I'd be surprised if they don't exist already. We already have AI rice cookers: "Zojirushi's top-of-the-line Induction Heating Pressure Rice Cooker & Warmer uses pressurized cooking and AI (Artificial Intelligence) to cook perfect rice." -- from https://www.zojirushi.com/app/product/npnvc
The term "AI" has become somewhat meaningless, but in this case they appear to be adjusting cooking time based on previous results. I'd guess they are probably adjusting a couple of parameters.
My basic understanding of how rice cookers work, is that they essentially apply full heating power until all the water has boiled away/been absorbed. They know when this happens by monitoring the temperature, the temperature wont rise above 100 degrees until all the water has boiled away. At this point they shut off.
I guess more "intelligent" rice cookers can do a little more than this, maybe if they see that it's consistently taking less time than expected to cook the rice they can heat to a lower temperature at the start to aid water absorption or something? Would be interested in knowing more.
They’re mostly doing systems, so it makes exactly 0 sense.
I hope not. It seems they can be a real pain in the ass:
Red Dwarf toaster: https://www.youtube.com/watch?v=LRq_SAuQDec
I would welcome a toaster that let me say "too burnt" or "too raw" or "just right" after each toasting, adjusted the cooking time and temperature accordingly, and generalized well to new kinds of bread and such.
The article misleads about the science being done, and people are better off not reading it. For example as others have pointed out, regression is not a black box and it is clear what we do and do not understand using this model.
1. The regression model used has absolutely nothing to do with decoding memory. The only signal here is high-frequency EEG activity, which does not provide information on the structure of human memory.
2. There is no evidence that the regression model was needed to enhance memory.
The obvious question is whether EEG-based stimulation makes any difference compared to always-on stimulation. It is very possible that the difference is negligible and that the EEG feedback doesn't matter.
The researchers fit a regression to predict word recall from high-frequency EEG activity when memorizing the word. We've known for several years that high-frequency activity predicts memory success, so this part isn't new.
In addition, several papers have tried to improve memory through high-frequency stimulation from brain implants, with various results. This paper proposes "closed-loop" stimulation, delivering stimulation only when the classifier predicts failure. They find that closed-loop is effective.
What the authors really want to claim is that closed-loop is more effective than open-loop, because otherwise their fancy "AI" classifier is useless. Surprisingly, this study does not compare closed-loop vs. open-loop.