I know I need to get ready for work, but I'm just so so tired. Maybe I can skip work to take a nap justice once. What's that? Oh, my depression monitors detecting early signs of a depressive state. I thought it was just tired, but this might be a bigger issue. Let me set in an appointment with my therapist. Hey there, I'm grain class and this is technically speaking an Intel podcast. The show is dedicated to highlighting the way technology is revolutionizing the way we live, work and move. In every episode, we'll connect with innovators in areas like artificial intelligence to better understand the human centered technology they've developed. Mental health care solutions remain underinvested in many communities around the world, yet so many suffer from issues that they don't even know. They have a lot of these conversations around health care, hinge on making therapy more accessible to those in need. However, it can be difficult to determine that one is experiencing depression or mental health crisis. Artificial intelligence is at the forefront of many different advancements in healthcare, but today we are going to dive into how it is working to make mental health care more accessible. To everyone. In order to do that, I have to introduce you to a special guest joining me now is Tinasawho. Tina Saho was a high school student when she started exploring coding and engineering with AI. She never considered herself to be overly interested in technology to start. However, she took her natural curiosity and eventually was invited to be a part of Intel's AI for Youth Pilot program, where she developed a tool that uses AI to detect and predict patterns of depression with around eighty percent accuracy. Since then, she's been awarded the Calm Trust Fellowship for Women. She currently attends to Ayabata College at the University of Delhi, where she's completing a bachelor's in computer science and exploring more opportunities to use AI as a tool for mental health and more.

I know I need to get ready for work, but I'm just so so tired. Maybe I can skip work to take a nap justice once. What's that? Oh, my depression monitors detecting early signs of a depressive state. I thought it was just tired, but this might be a bigger issue. Let me set in an appointment with my therapist. Hey there, I'm grain class and this is technically speaking an Intel podcast. The show is dedicated to highlighting the way technology is revolutionizing the way we live, work and move. In every episode, we'll connect with innovators in areas like artificial intelligence to better understand the human centered technology they've developed. Mental health care solutions remain underinvested in many communities around the world, yet so many suffer from issues that they don't even know. They have a lot of these conversations around health care, hinge on making therapy more accessible to those in need. However, it can be difficult to determine that one is experiencing depression or mental health crisis. Artificial intelligence is at the forefront of many different advancements in healthcare, but today we are going to dive into how it is working to make mental health care more accessible. To everyone. In order to do that, I have to introduce you to a special guest joining me now is Tinasawho. Tina Saho was a high school student when she started exploring coding and engineering with AI. She never considered herself to be overly interested in technology to start. However, she took her natural curiosity and eventually was invited to be a part of Intel's AI for Youth Pilot program, where she developed a tool that uses AI to detect and predict patterns of depression with around eighty percent accuracy. Since then, she's been awarded the Calm Trust Fellowship for Women. She currently attends to Ayabata College at the University of Delhi, where she's completing a bachelor's in computer science and exploring more opportunities to use AI as a tool for mental health and more.

Welcome to the show, Tina, Hi, thank you for inviting me to this show, and I'm feeling more than privileged to be a part of this podcast. So numbers stay to everyone.

Welcome to the show, Tina, Hi, thank you for inviting me to this show, and I'm feeling more than privileged to be a part of this podcast. So numbers stay to everyone.

I'm really interested in your story of how you got into the STEM field, what prompted your interest in that field and also in AI.

I'm really interested in your story of how you got into the STEM field, what prompted your interest in that field and also in AI.

To begin with, till my tenth class, I was a student or a person who was always against technology, like I always found in fact that the negative impacts that technology brings and whatever ethical concerns are there, they cannot be solved and they are simply too much to be on the side of technology. But I remember in twenty nineteen, the AAFO Youth program was launched and it was launched in our school, which is Salvangal Senior Secondary School. So when I participated in that program, I got to know what artificial intelligence is, how many astonishing and stounding possibilities AI can unlock and has already unlocked. And therefore, this program pivoted my you know journey, my career, my professional journey, and my cademic journey from being a non tech student to becoming a tech student finally pursuing computer science as my graduation.

To begin with, till my tenth class, I was a student or a person who was always against technology, like I always found in fact that the negative impacts that technology brings and whatever ethical concerns are there, they cannot be solved and they are simply too much to be on the side of technology. But I remember in twenty nineteen, the AAFO Youth program was launched and it was launched in our school, which is Salvangal Senior Secondary School. So when I participated in that program, I got to know what artificial intelligence is, how many astonishing and stounding possibilities AI can unlock and has already unlocked. And therefore, this program pivoted my you know journey, my career, my professional journey, and my cademic journey from being a non tech student to becoming a tech student finally pursuing computer science as my graduation.

Okay, was there a particular topic or person that really did spark that interest? What was the particular topic that actually got you really fired up?

Okay, was there a particular topic or person that really did spark that interest? What was the particular topic that actually got you really fired up?

So in my school there were two teachers who actually you know, molded my thinking and they actually infused this critical thinking aspect in me and they opened me to the world of possibilities that science, that technology and engineering offers. And I would like to take their name as well as a you know, token of respect. So it's one that Mam and Serbiam they empowered me. They encouraged me to think beyond what I see.

