This is an embarrasingly bad approach to face recognition for a small set of frequently photographed people.
Several comments from the article give me concern
- They seem to think Rekognition is a panacea for their problem, but there are many known issues with Rekognition celebrity detection. Not to mention that the cost-per-request is often highly unfavorable compared with building a higher-accuracy, situation-specific solution with extensions to pre-trained models.
- They say some interns took a “novel approach” by creating a hard coded look-up table for disambiguating similar politician-celebrity pairs. This creates awful tech debt and failure cases. I’m not knocking it too hard because it’s pragmatic, which is a good sign about those interns, but this should be seen as a necessary wart to be improved, not a point of pride.
- As others have pointed out, even considering turnover in Congress, it seems like people who report on Congress for their full time job should recognize them. It truly seems like a silly, wasteful use of resources to solve this with computer vision.
This is all consistent with what I’ve heard from colleagues at NYT data science. As well as people I’ve known in data science bootcamps around New York, like Insight, who heard recruiting pitches.
Their department seems self-aggrandizing, using highly overwrought personalization models and seeming to have 538-envy for how they want their data science work to come off despite 538 exiting, among other important figures like Mike Bostock.
It just comes off as a place that wants to do status signalling to seem like a machine learning or data science thought-leader, but they don’t pay competitively or do what’s needed to retain good people and would rather do patchwork stuff like this with interns than to take the work a little more seriously.
I don’t get the impression it’s a place serious ML practitioners would want to go.
Several comments from the article give me concern
- They seem to think Rekognition is a panacea for their problem, but there are many known issues with Rekognition celebrity detection. Not to mention that the cost-per-request is often highly unfavorable compared with building a higher-accuracy, situation-specific solution with extensions to pre-trained models.
- They say some interns took a “novel approach” by creating a hard coded look-up table for disambiguating similar politician-celebrity pairs. This creates awful tech debt and failure cases. I’m not knocking it too hard because it’s pragmatic, which is a good sign about those interns, but this should be seen as a necessary wart to be improved, not a point of pride.
- As others have pointed out, even considering turnover in Congress, it seems like people who report on Congress for their full time job should recognize them. It truly seems like a silly, wasteful use of resources to solve this with computer vision.
This is all consistent with what I’ve heard from colleagues at NYT data science. As well as people I’ve known in data science bootcamps around New York, like Insight, who heard recruiting pitches.
Their department seems self-aggrandizing, using highly overwrought personalization models and seeming to have 538-envy for how they want their data science work to come off despite 538 exiting, among other important figures like Mike Bostock.
It just comes off as a place that wants to do status signalling to seem like a machine learning or data science thought-leader, but they don’t pay competitively or do what’s needed to retain good people and would rather do patchwork stuff like this with interns than to take the work a little more seriously.
I don’t get the impression it’s a place serious ML practitioners would want to go.