NLP is not NLU and GPT-3 - Walid Saba

#machinelearning This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic Kilcher speak with veteran NLU expert Dr. Walid Saba.  Walid is an old-school AI expert. He is a polymath, a neuroscientist, psychologist, linguist,  philosopher, statistician, and logician. He thinks the missing information problem and lack of a typed ontology is the key issue with NLU, not sample efficiency or generalisation. He is a big critic of the deep learning movement and BERTology. We also cover GPT-3 in some detail in today's session, covering Luciano Floridi's recent article "GPT‑3: Its Nature, Scope, Limits, and Consequences" and a commentary on the incredible power of GPT-3 to perform tasks with just a few examples including the Yann LeCun commentary on Facebook and Hackernews.  Time stamps on the YouTube version 0:00:00 Walid intro  00:05:03 Knowledge acquisition bottleneck  00:06:11 Language is ambiguous  00:07:41 Language is not learned  00:08:32 Language is a formal language  00:08:55 Learning from data doesn’t work   00:14:01 Intelligence  00:15:07 Lack of domain knowledge these days  00:16:37 Yannic Kilcher thuglife comment  00:17:57 Deep learning assault  00:20:07 The way we evaluate language models is flawed  00:20:47 Humans do type checking  00:23:02 Ontologic  00:25:48 Comments On GPT3  00:30:54 Yann lecun and reddit  00:33:57 Minds and machines - Luciano  00:35:55 Main show introduction  00:39:02 Walid introduces himself  00:40:20 science advances one funeral at a time  00:44:58 Deep learning obsession syndrome and inception  00:46:14 BERTology / empirical methods are not NLU  00:49:55 Pattern recognition vs domain reasoning, is the knowledge in the data  00:56:04 Natural language understanding is about decoding and not compression, it's not learnable.  01:01:46 Intelligence is about not needing infinite amounts of time  01:04:23 We need an explicit ontological structure to understand anything  01:06:40 Ontological concepts  01:09:38 Word embeddings  01:12:20 There is power in structure  01:15:16 Language models are not trained on pronoun disambiguation and resolving scopes  01:17:33 The information is not in the data  01:19:03 Can we generate these rules on the fly? Rules or data?  01:20:39 The missing data problem is key  01:21:19 Problem with empirical methods and lecunn reference  01:22:45 Comparison with meatspace (brains)  01:28:16 The knowledge graph game, is knowledge constructed or discovered  01:29:41 How small can this ontology of the world be?  01:33:08 Walids taxonomy of understanding  01:38:49 The trend seems to be, less rules is better not the othe way around?  01:40:30 Testing the latest NLP models with entailment  01:42:25 Problems with the way we evaluate NLP  01:44:10 Winograd Schema challenge  01:45:56 All you need to know now is how to build neural networks, lack of rigour in ML research  01:50:47 Is everything learnable  01:53:02  How should we elevate language systems?  01:54:04 10 big problems in language (missing information)  01:55:59 Multiple inheritance is wrong  01:58:19 Language is ambiguous  02:01:14 How big would our world ontology need to be?  02:05:49 How to learn more about NLU  02:09:10 AlphaGo  Walid's blog: https://medium.com/@ontologik LinkedIn: https://www.linkedin.com/in/walidsaba/