognitive toolkit.
Doing it right:
Incentives a lookup table of correct moves.
Doing it wrong:
Incentivises cognitive tools.

70. Benefits of multi-agent environments:
- variety - the environment is parameterized by its agent
population, so an optimal strategy must be dynamically
derived, and cannot be statically “baked” as behaviors /
reflexes into a network.
- natural curriculum - the difficulty of the environment is
determined by the skill of the other agents.

71. Why? Trends.
Q: What about the optimization?
A: Optimize over the whole thing: the architecture, the
initialization, the learning rule.
Write very little (or none) explicit code.
(example small
tensorflow graph)

72. datasets
models
ImageNet
(~10^6 images)
Caltech 101
(~10^4 images)
(how large they are)
Google/FB
Images on the web
(~10^9+ images)
(how well they work)
Image Features
(SIFT etc., learning linear
classifiers on top)
ConvNets
(learn the features,
Structure hard-coded)
2013
2017
90s - 2012
CodeGen
(learn the weights
and the structure)
projection
Hard Coded
(edge detection etc.
no learning)
Lena
(10^0; single image)
70s - 90s
possibilityfrontier
Zone of “not going to happen.”
Pascal VOC
(~10^5 images)
In Computer Vision...

73. environments
agents
MuJoCo/ATARI
/Universe
(~few dozen envs)
Cartpole etc.
(and bandits, gridworld,
...few toy tasks)
(how much they measure / incentivise general intelligence)
more multi-agent / non-stationary / real-world-like.
(how impressive they are)
more learning.
more compute.
Value Iteration etc.
(~discrete MDPs, linear
function approximators)
DQN, PG
(deep nets, hard-coded
various tricks)
2013
2017
RL^2
(Learn the RL
algorithm.
structure fixed.)
90s - 2012
CodeGen
(learn structure and
learning algorithm)
projection
(simple multi-agent envs)
Digital worlds
(complex multi-agent envs)
Reality
Hard Coded
(LISP programs, no learning)
BlocksWorld
(SHRDLU etc)
70s - 90s
possibilityfrontier
Zone of “not going to happen.”
In Reinforcement Learning

74. With increasing computational resources, the trend
is towards more learning/optimization, and less
explicit design.
1970: One of Many explicit (LISP)
programs that made up SHRDLU.
50 years
“NEURAL ARCHITECTURE SEARCH WITH
REINFORCEMENT LEARNING”, Zoph & Le
Large-Scale Evolution of Image Classifiers

75. “Learning to Cooperate, Compete, and Communicate”
OpenAI blog post, 2017
- 4 red agents cooperate to
chase 2 green agents
- 2 green agents want to
reach blue “water”

76. What would this look like?
- Achieve completely uninterpretable “proto-AIs” first, similar
to simple animals, but with fairly complete cognitive toolkits.
- Evolved AIs are a synthetic species that lives among us.
- We will shape them to love humans, similar to how we
shaped dogs.
- “AI safety” will become a primarily empirical discipline, not a
mathematical one as it is today.
- Some might try to evolve bad AIs, equiv. to. combat dogs.
- We might have to make it illegal to evolve AI strains, or
upper bound the amount of computation per person and
closely track all computational resources on Earth.

77. Talk Outline:
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
- Artificial Life - “just do what nature did.”
- Something not on our radar
Where could AGI come from?

78. +
Data from very large VR MMORPG worlds?

79. Combination of some of the above?
- E.g. take the artificial life
approach, but allow agents to
access the high-level
representations of a big,
pre-trained generative model.

80. In order of promisingness:
- Artificial Life - “just do what nature did.”
- Something not on our radar
- Supervised learning - “it works, just scale up!”
- Unsupervised learning - “it will work, if we only scale up!”
- AIXI - “guys, I can write down optimal AI.”
- Brain simulation - “this will work one day, right?”
Conclusion

81. What do you think?
(Thank you!)
SL UL AIXI
BrainSim ALife Other
http://bit.ly/2r54rfe

82. Cool Related Pointers
Sebastian’s post, which inspired the title of this talk
http://www.nowozin.net/sebastian/blog/where-will-artificial-intelligence-come-from.html
Rodney Brooks paper
https://www.researchgate.net/publication/222486990_Intelligence_Without_Representation