I try not to immediately call BS on these types of studies…but in this case there are some concerns.
“The data sets were randomly divided into training (85%) and test (15%) sets. We used 10-fold cross-validation to obtain generalized results of model performance. Data splitting was performed at the participant level and stratified based on the outcome variables. Because the data classes were imbalanced for symptom severity (ADOS-2 and SRS-2), we performed a random undersampling of the data at the participant level before conducting data splitting. Moreover, we examined different split ratios (80:20 and 90:10) to assess the robustness and consistency of the predictive performances across diverse splitting proportions.”
* undersampling is problematic here and probably introduced some bias. These imbalanced class problems are just plain hard. Claiming one hundred percent on an imbalanced class problem should probably cause some concern.
* data split at the participant level has to be done really careful or you’ll over fit
* multiple comparisons bias by testing multiple split ratios on the same test data. Same with the 10-fold cross Val.
* not sure if they validated results on any external test data
* outcome variable stratification also has to be done really carefully or it will introduce bias; seems particularly sensitive in this case
* using severity of symptoms as class labels is problematic. These have to really have been diagnosed the same way / consistently to be meaningful.
I also note a long time history in collection of these images (15 years iirc). Hard to believe such a diverse set of images (collection, equipment etc) led to perfect results.
ML issues aside, super interested in the basic medical concept. I wasn’t aware retinal abnormalities could be indicative of issues like ASD.
This is a fantastic set of advice that every person earnest to make room for self-improvement in the year ahead should incorporate into their lives at some level
I think I speak for essentially everyone here when I say, "fk this guy's boss". Old mate here in this thread slacks off or 2 days, his boss is already very respectfully thinking of firing him.
It's not 1920. The modern middle manager has more invested in his fantasy football league than his direct reports -- and by some magnitude. Grow up.
The article is hand-wavingly vague — but even if they can intercept, e2e should render it useless. Thus, the implication is that they have some ability to render e2e pointless by being able to access the phone by other means. Thoughts?
I had a quick scan through the actual linked article [1] but couldn't find the actual SPU protocol? It seems like there are two variations but no details of the regimen (reps / sets / duration). Admittedly, it was a quick look through -- but I'd be really interested to know the protocol. From the YT video in OP it looks like an easy enough motion to learn.
Three of their best ML engineers /devs left in the last 12 months or so. The backend for anything deep-learning is MXNet...which is essentially a non-starter these days vs PyTorch or TensorFlow. All these functions you cite are very low level updates not real feature updates.
I would say in 2018-2019 the ML / Deep learning stack was actually very impressive compared with what was available in FOSS. Now -- it's languishing. Badly.
Rather than just bashing Stephen's personality and rehashing Cosma Shalizi's driveby on NKOS, can we try and focus on the technical accomplishment that Mathematica represents?
It really is an astounding system. If you enjoy functional programming, the language is super expressive and productive. The integrated libraries are fantastic -- especially if you want to chain together different knowledge domains through a consistent interface and syntax. You can get the entire Wolfram Alpha knowledgebase and curated datasets to use in your programs immediately.
The front-end is just lightyears in front of Jupyter notebooks in just about every conceivable way.
This isn't to hide that there are some frustrating things about the language (like, machine learning development has just stopped dead after a couple of really great years of feature dev...why?) and, especially, the organisation and some of its personalities. But the product -- it really should get a lot more kudos and wow factor based on real accomplishments and features that are live, in the real world.
To be fair, it's not the doctors doing the feeding; it's the hospital administrators who are almost always business folks representing the interests of the shareholders (increasingly, PE firms).
Hospitals are glorified hotels with super over-priced roomservice and activities. But the business model is very similar.
This article goes through a whole range of the classic tech used in the film. But the most entertaining part was discovering that the WOPR was a plywood box with a person inside operating an Apple II!
There's an interesting comment in the tweets that links to an article claiming high levels of uric acid in the blood of high achievers. The article is about the consequent prevalence of gout in that demographic.
Less well known is uric acid's effect on hypertension -- which would also perhaps (anecdotally) explain likely elevated levels of heart attack and stroke in that same demographic.
All terribly confounded, though. Hard to unravel chickens from eggs, lifestyle factors etc.
I’ve had a non-trivial “addiction” to Diet Coke in various times of my life. MDs and Neuroscientists please weigh in! When I put my Bayesian hat on, I think this claim falls over (lots of people in general drink Diet Coke so the background numbers are high and people are likely to self-select as being “more intelligent” etc etc)
I mean, hats off to one of the greatest pieces of overengineering (and the documenting thereof) in history. Not only does this ensure a comfortable reclining work position — it ensures absolutely no one will ever want to recline with you, either. Pure productivity.
> The people hiring them have no clue, hence why the marketing is the most important thing
This observation is absolutely spot on. An ignored element of meritocracy is visibility -- and, perhaps, as you suggest, inertia. And then we end up back at the black hole of designing the 'right' incentive schemes...
I think you're missing the point. For the amounts paid given the relatively small tasks involved this whole thing is outrageous from a tax-payer's point of view: how much of even your personal tax dollars went burned on this? What else could that money have been spent on?
Probably like many here, I read this and just had to vent outrage at the sheer injustice and inhumanity. This comment is for you to rage upvote.
Compassionate capitalism is more needed today than ever. Sure, this is likely just some low-level buffoon at a chain mouthing off in ignorance. But that's why it's dangerous; toxic capitalism has thoughtlessly metastasized to the middle.
Given the severe and extreme states most commodities markets have found themselves in of late, I expect an exacerbating withdrawal of liquidity by most market participants -- even posting margin for normal hedging requirements is beginning to require completely untenable amounts of free cash flow. I fear that severe inflation is very much looming at the door.
Genuinely, ask everyone you know for good recommendations. Even if they give you their uncle’s best friend in Cincinatti who does public defender work, that guy might know the woman who is the best litigator in the US who has a speciality in what you need. And then if she’s too expensive or won’t help you direct, she will happily refer you to someone who does.
And that kind of hustle is a prerequisite to solid business success anyway. So, get after it!
“The data sets were randomly divided into training (85%) and test (15%) sets. We used 10-fold cross-validation to obtain generalized results of model performance. Data splitting was performed at the participant level and stratified based on the outcome variables. Because the data classes were imbalanced for symptom severity (ADOS-2 and SRS-2), we performed a random undersampling of the data at the participant level before conducting data splitting. Moreover, we examined different split ratios (80:20 and 90:10) to assess the robustness and consistency of the predictive performances across diverse splitting proportions.”
* undersampling is problematic here and probably introduced some bias. These imbalanced class problems are just plain hard. Claiming one hundred percent on an imbalanced class problem should probably cause some concern. * data split at the participant level has to be done really careful or you’ll over fit * multiple comparisons bias by testing multiple split ratios on the same test data. Same with the 10-fold cross Val. * not sure if they validated results on any external test data * outcome variable stratification also has to be done really carefully or it will introduce bias; seems particularly sensitive in this case * using severity of symptoms as class labels is problematic. These have to really have been diagnosed the same way / consistently to be meaningful.
I also note a long time history in collection of these images (15 years iirc). Hard to believe such a diverse set of images (collection, equipment etc) led to perfect results.
ML issues aside, super interested in the basic medical concept. I wasn’t aware retinal abnormalities could be indicative of issues like ASD.