beefcafe

This is a killer feature for CTFs/OSCP/etc and why I started using it.

Probably because a new version was just released. Details here* but big news is debugger support. Looking forward to taking it for a spin.

https://htmlpreview.github.io/?https://github.com/NationalSe...

> DJI offered a bounty for researchers to uncover bugs in its drones

I'm not sure they understand how bug bounties are supposed to work...

https://arstechnica.com/information-technology/2017/11/dji-l...

Given your perspective, a more important question might be what underlying algorithms are being used? I’m only a rookie data scientist but knowing that (for example) a random forest outperformed a neural net in this case, or even just a set of heuristics, is solid information. These can be built in Python, R, even C depending on the application, developer experience and a bunch of other stuff.

That's like saying book A is in Spanish and book B is also in Spanish, so why did you like book B better? Python is just a language, what you do with it is what matters. Sounds like they built something completely distinct from Watson and it did the job.

I love the phrase “explainable AI”. We still can’t explain how our intelligence works with any degree of biological detail.

Show me a human that can win a Starcraft championship after only playing ten games. If you find any, they learned the mechanics and strategy somewhere else. That’s transfer learning, appears to be in its infancy in the ML community but making progress.

Also “How to Build a Brain” by Chris Eliasmith. Interesting theories on how neurons represent functions and then build into a greater whole. I’m not a neuroscientist but my intuition says he might be getting closer to a solid mid-level (above the level of ion channels) understanding of brain function.

An iOS dev is going to read this and build that feature into Siri just to mess with you.