I think there is a big problem in how people view AI. It's not artificial or intelligence. It is a powerful "structure" or tool for pattern discovery. A good re

I think there is a big problem in how people view AI. It's not artificial or intelligence. It is a powerful "structure" or tool for pattern discovery. A good read might be a critique of recent advances within natural language processing. The Guardian even published an article "written by AI" but all of these things hide the fact that there isn't understanding associated with these structures. The same way a car goes from A to B faster than you would, it doesn't mean it holds any understanding of WHY you would travel, it doesn't hold any understanding on HOW it should choose the path and so on. While these are great at aiding a competent user, they are not tools for original and new creation. Someone will point me to the written article, drawn image and so on and claim originality, but it's all derivative, there isn't real experimentation, there isn't something as shocking and mind bending as we had when Jazz came into being, nothing as new as the stream of consciousness type of writing of Tristam Shandy or nothing as gut wrenching or disturbing as Zdzisław Beksiński's work. We are, however, living in an age where lack of imagination is rampant. We are boring, unimaginative and we can't seem to come up with anything new. Everything we do, is similar to what "AI" does, we create derivation, nothing new or original. I must agree here with some of the things Jaron Lanier highlights in his books whereby we haven't had anything as good in a long time. Also why my examples are from quite a few years ago. So I'm not worried. It's not intelligence and as far as preparing for it - expand your imagination. Expand your boundaries and feel more at ease being uncomfortable. These are tools and it's up to your imagination to push the envelope and create new things with the help of them. --- LM - language model. Taken from a paper I wrote about NLP: Gary Marcus acknowledges that the system manages to achieve impressive results by providing fluent answers to previously unseen questions. It sticks to topics well and it achieved surpassingly accurate behaviour (Marcus, G. 2020, para. 31). Despite this, Marcus criticizes a very important aspect of the system. Despite its vast database of information, it cannot extract meaning from the sentences it manages to present to questions the user asks. GPT-2 will provide different answers of varying relevance to the same question. The answers it provides could indeed be conceived as potential continuations of the sentences presented, but sometimes the meaning is completely lost as the LM does not hold information on concepts and objects. If GPT-2 is viewed as an example of what is possible when we have access to a very large database, modern neural networks can come up with general rules even when there is no guidance or supervision. In this respect, the achievement is quite extraordinary, as GPT-2 can provide answers that are meaningful with little to no background information. The simple fact that this is possible tabula rasa is quite impressive. However, based on the examples provided by Marcus in his article, it is clear that all that we’re looking at is an extremely elaborate version of ELIZA1. The answers show that there is no substance to the information that is replicated by the LM – it simply doesn’t understand the information it provides the user with. The phrase that made me side with the points brought forth by Marcus in his article was the quote attributed to Ilya Sutkever the co- founder of Open-AI “If a machine like GPT-2 could have enough data and computing power to perfectly predict the next word, that would be the equivalent of understanding.” (Marcus, G 2020, para. 76) I find the statement to be, if not incorrect then wildly superficial, as word prediction does not equate to understanding. One of the examples provided in the article highlights this even further: A is bigger than B. B is bigger than C. Therefore A is bigger than _ B. A is bigger than B. B is bigger than C. Therefore A is bigger than _ which can also become a huge hit. The two above are question and answer pairs taken from Marcus, G 2020 (Marcus, G. 2020, ‘GPT-2 and the Nature of Intelligence’)