It gives an idea about various approaches to achieve Human Level Artificial Intelligence.

1. Dhole Patil College of Engineering
HUMAN LEVEL ARTIFICIAL
INTELLIGENCE
Presented by – Rahul Chaurasia
T.E Computer Science
Div - B
R.No – T120604282
(Guide – Prof. Manisha Singh)

2. Contents
1. Definition of Artificial Intelligence
2. Goals of Artificial Intelligence
3. Today's Artificial Intelligence
4. Future Artificial Intelligence
5. Obstacles
6. HUMAN LEVEL MACHINE INTELLIGENCE (HLMI)
7. Assessment of Intelligence
8. Brain vs Hardware of a system
9. Ways towards Human level Artificial Intelligence

3. What is Artificial Intelligence(AI)?
 John McCarthy, who coined the term in
1955, defines it as "the science and
engineering of making intelligent machines.

4.  Artificial intelligence (AI) is
the intelligence exhibited by machines or software.
It is basically a study of how to create computers
and computer software that are capable of
intelligent behavior. It is "the study and design of
intelligent agents", in which an intelligent agent is
a system that perceives its environment and takes
actions that maximize its chances of success.

5. GOALS
 Deduction, reasoning, problem solving
 Knowledge representation
 Planning
 Learning
 Natural language processing (communication)
 Perception
 Long-term goals
1) Social intelligence
2) Creativity
3) General intelligence

6. Today's AI(Narrow AI)
1. Siri( Speech Interpretation
and Recognition Interface)
2. Cortana
3. Self Driving cars
4. IBMs Watson
5. Autonomous Weapons
6. Facial Reorganization
7. Deep Blue - defeated the reigning world chess
champion Garry Kasparov in 1997
8. Proverb - solves crossword puzzles better than most
humans

7. Future AI(Human level AI)
 What are we looking for?
We are looking for a machine that can outperform
humans at multiple tasks and ideally at nearly every
task.

8. Ways towards human level AI
 Deep learning
 Symbolic Reasoning
 Brain Inspired Computing
 Structured Gel
 Quantum Weird stuff

9. Obstacles
 Common Sense
 Creativity

10. We will consider
1) Machine Learning
 Architecture of General Reinforcement Learning
 Deep reinforcement learning model
 Enhancing DRL with Predictive Model
2) Language Translation
3) Googles DeepDream

11. HUMAN LEVEL MACHINE INTELLIGENCE (HLMI)
KEY POINTS
Informally
 Human level machine intelligence = Machine with a
human brain
More concretely,
 A machine, M, has human level machine
intelligence if M has human-like capabilities to
Understand Converse
Learn Reason
Answer questions Remember
Organize Recall
Summarize

12. Machine Learning

13. Architecture of general
reinforcement learning

14. Explanation
 Agent performs action which influences the
environment.
 From the environment we get state
update(modifications to the state) and then we get a
certain type of reward.
 Its task is to learn a policy over an action that will
maximize the reward over time. It's a trial and error
learning.

15. Optical Action Component
 A component that finds an action that will maximize
the reward over time.

16. Compressive model/Predictive
model
 It is a predicted model. It predicts basically how the
world is going to carry on

17. Deep reinforcement learning
 Example. Deep minds DQN (Google bought it for 400
Million Pounds)
Q) What Deep Minds DQN does?
Ans) It learns to play video game. Only input the DQN
has is the pixels on the screen and the reward(score).
 It’s a reinforcement learning agent and it keeps on
trying different actions and improves the ability to play
the game better and better with time. And it does it by

19. Predictive Model

20. Deep reinforcement learning

21. Explanation
 We have the input to the agent which are the real
pixels on the screen.
 Convolution Neural Network(CNN) – It feeds on the
data to other layers of neurons that ultimately feeds
the data to the function Q*.
 Q* functions maps the action to their expected
rewards over time. Basically its gonna learn what the
best possible action can be to extract the best possible
reward in certain condition.

22. Conclusion
 So basically DQN helps the system to learn the game
from scratch and can get to a human level ability at
that game.
 Lets go back to the limitations on slide number 15.
 Lets check the solution to the limitations.

23. How to build the predictive model?

25. Explanation
 It is basically an augmentation of basic architecture
with a predictive model.
 Here Q* function doesn’t give the result directly but
rather also considers a predictive model which looks
ahead in time and predicts a result. Now we have two
results one is a basic result and other is the predicted
result. The best result is chosen and action is selected.

28. Explanation
 RNN – Recurrent Neural Networks.
 They are good at learning sequential data.
 Consider we are translating from English to French,
large data (English words) will be fed to the encoder
RNN. And this data will be paired with data(French
words) in Decoder RNN. It also predicts what the
future words are going to be based on the current or
prior words that’s why the name Thought Vectors.

31. Exercising the Imagination

34. Deep Dream
 DeepDream is a computer vision program created
by Google which uses a convolutional neural
network to find and enhance patterns in images
via algorithmic approach, thus creating a dreamlike
hallucinogenic appearance in the deliberately over-
processed images.

36. Using the Model
1. Look ahead for threats and opportunities
2. Rehearse actions and plans
3. Search a tree of possibilities
4. Explore novel recombination's of behavioral
repertoire.
5. Think and Imagine

37. ASSESSMENT OF INTELLIGENCE
 Every day experience in the use of automated
consumer service systems
 The Turing Test (Turing 1950)
 Machine IQ (MIQ) (Zadeh 1995)
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38. THE CONCEPT OF MIQ
 IQ and MIQ are not comprovable
 A machine may have superhuman intelligence in some
respects and subhuman intelligence in other respects.
Example: Google
 MIQ of a machine is relative to MIQ of other
machines in the same category, e.g., MIQ of Google
should be compared with MIQ of other search
engines.
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human machine
IQ MIQ

39. Can we build hardware as complex as the
brain?

40.  How complicated is our brain?
 a neuron, or nerve cell, is the basic information processing unit
 estimated to be on the order of 10 12 neurons in a human brain
 many more synapses (10 14) connecting these neurons
 cycle time: 10 -3 seconds (1 millisecond)
 How complex can we make computers?
 108 or more transistors per CPU
 supercomputer: hundreds of CPUs, 1012 bits of RAM
 cycle times: order of 10 - 9 seconds
 Conclusion
 YES: in the near future we can have computers with as many basic
processing elements as our brain, but with
 far fewer interconnections (wires or synapses) than the brain
 much faster updates than the brain
 but building hardware is very different from making a computer behave
like a brain!

41. References
 www.wikipedia.org
 www.youtube.com
 Prof. Murray Shanahan - Professor of Cognitive
Robotics in the Dept. of Computing at Imperial
College London, where he heads the Neuro dynamics
Group