Gurpreet Kaur


Do we dream like Dall-E?

Dreaming is still a little understood phenomenon of the human psyche, although there is a steady but slow uptake in releated research [1,2]. Especially its purpose remains somewhat a mystery and many suggetions have been put forth [3,4].In this short article I do not want to sketch a general answer to all these questions nor speculate on the nature and function of dreams. I rather want to spend some time exploring the a certain aspect of dreams, that is their ‘detail-specificity’.

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Talking counterfactual #2: Microsoft’s DialoGPT

In episode two of our new series, we take on Microsoft’s DialoGPT. It is a GPT-2 transformer-style architecture trained on 147 million Reddit discussion threads released in November 2019 [1,2]. Now, Reddit might not be the best spot to train your AI (so we heard 😉), but it is one of the largest data sources for human discussions. Let’s see if DialoGPT can cope with’s flavour of counterfactual conditionals.

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Talking counterfactual #1: facebook’s BlenderBot

With getting ready for public beta, we thought it’s time to take it for a spin and have a dance with some of the recent LLMs (large language models).As a starting point, we chose facebook’s BlenderBot (for convenience followingly written without the second capital “B”). The Blenderbot is a multipurpose Q&A open-source chat bot, so it is an ideal fit for the question-styled counterfactual conditionals employed by inaugural test set. (btw. counterfactual conditionals do not have to be framed as questions, as you could say: “John would have picked the red team, if he had known they would win.”, but construing them as questions makes it easier to have a starting point for judging the strength of an LLM, as the potential answer is easier to classify.)

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