Speaker 1: I know I need to get ready for work, but
Speaker 1: I'm just so so tired. Maybe I can skip work
Speaker 1: to take a nap justice once. What's that? Oh, my
Speaker 1: depression monitors detecting early signs of a depressive state. I
Speaker 1: thought it was just tired, but this might be a
Speaker 1: bigger issue. Let me set in an appointment with my therapist.
Speaker 1: Hey there, I'm grain class and this is technically speaking
Speaker 1: an Intel podcast. The show is dedicated to highlighting the
Speaker 1: way technology is revolutionizing the way we live, work and move.
Speaker 1: In every episode, we'll connect with innovators in areas like
Speaker 1: artificial intelligence to better understand the human centered technology they've developed.
Speaker 1: Mental health care solutions remain underinvested in many communities around
Speaker 1: the world, yet so many suffer from issues that they
Speaker 1: don't even know. They have a lot of these conversations
Speaker 1: around health care, hinge on making therapy more accessible to
Speaker 1: those in need. However, it can be difficult to determine
Speaker 1: that one is experiencing depression or mental health crisis. Artificial
Speaker 1: intelligence is at the forefront of many different advancements in healthcare,
Speaker 1: but today we are going to dive into how it
Speaker 1: is working to make mental health care more accessible. To everyone.
Speaker 1: In order to do that, I have to introduce you
Speaker 1: to a special guest joining me now is Tinasawho. Tina
Speaker 1: Saho was a high school student when she started exploring
Speaker 1: coding and engineering with AI. She never considered herself to
Speaker 1: be overly interested in technology to start. However, she took
Speaker 1: her natural curiosity and eventually was invited to be a
Speaker 1: part of Intel's AI for Youth Pilot program, where she
Speaker 1: developed a tool that uses AI to detect and predict
Speaker 1: patterns of depression with around eighty percent accuracy. Since then,
Speaker 1: she's been awarded the Calm Trust Fellowship for Women. She
Speaker 1: currently attends to Ayabata College at the University of Delhi,
Speaker 1: where she's completing a bachelor's in computer science and exploring
Speaker 1: more opportunities to use AI as a tool for mental
Speaker 1: health and more.
Speaker 2: Welcome to the show, Tina, Hi, thank you for inviting
Speaker 2: me to this show, and I'm feeling more than privileged
Speaker 2: to be a part of this podcast. So numbers stay
Speaker 2: to everyone.
Speaker 1: I'm really interested in your story of how you got
Speaker 1: into the STEM field, what prompted your interest in that
Speaker 1: field and also in AI.
Speaker 2: To begin with, till my tenth class, I was a
Speaker 2: student or a person who was always against technology, like
Speaker 2: I always found in fact that the negative impacts that
Speaker 2: technology brings and whatever ethical concerns are there, they cannot
Speaker 2: be solved and they are simply too much to be
Speaker 2: on the side of technology. But I remember in twenty nineteen,
Speaker 2: the AAFO Youth program was launched and it was launched
Speaker 2: in our school, which is Salvangal Senior Secondary School. So
Speaker 2: when I participated in that program, I got to know
Speaker 2: what artificial intelligence is, how many astonishing and stounding possibilities
Speaker 2: AI can unlock and has already unlocked. And therefore, this
Speaker 2: program pivoted my you know journey, my career, my professional journey,
Speaker 2: and my cademic journey from being a non tech student
Speaker 2: to becoming a tech student finally pursuing computer science as
Speaker 2: my graduation.
Speaker 1: Okay, was there a particular topic or person that really
Speaker 1: did spark that interest? What was the particular topic that
Speaker 1: actually got you really fired up?
Speaker 2: So in my school there were two teachers who actually
Speaker 2: you know, molded my thinking and they actually infused this
Speaker 2: critical thinking aspect in me and they opened me to
Speaker 2: the world of possibilities that science, that technology and engineering offers.
Speaker 2: And I would like to take their name as well
Speaker 2: as a you know, token of respect. So it's one
Speaker 2: that Mam and Serbiam they empowered me. They encouraged me
Speaker 2: to think beyond what I see.
Speaker 1: And that led you to the Intel's AI for Youth program.
Speaker 1: What sort of projects have you worked on? Are you
Speaker 1: working on right now?
