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AI evolution towards Singularity

AI evolution towards Singularity

DEFINITIONS
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ARTIFICIAL - made or produced by human beings rather than occurring naturally, especially as a copy of something natural.
INTELLIGENCE - is a faculty of the mind that implies learning, reasoning, understanding of the information perceived through the body sensory processes, vision (sight), audition (hearing), tactile stimulation (touch), olfaction (smell), and gustation (taste).

The 3 stages of Artificial Intelligence:
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1) ANI - Artificial Narrow Intelligence
This type of AI has a limited per process range of abilities, specifically designed for a narrow use. It is able to reach a level of performance of a human, and even better, but only within this limited field that is its specialty.

MindMap

2) AGI - Artificial General Intelligence
The next step after ANI that people are trying to achieve is Artificial General Intelligence (AGI), which would be good at a vast range of things, much more similar to human intelligence and not focused on specific tasks. It would be kind of similar to a human mind, and in theory it should be able to think and function like a human mind, being able to make sense of different content, understand issues and decide what is best in a complex situation. This is exactly why AGI hasn’t been achieved yet. We are not technically capable of producing something as complex yet, and we aren’t really sure how the human brain actually works either. AGI is a relatively logical and rational future though, and it could be attained at some point if humans develop their knowledge and understanding, as well as technical skills to a high enough level.

3) ASI - Artificial Super Intelligence also known as Singularity
When AGI is achieved and computers are able to learn independently at a very quick rate, and exponentially improve on their own without human intervention or help, the final step that AI could hypothetically reach is Artificial Super Intelligence (ASI). At this stage AI would be capable of vastly outperforming the best human brains in practically every field. The evolution from AGI to ASI would in theory be much faster than it is taking us to get from ANI to AGI right now, since AGI would allow computers to “think” and exponentially improve themselves once they are able to really learn from experience and by trial and error. If a transition to ASI ever happens, the exponential growth that is in theory expected to occur at this point is often called an “intelligence explosion”.

Artificial Intelligence as an analogy to the eukaryotic cell is the representation of an individual cybernetic cellular system in the same way as the DNA is the software of the biological life and Binary Code the universal language of computational systems.

Representation of the AI-Cell Layers
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Deep learning: is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

Neural networks: also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Machine learning: is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behaviour.

Impacts of AGI and ASI
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Economy: The economic consequences of artificial general intelligence arise from their fundamentally new properties compared to the human brains currently driving the economy. Once such digital minds become generally intelligent enough to perform a wide range of economic functions, they are likely to bring radical changes, creating great wealth, but also displacing humans out of more and more types of job. An important aspect of the question is that of economic growth. The invention of AGI or WBE could cause a sudden increase in growth by adding machine intelligence to the pool of human innovators. Machine intelligence could be much cheaper to produce, faster, and qualitatively smarter than human talent.

Cybersecurity: The rise of AI will drastically increase the surface of cyber threats, organise criminal  groups will make use of AI, cyber attacks will become more sophisticated and complex this will directly result in the need of developing Cognitive Cybersecurity defence systems.

References
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1) ANI, AGI and ASI - what do they mean?
2) What is a neural network?
3) Economic Consequences of AG
4) What is Cognitive Cybersecurity