Here is my annoyance with AI Hype: people seeking extra tailwinds pitch their startups as "ML companies" even with the most tangential usage of ML. It drowns out real ML companies. Most people cannot tell the difference.
At a hackathon recently, someone pitched their app as an "ML-driven app" though the only ML in there was some 1-line language translation feature they were consuming off Watson REST services for a tangential feature on their app.
Meanwhile, my submission actually used a self-trained CNN on a custom dataset using TensorFlow and changes to start/end layers on the NN. The image classification features were the core of the app and it wasn't something that was just a wrapper over an Off-the-shelf API. We actually tried multiple networks and went thru the trouble of parameterizing everything.
At the end, I wonder how many judges actually understood the difference in effort/value to the two attempts at ML.
At a hackathon recently, someone pitched their app as an "ML-driven app" though the only ML in there was some 1-line language translation feature they were consuming off Watson REST services for a tangential feature on their app.
Meanwhile, my submission actually used a self-trained CNN on a custom dataset using TensorFlow and changes to start/end layers on the NN. The image classification features were the core of the app and it wasn't something that was just a wrapper over an Off-the-shelf API. We actually tried multiple networks and went thru the trouble of parameterizing everything.
At the end, I wonder how many judges actually understood the difference in effort/value to the two attempts at ML.