rgarrett88

They sure are designed to sound like people

Maintenance on high energy movement hasn't exactly had a great track record either.

Get the looms while you're at it

I prefer my clothes hand woven but it's so hard to find artisanal weavers these days. And the rate they want! Outrageous when other clothes cost next to nothing.

They don't because they don't take a 30% cut of everything purchased on your MacBook.

I'm of the opinion that Apple will never natively allow unmanaged code outside macOS due to app store revenue. I mean if the AVP fails it would be a huge write down but if it wins and allows people to circumvent the Apple tax that's still a fail for the company.

>and no industry corporation has an incentive to run this either.

Are insurance companies not incentivized to measure cost effectiveness of treatments in the US?

I think AI will make every complicated process much more accessible as you can interact with a, rounds to free, teacher that can explain each piece in the appropriate level of detail for your skill level.

I think the difference is everyone knew market penetration on cell phones would be close to 90%. This may be better than the Quest but is it going to take AR/VR mainstream? Seems iffy. In which case drawbacks may never get ironed out.

Presumably they're looking at comparable shoppers before and after one starts Ozempic during the same time period. Or are you implying people are starting Ozempic to avoid buying food?

>computer

Has always felt like a stretch to me for a device I'm not allowed to run my own code on.

>4.4 Cross-Subject Performance Cross-subject performance is of vital importance for practical usage. To further report the We further provide a comparison with both baseline methods and a representative meta-learning (DA/DG) method, MAML [9], which is widely used in cross-subject problems in EEG classification below. Table 2: Cross-subject performance average decreasing comparison on 18 human subjects, where MAML denotes the method with MAML training. The metric is the lower the better. Calib Data Method Eye fixation −∆(%) ↓ Raw EEG waves −∆(%) ↓ B-2 B-4 R-P R-F B-2 B-4 R-P R-F × Baseline 3.38 2.08 2.14 2.80 7.94 5.38 6.02 5.89 Baseline+MAML [9] 2.51 1.43 1.08 1.23 6.86 4.22 4.08 4.79 × DeWave 2.35 1.25 1.16 1.17 6.24 3.88 3.94 4.28 DeWave+MAML [9] 2.08 1.25 1.16 1.17 6.24 3.88 3.94 4.28 Figure 4: The cross-subjects performance variance without calibration In Table 2, we compare with MAML by reporting the average performance drop ratio between withinsubject and cross-subject translation metrics on 18 human subjects on both eye-fixation sliced features and raw EEG waves. We compare the DeWave with the baseline under both direct testing (without Calib data) and with MAML (with Calib data). The DeWave model shows superior performance in both settings. To further illustrate the performance variance on different subjects, we train the model by only using the data from subject YAG and test the metrics on all other subjects. The results are illustrated in Figure 4, where the radar chart denotes the performance is stable across different subjects.

Looks like it crosses over. That's wild.

You know that population isn't uniformly distributed across the states right?