Speaker 2: This isn't my participating in this program. I build Happiness Guru,
Speaker 2: which is a model that predicts depression. Apart from that too,
Speaker 2: I was also a part of Utul Tinkling Labs. Through
Speaker 2: the Detail Lab of our school. I basically got to
Speaker 2: know about Intel's AfOR Youth program only and in there itself,
Speaker 2: I build a few projects and one of them was
Speaker 2: Happiness Guru.
Speaker 1: And with the Happiness Crew, is that related to the
Speaker 1: depression detection research you've been doing? What kind of prompted
Speaker 1: you to look in that direction in the field of
Speaker 1: depression and then using technology? Because generally, speaker, we don't
Speaker 1: associate technology with treating depression or even detecting depression. So
Speaker 1: what was the spark for you there?
Speaker 2: So while I was in this program, I was in
Speaker 2: Class ten and my Plus ten results were out and
Speaker 2: they were not as much as I expected, and I
Speaker 2: went into a phase of depression because I associated myself
Speaker 2: worth with the Marxi score, so my own personal experience
Speaker 2: of dealing with depression. And then at that time, you
Speaker 2: know that society rates were very alarming among the youth,
Speaker 2: especially aged between fifteen to twenty nine. And we found
Speaker 2: that the driving forces behind you know these when these
Speaker 2: societies were you know, peer pressure, overburdening academics, financial stress,
Speaker 2: and too much expectations that we have from youth, you know,
Speaker 2: especially if you talk about teenage and someone who is
Speaker 2: in between eighteen to twenty five. So on researching, we
Speaker 2: found that these societ rates are very alarming, they're very distressing,
Speaker 2: and these are the leading cause of the depression or stress.
Speaker 2: And thereby we thought that we must come up with
Speaker 2: some solution that can basically help us predict which person
Speaker 2: is going through depression, and that to in a very
Speaker 2: human friendly manner, not making someone uncomfortable with the kind
Speaker 2: of procedures or with the kind of system we have.
Speaker 2: So these were the I would say, the enablers that
Speaker 2: led our team building this solution.
Speaker 1: And in terms of the happiness grew app can you
Speaker 1: just explain how it actually works, you know, to try
Speaker 1: and detect the early signs of depression.
Speaker 2: First of all, it's our web based application. While building
Speaker 2: this project, the queue that we took, you know, to
Speaker 2: build the entire model was that after our research, we
Speaker 2: got to know that a person's vocabulary can be a
Speaker 2: mirror into their mental state. And taking this as the queue,
Speaker 2: we build this project which tries to analyze the emotional
Speaker 2: quotient of a person of a user through their facial
Speaker 2: expression and then their textual responses that the user is
Speaker 2: going to provide to the AA machine. So the working
Speaker 2: of the project is divided into three steps. The first
Speaker 2: step is emotion detection stage, and in this stage you
Speaker 2: basically need to stand in front of your laptop or
Speaker 2: whatever device you are using this web application, and then
Speaker 2: it detects your current mode, whether you're happy or sad,
Speaker 2: you're neutral, angry. Then the next step is that user
Speaker 2: is asked to answer nine questions and there's a scale
Speaker 2: of relevance and then they need to select how much
Speaker 2: relevant or how much they are able to relate this
Speaker 2: to this situation. Then, after these two steps, a threshold
Speaker 2: score is generated which gives the initial lead. If the
Speaker 2: person is stressed or not, and if the score is
Speaker 2: below the threshold that we have said, the person is
Speaker 2: predicted as happy, while in the other case, user is
Speaker 2: taken to the third step, which is the final step.
Speaker 2: And this step consists of four descriptive questions which he
Speaker 2: or she can use as a platform to went out
Speaker 2: all his or her feelings and thoughts. So whatever answers
Speaker 2: user will give to these four descriptive questions, these answers
Speaker 2: will be used as a basis of classification. Then the
Speaker 2: machine will predict whether the user is depressed or not.
Speaker 2: So this AI machine, whatever you know input we are
Speaker 2: giving in this step. There's a model namely SVM, which
Speaker 2: is support vector machine. It's a non contextual classification model.
Speaker 2: It is basically used to classify things. And then we
Speaker 2: are using this model on the kind of you know,
Speaker 2: language or keywords that are used in the answers. And
Speaker 2: then accordingly the results are given out that whether the
Speaker 2: person is stressed or not, and if the person is stressed,
Speaker 2: automatically the person is consulted to the concerned authorities or
Speaker 2: counselor otherwise the person is predicted as happy or not stressed.
