Suramya's Blog : Welcome to my crazy life…

I am not the most fervent fan of emojis in the world and for the most part I have gone through the last 20+ years with about 3-4 emojis that I use on a regular basis. But I know people who communicate wholly using them and while I am ok with folks using them in personal communications I dislike them immensely in professional communications (except for the occasional smiley face). However not everyone agrees with me and there have been books published in the past using just Emoji’s and now there is a programming language that is written entirely in emoji.

Emojicode, which first appeared as a Github project back in 2016 has been around for a while now and has a fairly strong following in the tech world. I realize that I sound like one of the old men screaming ‘Get off my lawn’ but I really don’t understand why anyone would want to code in a language that uses emoji’s to define text as a serious programming language. As a joke it would be fun to learn but I can’t really imagine coming in to work one day and writing code using Emojocode for a work project.

Here’s an example of the “Hello World” program written using Emojicode.

Hello World using Emojicode

If you want to learn coding in Emojicode, you can check out their impressive documentation or do a Code Academy course on the language. Emojicode is open-source, so you can also contribute to the development via their GitHub repository.

Yes, learning new programming languages is cool, but I don’t think I will be spending the effort to learn Emojicode anytime in the near future.

– Suramya

Depression is brutal, it can range from feelings of sadness, loss, or anger that interfere with a person’s everyday activities to suicidal tendencies. In the past few months there have been multiple cases of famous people committing suicide because they were depressed and unable to cope with the feelings of isolation and stress brought about by the current pandemic. Unfortunately depression is not an easy thing to diagnose and there isn’t a single test to diagnose it. Doctors can diagnose it based on your symptoms and a psychological evaluation which in most cases includes questions about your:

• moods
• appetite
• sleep pattern
• activity level
• thoughts etc

But all of this requires a person to be open about their thoughts and that can be difficult at times due to the stigma associated with mental health issues. In all of the cases I was referring to earlier the common theme from the friends & acquaintances have been about how they wish they had known that xyz was depressed and if they had then maybe they could have helped.

The problem is that people don’t always come out and say that they are depressed and sometime the signals are very faint. So its very interesting to see the various efforts that are underway to identify these symptoms earlier and get the people the help they need faster so that they don’t have to face it alone. As part of this effort scientists at Canada’s University of Alberta have created a machine learning model that uses linguistic clues to indicate signs of depression in text communications like twitter messages and have published a paper on it (Augmenting Semantic Representation of Depressive Language: From Forums to Microblogs) in the ‘European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database’.

We discuss and analyze the process of creating word embedding feature representations specifically designed for a learning task when annotated data is scarce, like depressive language detection from Tweets. We start from rich word embedding pre-trained from a general dataset, then enhance it with embedding learned from a domain specific but relatively much smaller dataset. Our strengthened representation portrays better the domain of depression we are interested in as it combines the semantics learned from the specific domain and word coverage from the general language. We present a comparative analyses of our word embedding representations with a simple bag-of-words model, a well known sentiment lexicon, a psycholinguistic lexicon, and a general pre-trained word embedding, based on their efficacy in accurately identifying depressive Tweets. We show that our representations achieve a significantly better F1 score than the others when applied to a high quality dataset.

This is not the first study on the topic and it won’t be the last. The paper is fairly technical and from what I can understand they can identify potential signs of depression based on words used and phrasing. But am not sure how they are taking into account sarcasm and contextual clues. For example without the appropriate context things being said can be taken in many different ways and identifying the correct emotion behind the words can be tricky. When we interact in person or over phone things like body language or verbal cues give us additional context about how a person is feeling, unfortunately that is not the case with text and there is a huge potential for things to be taken out of context or in the wrong way. Another issue is how to differentiate between feelings of sadness and depression as the symptoms might be very similar.

We need human interactions, connections etc to address this issue and not another technology claiming to be a silver bullet as not everything can be solved by AI/ML and the low accuracy level on such solutions can only cause trouble down the line. Imagine such a system being implemented at workplaces, during interviews or on dating sites. If a system flagged you as a depressive then it could cost you your job, or your relationship.

What do you think?

– Suramya

Over the past few months/years we have been reading a lot about AI being used to identify emotions like fear, confusion and even traits like lying or trustworthiness of a person by analyzing video & audio recordings. This is driving innovations in Recruiting, Criminal investigations etc. In fact the global emotion detection and recognition market is estimated to witness a compound annual growth rate of 32.7% between 2018 – 2023, driving the market to reach USD 24.74 billion by 2020. So a lot of companies are focusing their efforts in this space as AI applications that are emotionally aware give a more realistic experience for users. However, there are multiple privacy implications of having a system detect a person’s emotional state when interacting with an online system.

So to counter this trend of systems becoming more and more aware there is now a group of researchers who have come up with an AI-based countermeasure to mask emotion in spoken words, kicking off an arms race between the two factions. The idea is to automatically converting emotional speech into “normal” speech using AI.

Their method for masking emotion involves collecting speech, analyzing it, and extracting emotional features from the raw signal. Next, an AI program trains on this signal and replaces the emotional indicators in speech, flattening them. Finally, a voice synthesizer re-generates the normalized speech using the AIs outputs, which gets sent to the cloud. The researchers say that this method reduced emotional identification by 96 percent in an experiment, although speech recognition accuracy decreased, with a word error rate of 35 percent.

In a way its quite cool because it removes a potential privacy issue, but if you extrapolate from existing research then we have the potential of bigger headaches in the future. Currently we have the capability of removing emotion from a audio recording, how difficult would it be to add emotion to a recording? Not too difficult if you go through the ongoing research. So, now we have a system that can take a audio/video recording and change the emotion from sadness to mocking or from happy to sad. This combined with the deepfakes apps that are already there in the market will cause huge headaches for the public as it would be really hard for us to determine if a given audio/video is authentic or altered.

Article: Researchers Created AI That Hides Your Emotions From Other AI
Paper: Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants

Well this is all for now. Will write more later.

– Suramya