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Comment by allenz | Hacker News Reader

This study is interesting, but it's not really AI and it's not really novel.

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.

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I'm sad that the AI acronym has become overused, and has lost its credibility. Back in 2004 even the expression "Expert System" was warily used, and only when appropriate. The way this is going we're going to have AI toasters by the end of the year.

The way this is going we're going to have AI toasters by the end of the year.

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

I realize this is somewhat ridiculous, but I actually found their FAQ [0] and the product very interesting.

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.

[0] https://www.zojirushi.com/app/faq/rice-cookers

There is kind of a Gresham'a Law with these buzzwords, where if you actually have new and interesting ideas around them, it's much harder to get people to take you seriously.

Every new faculty candidate we’ve had this term has mentioned wanting to apply AI or ML to their future research.

They’re mostly doing systems, so it makes exactly 0 sense.

>> "we're going to have AI toasters by the end of the year."

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

AI in toasters is better than many use cases being considered or publicized.

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.

Yesterday's fuzzy logic is today's AI?

fuzzy logic is a very ... fuzzy term.

A side-note: Statistical classification is machine learning, which is a subset of AI. Or atleast, was a subset of AI when it was classified itself. Machine learning has a lot of overlap with artificial intelligence, and statistical classification is, pedantically, AI. On a more general note, AI is an extremely broad field and -- I am assuming this is where you're coming from -- is not limited to the whole General/Narrow/Vertical/Foo/Baz/Bar jumble mumble.

Semantics aside, AI is being name-dropped to drive clicks. It's misleading. Doing logististic regression is not noteworthy and has not been for many decades. Further, we already knew how to improve memory without the regression, so the study doesn't accomplish much.

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.

Update on the slightly better HN title:

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.

could you clarify what you mean by an open-loop system, and why it must be compared with a closed-loop one?

Sure, those terms are used in the paper. In general, open-loop vs. closed-loop refer to systems without and with feedback, respectively. Previous studies already showed that high-frequency stimulation could improve memory. Those were open-loop because they didn't use EEG feedback; stimulation is always on. The alternative is stimulation only when the subject is predicted to forget the word, based on EEG feedback.

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.

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