AI-complete | Wikipedia audio article
Channel: wikipedia tts
Date: 13/01/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/AI-complete


00:01:16 1 History
00:01:55 2 AI-complete problems
00:03:01 2.1 Machine translation
00:04:47 3 Software brittleness
00:06:09 4 Formalization
00:08:08 4.1 Results
00:11:49 5 See also



Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.7022464606164619
Voice name: en-US-Wavenet-B


"I cannot teach anybody anything, I can only make them think."
- Socrates


SUMMARY
=======
In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI.  To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.  
AI-complete problems are hypothesised to include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.Currently, AI-complete problems cannot be solved with modern computer technology alone, but would also require human computation.  This property can be useful, for instance to test for the presence of humans as with CAPTCHAs, and for computer security to circumvent brute-force attacks.