Can deep learning help find the perfect date?
When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app - the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the pursuit of the perfect match.
By Harm de Vries
One of the first things I did when I moved to Montreal was installing Tinder. For those of you not familiar with the online meat market, Tinder is a dating app showing nearby users you can like or dislike by their profile pictures. After using the app for a while, I discovered that although I consistently disliked girls with a lot of piercings and tattoos (no offense, just not my type), the app kept showing me these profiles. It surprised me that Tinder did not use my history of swipes to learn what type of girls I like.
This observation made me think: can a computer learn to which girls I'm attracted? I have tried by labeling over 9K profile pictures on Tinder and using deep learning, the latest revolution in artificial intelligence. In this blog post, I will provide a high-level view how I used these techniques to predict my Tinder swipes. You can find the technical details in this paper, which was accepted for the ICML deep learning workshop.
Computers have been around for many decades, but there still exists a lot of problems that the most powerful computers struggle to solve, yet we (as human beings) can do without effort. You can think of detecting faces in pictures, recognizing speech and translating text. The problem is that although we perform such tasks easily, we cannot explain how we do it. That means, we can not come up with a set of rules that can be transformed into a computer program.
Is predicting attractiveness from a profile picture such a task? Yes, it is simply too difficult to specify a set of rules to who you're attracted. For some of you this might sound counter-intuitive because you can specify a clear list of traits e.g. blue eyes, blond hair, etc. However, even if you could do that, you have to believe me that it is too complex to construct a program by hand that detects such characteristics in images.
On the other hand, if I show you a picture of a girl/boy you can decide in a split of a second whether you like her/him. This suggests a better strategy: we let the computer program itself by showing it profile pictures of girls you do or do not find attractive. We call this machine learning because the program learns a set of attractiveness rules from these profile pictures.
Deep learning is a particular class of learning algorithms that has recently lead to a breakthrough in image recognition (and many other artificial intelligence problems). If you want to see yourself how well these techniques work nowadays, I encourage you to upload a picture to
In deep learning, the structure of the program is an artificial neural network which is inspired by a biological neural network i.e. the brain. Neural networks are called deep when they consists of several layers of connected neurons as shown in the figure above. Each neuron is connected to a set of neurons in the previous layer. The strength of those connections (shown in red) determine to what image pattern the neuron responds. You can think of it as an adjustable filter, only responding when it sees a particular pattern.
The connection strengths of the network are randomly initialized, meaning that all neurons are just random filters. Therefore, when we feed an image to the network (by inputting the intensity of its pixels), only a few neurons in the first layer will respond, which in turn let some random neurons in the next layer to be active (and so forth till the output). As a result, the output of the network will be random like or dislike.
Therefore, in order to make good predictions, we need to train the network. We do so by inputting a profile picture and propagating it through the network to get the output. If the network's prediction is different from the desired output (like or dislike), we adjust its filters (i.e. its connection strengths) to make the prediction more correct. How we exactly adjust the filters is rather difficult and outside the scope of this blog post, all you need to know is that we can do it. The training phase is nothing more than repeating this process for many examples, and then hope that the network predicts well for profile pictures it has not seen yet.
After training, we often observe that the neurons in the first layer respond to edges and color blobs, while the next layer uses these observations to detect more complex patterns such as eyes and ears. The last layer might detect complete faces or piercings and tattoos (which is important to predict my preferences).
See a continuation: Can Deep Learning Help you Find the Perfect Girl? – Part 2.
Harm de Vries is a PhD student supervised by Aaron Courville and Roland Memisevic, and a member of the Montreal Institute for Learning Algorithms headed by Yoshua Bengio. His main interests are in the theory and application of deep learning.
Original by Harm de Vries.
Related:
One of the first things I did when I moved to Montreal was installing Tinder. For those of you not familiar with the online meat market, Tinder is a dating app showing nearby users you can like or dislike by their profile pictures. After using the app for a while, I discovered that although I consistently disliked girls with a lot of piercings and tattoos (no offense, just not my type), the app kept showing me these profiles. It surprised me that Tinder did not use my history of swipes to learn what type of girls I like.
This observation made me think: can a computer learn to which girls I'm attracted? I have tried by labeling over 9K profile pictures on Tinder and using deep learning, the latest revolution in artificial intelligence. In this blog post, I will provide a high-level view how I used these techniques to predict my Tinder swipes. You can find the technical details in this paper, which was accepted for the ICML deep learning workshop.
Machine learning
Computers have been around for many decades, but there still exists a lot of problems that the most powerful computers struggle to solve, yet we (as human beings) can do without effort. You can think of detecting faces in pictures, recognizing speech and translating text. The problem is that although we perform such tasks easily, we cannot explain how we do it. That means, we can not come up with a set of rules that can be transformed into a computer program.
Is predicting attractiveness from a profile picture such a task? Yes, it is simply too difficult to specify a set of rules to who you're attracted. For some of you this might sound counter-intuitive because you can specify a clear list of traits e.g. blue eyes, blond hair, etc. However, even if you could do that, you have to believe me that it is too complex to construct a program by hand that detects such characteristics in images.
On the other hand, if I show you a picture of a girl/boy you can decide in a split of a second whether you like her/him. This suggests a better strategy: we let the computer program itself by showing it profile pictures of girls you do or do not find attractive. We call this machine learning because the program learns a set of attractiveness rules from these profile pictures.
Deep learning
Deep learning is a particular class of learning algorithms that has recently lead to a breakthrough in image recognition (and many other artificial intelligence problems). If you want to see yourself how well these techniques work nowadays, I encourage you to upload a picture to
- how-old.net to estimate the age of the persons in the picture
- imageidentify.com to detect what's in the picture
In deep learning, the structure of the program is an artificial neural network which is inspired by a biological neural network i.e. the brain. Neural networks are called deep when they consists of several layers of connected neurons as shown in the figure above. Each neuron is connected to a set of neurons in the previous layer. The strength of those connections (shown in red) determine to what image pattern the neuron responds. You can think of it as an adjustable filter, only responding when it sees a particular pattern.
The connection strengths of the network are randomly initialized, meaning that all neurons are just random filters. Therefore, when we feed an image to the network (by inputting the intensity of its pixels), only a few neurons in the first layer will respond, which in turn let some random neurons in the next layer to be active (and so forth till the output). As a result, the output of the network will be random like or dislike.
Therefore, in order to make good predictions, we need to train the network. We do so by inputting a profile picture and propagating it through the network to get the output. If the network's prediction is different from the desired output (like or dislike), we adjust its filters (i.e. its connection strengths) to make the prediction more correct. How we exactly adjust the filters is rather difficult and outside the scope of this blog post, all you need to know is that we can do it. The training phase is nothing more than repeating this process for many examples, and then hope that the network predicts well for profile pictures it has not seen yet.
After training, we often observe that the neurons in the first layer respond to edges and color blobs, while the next layer uses these observations to detect more complex patterns such as eyes and ears. The last layer might detect complete faces or piercings and tattoos (which is important to predict my preferences).
See a continuation: Can Deep Learning Help you Find the Perfect Girl? – Part 2.
Harm de Vries is a PhD student supervised by Aaron Courville and Roland Memisevic, and a member of the Montreal Institute for Learning Algorithms headed by Yoshua Bengio. His main interests are in the theory and application of deep learning.
Original by Harm de Vries.
Related:
- Love, Sex and Predictive Analytics
- Thomas Levi, PlentyOfFish on What does Big Data tell us about Romance
- Cartoon: Where humans are still ahead of Deep Learning