In the next two months I am going to attend as a speaker five virtual events, with three different talks and different audiences. Some events are free, some are not. Details and registration links or discount codes below, all talks are in English.
The conference will run for 28 hours on 20-21 October 2020 and will cover topics on Open Source Databases and Applications using MySQL, PostgreSQL, MongoDB, and MariaDB. You can find the full agenda online and register for free.
Is serverless the future of relational databases? Looking forward to be part of DeveloperWeek Global: Cloud Conference, one of the world’s largest virtual software developer event series, and discuss relational databases on the cloud. See you online at the end of September! More about my talk here.
AWS has recently made Contact Center Intelligence (CCI) solutions available, a combination of services that enables companies to integrate contact centers with AI.
AWS has recently made AWS Wavelength zones in San Francisco and Boston available to provide a subset of their computing services on Verizon datacenters. The new zones will allow developers to build applications that can benefit from the ultra-low latency of the mobile carriers.
HashiCorp, the company behind the software tool Terraform, introduces a platform to run their products on AWS, Azure, and GCP as managed services. This will extend their enterprise offer with a focus on multi-cloud environments.
In a series of three technical articles, AWS has recently introduced the new “Serverless LAMP stack”. But not everyone in the open-source community believes that the successor of the LAMP stack is proprietary technologies from a single vendor, and alternative approaches have been suggested.
The AWS Serverless LAMP Stack: The Future of PHP or Vendor Lock-in?
Amazon RDS Proxy is a new fully managed, highly available database proxy for MySQL and PostgreSQL databases running on Amazon RDS and Aurora. The service is tailored to serverless architectures and other applications that open and close database connections at a high rate
With the sensitive topic of biased results for face recognition, Amazon Rekognition has been in the news a lot recently. Amazon announced a one-year moratorium on allowing law enforcement to use the facial recognition platform. IBM decided not to offer facial recognition technology anymore. Microsoft took a conservative approach too.
But Amazon Rekognition is not limited to face recognition, and the benefits and risks for image analysis are much broader. Object and scene detection is a powerful and useful technology to search and filter large image collections. But even with objects, there might be side effects.
The obvious benchmark is the flag of the United States of America. Amazon Rekognition labels it as a “Flag” with a 99.7% confidence and “American Flag” with a 92.5% confidence. A good and reliable result. But is it enough to trust the service for labelling flags worldwide?
What about Cuba?
Let us cross the Florida Straits and use the flag of Cuba as a comparison. Some similarities in colors and patterns, but still a very different flag. Not only politically.
The confidence level for the label “Flag” is still very high (99.4%) but surprisingly Rekognition has a 82.5% confidence of being the American flag. A glitch or a side effect of similarities in shape and design?
Il Tricolore
As an Italian, the natural choice for a different flag is il Tricolore: different colors, different patterns. But the confidence for the label “American flag” is even higher than for Cuba: 87.6%.
Malaysia
You can compare many other national flags but the results will be very similar. All are correctly labelled as “Flag” but they are labelled as well “American Flag” with various levels of confidence.
No other national flag label is detected, neither an “Italian Flag”, nor a “Cuban Flag”. Amazon Rekognition uses a hierarchical taxonomy of ancestor labels to categorize labels, but apparently the only child of “Flag” is “American Flag”. This likely reflects the main market for the product and the initial dataset for training.
The closer a flag resembles the Stars and Stripes, the higher the confidence level: for the flag of Malaysia (92.4%) is very similar to the one of the United States (92,5%). A demonstration that setting an arbitrary high confidence level might help but will not be safe in every scenario.
Any feedback from Amazon?
Last year I raised a ticket to AWS Support and the feedback was straightforward and honest:
I was in touch with the Rekognition engineering team as well as the Rekognition product team and I have relayed your message over to them. They acknowledged that Rekognition is currently not trained to identify the flags.
This is not an Amazon problem, this is your problem as a developer relying on an external service. If you integrate image recognition capabilities in your application, you have to manage the risks and challenges yourself. You cannot bury your head in the sand and hope for the best.
Accuracy is always relative
Setting an artificial confidence level number for the results of Amazon Rekognition is not enough. National flags are not the most important challenge for AI but they are an example of the risks when image detection is not handled properly. And mislabelled flags could even escalate tensions in conflict zones, disputed territories, or partially recognized states.
Fur further posts and talks on the challenges of geolocations, check saorico.com
