dataqa VS habitat-sim

Hey, I am the creator and (only contributor today) of open-source https://github.com/dataqa/dataqa, a Python library to explore and annotate documents. It uses weak supervision, is based on spacy, and has a lot of opportunities to add more deep learning and ML functionality. I can guide you through it :-). This would be a great opportunity to be first and lead contributor of an open-source library (outside the creator).

I recently added a new feature to DataQA (https://github.com/dataqa/dataqa) to be able to extract entities from Wikipedia. All you need to do is upload a file with Wikipedia urls:

The open-source project is here: https://github.com/dataqa/dataqa. I have just released a feature which I have been working on for a while to solve a problem which I've seen a lot in industry: how to map entities found in text to large knowledge base ontologies.

The tool I developed and used for this problem: https://github.com/dataqa/dataqa

I have seen first hand at small and large companies how problems have been tackled with ML without trying a simple rule or heuristic first. And then, further down the line, the system has been compared to a few business rules put together, to find that the difference in performance did not explain the deployment of an ML system in the first place.

It's true that if your rules grow in complexity, this might make it harder to maintain, but the good thing about rules is that they tend to be fully explainable, and they can be encoded by domain experts. So the maintenance of such a system does not need to be done exclusively by an ML engineer anymore.

Here is where I insert my plug: I have developed a tool to create rules to solve NLP problems: https://github.com/dataqa/dataqa

After working in ML for more than a decade, I became frustrated over time with the lack of tools to create baselines using simple rules and heuristics. It is well known that most business problems out there can achieve decent baselines using only heuristics. This is why I have developed DataQA (https://github.com/dataqa/dataqa), which uses NLP rules to do common NLP annotation tasks, such as multiclass classification or named entity recognition.

Spot on! I think you grasped the general idea. During some of my 3d studies, I collected data about hand movements, eye movements and navigation paths within scenes, which could potentially be used for training data in robots (e.g. to train robot arm-suction grip, visual input and navigation respectively). I see projects like this https://aihabitat.org/, where my research seems quite relevant.

I need an easy to set up simulation of a 3d environment and I was wondering what you guys are using. Something like https://aihabitat.org/ . It already comes with rich visuals, which is quite important in my case and it works out of the box so I don't need to waste time developing my own models and graphics. Unfortunately habitat ai doesn't work on windows. Are there some alternatives?

There are plenty of them out there. I spend a lot of time contributing to open source projects like Habitat-Sim https://github.com/facebookresearch/habitat-sim and Habitat-Lab https://github.com/facebookresearch/habitat-lab which have a ton of open issues and code maintaince stuff that we would welcome contributions of.

With some hacks it is pretty easy to get drones working in Habitat-Sim: https://github.com/facebookresearch/habitat-sim