So in my school there were two teachers who actually you know, molded my thinking and they actually infused this critical thinking aspect in me and they opened me to the world of possibilities that science, that technology and engineering offers. And I would like to take their name as well as a you know, token of respect. So it's one that Mam and Serbiam they empowered me. They encouraged me to think beyond what I see.

And that led you to the Intel's AI for Youth program. What sort of projects have you worked on? Are you working on right now?

And that led you to the Intel's AI for Youth program. What sort of projects have you worked on? Are you working on right now?

This isn't my participating in this program. I build Happiness Guru, which is a model that predicts depression. Apart from that too, I was also a part of Utul Tinkling Labs. Through the Detail Lab of our school. I basically got to know about Intel's AfOR Youth program only and in there itself, I build a few projects and one of them was Happiness Guru.

This isn't my participating in this program. I build Happiness Guru, which is a model that predicts depression. Apart from that too, I was also a part of Utul Tinkling Labs. Through the Detail Lab of our school. I basically got to know about Intel's AfOR Youth program only and in there itself, I build a few projects and one of them was Happiness Guru.

And with the Happiness Crew, is that related to the depression detection research you've been doing? What kind of prompted you to look in that direction in the field of depression and then using technology? Because generally, speaker, we don't associate technology with treating depression or even detecting depression. So what was the spark for you there?

And with the Happiness Crew, is that related to the depression detection research you've been doing? What kind of prompted you to look in that direction in the field of depression and then using technology? Because generally, speaker, we don't associate technology with treating depression or even detecting depression. So what was the spark for you there?

So while I was in this program, I was in Class ten and my Plus ten results were out and they were not as much as I expected, and I went into a phase of depression because I associated myself worth with the Marxi score, so my own personal experience of dealing with depression. And then at that time, you know that society rates were very alarming among the youth, especially aged between fifteen to twenty nine. And we found that the driving forces behind you know these when these societies were you know, peer pressure, overburdening academics, financial stress, and too much expectations that we have from youth, you know, especially if you talk about teenage and someone who is in between eighteen to twenty five. So on researching, we found that these societ rates are very alarming, they're very distressing, and these are the leading cause of the depression or stress. And thereby we thought that we must come up with some solution that can basically help us predict which person is going through depression, and that to in a very human friendly manner, not making someone uncomfortable with the kind of procedures or with the kind of system we have. So these were the I would say, the enablers that led our team building this solution.

So while I was in this program, I was in Class ten and my Plus ten results were out and they were not as much as I expected, and I went into a phase of depression because I associated myself worth with the Marxi score, so my own personal experience of dealing with depression. And then at that time, you know that society rates were very alarming among the youth, especially aged between fifteen to twenty nine. And we found that the driving forces behind you know these when these societies were you know, peer pressure, overburdening academics, financial stress, and too much expectations that we have from youth, you know, especially if you talk about teenage and someone who is in between eighteen to twenty five. So on researching, we found that these societ rates are very alarming, they're very distressing, and these are the leading cause of the depression or stress. And thereby we thought that we must come up with some solution that can basically help us predict which person is going through depression, and that to in a very human friendly manner, not making someone uncomfortable with the kind of procedures or with the kind of system we have. So these were the I would say, the enablers that led our team building this solution.

And in terms of the happiness grew app can you just explain how it actually works, you know, to try and detect the early signs of depression.

And in terms of the happiness grew app can you just explain how it actually works, you know, to try and detect the early signs of depression.

First of all, it's our web based application. While building this project, the queue that we took, you know, to build the entire model was that after our research, we got to know that a person's vocabulary can be a mirror into their mental state. And taking this as the queue, we build this project which tries to analyze the emotional quotient of a person of a user through their facial expression and then their textual responses that the user is going to provide to the AA machine. So the working of the project is divided into three steps. The first step is emotion detection stage, and in this stage you basically need to stand in front of your laptop or whatever device you are using this web application, and then it detects your current mode, whether you're happy or sad, you're neutral, angry. Then the next step is that user is asked to answer nine questions and there's a scale of relevance and then they need to select how much relevant or how much they are able to relate this to this situation. Then, after these two steps, a threshold score is generated which gives the initial lead. If the person is stressed or not, and if the score is below the threshold that we have said, the person is predicted as happy, while in the other case, user is taken to the third step, which is the final step. And this step consists of four descriptive questions which he or she can use as a platform to went out all his or her feelings and thoughts. So whatever answers user will give to these four descriptive questions, these answers will be used as a basis of classification. Then the machine will predict whether the user is depressed or not. So this AI machine, whatever you know input we are giving in this step. There's a model namely SVM, which is support vector machine. It's a non contextual classification model. It is basically used to classify things. And then we are using this model on the kind of you know, language or keywords that are used in the answers. And then accordingly the results are given out that whether the person is stressed or not, and if the person is stressed, automatically the person is consulted to the concerned authorities or counselor otherwise the person is predicted as happy or not stressed.

