Here I analyze the given question in current perspective (2020), by juxtaposing it against Turing’s seminal take and arguments on the question: ‘*Can machines think*?’ using the ‘imitation game’ in 1950 [7]. Due to the relative subjectiveness and broadness of the notion ‘*the way people do*’ (hereafter, *humanlike*), for this write-up, I’ll presume the setup similar to the imitation game, and constrain the discussion as such.

**Arguments for ‘Yes’**: Of the contrary views posited by Turing, the one most relevant even after 50+ years is the ‘*argument of consciousness*’ (AoC). It has seen reincarnations in various forms and arguments in AI research and philosophy by many luminaries following Turing, like Dreyfus, Searle, Harnad, Haugeland [2, 6, 4, 5] to name a few. …

Graphical representations are increasingly getting popular in machine learning (ML) and data science research. Skimming recent literature in geometric learning and/or Graphical Neural Networks (GNNs) techniques like GCN [1], GAT[2] can be befuddling for fresh eyes. Thus, in a series of succinct posts, I will elucidate atomic/foundational concepts that can be helpful in the long run to grasp the overall concepts.

Before delving into topics like ‘**Graph Laplacians**’ and subsequent computations, let’s start things off with a very simple atomic concept: **row normalizing adjacency matrices of a graph**.

As a recap, for a graph with `n`

vertices, the entries of the `n * n`

**adjacency matrix**, A are defined…

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