Speaker 1: Detecting and treating mental health is something with which many
Speaker 1: societies around the world struggle. According to the World Health Organization,
Speaker 1: approximately two hundred and eighty million people in the world
Speaker 1: suffer from depression and more than three hundred million are
Speaker 1: living with anxiety. Many people with these mental health conditions
Speaker 1: exhibit some symptoms as children or young adults, but based
Speaker 1: on guidance from the US National Institute of Mental Health,
Speaker 1: depression can only be diagnosed once an individual exhibits the
Speaker 1: five major symptoms of depression every day, all day for
Speaker 1: a minimum of two weeks. Imagine how we could help
Speaker 1: people earlier if we were able to identify depression with
Speaker 1: the help of AI tools like the Happiness Guru model.
Speaker 1: How does one actually create that model? What data is
Speaker 1: needed to train that model so that it can get
Speaker 1: that output.
Speaker 2: So basically, whenever we build any project, we were taught
Speaker 2: this thing in the program itself that there's a whole
Speaker 2: project cycle that needs to be taken into you know,
Speaker 2: account while we're building any project. So the first step
Speaker 2: that comes into the AA project cycle is problem scoping.
Speaker 2: So we have problem statements, we have a stakeholders, and
Speaker 2: we have our ideal solution as well. Now comes data
Speaker 2: acquisition so basically to make this project work the way
Speaker 2: it is working right now, data was collected you know,
Speaker 2: anonymously through offline and online surveys and across five different
Speaker 2: schools across India. So during these surveys, we briefed students
Speaker 2: in the school what this survey is about and then
Speaker 2: they were asked to fill out that form which contained
Speaker 2: descriptive questions. Now, these descriptive questions that we selected, these
Speaker 2: were validated by a team of psychiatrists and counselors and
Speaker 2: then with the help of this survey process, we were
Speaker 2: able to develop an authentic data set of seven hundred
Speaker 2: plus centuries where the students basically wrote whatever they felt
Speaker 2: during that time and you know, went out their thoughts
Speaker 2: in that survey. The responses were labeled the on the
Speaker 2: scale of A two D, with A being least sever
Speaker 2: like perfectly healthy mentally and to D being needing immediate
Speaker 2: support from professionals and family. And this was done with
Speaker 2: the help of our school counselor, Ishitan Atara, So she
Speaker 2: helped us in you know, laboring these responses and then
Speaker 2: this data was used to train that SVM model that
Speaker 2: I was talking about that is a part of step three,
Speaker 2: So we need to convert this offline data into a
Speaker 2: digitized format because that's how model gets trained. So we
Speaker 2: did that, we started classifying, and then we trained the
Speaker 2: SVM model. Apart from that, there's one more thing that
Speaker 2: has went into this. The step one which I talked
Speaker 2: about is about, you know, recognizing whatever current emotion the
Speaker 2: user has, whatever their emotion is currently while they're using
Speaker 2: So this is turned with the help of library basically
Speaker 2: fast a dot Vision. So fast a dot Vision is
Speaker 2: a library that is used for computer vision tasks. And
Speaker 2: then we have trained this module using a data set.
Speaker 2: So this data set consisted of two thousand rows i
Speaker 2: would say, which consisted of facial expressions of different people,
Speaker 2: like there were videos and images of people from different
Speaker 2: genders and heritages of different backgrounds, and then they were
Speaker 2: classified as happy saturn nedle to train our module, which
Speaker 2: was fast a Dot Vision.
Speaker 1: What Tina is describing in her design philosophy is very
Speaker 1: interesting because in a way it mirrors processes used by
Speaker 1: psychiatrists and counselors to identify depression in young people at schools. However,
Speaker 1: in her system the effectiveness is amplified. Oftentimes people experiencing
Speaker 1: depression are not able to recognize the symptoms in themselves,
Speaker 1: and for young people particular, having access to a professional
Speaker 1: who could observe and identify the science is not guaranteed.
Speaker 1: For cultural, social and economic reasons, mental health is largely ignored.
Speaker 1: I can see the benefit of an automated system being
Speaker 1: used to identify it and how that can help those
Speaker 1: with reservations around mental healthcare take that crucial first step.
Speaker 1: You're listening to technically Speaking an Intel podcast will be
Speaker 1: right back. Welcome back to technically Speaking an Intel podcast.
