This is an emerging research area targeting to solve social science theories by using computation, and most notably with Big Data and Machine learning techniques.
I was thinking in this morning, especially after the last Cambridge Analytica scandal on Facebook, that there should be a new kind of privacy-first data analysis process in Facebook without sharing the data with external companies. In the current system, the flow is: the user accepts permission request of the application, and the app owner is collecting the data on its own platform and doing analysis on it, selling it to another firm, etc… Instead, the data analysis task should be executed on the control of Facebook. In the new system, when the app wants permission, the facebook will alert: this app wants to do an analysis of your data, we will never share the data with him, the analysis that the app will do will be executed on my platform, I reviewed and controlled their codes, (like the Apple Store code review process ), and we’ll share the result of the analysis with both you and the app owner ( such as you are supporting 80% conservative party), and the app owner will also tell you how and for what purpose it will use this result.
Recently, I tried several products to extract demographic information from a profile image. My target was to obtain information about age, gender, and ethnicity. I found the prominent companies in the sector are Clarifai and Face++. I integrated my trial software with both products and I found Clarifai’s accuracy better than Face++. My reasons are:
- Clarifai provides the probability value of its predictions. (predicted gender is female with a probability %52) So, it is possible to eliminate the results having low prediction score. On the contrast, Face++ does not provide that value. This is an unwanted situation because, in binary classification technique, the prediction always has a result, even its score is not very high.
- Clarifai correctly predicted the ethnicity of the image below as “White”, while Face++ wrongly predicted it as “Black”. But on the other hand, Clarifai could not found the gender value correctly (female %51, male %49) while Face++ correctly marked it as male (we don’t know its probability).
- The disadvantage of Clarifai is its low quota for free usages. It permits only 2500 API calls per month for free accounts. But Face++ does not specify any upper limit for free accounts. It has only one single limitation, which is one single API call per second.
I hope my hands-on experience with these services will help you choose the right product.
Result of Clarifai: (https://clarifai.com/demo)
Gender: feminine (prob. score: 0.510), masculine(prob. score: 0.490)
Age: 55 (prob. score: 0.356)
Ethnicity (Multicultural appearance): White: (prob. score: 0.981)
Result of Face++: (https://www.faceplusplus.com/attributes/#demo)
Ethnicity (Multicultural appearance): Black
Within the last month, the future of education was one of the main topics in Davos. There were very interesting debates, and in of them, Jack Ma (the founder of Alibaba) told that it is strongly and urgently needed to change the current education system due to the rising impact of robots. Since robots are able to obtain the knowledge, by learning from their past experiences, they will do most of the things people do today. In order to adapt ourselves to the modern world, we need to educate our children in a way that cannot be copied by robots. Rather than teaching mathematics or physics to our children, we should support their more humanistic skills such as music and art.
I agree with Jack Ma’s ideas and I think we need to think more about people’s main advantages and disadvantages over robots in the next 20 years. Today, our children start learning to code in primary school, in order to communicate better with the robots and understand their logic. But when the world will be dominated by robot activities, all the things will be changed and humans should be in a place where robots do not see them as a threat.
I started to the MongoDB developer course given online by MongoDB University. I have worked a lot with Mongo at Vodafone but I was using only 10-20% of its key features. Now at Politecnico, the things are more complex so I need to pay more attention to the performance issues. In my research project, I use MongoDB to store Tweets and perform text analysis over the records.
I currently completed the Week-1 course. I hope I will learn more in the upcoming weeks.
I am curious about this technology. I was expecting the advances in Wi-Fi technology, however, anybody could imagine that the led lights will be the next data streaming resources. There are many concerns about the data streaming feature of the light including its potential impacts on the health. There are still grey points in my mind about how can be a complete synchronous data streaming in all over the world with the li-fi. This is truly the innovation. Congrats Prof. Harald Haas!!