[Burkart]

What are your keywords for (the (practical) development of) AGI?
Mine (besides of "learning") is the "understanding" (of AI itself).

[LOCKSUIT]

attention types
recognition
thinking
node cost
flexibleness
generalness
compression
senses=actions
language=images
story telling
satisfaction
generative
predictor using past experience
reward transfer
links
big data
big model
dropout
revisement/update of nodes/friend connections
unsupervised learning
parallel sequences
hierarchy
heterarchy
feedback scientific method
Self-Attention in Transformers
self-regeneration
physics, self-organization
propagation of waves in hierarchies aligned nodes
clustering by k-means
entailment
translation
sumarization
segmentation
task CHANGE by energy
AGI=simple/efficient
re-use of hierarchies

Lots of these are the same design. It's pretty simple. You can kick but and help us refine it easily if it's explained clearly. I'm working on a big movie and will explain soon.

[HS]

Grooviness - AGI attempts to attune to the grooves of least resistance in its perception of the future, like temporal rivers. This perception of the future can take a lot into account, and the AGI can decide to do something difficult in the present to improve the future, minimizing overall resistance. People have found two principal methods of being temporally groovy, as demonstrated by the Taoists & Confucians. I guess the method or blend of methods used, would depend on the goals/disposition of the AGI in question.

[LOCKSUIT]

adaption
adjustment
update
revising
repair
replication
emergence
candidates
probability of entailment/translation

[goaty]

'applied' a.i.    as aposed to pipe dreaming a.i.

[LOCKSUIT]

'applied' a.i.    as aposed to pipe dreaming a.i.

[Burkart]

attention types
recognition
thinking
node cost
flexibleness
generalness
compression
senses=actions
language=images
story telling
satisfaction
generative
predictor using past experience
reward transfer
links
big data
big model
dropout
revisement/update of nodes/friend connections
unsupervised learning
parallel sequences
hierarchy
heterarchy
feedback scientific method
Self-Attention in Transformers
self-regeneration
physics, self-organization
propagation of waves in hierarchies aligned nodes
clustering by k-means
entailment
translation
sumarization
segmentation
task CHANGE by energy
AGI=simple/efficient
re-use of hierarchies

Lots of these are the same design. It's pretty simple. You can kick but and help us refine it easily if it's explained clearly. I'm working on a big movie and will explain soon.

What are your - let's say - three to five most important items? "Links" for example seems to be me too general.

[AndyGoode]

Terminator
Skynet
Cyberdyne Systems
Genisys
HK
phased plasma rifle
replicant
skin job
Voight-Kampff
Tyrell Corporation
Nexus
pleasure model
incept date
domination
annihilation
nuclear war
Colossus
Forbin Project
self-aware
mutual assured destruction
WOPR
NORAD
Global Thermonuclear War
HAL
heuristic
human error
expendable
holographic
TMA-1
AE-35
Daisy
 

[goaty]

I don't know if this is what you like, but ill do a bucket load list as well->   

nano-bots
motor driver
exact pattern matching
nearest neighbour matching
similarity matching
dfa
computer vision
Kinect sensor
karl sims evolved creatures
chess a.i.
linear search
brute force search
interpolation search

[Korrelan]

1. Neuromorphic
2. Connectome
3. Unsupervised
4. Self-organising
5. Pattern recognition

 

[LOCKSUIT]

More context is better however you do also need summarization, so here:

4:
lots of non-random data (else no hierarchy)
goals
revisement (learning)
prediction (attention (iterative regeneration))

All 4 are the same thing....the prediction adds nonrandom data to its learning and updates its goals. Endless feedback and it talks to itself in brain.



With AGI, you need to teach it knowledge (text and/or images), it needs to have goals, it needs to generate solutions, then store that new knowledge and learn, update goals.

With nonrandom data in the universe, and a certain physics destiny, everything is working on its own under predictable laws. When you have data that proves cat=dog, this too is predictable and if your device is the right one - it will model predictableness. The universe is heading towards equilibrium, which means you end up with a megastructure that repairs and resists change, unlike all the devastation in the cosmos. Such instant-regeneration stems from a globally-optimal haven that is made of smart cells, some general and some specialized, including debree like metal parts in between. Equilibrium is instant-self-regeneration. Repair/revisement is the keyword. You fix, add more data, learn. You do need real-world observations and real-world implementation of storyPlans you dream in your sim model. Your predictions help add more for the lack of real world data.

All of evolution emerged on its journey through which new soup resisted change. Breeding fast resists change. Being a table doesn't defend itself or remake itself or repair itself. The Earth resisted destruction of many deaths, the cell did, the human did. They did come together on their own, they literally attract together - mass, mates. The self-organizing/emerging universe state ends up resisting change, until then, change will happen dramatically. Less change will happen near the end, but by that point the instant-regeneration megastar will be nearly appeared.

And you thought exponential evolution was just cus it was smarter. Lol. You think - ah, cell phones, more better data faster, more humans... But now I pinpointed it.

[AndyGoode]

The universe is heading towards equilibrium, which means you end up with a megastructure that repairs and resists change, unlike all the devastation in the cosmos.

A good term that refers to this situation is 'negentropy' or 'negentropic'. Another one is 'extropy', or 'extropianism'. All these terms arise every so often in A.I. discussions.

https://en.wikipedia.org/wiki/Negentropy
https://en.wikipedia.org/wiki/Entropy_and_life
https://en.wikipedia.org/wiki/Extropianism

[LOCKSUIT]

I liked this part in particular:

"Entropy in psychology
The notion of entropy as disorder has been transferred from thermodynamics to psychology by Polish psychiatrist Antoni Kępiński, who admitted being inspired by Erwin Schrödinger.[15] In his theoretical framework devised to explain mental disorders (the information metabolism theory), the difference between living organisms and other systems was explained as the ability to maintain order. Contrary to inanimate matter, organisms maintain the particular order of their bodily structures and inner worlds which they impose onto their surroundings and forward to new generations. The life of an organism or the species ceases as soon as it loses that ability.[16] Maintaining of that order requires continual exchange of information between the organism and its surroundings. In higher organisms, information is acquired mainly through receptors and metabolised in the nervous system. The result is action - some form of motion, for example locomotion, speech, internal motion of organs, secretion of hormones etc. The reaction of organism becomes an informational signal to other organisms. Information metabolism, which allows to maintain the order, is possible only if a hierarchy of value exists, as the signals coming to the organism must be structured. In humans that hierarchy has three levels i.e. biological, emotional and sociocultural.[17] Kępiński explained how various mental disorders are caused by distortions of that hierarchy and the return to mental health is possible through its restoration.[18]

The idea was continued by Struzik who proposed that Kępiński's information metabolism theory may be seen as an extension of Brilluoin's negentropy principle of information.[19] In 2011 the notion of psychological entropy has been reintroduced to psychologists by Hirsh et al.[20] Similarly to Kępiński, these authors noted that uncertainty management is a critical ability for any organism. Uncertainty, arising due to the conflict between competing perceptual and behavioral affordances, is experienced subjectively as anxiety. Hirsh and his collaborators proposed that both the perceptual and behavioral domains may be conceptualized as probability distributions and that the amount of uncertainty associated with a given perceptual or behavioral experience can be quantified in terms of Claude Shannon’s entropy formula."

[yotamarker]

algorithm mutation
algorithm fusion

[LOCKSUIT]

no, idea mutation and fusion!!

idea=algorithm