Ask HN: What are the challenges of building a personal assistant like Siri?

You should go for it. Even if you never really get it to work you'll most likely learn a ton along the way. And if you even halfway pull it off it would make an epic "Show HN". Also, a lot of the required technologies are advancing at an incredible rate (both the state of the art and public accessibility). It may well be an order of magnitude easier now than it would have been a few years ago.

I don't have any specific advice other than the usual for any large project: start small and focused, then take it one step at a time. Oh, and read lots of academic research papers.

Good luck!

Always. NLP is a valid way to accept requests. Web crawling is a valid way to get data into the system. How do you tie these two together? How is the data stored? How do create relationships between the data in your system?

2. is the project feasible?

You have not yet defined your project beyond "like Siri or Cortana". What does that mean to you? How will you know when that has been accomplished? Can you first define a more limited scope for testing the feasibility of the different parts of your system?

3. will it succeed?

First you must define success.

It's a fantastic idea. I wonder if there's a way to share the data generated as people use it. I would imagine that everyone would benefit from this, though it would have to be anonimized effectively.

For Deep NLP, you will need to be solid on linear algebra and machine learning. For introduction to machine learning, check out Andrew Ng at Coursera: https://www.coursera.org/learn/machine-learning and my favorite talks on Linear Algebra are the ones done by Gilbert Strang: http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-...

For web crawling, there are plenty of open source libraries. If you are not familiar with it, check out the Common Crawl: http://commoncrawl.org/ This is a great source of data to crawl.

If you focus solely on NLP and data from the Common Crawl (or even Wikipedia), then you will see where you stand as the smoke clears and you feel comfortable with the state of the art techniques.

Ignore all the naysayers. The good news is that it has never been easier to get started in deep NLP. Once you have the experience of training a model and seeing how well it works, you can decide on the next steps. Perhaps, you will find a niche that is not well-served that you can go after as a first step of a personal assistant.

Good luck.

Yes, we will absolutely make it easy to share training data via opt-in! If I train a plugin, everyone should benefit without needing a GPU rig or 2 TB of data.