edrone: that’s how we are

Not so long ago the edrone team had finally the opportunity to leave our virtual workspace behind and meet in the real world. We had an unbelievably good time, and that got me thinking about how it’s possible that a 110-person team, mainly created during lockdowns, is so close to each other.

Dispatches from the ULMFiT trenches

Don’t just look at the benchmark figures when you evaluate your language model. A subjective judgement can also lead to interesting insights.

Continual Learning

Continual learning is a machine learning domain that aims to mitigate catastrophic forgetting and enable models to be trained with an incoming stream of training data.

Ghost in the Marketing Machine

One of the many views regarding the philosophy of the mind is called „psychophysical dualism.” It was especially present in the texts of Descartes and postulated the separation of the psychological aspect of humanity from its physicality.

Arguing with Language: Inter Annotator Agreement

When it comes to processing natural language and developing smart voice assistants for eCommerce, sooner or later, you will encounter the term Inter-Annotator Agreement. Such an agreement is critical for the illusion of understanding words by a machine.

Hey Facebook: a few words about wake words

Facebook stuck out from the other giants, commonly referred to as the “big four” in terms of voice solutions. It’s a euphemism anyway because not everyone is aware that Facebook has devices with a voice interface at all.

Benford’s Law: Is the reality hardcoded?

At edrone, we collect tons of data. There is information about the order value among this data, and as you probably assume, there are lots of random values there. But in fact they aren’t entirely random.

Word2vec: Loss Function

The difference between probabilistic output vectors and target vectors is crucial for the network to learn. How can you measure this difference? What does the learning process look like? How do we indicate when our model is smart enough?

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