First of all, it's our web based application. While building this project, the queue that we took, you know, to build the entire model was that after our research, we got to know that a person's vocabulary can be a mirror into their mental state. And taking this as the queue, we build this project which tries to analyze the emotional quotient of a person of a user through their facial expression and then their textual responses that the user is going to provide to the AA machine. So the working of the project is divided into three steps. The first step is emotion detection stage, and in this stage you basically need to stand in front of your laptop or whatever device you are using this web application, and then it detects your current mode, whether you're happy or sad, you're neutral, angry. Then the next step is that user is asked to answer nine questions and there's a scale of relevance and then they need to select how much relevant or how much they are able to relate this to this situation. Then, after these two steps, a threshold score is generated which gives the initial lead. If the person is stressed or not, and if the score is below the threshold that we have said, the person is predicted as happy, while in the other case, user is taken to the third step, which is the final step. And this step consists of four descriptive questions which he or she can use as a platform to went out all his or her feelings and thoughts. So whatever answers user will give to these four descriptive questions, these answers will be used as a basis of classification. Then the machine will predict whether the user is depressed or not. So this AI machine, whatever you know input we are giving in this step. There's a model namely SVM, which is support vector machine. It's a non contextual classification model. It is basically used to classify things. And then we are using this model on the kind of you know, language or keywords that are used in the answers. And then accordingly the results are given out that whether the person is stressed or not, and if the person is stressed, automatically the person is consulted to the concerned authorities or counselor otherwise the person is predicted as happy or not stressed.

Detecting and treating mental health is something with which many societies around the world struggle. According to the World Health Organization, approximately two hundred and eighty million people in the world suffer from depression and more than three hundred million are living with anxiety. Many people with these mental health conditions exhibit some symptoms as children or young adults, but based on guidance from the US National Institute of Mental Health, depression can only be diagnosed once an individual exhibits the five major symptoms of depression every day, all day for a minimum of two weeks. Imagine how we could help people earlier if we were able to identify depression with the help of AI tools like the Happiness Guru model. How does one actually create that model? What data is needed to train that model so that it can get that output.

Detecting and treating mental health is something with which many societies around the world struggle. According to the World Health Organization, approximately two hundred and eighty million people in the world suffer from depression and more than three hundred million are living with anxiety. Many people with these mental health conditions exhibit some symptoms as children or young adults, but based on guidance from the US National Institute of Mental Health, depression can only be diagnosed once an individual exhibits the five major symptoms of depression every day, all day for a minimum of two weeks. Imagine how we could help people earlier if we were able to identify depression with the help of AI tools like the Happiness Guru model. How does one actually create that model? What data is needed to train that model so that it can get that output.

So basically, whenever we build any project, we were taught this thing in the program itself that there's a whole project cycle that needs to be taken into you know, account while we're building any project. So the first step that comes into the AA project cycle is problem scoping. So we have problem statements, we have a stakeholders, and we have our ideal solution as well. Now comes data acquisition so basically to make this project work the way it is working right now, data was collected you know, anonymously through offline and online surveys and across five different schools across India. So during these surveys, we briefed students in the school what this survey is about and then they were asked to fill out that form which contained descriptive questions. Now, these descriptive questions that we selected, these were validated by a team of psychiatrists and counselors and then with the help of this survey process, we were able to develop an authentic data set of seven hundred plus centuries where the students basically wrote whatever they felt during that time and you know, went out their thoughts in that survey. The responses were labeled the on the scale of A two D, with A being least sever like perfectly healthy mentally and to D being needing immediate support from professionals and family. And this was done with the help of our school counselor, Ishitan Atara, So she helped us in you know, laboring these responses and then this data was used to train that SVM model that I was talking about that is a part of step three, So we need to convert this offline data into a digitized format because that's how model gets trained. So we did that, we started classifying, and then we trained the SVM model. Apart from that, there's one more thing that has went into this. The step one which I talked about is about, you know, recognizing whatever current emotion the user has, whatever their emotion is currently while they're using So this is turned with the help of library basically fast a dot Vision. So fast a dot Vision is a library that is used for computer vision tasks. And then we have trained this module using a data set. So this data set consisted of two thousand rows i would say, which consisted of facial expressions of different people, like there were videos and images of people from different genders and heritages of different backgrounds, and then they were classified as happy saturn nedle to train our module, which was fast a Dot Vision.

