2. Page no | 01
Content
1. Definition of AGI
2. AI vs AGI
3. Technologies used in the development of AGI
4. Ethics of AGI
5. Distinguishing Feature
6. Roadmap of creating AGI
7. Development in the field of AGI so far
8. Limitations
9. Jarvis – Ironman
10. Conclusion
11. References
4. Page no | 03
AGI
Machines Brain Emotions
“An artificial general intelligence (AGI) is a hypothetical intelligent agent which can understand or learn any
intellectual task that human beings or other animals can.”
5.
6. Page no | 05
VS
AGI
(Artificial General
Intelligence)
AI
(Artificial Intelligence)
7. Page no | 06
• AGI (Artificial General Intelligence) and AI (Artificial Intelligence) are both forms of artificial
intelligence, but they differ in their capabilities and limitations.
• The key difference between Artificial General Intelligence (AGI) and Artificial Intelligence (AI) is
their level of generalization.
• AI is designed to perform specific tasks, while AGI is designed to be versatile.
10. Page no | 09
Of
Artificial General Intelligence
11. Page no | 10
Fairness and Bias Trust and Transparency Accountability
Social Benefit Privacy and Security
12. Alberta Plan
for AI Research
1
• Emphasis on ordinary experience
2
• Temporal Uniformity
3
• Cognizance of Computational
Consideration
4
• Model Other Agents
Distinguishing
feature
14. Page no | 13
1. Representation I: Continual supervised learning with given features.
Find best algorithm
More fast, robust and continue over period of time.
Meta-learning
Normalization of features
2. Representation II: Supervised feature finding.
Create new feature
Adjust neurons to get the optimal result
Init, backdrop, utility, drop, re-init (meta-learning)
3. Prediction I: Continual Generalized Value Function (GVF) prediction learning.
Working with RL
How good a state is by the rewards it collected till now.
4. Control I: Continual actor-critic control.
We are not actually interacting with environment, we are just predicting what was happening
5. Prediction II: Average-reward GVF learning.
Maximize the average reward
15. Page no | 14
6. Control II: Continuing control problems.
Model free RL agent is ready
Combine and convert all the algorithm into continual learning
7. Planning I: Planning with average reward.
Maximizing average reward using model or finding the new model which are unsolved
8. Prototype-AI I: One-step model-based RL with continual function approximation.
Recursive update function
Predicting the next step, update based on certain visions
9. Planning II: Search control and exploration.
Search would be done
What we are missing till this date and fill the gaps
Priories things
10. Prototype-AI II: The STOMP Progression
Subtask, Option Model, and planning
Driverless car
11. Prototype-AI III: Oak.
Set of options ( a. fish, b. use computer)
12. Prototype-IA: Intelligence amplification.
Model should do things very well.
Agent to agent interaction
Multiplicative Scaling
16. Development in the
field of AGI so far
• ChatGPT by Open AI
• Brad by Google
• Sophia by Hanson Robotics
17. • Bard's error wiped $100bn (£82bn) off Google's
parent company Alphabet (GOOGL) as shares plunged
by 7.44% on Wednesday.
• OpenAI Chief Executive Officer Sam Altman said GPT-
4 was "most capable and aligned" with human values
and intent, though "it is still flawed." GPT-4 generally
lacks knowledge of events that occurred
after September 2021, when the vast majority of its
data was cut off. It also does not learn from experience.
• Sophia becomes very desirable, Sophia
robot comes with some face recognition capability
and a chatbot engine, but Sophia appears to either
deliver scripted answers to set questions or works in
simple chatbot mode where keywords trigger
language segments, sometimes inappropriate, and
sometimes there is just silence.
19. Conclusion
Is AGI really Possible ?
Well as of now, it seem difficult but it is possible as Elon
Musk has said in one of his interview that AGI is our future
and we can’t ignore it.
AGI main focus is to replicate human brain and do things
like human or even better but human’s brain is composed
of around 100 billion neurons and each neuron can
generate technically up to 5-50 messages each and
processes it at the same time.
It is very hard to replicate human brain and even if we
start to do it would require a large amount of
computational power and storage and even if simulated
brain is built it is much slower than actual ones.
But achieving true AGI would be the most defining
moment in the history of humankind and that moment is
closer each day.