Speaker 1: I'm here now with Tina. So, So, in terms of
Speaker 1: your research or next phase, do you think these sorts
Speaker 1: of wearable devices or things that can detect people's emotions,
Speaker 1: do you see a future where that could be a
Speaker 1: possibility where we could get in early in terms of
Speaker 1: detecting depression.
Speaker 2: Yes, there can be. In fact, there's been a rise
Speaker 2: in it lately. Like I've been following up the news
Speaker 2: around this, and I've got to know that there was
Speaker 2: some institute in New York itself which conducted a study
Speaker 2: which basically built an machine learning model that took the
Speaker 2: data of thousands users, and then this model was able
Speaker 2: to tell whether a person was mentally healthy or not.
Speaker 2: So we need to understand how this works for us
Speaker 2: to fall like, we are basically collecting data points in
Speaker 2: terms of different variables, and these variables are like you know,
Speaker 2: what at our pulse rate, what is our heart beat?
Speaker 2: And I mean different things that can be measured by
Speaker 2: these devices, by these variables to find the relation between
Speaker 2: someone's mental health and whatever data points we are collecting.
Speaker 2: So there's a possibility that in the coming year we
Speaker 2: can lead mental health care services. Apart from this, a
Speaker 2: similar thing that strucks to me right now is that
Speaker 2: brain computer interface. I mean well, brain computer interface is
Speaker 2: a machine that actually helps us to control a device
Speaker 2: or machine using our brain. So if something of that
Speaker 2: sort can be infused with machine learning, and then if
Speaker 2: we can build some solution that is oriented towards solving
Speaker 2: mental health problems that exist, that is oriented towards providing
Speaker 2: more healthcare services, like those that accessible enough and affordable
Speaker 2: as well, So I think majority of problems can be
Speaker 2: solved in this area.
Speaker 1: Tina mentioning BCI or brain computer interface reminds me of
Speaker 1: the conversation in episode three with Jaggedish and Lama. We
Speaker 1: tend to think of BCI as human brains controlling the
Speaker 1: function of a machine, like moving a mouse cursor or
Speaker 1: controlling a robotic limb. However, Tina imagines a world where
Speaker 1: our brains can simply inform machines on how to service us.
Speaker 1: It is not so much that you would need to
Speaker 1: even think about being helped, but the machine learning process
Speaker 1: would allow a tool to remind you of a service
Speaker 1: you need. It's almost like having a second brain. I
Speaker 1: can't wait to see all of the medical applications this
Speaker 1: open up to the world. Particularly through the pandemic and
Speaker 1: post pandemic, there was a rise in mental health issues
Speaker 1: which needed expert care. Now do you think that AI
Speaker 1: can play a role in actually providing therapy for people
Speaker 1: with mental health concerns. I recently read an article in
Speaker 1: Time magazine about robot, which is a AI personal therapist.
Speaker 1: I'd like to get your thoughts as to whether they
Speaker 1: could actually provide useful advice for people to help manage
Speaker 1: their depression and mental health issues.
Speaker 2: So, when we think of mental health care, you know,
Speaker 2: the corner store of this is communication. It's not depending
Speaker 2: on the procedures, but more on the communication. Like if
Speaker 2: we know that therapist and the patient that there should
Speaker 2: be a strong relationship between them. The relationship should be
Speaker 2: good enough so that the patient can communicate with their
Speaker 2: therapists and then the problem can whatever problem the patient
Speaker 2: is going through. So like if you talk about therapists
Speaker 2: in terms of air, So there are chadbots which are
Speaker 2: coming up, like robot and new par So these chadbots
Speaker 2: that are increasingly being used to offer advice and a
Speaker 2: line of communication for mental health patients during their treatment.
Speaker 2: So they can also help with coping up with symptoms
Speaker 2: as well as they can look out for keyword that
Speaker 2: could trigger a possible help that patient needs. So chargipity
Speaker 2: can be used like a therapist. Like there have been
Speaker 2: certain use cases, like I've been reading on Reddit and
Speaker 2: there have been people who have been like sharing their
Speaker 2: stories around how they use chargity as a therapist. So
Speaker 2: when we see that chatbot can be used as a therapist,
Speaker 2: it is like we are giving them some inputs and
Speaker 2: they're basically doing sentiment analygies on the basis of textual
Speaker 2: responses that we're giving to them, and then they are
Speaker 2: basically modifying their answers to make it more human like
Speaker 2: and that's how they can work as AI therapist. But
Speaker 2: there are concerns around it as well. Like the first
Speaker 2: thing that comes up with is reliability. How much accurate
Speaker 2: of the solution that chatbot is providing us or any
Speaker 2: tool that we have built as a form of therapist
Speaker 2: is providing us. So first is reliability and then comes accountability.