So basically, whenever we build any project, we were taught this thing in the program itself that there's a whole project cycle that needs to be taken into you know, account while we're building any project. So the first step that comes into the AA project cycle is problem scoping. So we have problem statements, we have a stakeholders, and we have our ideal solution as well. Now comes data acquisition so basically to make this project work the way it is working right now, data was collected you know, anonymously through offline and online surveys and across five different schools across India. So during these surveys, we briefed students in the school what this survey is about and then they were asked to fill out that form which contained descriptive questions. Now, these descriptive questions that we selected, these were validated by a team of psychiatrists and counselors and then with the help of this survey process, we were able to develop an authentic data set of seven hundred plus centuries where the students basically wrote whatever they felt during that time and you know, went out their thoughts in that survey. The responses were labeled the on the scale of A two D, with A being least sever like perfectly healthy mentally and to D being needing immediate support from professionals and family. And this was done with the help of our school counselor, Ishitan Atara, So she helped us in you know, laboring these responses and then this data was used to train that SVM model that I was talking about that is a part of step three, So we need to convert this offline data into a digitized format because that's how model gets trained. So we did that, we started classifying, and then we trained the SVM model. Apart from that, there's one more thing that has went into this. The step one which I talked about is about, you know, recognizing whatever current emotion the user has, whatever their emotion is currently while they're using So this is turned with the help of library basically fast a dot Vision. So fast a dot Vision is a library that is used for computer vision tasks. And then we have trained this module using a data set. So this data set consisted of two thousand rows i would say, which consisted of facial expressions of different people, like there were videos and images of people from different genders and heritages of different backgrounds, and then they were classified as happy saturn nedle to train our module, which was fast a Dot Vision.

What Tina is describing in her design philosophy is very interesting because in a way it mirrors processes used by psychiatrists and counselors to identify depression in young people at schools. However, in her system the effectiveness is amplified. Oftentimes people experiencing depression are not able to recognize the symptoms in themselves, and for young people particular, having access to a professional who could observe and identify the science is not guaranteed. For cultural, social and economic reasons, mental health is largely ignored. I can see the benefit of an automated system being used to identify it and how that can help those with reservations around mental healthcare take that crucial first step. You're listening to technically Speaking an Intel podcast will be right back. Welcome back to technically Speaking an Intel podcast. I'm here now with Tina. So, So, in terms of your research or next phase, do you think these sorts of wearable devices or things that can detect people's emotions, do you see a future where that could be a possibility where we could get in early in terms of detecting depression.

What Tina is describing in her design philosophy is very interesting because in a way it mirrors processes used by psychiatrists and counselors to identify depression in young people at schools. However, in her system the effectiveness is amplified. Oftentimes people experiencing depression are not able to recognize the symptoms in themselves, and for young people particular, having access to a professional who could observe and identify the science is not guaranteed. For cultural, social and economic reasons, mental health is largely ignored. I can see the benefit of an automated system being used to identify it and how that can help those with reservations around mental healthcare take that crucial first step. You're listening to technically Speaking an Intel podcast will be right back. Welcome back to technically Speaking an Intel podcast. I'm here now with Tina. So, So, in terms of your research or next phase, do you think these sorts of wearable devices or things that can detect people's emotions, do you see a future where that could be a possibility where we could get in early in terms of detecting depression.

Yes, there can be. In fact, there's been a rise in it lately. Like I've been following up the news around this, and I've got to know that there was some institute in New York itself which conducted a study which basically built an machine learning model that took the data of thousands users, and then this model was able to tell whether a person was mentally healthy or not. So we need to understand how this works for us to fall like, we are basically collecting data points in terms of different variables, and these variables are like you know, what at our pulse rate, what is our heart beat? And I mean different things that can be measured by these devices, by these variables to find the relation between someone's mental health and whatever data points we are collecting. So there's a possibility that in the coming year we can lead mental health care services. Apart from this, a similar thing that strucks to me right now is that brain computer interface. I mean well, brain computer interface is a machine that actually helps us to control a device or machine using our brain. So if something of that sort can be infused with machine learning, and then if we can build some solution that is oriented towards solving mental health problems that exist, that is oriented towards providing more healthcare services, like those that accessible enough and affordable as well, So I think majority of problems can be solved in this area.

Yes, there can be. In fact, there's been a rise in it lately. Like I've been following up the news around this, and I've got to know that there was some institute in New York itself which conducted a study which basically built an machine learning model that took the data of thousands users, and then this model was able to tell whether a person was mentally healthy or not. So we need to understand how this works for us to fall like, we are basically collecting data points in terms of different variables, and these variables are like you know, what at our pulse rate, what is our heart beat? And I mean different things that can be measured by these devices, by these variables to find the relation between someone's mental health and whatever data points we are collecting. So there's a possibility that in the coming year we can lead mental health care services. Apart from this, a similar thing that strucks to me right now is that brain computer interface. I mean well, brain computer interface is a machine that actually helps us to control a device or machine using our brain. So if something of that sort can be infused with machine learning, and then if we can build some solution that is oriented towards solving mental health problems that exist, that is oriented towards providing more healthcare services, like those that accessible enough and affordable as well, So I think majority of problems can be solved in this area.

Tina mentioning BCI or brain computer interface reminds me of the conversation in episode three with Jaggedish and Lama. We tend to think of BCI as human brains controlling the function of a machine, like moving a mouse cursor or controlling a robotic limb. However, Tina imagines a world where our brains can simply inform machines on how to service us. It is not so much that you would need to even think about being helped, but the machine learning process would allow a tool to remind you of a service you need. It's almost like having a second brain. I can't wait to see all of the medical applications this open up to the world. Particularly through the pandemic and post pandemic, there was a rise in mental health issues which needed expert care. Now do you think that AI can play a role in actually providing therapy for people with mental health concerns. I recently read an article in Time magazine about robot, which is a AI personal therapist. I'd like to get your thoughts as to whether they could actually provide useful advice for people to help manage their depression and mental health issues.

