<p>No. The reasons include but are not limited to:</p>
<ul>
<li><p>lack of understanding of how the brain works</p>
</li>
<li><p>current ANNs are mostly good at pattern recognition and generative   tasks, but lack the capacity to create abstractions on their own</p>
</li>
<li><p>we can't match the size/number of artificial neurons to the number of biological neurons</p>
</li>
<li><p>even with a much smaller ANN size, performance is an issue (i.e. state of the art image categorization ANNs have to be trained few weeks on multi GPU rigs to match human level).</p>
</li>
</ul>

CC BY-SA 3.0
2016-10-20T21:17:06.703Z
Both the question and answer might benefit from distinguishing between "possible someday" and "possible right now"
antlersoft
CC BY-SA 4.0
2016-10-20T18:54:22.163Z
2021-01-26T01:49:33.417Z
2021-01-26T01:49:33.417Z
Iliyan Bobev
<p>I read a lot about the structure of the human brain and artificial neural networks.</p>
<p>Is it possible to build an AGI (or human-level AI) with artificial neural networks on neuromorphic chips, which would be divided into centers, like the brain is, e.g. centers responsible for feelings, abstract thinking, speech, memory, etc.?</p>

<neural-networks><agi><neuromorphic-engineering><neuroscience><brain>
Is it possible to build an AGI with neural networks on neuromorphic chips?
Bartosz Królak
# Is it possible to build an AGI with neural networks on neuromorphic chips?

(Asked by: username_0 on 2016-10-20)

I read a lot about the structure of the human brain and artificial neural networks.

Is it possible to build an AGI (or human-level AI) with artificial neural networks on neuromorphic chips, which would be divided into centers, like the brain is, e.g. centers responsible for feelings, abstract thinking, speech, memory, etc.?

**Tags:** neural-networks, agi, neuromorphic-engineering, neuroscience, brain


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## 1 answers

### Answer by username_1 on 2016-10-20. Score: 2 | Accepted answer

No. The reasons include but are not limited to:

* lack of understanding of how the brain works
* current ANNs are mostly good at pattern recognition and generative tasks, but lack the capacity to create abstractions on their own
* we can't match the size/number of artificial neurons to the number of biological neurons
* even with a much smaller ANN size, performance is an issue (i.e. state of the art image categorization ANNs have to be trained few weeks on multi GPU rigs to match human level).

> **Comments:**
> **username_2**: Both the question and answer might benefit from distinguishing between "possible someday" and "possible right now"  

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