Speaker 2: What if you know, something wrong happens, Who's responsible for
Speaker 2: all of this? But apart from this, the concern that
Speaker 2: always struck me is that these are privately funded apps,
Speaker 2: Like these are the apps that have been used at
Speaker 2: commercial level. I mean, there are certain subscription charges that
Speaker 2: need to be paid to use these apps. So I've
Speaker 2: always had this view that once you start commercializing and
Speaker 2: start making out profits from healthcare services, then things turn problematic,
Speaker 2: you know, and when something as vulnerable and as volatile
Speaker 2: as mental health is involved, I think we must be
Speaker 2: very much cautious. We must be very much vigilant about
Speaker 2: the kind of apps we are using and the kind
Speaker 2: of tools that are coming in in terms of mental
Speaker 2: health care services.
Speaker 1: And that leads me to if you are going to
Speaker 1: be using these sorts of chat pots like chat GPT,
Speaker 1: as you mentioned, to make sure that you're well aware
Speaker 1: of who's got your data, what the privacy concerns may be,
Speaker 1: and how you can make an informed decision. I like
Speaker 1: to get your thoughts around that, particularly around privacy and
Speaker 1: data security, and maybe you could start with how you
Speaker 1: tackled it with your app.
Speaker 2: This is one of the main concerns that come up.
Speaker 2: Like you also mentioned that whenever we are using such apps,
Speaker 2: we need to be aware that what kind of data
Speaker 2: we are feeding into it and what kind of formissions
Speaker 2: we're giving to such a tool. But someone who is
Speaker 2: going through a mental health problem mental illness, I mean
Speaker 2: we cannot say that the person is healthy enough or
Speaker 2: stable enough to be able to make a decision on this,
Speaker 2: and therefore privacy concerns will come later in the stage.
Speaker 2: But the first thing is that are we able to
Speaker 2: make the patients familiarize with the kind of data they're
Speaker 2: feeding into the apps and what are the consequences or
Speaker 2: ramifications that this data can lead to.
Speaker 1: Yeah, because I actually heard some stories around people using
Speaker 1: these chat says therapy and the concept of this transference,
Speaker 1: so they're actually falling in love with the bots. There's
Speaker 1: a similar experience with psychologists where patients fall in love
Speaker 1: with the therapist. So that's just another potential challenge that
Speaker 1: we all have to come to deal with if you're
Speaker 1: going to start using these things.
Speaker 2: Yeah, they're a virtual entities that are coming into this
Speaker 2: scenario and we are able to see them and they've
Speaker 2: been living their own life. People are becoming so comfortable
Speaker 2: with chatbirds now because definitely there's a lack of communication
Speaker 2: that is happening, and ever since the pandemic gets strung,
Speaker 2: this communication gap has increased, it has profoundly increased, so
Speaker 2: people are finding way to escape this and then these
Speaker 2: AI therapists come as a rescue and therefore people use
Speaker 2: it blindly without being enough aware about what kind of
Speaker 2: data they're feeling it and what kind of algorithms these
Speaker 2: applications are using. Because we know that to these algorithms
Speaker 2: may not be explainable, they're not transparent, so we have
Speaker 2: to be aware about this as well. Literacy and education
Speaker 2: is needed in these aspects as well.
Speaker 1: Yeah, just on that you talked about explainability and transparency,
Speaker 1: do we just explain to the audience who may be
Speaker 1: not so familiar with those terms when it comes to
Speaker 1: AI models, what that actually means transparency.
Speaker 2: Okay, So there's a term that goes with algorithms, and
Speaker 2: that's black box. So algorithms are like black blocks. We
Speaker 2: know what is going out, but we do not know how
Speaker 2: all of this is functioning, what is actually into the algorithm,
Speaker 2: and what is the procedure and how on what basis
Speaker 2: they're doing everything. Transparency is related to the kind of
Speaker 2: data we're feeding it and the way we are using
Speaker 2: it and how algorithm is working. To know this and
Speaker 2: explainability means that any user, because there are two categories
Speaker 2: of population who are associated with any AA system. The
Speaker 2: first one are users and the second one are the
Speaker 2: developers and stakeholders. So stakeholders must know that what kind
Speaker 2: of algorithm it is and there should be transparency in it.