Tina mentioning BCI or brain computer interface reminds me of the conversation in episode three with Jaggedish and Lama. We tend to think of BCI as human brains controlling the function of a machine, like moving a mouse cursor or controlling a robotic limb. However, Tina imagines a world where our brains can simply inform machines on how to service us. It is not so much that you would need to even think about being helped, but the machine learning process would allow a tool to remind you of a service you need. It's almost like having a second brain. I can't wait to see all of the medical applications this open up to the world. Particularly through the pandemic and post pandemic, there was a rise in mental health issues which needed expert care. Now do you think that AI can play a role in actually providing therapy for people with mental health concerns. I recently read an article in Time magazine about robot, which is a AI personal therapist. I'd like to get your thoughts as to whether they could actually provide useful advice for people to help manage their depression and mental health issues.

So, when we think of mental health care, you know, the corner store of this is communication. It's not depending on the procedures, but more on the communication. Like if we know that therapist and the patient that there should be a strong relationship between them. The relationship should be good enough so that the patient can communicate with their therapists and then the problem can whatever problem the patient is going through. So like if you talk about therapists in terms of air, So there are chadbots which are coming up, like robot and new par So these chadbots that are increasingly being used to offer advice and a line of communication for mental health patients during their treatment. So they can also help with coping up with symptoms as well as they can look out for keyword that could trigger a possible help that patient needs. So chargipity can be used like a therapist. Like there have been certain use cases, like I've been reading on Reddit and there have been people who have been like sharing their stories around how they use chargity as a therapist. So when we see that chatbot can be used as a therapist, it is like we are giving them some inputs and they're basically doing sentiment analygies on the basis of textual responses that we're giving to them, and then they are basically modifying their answers to make it more human like and that's how they can work as AI therapist. But there are concerns around it as well. Like the first thing that comes up with is reliability. How much accurate of the solution that chatbot is providing us or any tool that we have built as a form of therapist is providing us. So first is reliability and then comes accountability. What if you know, something wrong happens, Who's responsible for all of this? But apart from this, the concern that always struck me is that these are privately funded apps, Like these are the apps that have been used at commercial level. I mean, there are certain subscription charges that need to be paid to use these apps. So I've always had this view that once you start commercializing and start making out profits from healthcare services, then things turn problematic, you know, and when something as vulnerable and as volatile as mental health is involved, I think we must be very much cautious. We must be very much vigilant about the kind of apps we are using and the kind of tools that are coming in in terms of mental health care services.

So, when we think of mental health care, you know, the corner store of this is communication. It's not depending on the procedures, but more on the communication. Like if we know that therapist and the patient that there should be a strong relationship between them. The relationship should be good enough so that the patient can communicate with their therapists and then the problem can whatever problem the patient is going through. So like if you talk about therapists in terms of air, So there are chadbots which are coming up, like robot and new par So these chadbots that are increasingly being used to offer advice and a line of communication for mental health patients during their treatment. So they can also help with coping up with symptoms as well as they can look out for keyword that could trigger a possible help that patient needs. So chargipity can be used like a therapist. Like there have been certain use cases, like I've been reading on Reddit and there have been people who have been like sharing their stories around how they use chargity as a therapist. So when we see that chatbot can be used as a therapist, it is like we are giving them some inputs and they're basically doing sentiment analygies on the basis of textual responses that we're giving to them, and then they are basically modifying their answers to make it more human like and that's how they can work as AI therapist. But there are concerns around it as well. Like the first thing that comes up with is reliability. How much accurate of the solution that chatbot is providing us or any tool that we have built as a form of therapist is providing us. So first is reliability and then comes accountability. What if you know, something wrong happens, Who's responsible for all of this? But apart from this, the concern that always struck me is that these are privately funded apps, Like these are the apps that have been used at commercial level. I mean, there are certain subscription charges that need to be paid to use these apps. So I've always had this view that once you start commercializing and start making out profits from healthcare services, then things turn problematic, you know, and when something as vulnerable and as volatile as mental health is involved, I think we must be very much cautious. We must be very much vigilant about the kind of apps we are using and the kind of tools that are coming in in terms of mental health care services.

And that leads me to if you are going to be using these sorts of chat pots like chat GPT, as you mentioned, to make sure that you're well aware of who's got your data, what the privacy concerns may be, and how you can make an informed decision. I like to get your thoughts around that, particularly around privacy and data security, and maybe you could start with how you tackled it with your app.

And that leads me to if you are going to be using these sorts of chat pots like chat GPT, as you mentioned, to make sure that you're well aware of who's got your data, what the privacy concerns may be, and how you can make an informed decision. I like to get your thoughts around that, particularly around privacy and data security, and maybe you could start with how you tackled it with your app.

This is one of the main concerns that come up. Like you also mentioned that whenever we are using such apps, we need to be aware that what kind of data we are feeding into it and what kind of formissions we're giving to such a tool. But someone who is going through a mental health problem mental illness, I mean we cannot say that the person is healthy enough or stable enough to be able to make a decision on this, and therefore privacy concerns will come later in the stage. But the first thing is that are we able to make the patients familiarize with the kind of data they're feeding into the apps and what are the consequences or ramifications that this data can lead to.

