As a food lover, I found this an interesting article and had never thought about the applications of 3D printing to food. I also appreciate the further thoughts on the applications and limitations of the application, which can be sensitive. In particular, how does the business convince the consumers that this is a legitimate and scalable solution for day to day consumption.
Great post on ML application in social media space. To your question, there are predictive capability from machine learning by detecting the pattern and training historical datasets. Through the automatic training algorithm, the modeling all are going through the optimization process overtime, which cannot be replaced by human being. The massive info consumed, analyzed and optimized in parallel are the true beauties of machine learning.
This is an interesting article exploring 3D printing application in Boeing. I particularly enjoyed how to author brought up its in limitation in critical vs non critical parts of the airplane. Would also be interested to hear how Boeing try to address the quality assurance issue of 3D in this very sensitive space. Especially, for barriers to entry for military aircrafts, what are the effective ways to mitigate the risk and concerns.
Very interesting read on applying AI solution for a very challenging systematic problem we are facing today. I appreciate the thought provoking process exhibited in this blog. In particular, the author questioned whether the fact check process should solely based on machine learning algorithm. I am personally interested in how humanity play in the fact checking role and whether it needs to be prioritized first. Especially, when Factmata performed on the text detects up to four dimensions:
Hate speech and abusive content
Propaganda and extremely politically biased content
Spoof websites and content spread by known fake news networks
Extreme clickbait content
How does text classification process work when you try to detect the four dimensions; it’s predefined by algorithm or this is also a self-training process. I would imagine the process itself matters more than the result?
This is a very interesting read on leveraging machine learning, mostly image and text recognition and classification, to drive the success of the business from both supply (seller) and demand (consumer) side. I also appreciate the thoughts on exploring the predicting algorithm to driver the ML to the next level. In addition, the author emphasized the rank (priority) of different meta datasets being used, which is often neglected in a lot of business cases. Lastly, the author took the step back and really question the level of effectiveness of personalization for this particular business model, which I found it very thoughtful!
Very interesting read on AI application in shipping & cargo space. I also appreciate the thought on potentially looking for outsourcing model rather than trying to develop in-house solution. Would also be curious how does competitors place in this space and case example of a successful application of leveraging AI for shipping cost tracking & optimization.