It does matter if you run your ML models in production. After we upgraded to 3.12 the average response time ~4-6ms decreased to stable sub 2ms. Latency decrease for p95 and p99 were even more significant
If this is the state-of-art then I have some bad news. I recently finished writing my thesis and copy-pasted several paragraphs that did not included any mathematical formulas. Most of them were classified as written by a machine (8 out of 12). It might be due that I am not a native speaker. It seems that the model struggled the most where there are mathematical details discussed but on the topics where I wrote more freely as conclusions and some analysis, it classified as written by human.
Isn't that an issue? for instance if someone made a video 'out of your domain' (e.g. different model than the interal training example) how would the model perform? Would the AUC be impacted? what is the PPV? It seems common in these results that people are experiencing false positives, i did as well. if the percentage of fake news that we read is 10% and the model (auc + operating point on a test set unpublished) has 92% sens and spec we would still expect that ~50% of model positives are true negatives. If the "accuracy" is computed in an unblanaced dataset, what is to be taken from it ?
What happens is it essentially collapses as it requires a set of people to train the model. Meaning that set of people with their biases are training an AI to determine what is fake and what isn't.
Sounds like a pretty bad idea especially if they decide to be gatekeepers of factual articles. It requires the entire team to know their biases one way or another. Regardless if they think it's "right" or not.
I had same problem few years ago but after installing StayFocusd[1] plugin in chrome I solved this issue. I set allowed time per day to 40 minutes to visit such websites as: reddit, fb, 9gag, youtube. If I need to watch video related to work/studies I use viewpure[2] that way I only watch only that particular video and don't lose my binge time. I also communicate with my friends a lot using fb so I just use messenger website instead of having fb opened directly.
Cool project! Last year I built self driving robot for my bachelor's thesis. Instead of building end-to-end deep learning pipeline I used two neural nets: one trained with genetic algorithm to drive a robot based on ultrasonic sensors, another for object recognition and detection. Based on detected items (like road signs) robot took different actions.
Nice project and cool video! (our video is not so well produced :P). Our project was a summer project, we did it in this January. The evolution of the project will be something like yours, we didn't apply any object detection technique.