This is one of the main concerns that come up. Like you also mentioned that whenever we are using such apps, we need to be aware that what kind of data we are feeding into it and what kind of formissions we're giving to such a tool. But someone who is going through a mental health problem mental illness, I mean we cannot say that the person is healthy enough or stable enough to be able to make a decision on this, and therefore privacy concerns will come later in the stage. But the first thing is that are we able to make the patients familiarize with the kind of data they're feeding into the apps and what are the consequences or ramifications that this data can lead to.

Yeah, because I actually heard some stories around people using these chat says therapy and the concept of this transference, so they're actually falling in love with the bots. There's a similar experience with psychologists where patients fall in love with the therapist. So that's just another potential challenge that we all have to come to deal with if you're going to start using these things.

Yeah, because I actually heard some stories around people using these chat says therapy and the concept of this transference, so they're actually falling in love with the bots. There's a similar experience with psychologists where patients fall in love with the therapist. So that's just another potential challenge that we all have to come to deal with if you're going to start using these things.

Yeah, they're a virtual entities that are coming into this scenario and we are able to see them and they've been living their own life. People are becoming so comfortable with chatbirds now because definitely there's a lack of communication that is happening, and ever since the pandemic gets strung, this communication gap has increased, it has profoundly increased, so people are finding way to escape this and then these AI therapists come as a rescue and therefore people use it blindly without being enough aware about what kind of data they're feeling it and what kind of algorithms these applications are using. Because we know that to these algorithms may not be explainable, they're not transparent, so we have to be aware about this as well. Literacy and education is needed in these aspects as well.

Yeah, they're a virtual entities that are coming into this scenario and we are able to see them and they've been living their own life. People are becoming so comfortable with chatbirds now because definitely there's a lack of communication that is happening, and ever since the pandemic gets strung, this communication gap has increased, it has profoundly increased, so people are finding way to escape this and then these AI therapists come as a rescue and therefore people use it blindly without being enough aware about what kind of data they're feeling it and what kind of algorithms these applications are using. Because we know that to these algorithms may not be explainable, they're not transparent, so we have to be aware about this as well. Literacy and education is needed in these aspects as well.

Yeah, just on that you talked about explainability and transparency, do we just explain to the audience who may be not so familiar with those terms when it comes to AI models, what that actually means transparency.

Yeah, just on that you talked about explainability and transparency, do we just explain to the audience who may be not so familiar with those terms when it comes to AI models, what that actually means transparency.

Okay, So there's a term that goes with algorithms, and that's black box. So algorithms are like black blocks. We know what is going out, but we do not know how all of this is functioning, what is actually into the algorithm, and what is the procedure and how on what basis they're doing everything. Transparency is related to the kind of data we're feeding it and the way we are using it and how algorithm is working. To know this and explainability means that any user, because there are two categories of population who are associated with any AA system. The first one are users and the second one are the developers and stakeholders. So stakeholders must know that what kind of algorithm it is and there should be transparency in it. But when it comes to you user, AI systems and those algorithms must be explainable enough. I mean users are able to understand in a very human like language, that's what this algorithm is doing.

Okay, So there's a term that goes with algorithms, and that's black box. So algorithms are like black blocks. We know what is going out, but we do not know how all of this is functioning, what is actually into the algorithm, and what is the procedure and how on what basis they're doing everything. Transparency is related to the kind of data we're feeding it and the way we are using it and how algorithm is working. To know this and explainability means that any user, because there are two categories of population who are associated with any AA system. The first one are users and the second one are the developers and stakeholders. So stakeholders must know that what kind of algorithm it is and there should be transparency in it. But when it comes to you user, AI systems and those algorithms must be explainable enough. I mean users are able to understand in a very human like language, that's what this algorithm is doing.

That's really good And as AI emerges as this tool to help people struggling with their mental health, I'd like a few more comments just around how you see it working in tandem with the medical community to better serve their patients and their communities. Do you have any thoughts on how you know this tool can actually be used together rather than a replacement.

That's really good And as AI emerges as this tool to help people struggling with their mental health, I'd like a few more comments just around how you see it working in tandem with the medical community to better serve their patients and their communities. Do you have any thoughts on how you know this tool can actually be used together rather than a replacement.

