NLP and Conversational AI for Compassionate AI Psychologist with Human-like Memory

NLP and Conversational AI for Compassionate AI Psychologist with Human-like Memory

Build an AI psychologist which will be able to conduct compassionate conversations by using a voice-based conversational AI approach, with the ability to identify the emotions of the users and their underlying mental health state. In this 8-week challenge, you will join a collaborative team of 50 AI engineers.

 

The problem

During the pandemic, we saw a mental health ‘surge’ with a sharp increase in people experiencing anxiety (health anxiety, loneliness, etc.) and stress (including Post Traumatic Stress).

Surveys by such organizations as YouGov and Mental Health America show that, during the pandemic, workers were more stressed than ever. This included anxiety regarding health, finance, and employment, as well as broader issues associated with PTSD, burnout, and moderate depression.

Nearly two-thirds of the UK population (63%) felt anxious at least several times a month during the Spring of 2020. One in five (20%) reported feeling anxious on most days of the week or even more frequently.

Mental Health America’s 2021 study shows the strain on employees, with burnout prevalent and more than half of the respondents actively looking for another job.

There is an increased strain on an already-overstretched mental health service that, in some part, has been accommodated by a growing number of apps as well as telehealth calls (Skype / Zoom calls to human therapists).

 

The project goals

The project goal is to build an AI psychologist which will be able to conduct compassionate conversations by using a voice-based conversational AI approach, with the ability to identify the emotions of the user and their underlying mental health state and the capacity to remember these users’ states by possessing a human-like memory.

 

The data

This project will require the manual collection of data.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Building an NLP Engine to Identify, Analyze, and Recommend Actions to Psychological Violence

Building an NLP Engine to Identify, Analyze, and Recommend Actions to Psychological Violence

This two-month Omdena AI Challenge developed an NLP model to identify early patterns of psychological violence. The challenge partner Violetta is a platform to fight psychological violence through technology in Mexico and Latin America.

 

The problem and impact

Violetta seeks to positively impact the population that experiences a situation of violence to a lesser degree, which is why it is often not identified as violence. Violetta provides information in an empathetic, friendly, and unbiased manner to promote prevention and early recognition of patterns of violence.

The impact of the platform also transcends the gender issue, since “Violetta” is aimed at women and men between 18 and 45 years of age who use social networks to interact and who seek some kind of support when they detect something unusual or some unpleasant behavior on the part of the people with whom they live, either in their family, partner and work nuclei.

 

The project outcomes

Help to develop a Multi-Label Text categorization model to identify psychological violence within a text sent by the user through a conversational platform. Next, the models give recommendations based on the specific category of violence. 

Outcomes:

  • A model trained with the given data and external violence data with accuracy near to 93% 
  • Categorize the text inputs into different categories.
  • Give recommendations to the user with the percentage of risk of psychological violence based on text classification and the model. 
  • Develop a dashboard to analyze the data. The dashboard measures the sentiment and shows the topics that are being talked about to understand trends in real-time.

 

Further: The NLP model will be integrated into a chatbot.

 

Violeta about the AI Challenge results