emeijer

Well.. assuming the room is about 3 meters high, that's a room volume of 15 cubic meters. Assuming it's filled with air, and air weighs about 1,29 kg per cubic meter, that's 19,35 kg or 19350 grams of air. 15 degrees C is 288 Kelvin, so that's 19350*288 = 5.572.800 gram-kelvins of energy in the room initially. Now we add 100.000.000 gram-kelvins (the one gram of hot stuff) to it, and assuming this energy distributes over the air contents, our air now has a heat energy of 105.572.800 gram-kelvins. Dividing it back the same way (over the 19350 grams of air) gives us 105572800/19350 = 5456 gram-kelvins per gram of air, so a temperature of 5456 kelvins or 5183 degrees C. Still pretty hot.

Vision based on visible light doesn't suffer from the problem in the same way as echolocation or lidar, since it is not dependant on observing signals emitted by the observer. I guess very smooth surfaces might act as mirrors, which will probably bring it's own set of difficulties for machines. Anecdotally, I can say my own stereo vision doesn't have big difficulties with most smooth surfaces though :)

Actually, as of the next version (10) you can :)

The harder problem, as usual, is to get enough high quality training data for a particular problem domain though.

Maybe as a POC we can try building a bot that generates relevant HN comments given the post and parent comments. Maybe I'm such a bot, how could I possibly know?

Very interesting approach, and intuitively it makes sense to treat language less as a sequence of words over time and more as a collection of words/tokens with meaning in their relative ordering.

Now I'm wondering what would happen if a model like this were applied to different kinds of text generation like chat bots. Maybe we could build actually useful bots if they can have attention on the entire conversation so far and additional meta data. Think customer service bots with access to customer data that can learn to interpret questions, associate it with their account information through the attention model and generate useful responses.

Optimizely for groups and events. A/B testing in the real world :)

"The bacteria operate at an efficiency of more than 80 percent"

Really? If this technology is able to convert energy from sunlight + co2 into carbon based fuels at 80% efficiency, that's quite astounding. Something like that could solve the whole energy storage problem we have with solar and wind energy. Almost sounds too good to be true.