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 :)
I know I have slammed into transparent surfaces because I couldn't see them.
It probably gets worse the higher you go, because there is less grassy people adding marks to them, and less "people are hitting this too much, we'd better reduce that glass size". Birds are famous for getting them wrong.
But bats also use vision based on visible light. I haven't read the article yet, but do they explain about why the bat wouldn't see the building with its eyes?
"researchers can rule out the possibility that the bats were visually confused, because the experiments were done under infrared light, which bats can't see, and so they would have been relying entirely on echolocation."
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.
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?
"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.
There is more than enough sun falling on the ground in deserts to supply all the energy humans use at ridiculously small conversion efficiencies. It doesn't do us any good because we don't know how to capture that.
No the problem there is actually transporting that. It's no use to build a solar farm there and then stretch a long copper powerline back to wherever it is needed because of the losses involved.
"The studies concluded that the extremely high solar radiation in the deserts of North Africa and the Middle East outweighs the 10–15% transmission losses between the desert regions and Europe. This means that solar thermal power plants in the desert regions are more economical than the same kinds of plants in southern Europe."
I think it's referring to how efficiently it uses the energy after it is collected by the solar panel. So the collection itself might be <20% efficient, but the synthesis of new materials can be very efficient after that.