Yeah, Basically, we always think that AI is a disruptor. We have always thought of this any technology that comes, but I've always believed that they are over here to augment our capabilities and to supplement whatever you know, roles are there. So I'm from India and the very first thing that I mean I have to cover up is that we need to educate people around mental health because in India, the most instrumental impedt in terms of mental health is lack of awareness and education. People do not know what exactly depression is, what exactly anxiety and stresses. They use it in a very casual way. And to be very honest, mental health is something which is stigmatized in India. So you know, if someone is suffering from mental health issue, they are often labeled as lunatics or crazy or possessed. So we need to educate people around this first of all. So I believe my project it's still it's working. I'm looking forward to deploying it into as many schools as I can. Because we know that annealgorithm, the more data we feed into it, the more accurate it becomes. Its current accuracy is seventy seven to eighty person. So we need to increase that accuracy first of all, and then we have to take care of the data. We need to have some regulations, we have some norms and rules. We have to inform our users also that the data that we're taking from them is in safe hans. Secondly, I believe I will be changing the working of this project. Currently it works on you know, facial recognition on current mood, and that can easily be fabricated. I mean something that is not reliable. That is not a thing that should be taken into account while you are assessing someone's mental health. So I think I need to eliminate this step and replace it with something better. It could possibly be like I find a BCI like brain computer interface, this technology. I find it very interesting, so I can possibly couple it with this and then I can, you know, find some solution.

Yeah, Basically, we always think that AI is a disruptor. We have always thought of this any technology that comes, but I've always believed that they are over here to augment our capabilities and to supplement whatever you know, roles are there. So I'm from India and the very first thing that I mean I have to cover up is that we need to educate people around mental health because in India, the most instrumental impedt in terms of mental health is lack of awareness and education. People do not know what exactly depression is, what exactly anxiety and stresses. They use it in a very casual way. And to be very honest, mental health is something which is stigmatized in India. So you know, if someone is suffering from mental health issue, they are often labeled as lunatics or crazy or possessed. So we need to educate people around this first of all. So I believe my project it's still it's working. I'm looking forward to deploying it into as many schools as I can. Because we know that annealgorithm, the more data we feed into it, the more accurate it becomes. Its current accuracy is seventy seven to eighty person. So we need to increase that accuracy first of all, and then we have to take care of the data. We need to have some regulations, we have some norms and rules. We have to inform our users also that the data that we're taking from them is in safe hans. Secondly, I believe I will be changing the working of this project. Currently it works on you know, facial recognition on current mood, and that can easily be fabricated. I mean something that is not reliable. That is not a thing that should be taken into account while you are assessing someone's mental health. So I think I need to eliminate this step and replace it with something better. It could possibly be like I find a BCI like brain computer interface, this technology. I find it very interesting, so I can possibly couple it with this and then I can, you know, find some solution.

Tina's recognition of the unsustainability of facial recognition is very valuable. My mother always said the eyes are the windows of the soul. But Tina understands that who we are has a lot more nuance to it. This is so important to how machine learning develops to become more inclusive. One of the biggest concerns with AI is a distrust of the machine's ability to understand humanity. What is great about hearing Tina speak is that her work is rooted in finding multiple ways to understand humans. This gives me a lot of hope for what AI can be, and we have people like Tina behind its development. Just to circle back round at the start, we talked about the start of your story and getting inspired by the AI Youth program run by Intel. I'd like to get a sense of in terms of your peer group, how much interest is there in AI development and STEM. I guess in your coh of friends and peers, is it something they're interested in and do you see a trend growing or are there's still more challenges for people to take up that sort of role in their career.

Tina's recognition of the unsustainability of facial recognition is very valuable. My mother always said the eyes are the windows of the soul. But Tina understands that who we are has a lot more nuance to it. This is so important to how machine learning develops to become more inclusive. One of the biggest concerns with AI is a distrust of the machine's ability to understand humanity. What is great about hearing Tina speak is that her work is rooted in finding multiple ways to understand humans. This gives me a lot of hope for what AI can be, and we have people like Tina behind its development. Just to circle back round at the start, we talked about the start of your story and getting inspired by the AI Youth program run by Intel. I'd like to get a sense of in terms of your peer group, how much interest is there in AI development and STEM. I guess in your coh of friends and peers, is it something they're interested in and do you see a trend growing or are there's still more challenges for people to take up that sort of role in their career.

Whatever peer groups I have, they all of them are quite interested in data science and machine learning. We know the data is the new oil, so like there are a huge number of job rules that have been coming up. And since many of my friends and acquaintances we are like financially weak, so all of us look towards earning some skill set and becoming job ready, increasing unemployability rather than you know, we do not focus on taking this up on a longer run. So, I mean there's a lot up in this because we know that machine learning, artificial intelligence, deep learning and whatever technologies that they are coming up, they hold the potential to change, to transform the landscape of every industry. So if we take it up as a profession, then we need to stay in it for a long run. But there are a multitude of impairments to it. So the very first one is like I being a girl. So in India, like especially from the place I belong to, girls are usually not encouraged to take up STEM fields. So we need to overcome that first of all. And then once we become employable, once we become like financially stable independent, I mean, then talking on a personal level, I can then you know, work in this field, and then I can possibly work in somewhere around mental health and machine learning. And therefore, in the coming future I plan to you know, launch a program to say which is shakti in STEM. So Shakti is a Hindi word and a literal meaning. It means feminine energy. Apart from this, it also has a different meaning, like in India, Shakti is used to represent strong and resilient young girls and women. So I would want to launch this program Shuck teen Stem, which aims at educating youngers who are based in rural areas who heal from financially weaker and economically weaker and socially backward start of the society and to educate them and to fuel their aspirations to enter into STEM careers.

