n-e-w

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.

The talk begins in earnest at 16:30

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 would have thought the title of the article would have been sufficient to prevent you clicking it in a work context :/

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?

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.

I love Wolfram stuff.