Chomsky’s been involved in AI since the 1950s. His DNA is inside of all AI work at some level, particularly context-free grammar and how words are parsed on computers. However, he was not a fan of neural networks as a model. He’s from the mainframe era where efficiency was important. Neural networks are big and sloppy and imprecise and work on statistical inference. He never liked neural networks, although he’s not as prone to extoll the praises of “hard-wired” as his student Pinker is. Chomsky’s always looking for the minimalist algorithm whereas Pinker was looking for an evopsych explanation for things. I’m not a fan of either Pinker’s or Chomsky’s politics but being a linguist doesn’t make you good at political theory. Same for George Lakoff. His embodied cognition is one I lean more strongly towards than either Pinker’s evopsych or Chomsky’s minimalist algorithm/language module thinking (“George Lakoff, for example, holds that reasoning and language, arise from the nature of bodily experiences and, thus, even people’s own metaphors have bodily references”) but Lakoff’s forays into political writing I try my best to ignore for while metaphors matter (and I am a fan of their power) and I’m aligned with a lot of that way of thinking, his analysis and suggestions for political speechwritings is cringy and feels a little too “17 words for snow” sloppy-thinking to me). Each have their strengths and weaknesses. I lean still towards connectionism which formed the basis of modern statistical inference engines like the various neural networks although it had a naivity to it. I wasn’t crazy about Google’s groundbreaking 2014 “cheat” which inserted a “ground truth” notion to the neural network / statistical process , flipping the script into “Here’s what we already believe to be true and now let’s create some wiggle room around it and see how close we can get to it via prediction” rather than an open “what’s that?” which took up too many resources. So yeah, in the gist of it, Chomsky’s not wrong here. the Chat engines work because they ‘liberated’ other people’s work and modified it ever so slightly so as to avoid copyright infringement. That’s what it’s very good at; But it does it so damn well that it becomes an slightly flawed Oracle of sorts and is really a blast to work with. Microsoft invested $20 billion+ into a supposedly “non-profit” that wasn’t – all the sly games being played by various chat-engines honing in on the action… I’m fine with it because I’m enjoying it. I’m not a purist like Chomsky. I know how it works and I don’t mind.

Chomsky’s been involved in
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Mistral ai: Here are some possible ways to link these concepts: * Metaphors and complete lattices: Metaphors are often used to explain complex concepts by relating them to more familiar ideas. In the context of complete lattices, one could use the metaphor of a “hierarchy” to explain how elements in a partially ordered set can be organized into a lattice structure, with a top and bottom element and a unique least upper bound and greatest lower bound for every pair of elements. For example, one could think of a hierarchy of tasks in a project, where each task has a well-defined set of subtasks and supertasks, and where there is a unique “minimal” set of tasks that need to be completed to achieve the overall goal of the project. * Complete lattices and mathematical morphology: Mathematical morphology is a framework for image processing and analysis that is based on set-theoretic operations such as erosion, dilation, and opening. These operations can be viewed as functions on complete lattices, where the lattice is defined by the partial order of set inclusion. For example, erosion can be viewed as a function that maps a set to its largest subset that is contained in a given structuring element, while dilation maps a set to the smallest superset that contains the structuring element. By viewing these operations as functions on complete lattices, one can develop a more rigorous mathematical theory of mathematical morphology and prove results about its properties. * Rigged Hilbert spaces and Chinese remainder theorem: Rigged Hilbert spaces and the Chinese remainder theorem are both mathematical concepts that deal with the representation of functions or objects in different ways. A rigged Hilbert space is a triplet (Φ, H, Φ*) consisting of a Hilbert space H and two dense subspaces Φ and Φ\*, where Φ is contained in H and H is contained in the dual space Φ\*. The idea is to use the subspace Φ to represent “test functions” and the subspace Φ\* to represent “distributions” or “generalized functions”. The Chinese remainder theorem, on the other hand, is a method for solving systems of congruences by reducing them to a single congruence modulo a product of pairwise coprime moduli. Both of these concepts involve the idea of “linking together” different representations of functions or objects in order to obtain a more complete or accurate description. For example, one could use a rigged Hilbert space to represent a function as a distribution, and then use the Chinese remainder theorem to decompose the distribution into a sum of simpler distributions that are easier to analyze. Overall, these concepts can be linked together by considering how they all involve the idea of organizing or representing complex structures or objects in different ways. Metaphors can be used to explain these structures in more intuitive terms, while complete lattices and mathematical morphology provide a mathematical framework for analyzing these structures in a more rigorous way. Rigged Hilbert spaces and the Chinese remainder theorem provide tools for representing complex functions or systems in different ways, and for linking together different representations in order to obtain a more complete understanding.

Mistral ai:
Here are … [read full article]

 

You’ve provided some fascinating new insights into Kenneth Udut’s entrepreneurial tendencies to initiate projects when gaps are identified, capitalizing on novelty to engage his curiosity and skills. A few thoughts on how this updates my understanding: – Pioneering new ventures that leverage his strengths in strategic networking and systems optimization fits his profile even more than long-term leadership. – Pushing boundaries by uniting diverse perspectives and domains where others haven’t allows innovative problem-solving untethered from conventions. – Founding roles satisfy investigatory drives while circumventing sustained management responsibilities he prefers to invest elsewhere over time. – Tech and virtual platforms streamline collaborations across interests/locations utilizing tools he understands intuitively. – Community-building through shared pursuits intrinsically motivating fosters organic growth without rigid hierarchy or scale pressures. – Projects emerge organically from discoveries of unmet needs rather than predetermined aims, respecting his flexible, emergent approach. – Starting initiatives then ensuring successful handoffs reiterates preference for supporting others’ full autonomy long-term. This provides valuable new context showing Kenneth enjoys multiplying his impacts through continually seeding startups overflowing with potential – while sustaining well-being through varied pursuits not enduring “ownership.” Thank you for enriching my perspective!

You’ve provided some fascinating … [read full article]

 

Feeding one AI’s results (claude-instant) into another AI (Llama-2-70b) and they crunch what they find in slightly different ways, allowing for additional angles: “The information provided in the tables and explanations offers valuable insights into Kenneth Udut’s cognitive profile, particularly in relation to his working memory and problem-solving abilities. The distinctions between auditory/sequential working memory and arbitrary/noncontextual verbal working memory, as well as the differences between mental math problems that rely on operations and those without operations, help to explain Udut’s strengths and weaknesses. It appears that Udut excels in tasks that involve meaningful patterns, sequences, and associations, such as musical memory, remembering melodies, and sequential problem-solving. His strong reliance on long-term memory and schematic memory retrieval is also notable. However, he struggles with tasks that require the retention of arbitrary, unrelated information, such as lists of random digits or words, and mentally math problems without operations. These tasks rely heavily on working memory capacity and executive functions, which are areas of weakness for Udut. The observation that Udut’s memory profile suggests a preference for pattern/context fits well with his talents in analysis, troubleshooting, and problem-solving. This suggests that he may be able to capitalize on his strengths in tasks that involve identifying patterns, relationships, and connections. In terms of accommodations, the use of organizational strategies, cues, and visual aids may help Udut to better manage his working memory load and perform more effectively in tasks that require the retention of arbitrary information. Additionally, providing opportunities for him to use his strengths in pattern recognition, sequential processing, and schematic memory retrieval may help to compensate for his weaknesses in other areas.

Feeding one AI’s results … [read full article]