New AI System Predicts Seizures
spectrum.ieee.org
spectrum.ieee.org
Also, it is definitely appropriate to use the term AI in this case. AI is not a technical term so it's really in the eye of the beholder, but I think it's safe to say that ML is a subset of AI. Perhaps people are conflating AI with AGI?
This set is far too small to say anything strong about the results, but the problem is interesting anyway.
Similarly sculpture is a subset of visual arts therefore when sculpting you may claim to be creating a work of art.
Secondly, while I know of (mostly historically) a small amount of serious non-ML AI and AGI work being done, it has almost nothing to do with the common parlance to day, which is nearly entirely ML. Is this what you mean when you talk about non-ML AI or is there something I'm missing?
For what it's worth, I'm happy with thinking of them as overlapping, although I do think the AI terminology is almost useless at the moment, and ML is slightly better defined.
While most recent successes in the field of AI have been brought about from advances in the subfield of Machine Learning, even today's most advanced AI systems have components that are not Machine Learning (e.g. Alpha Go still requires tree search techniques from "classic AI" to work).
In the definitions below, AI refers to any "intelligent agent", whereas ML refers to the subset of techniques that achieve this through learning/experience/data.
ML: "Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[0]
AI: "Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals."
[0] https://en.wikipedia.org/wiki/Machine_learning#Overview [1] https://en.wikipedia.org/wiki/Artificial_intelligence#Defini...
Unless something has changed radically since I stopped paying as much attention there is not actual agreement on these terminologies, at least broadly, in academic circles.
I certainly agree many current systems with a core ML component include other techniques from lots of areas including what you call "classic AI" as well as optimization, etc., but the ML is still the fundamental part of nearly everything recent I've seen. As pretty much every successful system of this type is a hybrid in the sense you mean, I don't find differentiating them from some putative "pure ML" approach very interesting.
There was some good work in very different approaches in the 70s through 80s, but that seems to have tapered off in the 90s really. I'm not very current though and would love to hear of newer interesting things in that vein.
Also, I've noticed the definition of the word "AI" has grown to encompass pretty much any type of software that does something with data.
Agreed about the sample size. It's not clear from the article whether the high accuracy was based on testing against the training set, too. I assume not, as that would be exceptionally sloppy. But it's not clear.
In this case I guess the “AI” label is relatively well applied (compared to the scope of ALL things I’ve seen it haphazardly slapped on), but yeah, still - people need to stop calling any kind of data analysis “AI”.
Of course, that does mean the results don't necessarily generalize to the whole population, which is why you do eventually need much larger sample sizes when you're working toward FDA approval.
In my experience it is far more likely to mean "doesn't generalize well, and moving to a reasonable training set will reveal flaws in the model.
Afaik AGI research is still a separate thing, so it's not really that misleading to use "AI" for applications like this were machine learning is involved, resulting in a very specialized "artificial intelligence" that can spot otherwise hidden patterns.
That's definitely the case in conferences like O'Reilly Strata. They seem to be 100% focused on AI/ML, which seems really limiting. I'm starting to skip attendance until things get a bit more balanced.
Edit: Perhaps this hints at some medication that is not currently viable but could be administered if the seisure was predicted: "Notably, seizures are controllable with medication in up to 70 percent of these patients."
https://ieeexplore.ieee.org/document/8765420
"Efficient Epileptic Seizure Prediction Based on Deep Learning"
* what is the input to the model? It sounds like it's EEG, but how practical is it to collect those data on a day-to-day basis?
* how far ahead of time are seizures predicted?
In other words -- how practical is this research?
"We are currently working on the design of an efficient hardware [device] that deploys this algorithm, considering many issues like system size, power consumption, and latency to be suitable for practical application in a comfortable way to the patient"