BigDog Overview (Updated March 2010)

> Can it get up if knocked over? Yes [link] . > Just how resilient is it? Watch it on ice ▶ this video I assumed the HN crowd had already seen all these videos.

in “Boston Dynamics' New Robot – Spot” · View on HN

The Big Dog has an internal combustion engine (what I take to be an electrical generator) that is about as loud as a chainsaw[1]. SpotMini makes servo noises, but it's all-electric, and quite a bit less noisy. 1: ▶ this video

in “Boston Dynamics will start selling its dog-like SpotMini robot in 2019” · View on HN

> At any given time there’s only a single running code block. > But here… there be dragons. That's one of the things Erlang solves pretty well. Sometimes I sort of envision it as that robot dog/mule thing that they keep kicking and it gets back up and keeps going. ▶ this video if you have never seen it before.

in “Gotchas from Two Years with Node” · View on HN

Having just watched this video[0] of some of their work this stuff is just amazing. I can't wait to see what Google does with it. Also this is what makes Google such an awesome company from a computer science perspective IMO. [0]: ▶ this video

in “Google Acquires Boston Dynamics” · View on HN

It says in the article that Big Dog is a robot made by a company Google bought. Here's a video: ▶ this video

in “The rise of robots and the future of jobs” · View on HN

Let's say you want to build a robot that walks around like a person or even a goat. There are basically two part's of that problem, building the hardware, and building the software. If you use neural networks for the software then it's going to take more resources to run, and take a while to train. However, while people may be able to write more efficient software that requires fewer resources developing that software is going to take longer and cost a lot more money. ▶ this video The idea that I can create software which works even if I don't understand why it works is a powerful idea. The point of AI as a field is stepping back and saying: "Computers are fast and cheap, people are slow and expensive, can't we automate this?" and continuing to do that not just to secretary's, or even coders, but also designers. As long as you can build a large enough training set and toss enough computing resources you can basically solve any problem. Granted, the need for large training set's and processing power can also be a huge limitations. But, in the end neural nets / AI is just another tool like your computer, Algebra, or a big stick. The it's a cool idea and solves useful problems is enough for people to spend a lot of time and money looking into it.

in “Google & Stanford create a digital brain that learns to identify a human face” · View on HN