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The Problems With Artificial General Intelligence

We can’t describe what we don’t know.

Keno Leon
Keno Leon
Oct 28, 2020 · 12 min read

Preface

What do we currently have ?

Out of the elements I just mentioned only a few are inspired by how the brain works, ANNs for instance do use neurons and connections in between neurons and even synapse (in mathematically terms represented as tensors, matrices, vectors ), they also employ Machine Learning concepts that can be traced to biological memory research. But they employ a fraction of the current knowledge in Neuroscience and Biology, quickly deviate from them and these branches of science are far from being done discovering all there is to discover in their respective fields. One first big opportunity and challenge for the willing.

Tests & Tasks

The Turing test: In its simplest form consists of a text based conversational AI that emulates a human and if the evaluator cannot reliably tell the machine from a human the AI is said to have passed the test.
Anthropomorphism aside there are conditionals and sets of subroutines in conversational AIs that help the Ai achieve its task…
The terms Strong AI and Weak AI are related to how the AI achieves its task, a Strong AI can think and have a mind whereas a weak one just acts like it has one, to this date all our AIs are weak.

Intelligent behavior.

Some but not all Intelligent Elements:Reason, memory (learn and recall), solve problems, solve problems fast, use logic, be capable of language and communication, be able to create new things, express itself into the real world or environment, be able to perceive the environment, be conscious, be aware of itself and others, understand and have emotions, have common sense... 
A psychologist AI ?

What’s so General about AGI ?

Narrow AIs fall flat when asked about something else, if you give an image recognition AI a slightly different task like smell ( recognize odors ) it wont know what to do with the information given.
3 levels of generalization from narrow to multi domain, notice how more complex the information and capabilities required for the last AI are.

To emulate the brain or not ?

A more formal framework for the discovery/invention of AIs can be found in David Marr's (Vision) 3 Levels of analysis mainly computational theory, representation/algorithm and hardware implementation. If you add some of the other levels needed outside of pure research to create a finished product like current image classifiers, you might end up with something like the following illustration:

What needs to happen ?

The current narrow AIs  ( Mainly ML detectors and forecasters) can trace their origins to different periods, Neuroscience in the late 1800, AI in the 1960’s, technological advances in several other periods and on an on, it's sort of a cauldron with a mix of ingredients and every now and then something bubbles up, who knows, it could even be accelerating with the late inclusion of commerce and ample computing power.https://en.wikipedia.org/wiki/History_of_artificial_intelligence
Let's say we solve these starter problems, the next steps would be to build the higher brain areas and cognitive behaviors into software/hardware and we unfortunately go back to the neuroscience/computer science challenge of knowing what to code/build next.
You might have heard the following: The processing power needed to emulate the brain is insufficient but in the near future we will achieve it and presumably an AGI or brain analog.I hope that by now you are suspicious of the totality of such claims, but let's briefly talk about numbers. A simple organism ( the small worm C.elegans - about 1mm in width or the size of this point: . ) contains about 300 neurons and a few thousand cells. Recreating one virtually would need very achievable computing power but a lot more in research of the inner connections and work in building one, plus and environment.A human brain has about 85 billion neurons, 16 billion if we take out the cerebellum and focus on the cortex where the bulk of higher cognitive behavior is thought to reside; a modern cpu has about 7 billion transistors but that doesn't really help in comparison because well a transistor is not a neuron. To equate them we would need to add more functions to the transistor, memory is equally hard to measure since we store information in a fundamentally different way than computers, so the numbers game only takes us so far.Then there is the matter of purpose built hardware vs the common hardware denominator, if you are trying to build an AGI out of your average computer/webcam/microphone and software (let's say python or some C flavor), you will very quickly find it's like trying to fit a square peg in a round hole, for our general computing needs we went a completely different route than nature and trying to reconcile them is frustrating and challenging. Going the other route (custom hardware/software) might get us there faster but at a considerably larger research cost and might not be possible for a single person or small team, pick your poison, the first results might even be underwhelming in comparison to narrow AIs.

Final thoughts

A draft, also here be dragons everywhere.

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Keno Leon

Written by

Keno Leon

AI, Software Developer, Designer : www.k3no.com

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +800K followers.

Keno Leon

Written by

Keno Leon

AI, Software Developer, Designer : www.k3no.com

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +800K followers.

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