Whatever peer groups I have, they all of them are quite interested in data science and machine learning. We know the data is the new oil, so like there are a huge number of job rules that have been coming up. And since many of my friends and acquaintances we are like financially weak, so all of us look towards earning some skill set and becoming job ready, increasing unemployability rather than you know, we do not focus on taking this up on a longer run. So, I mean there's a lot up in this because we know that machine learning, artificial intelligence, deep learning and whatever technologies that they are coming up, they hold the potential to change, to transform the landscape of every industry. So if we take it up as a profession, then we need to stay in it for a long run. But there are a multitude of impairments to it. So the very first one is like I being a girl. So in India, like especially from the place I belong to, girls are usually not encouraged to take up STEM fields. So we need to overcome that first of all. And then once we become employable, once we become like financially stable independent, I mean, then talking on a personal level, I can then you know, work in this field, and then I can possibly work in somewhere around mental health and machine learning. And therefore, in the coming future I plan to you know, launch a program to say which is shakti in STEM. So Shakti is a Hindi word and a literal meaning. It means feminine energy. Apart from this, it also has a different meaning, like in India, Shakti is used to represent strong and resilient young girls and women. So I would want to launch this program Shuck teen Stem, which aims at educating youngers who are based in rural areas who heal from financially weaker and economically weaker and socially backward start of the society and to educate them and to fuel their aspirations to enter into STEM careers.

Yeah, that's awesome because I mean, I have two daughters and I'm really encouraging them to get into the STEM side of things. And you know, anything to help anyone get into coding and developing and actually creating something from new is quite a exciting feeling. So thanks Tina for joining us today. I really enjoyed that and I learned quite a lot from this.

Yeah, that's awesome because I mean, I have two daughters and I'm really encouraging them to get into the STEM side of things. And you know, anything to help anyone get into coding and developing and actually creating something from new is quite a exciting feeling. So thanks Tina for joining us today. I really enjoyed that and I learned quite a lot from this.

Thank you, Thank you.

Thank you, Thank you.

Thank you to my guest Tina Sahu for joining me on this episode of Technically Speaking, an Intel podcast. This episode brilliantly highlighted the potential of AI in supporting those facing mental health challenges. I firmly believe that within the next decade will witness a surge in AI powered therapeutic tools designed especially for the younger generation navigating life hurdles. One heartening development is society's evolving recognition of mental health as a genuine concern. I remember the nineties as a fresh faced teenager. It was a time when such discussions were almost taboo and laden with stigma. Yet there's a pressing issue the shortage of well trained mental health professionals to cater to the increasing demand. AI and tech can serve as invaluable aids for these professionals, ultimately benefiting our community at large. Tina's transition from technology skeptic to its ardent supporter was a highlight for me as a father of three. I'm hopeful not just about the job prospects AI will offer them, but also the tech savvy liars they will lead, with AI becoming second nature to them. Observing the innovative solutions emerging from young minds like Tina's, I'm convinced we're on the cusps discovering awesome new technologies, apps, and remedies for many of life's challenges. Please join us on Tuesday, November twenty eighth for the next two episodes of Technically Speaking, an Intel podcast we'll be sharing two special episodes exploring the future of transportation and how technology like AI has already created modern day and mobility marvels like flying cars and autonomous shuttles. Technically Speaking, was produced by Ruby Studios from iHeartRadio in partnership with Intel, and hosted by me Graham Class. Our executive producer is Moley Sosha, our ep of Post production is James Foster, and our supervising producer is Nikias Swinton. This episode was edited by Cira Spreen and written and produced by Tiree Rush.

Thank you to my guest Tina Sahu for joining me on this episode of Technically Speaking, an Intel podcast. This episode brilliantly highlighted the potential of AI in supporting those facing mental health challenges. I firmly believe that within the next decade will witness a surge in AI powered therapeutic tools designed especially for the younger generation navigating life hurdles. One heartening development is society's evolving recognition of mental health as a genuine concern. I remember the nineties as a fresh faced teenager. It was a time when such discussions were almost taboo and laden with stigma. Yet there's a pressing issue the shortage of well trained mental health professionals to cater to the increasing demand. AI and tech can serve as invaluable aids for these professionals, ultimately benefiting our community at large. Tina's transition from technology skeptic to its ardent supporter was a highlight for me as a father of three. I'm hopeful not just about the job prospects AI will offer them, but also the tech savvy liars they will lead, with AI becoming second nature to them. Observing the innovative solutions emerging from young minds like Tina's, I'm convinced we're on the cusps discovering awesome new technologies, apps, and remedies for many of life's challenges. Please join us on Tuesday, November twenty eighth for the next two episodes of Technically Speaking, an Intel podcast we'll be sharing two special episodes exploring the future of transportation and how technology like AI has already created modern day and mobility marvels like flying cars and autonomous shuttles. Technically Speaking, was produced by Ruby Studios from iHeartRadio in partnership with Intel, and hosted by me Graham Class. Our executive producer is Moley Sosha, our ep of Post production is James Foster, and our supervising producer is Nikias Swinton. This episode was edited by Cira Spreen and written and produced by Tiree Rush.