27 karma · joined January 27, 2019
For example in predictive typing anything above ~100ms (don't remember the number but there's an exact measurement on the Gmail paper for smart compose) is noticeable and very annoying to a user.
Or video chat, I'd be curious to see the video latency required for a user to notice lag between audio & video.
I know in game dev, I was always taught that 60fps is fine, but then there are gamers swearing up and down when they drop below 140fps. Would be curious to see what the max allowed latency is here as well.
Another common one is the microexpressions latency which is frequently quoted as 1/20 of a second (max latency before an emotion detector starts to miss the microexpression).
I feel like these are the real goalposts in tech; especially in ML, we've got the accuracy/quality and now we need the speed. Every research paper likes to brag about how many ms it takes for one step, I'd like to see this compared to the maximum latency required for practical HCI.
We store no speech data at all! On Google Chrome, speech recog is done via Web Speech API. They claim not to store speech data in their privacy whitepaper: https://www.google.com/chrome/privacy/whitepaper.html#speech
- works right in your browser with no downloads or installs - comes with all the cool video call features (screenshare, chat, picture in picture) - integrates speech recognition to provide subtitles and a full call transcript!
We're also trending #1 on Product Hunt right now! There's a 7 day trial so you can try it out, and we'd love to get feedback: https://www.producthunt.com/posts/aiko-meet
Also do AWS credits apply to this? I and most startup founders I know have upwards of 30k$ in AWS credits usually sitting around so this would definitely go a long way as to integrating translation without running a pre-scale cost.
According to their support the only way to change basically anything is to open an entirely new account. So we're currently tracking down and collecting documents all over again, except this time we have to spend a bunch on legal fees since we had Atlas to help us through the first time.
And thank god, HTTPS support built in... it never ceases to amaze me why some HTTP libs don't have TLS as a given.
Let the chaos ensue!
i.e. if I was a total noob and I wanted to make an application using AI to detect if people in the crowd were bored, I would have no idea where to start without reading/researching for hours online on different fields and models that work and how they work. It would be neat if there was a tool that just asked you a few questions, then took that info and gave you a roadmap, i.e. "Feed Forward Neural Networks, Digit Classification, Image Classification w/ Inception, Object Detection with ResNet + Inception, Optimizing TensorFlow code for Servers, Deploying TensorFlow with Docker, Protecting Against Adversarial Input"
This way someone with a time sensitive project doesn't have to learn TF for 6 months before being able to accomplish what they wanted! Just something I think would be neat and also possible to add to TF World.
For example, et's take Paladins and Overwatch. Developed/released at the same time, with similar playstyles and roles.
Paladins, although having more features and variations allowing changing playstyles, suffers from so many bugs that it's basically all you hear about. On the other hand Overwatch has somewhat less variation in playstyle and a more toxic playerbase, but is very reliable with excellent quality, and the devs fix bugs as fast as they can with a focus more on polish and gameplay than putting out tons of skins.
At a high level it seems like pro/cons, and it is difficult to gauge interest because Overwatch is much more popular/instated, but then looking at the trends: https://redditprofile.com/compare?search=paladins,overwatch
It becomes super clear that Overwatch has way higher sentiment. I assume this may be slightly biased as Paladins, having a smaller playerbase, probably mostly has posters who are dedicated to the game and so are more heavily influenced by bugs and QoL issues; but I don't see it being enough to justify a 100:15 ratio in relative sentiment. You can also see clear dips around the times that game-breaking bugs were discovered, or when HiRez/EvilMojo did an oopsie.
This is just an example but I figure offsetting trends in this way is going to be very powerful. It better visualizes product sentiment across competing products in a way that is mostly offset at scale from "popularity" (which makes other trend metrics i.e. Google Trends too biased to use).