orrr....you could take the criticism on the chin and learn from it, and outshine next time round? Not the shit you want to hear now, right?
When it comes to systems design and specification, I'm globally competitive. I'd be happy to help show you a way over this small hurdle. If you have a star inside of you, respect it and learn how to make it shine. You owe it to your gift. Please just don't look in the mirror and become a victim. You'll end up like some bitching old dudes on this forum, forever victims. Dead stars. And if you do feel the need to victim yourself, keep mum about it. It's but a moment. It'll soon pass. Now's the time you need to dig deep and show some character, which is the real purpose of education, is it not? From: YKY (Yan King Yin, 甄景贤) <[email protected]> Sent: Monday, 17 June 2019 07:10 To: AGI Subject: Re: [agi] My AGI 2019 paper draft I am really disappointed that my AGI 2019 paper has been rejected. The reasons given by the reviewers are very superficial and vacuous, and given that I have posted my presentation slides here which explained the theory in very simple terms, and they have not given me a chance to explain any unclear details to them (it could be argued that they lack certain basic AGI background notions, and it's not my fault to omit them). Either the reviewers don't understand my ideas or they are biased by political reasons. Anyway, I will continue to publish my ideas through other channels, to the global community. See you around 😅 PS: my paper has an unconventional style which was *deliberate* to make it more understandable. ----------------------- REVIEW 1 --------------------- SCORE: 1 (Weak Accept) 1. The paper is not in the required format. 2. The paper only described what is included in the proposed model, but does not clearly explain how these parts work together as a complete system, nor that why the system can be taken as an AGI. 3. Not sure how "inductive bias" is implemented in the system? Is it "learned" by the system during the learning process, based on a control mechanism of the system, or pre-defined when handling different problems before the system starts learning? 4. "In principle, every state is potentially reachable from every other state, if a logic rule exists between them. Now we use a deep FFNN to represent the set of all logic rules." Theoretically speaking, yes, but are inference rules between two states are handled continuously or discretely? 5. How interestingness is defined within the system? Why some propositions have low interestingness and what is the purpose of having "forgetting mechanism" in the addressed model? 6. Experiments or use cases implemented by the addressed model is preferred. 7. References from Wikipedia is not encouraged. ----------------------- REVIEW 2 --------------------- SCORE: -1 (Weak Reject) There are a lot of promising statements in the abstract to the paper but there is absolutely no justification of this statements in the main text. Some of the ideas are looking plausible, but have no theoretical or experimental verification. Some of the stetements are simply not correct, e.g. statement about Turing completeness of RNNs (they really are but with some additional refinement). Some of the statements are self-containing and trivial. Also paper lacks of motivation for some of the presented models, e.g. 15, which should be a picture (isn't it?). And two more major drawbacks of the paper are its organization and appearance. ----------------------- REVIEW 3 --------------------- SCORE: -2 (Reject) This paper is about inductive learning in the framework of AGI. This paper does not contain any introduction and the reader is very quickly a bit lost and does not know how to read and understand it. The form of this paper should be entirely revised. Then the paper is not easy to read and to follow, as it proposes an unordered sequence of paragraphs with different topics and not necessarily related. At least, it is not indicated by the authors how we should read the paper and what is the objective which is followed. There is no much ore to say, this paper should be totally revised, on the form and the content, to be at least readable and then evaluated in good conditions. Artificial General Intelligence List<https://agi.topicbox.com/latest> / AGI / see discussions<https://agi.topicbox.com/groups/agi> + participants<https://agi.topicbox.com/groups/agi/members> + delivery options<https://agi.topicbox.com/groups/agi/subscription> Permalink<https://agi.topicbox.com/groups/agi/T3cad55ae5144b323-M3e49379ebd86a9f57f10499b> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T3cad55ae5144b323-M73b1f9d2dfd163171c44eeed Delivery options: https://agi.topicbox.com/groups/agi/subscription