Speaker 2: But when it comes to you user, AI systems and
Speaker 2: those algorithms must be explainable enough. I mean users are
Speaker 2: able to understand in a very human like language, that's
Speaker 2: what this algorithm is doing.
Speaker 1: That's really good And as AI emerges as this tool
Speaker 1: to help people struggling with their mental health, I'd like
Speaker 1: a few more comments just around how you see it
Speaker 1: working in tandem with the medical community to better serve
Speaker 1: their patients and their communities. Do you have any thoughts
Speaker 1: on how you know this tool can actually be used
Speaker 1: together rather than a replacement.
Speaker 2: Yeah, Basically, we always think that AI is a disruptor.
Speaker 2: We have always thought of this any technology that comes,
Speaker 2: but I've always believed that they are over here to
Speaker 2: augment our capabilities and to supplement whatever you know, roles
Speaker 2: are there. So I'm from India and the very first
Speaker 2: thing that I mean I have to cover up is
Speaker 2: that we need to educate people around mental health because
Speaker 2: in India, the most instrumental impedt in terms of mental
Speaker 2: health is lack of awareness and education. People do not
Speaker 2: know what exactly depression is, what exactly anxiety and stresses.
Speaker 2: They use it in a very casual way. And to
Speaker 2: be very honest, mental health is something which is stigmatized
Speaker 2: in India. So you know, if someone is suffering from
Speaker 2: mental health issue, they are often labeled as lunatics or
Speaker 2: crazy or possessed. So we need to educate people around
Speaker 2: this first of all. So I believe my project it's
Speaker 2: still it's working. I'm looking forward to deploying it into
Speaker 2: as many schools as I can. Because we know that annealgorithm,
Speaker 2: the more data we feed into it, the more accurate
Speaker 2: it becomes. Its current accuracy is seventy seven to eighty person.
Speaker 2: So we need to increase that accuracy first of all,
Speaker 2: and then we have to take care of the data.
Speaker 2: We need to have some regulations, we have some norms
Speaker 2: and rules. We have to inform our users also that
Speaker 2: the data that we're taking from them is in safe hans. Secondly,
Speaker 2: I believe I will be changing the working of this project.
Speaker 2: Currently it works on you know, facial recognition on current mood,
Speaker 2: and that can easily be fabricated. I mean something that
Speaker 2: is not reliable. That is not a thing that should
Speaker 2: be taken into account while you are assessing someone's mental health.
Speaker 2: So I think I need to eliminate this step and
Speaker 2: replace it with something better. It could possibly be like
Speaker 2: I find a BCI like brain computer interface, this technology.
Speaker 2: I find it very interesting, so I can possibly couple
Speaker 2: it with this and then I can, you know, find
Speaker 2: some solution.
Speaker 1: Tina's recognition of the unsustainability of facial recognition is very valuable.
Speaker 1: My mother always said the eyes are the windows of
Speaker 1: the soul. But Tina understands that who we are has
Speaker 1: a lot more nuance to it. This is so important
Speaker 1: to how machine learning develops to become more inclusive. One
Speaker 1: of the biggest concerns with AI is a distrust of
Speaker 1: the machine's ability to understand humanity. What is great about
Speaker 1: hearing Tina speak is that her work is rooted in
Speaker 1: finding multiple ways to understand humans. This gives me a
Speaker 1: lot of hope for what AI can be, and we
Speaker 1: have people like Tina behind its development. Just to circle
Speaker 1: back round at the start, we talked about the start
Speaker 1: of your story and getting inspired by the AI Youth
Speaker 1: program run by Intel. I'd like to get a sense
Speaker 1: of in terms of your peer group, how much interest
Speaker 1: is there in AI development and STEM. I guess in
Speaker 1: your coh of friends and peers, is it something they're
Speaker 1: interested in and do you see a trend growing or
Speaker 1: are there's still more challenges for people to take up
Speaker 1: that sort of role in their career.
Speaker 2: Whatever peer groups I have, they all of them are
Speaker 2: quite interested in data science and machine learning. We know
Speaker 2: the data is the new oil, so like there are
Speaker 2: a huge number of job rules that have been coming up.
