UPDATED Feb. 2023 - I'm excited by ChatGPT 's possibilities in terms of facilitating advanced learning . For example, I got enlightening answers to questions that I had confronted when I first studied neuroscience. The examples below are taken from a very recent session I had with ChatGPT (mid Jan. 2023.) Source: https://neurosciencestuff.tumblr.com In case you're not familiar with ChatGPT, it is a very sophisticated "chatbot" - though, if you call it that way, it'll correct you! 'I am not a "chatbot", I am a language model, a sophisticated type of AI algorithm trained on vast amounts of text data to generate human-like text'. UPDATE: this article focuses on some of the impressive abilities of ChatGPT. For a good glimpse of its weaknesses, in the context of poor intuition about Physics, as well as Math errors, check out this great short video: ChatGPT does Physics For a high-level explanation of how ChatGPT actually works -
Graph databases have an easygoing laissez-faire attitude: "express yourself (almost) however you want"... By contrast, relational databases come across with an attitude along the lines of a micro-manager: "my way or the highway"... Is there a way to take the best of both worlds and distance oneself from their respective excesses, as best suited for one's needs? (Note: this is part2 of a 2-part series on Graph Databases and Neo4j. For part 1, see here . This part2 is currently at a draft stage) Let's Get Concrete Consider a simple scenario with data such as the Sample, Experiment, Study, Run Result , where Samples are used in Experiments, and where Experiments are part of Studies and produce Run Results. That’s all very easy and intuitive to represent and store in a Labeled Graph Database such as Neo4j . For example, a rough draft might go like this: The “labels” (black tags) represent the Class of the data – akin to table names of relational d