Thanks for the suggestion, I will check it out. It is a very bad habit of mine to spout off at the mouth without much forethought or consideration when I get irritated. Look for a retraction or comment modification if I need to reconsider my characterization of Alkhateeb’s position.

I see that you are a Cambridge man and a linguist no less, no doubt you are well versed in the works of LW. I consider myself the world’s foremost non academic, non credentialed, semi-capable non authority on his philosophy (early and late). It is a very prestigious position and no one has challenged my claim yet. Lol!

Can I pick your brain very quickly on a subject I have been seeing a lot more of recently? I don’t know how much language theory you get as part of the curriculum or even if this topic would fall under that rubric but what is your opinion of word vectors? Specifically their usefulness or lack thereof for natural language processing. It appears they have become a favorite tool of the [machine learning] crowd and algorithms employing various word vector models and/or combinations of models are appearing at a rapid clip. A good text or web resource recommendation would be awesome too if you know of any. Keep in mind my current knowledge level in the area is essentially zero so articles from the primary literature are most likely going to be beyond my level of comprehension at the moment. A review article, introductory text, or even media resource would probably be best to get me started. I have seen some pretty wild claims about just how powerful word vectors may be/are coming from the usual band of tech cheerleaders (mostly the AI and ML hypers) and I am skeptical to say the least. Perhaps they are right to be so enthusiastic, but if they are not I want to have the ammunition to challenge with actual facts and data any more overhyped or bogus claims I see.

Thanks again!

p.s. I bracket [machine learning] because it is a nonsense term (or at the very least senseless. I go back and forth on that). The two words when combined in that order result in a logical contradiction. The very definition of each word in the term excludes the possibility of the use of the other word in the term. The concept ‘machine learning’ is also logically incoherent and thus absurd. Simply put machines can’t learn. Some may argue that the term machine learning “transforms” or “erases” the original definitions of the words of which it is composed and thus is rendered non problematic. They are free to do so however I would argue that the way the term is actually used says otherwise. At least to the general public (Joe Six-Pack and the mass media), machine learning could best be represented by computers sitting down in a classroom at Cambridge and learning about the philosophy of LW from a distinguished professor. I prefer neuroarchitecture inspired computing (NiC) and have decreed that it shall be the new designation for any and all activities that once were described as [machine learning]. Much like my ability to designate myself an expert in whatever, my authority to modify the language at will is absolute and not subject to challenge or dispute.* No doubt you have already noticed the change in the technical journals and this will spread to the mass media and general populace soon. Lol!

*would that be sweet or what

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Research scientist (Ph.D. micro/mol biology), Thought middle manager, Everyday junglist, Selecta (Ret.), Boulderer, Cat lover, Fish hater

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