Speaker 2: And since many of my friends and acquaintances we are
Speaker 2: like financially weak, so all of us look towards earning
Speaker 2: some skill set and becoming job ready, increasing unemployability rather
Speaker 2: than you know, we do not focus on taking this
Speaker 2: up on a longer run. So, I mean there's a
Speaker 2: lot up in this because we know that machine learning,
Speaker 2: artificial intelligence, deep learning and whatever technologies that they are coming up,
Speaker 2: they hold the potential to change, to transform the landscape
Speaker 2: of every industry. So if we take it up as
Speaker 2: a profession, then we need to stay in it for
Speaker 2: a long run. But there are a multitude of impairments
Speaker 2: to it. So the very first one is like I
Speaker 2: being a girl. So in India, like especially from the
Speaker 2: place I belong to, girls are usually not encouraged to
Speaker 2: take up STEM fields. So we need to overcome that
Speaker 2: first of all. And then once we become employable, once
Speaker 2: we become like financially stable independent, I mean, then talking
Speaker 2: on a personal level, I can then you know, work
Speaker 2: in this field, and then I can possibly work in
Speaker 2: somewhere around mental health and machine learning. And therefore, in
Speaker 2: the coming future I plan to you know, launch a
Speaker 2: program to say which is shakti in STEM. So Shakti
Speaker 2: is a Hindi word and a literal meaning. It means
Speaker 2: feminine energy. Apart from this, it also has a different meaning,
Speaker 2: like in India, Shakti is used to represent strong and
Speaker 2: resilient young girls and women. So I would want to
Speaker 2: launch this program Shuck teen Stem, which aims at educating
Speaker 2: youngers who are based in rural areas who heal from
Speaker 2: financially weaker and economically weaker and socially backward start of
Speaker 2: the society and to educate them and to fuel their
Speaker 2: aspirations to enter into STEM careers.
Speaker 1: Yeah, that's awesome because I mean, I have two daughters
Speaker 1: and I'm really encouraging them to get into the STEM
Speaker 1: side of things. And you know, anything to help anyone
Speaker 1: get into coding and developing and actually creating something from
Speaker 1: new is quite a exciting feeling. So thanks Tina for
Speaker 1: joining us today. I really enjoyed that and I learned
Speaker 1: quite a lot from this.
Speaker 2: Thank you, Thank you.
Speaker 1: Thank you to my guest Tina Sahu for joining me
Speaker 1: on this episode of Technically Speaking, an Intel podcast. This
Speaker 1: episode brilliantly highlighted the potential of AI in supporting those
Speaker 1: facing mental health challenges. I firmly believe that within the
Speaker 1: next decade will witness a surge in AI powered therapeutic
Speaker 1: tools designed especially for the younger generation navigating life hurdles.
Speaker 1: One heartening development is society's evolving recognition of mental health
Speaker 1: as a genuine concern. I remember the nineties as a
Speaker 1: fresh faced teenager. It was a time when such discussions
Speaker 1: were almost taboo and laden with stigma. Yet there's a
Speaker 1: pressing issue the shortage of well trained mental health professionals
Speaker 1: to cater to the increasing demand. AI and tech can
Speaker 1: serve as invaluable aids for these professionals, ultimately benefiting our
Speaker 1: community at large. Tina's transition from technology skeptic to its
Speaker 1: ardent supporter was a highlight for me as a father
Speaker 1: of three. I'm hopeful not just about the job prospects
Speaker 1: AI will offer them, but also the tech savvy liars
Speaker 1: they will lead, with AI becoming second nature to them.
Speaker 1: Observing the innovative solutions emerging from young minds like Tina's,
Speaker 1: I'm convinced we're on the cusps discovering awesome new technologies, apps,
Speaker 1: and remedies for many of life's challenges. Please join us
Speaker 1: on Tuesday, November twenty eighth for the next two episodes
Speaker 1: of Technically Speaking, an Intel podcast we'll be sharing two
Speaker 1: special episodes exploring the future of transportation and how technology
Speaker 1: like AI has already created modern day and mobility marvels
Speaker 1: like flying cars and autonomous shuttles. Technically Speaking, was produced
Speaker 1: by Ruby Studios from iHeartRadio in partnership with Intel, and
Speaker 1: hosted by me Graham Class. Our executive producer is Moley Sosha,
Speaker 1: our ep of Post production is James Foster, and our
Speaker 1: supervising producer is Nikias Swinton. This episode was edited by
Speaker 1: Cira Spreen and written and produced by Tiree Rush.