In this enlightening episode, we delve into the transformative potential of AI, Data Science, Big Data, and NoSQL on senior care with our esteemed guest, Akmal Chaudhri, Esq., a titan in database technology. We explore the impact of these tools on health prediction, personalizing care, and enhancing the independence of older adults.
We journey through Akmal's expansive career, reflecting on the evolution of database technology. The discussion extends into how AI and Data Science anticipate and address the needs of our aging population, and the role of Big Data in improving senior living conditions.
This podcast illuminates the balance between embracing new technologies and ensuring the best care for our aging society. We conclude with an optimistic look at a future where technology empowers our seniors to lead dignified and fulfilling lives. This thought-provoking conversation promises to inspire all listeners with its unique blend of technology and compassion.
You can join Akmal Chaudhri, Esq. on Wednesday, 2 August, 2023 at this exclusive event "AI: Using Generative Pre-trained Transformers (GPT) Without Hallucinations": https://www.eventbrite.co.uk/e/ai-using-generative-pre-trained-transformers-gpt-without-hallucinations-registration-672983360347
You can find Akmal on LinkedIn: https://www.linkedin.com/in/akmalchaudhri/
👉 See our Website: https://podcast.boomerliving.tv/
🎙 Boomer Living Podcast: https://hanhdbrown.com/
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Hello, I'm Hanh Brown, the host of the Boomer Living Broadcast. It's great to have you here. In the fast paced digital world we live in, technologies like AI, data science, big data, and NoSQL are becoming integral to reshaping numerous sectors. One of which is the realm of senior care. With the potential to revolutionize health predictions, personalize senior
care, and amplify the independence of our older adults, these advanced tools are becoming indispensable in harnessing the power of data for good. They allow us to glean crucial insights from mountains of data, paving the way for informed decision making, resource allocation, and meaningful changes in the lives of the elderly population. Well, our esteemed guest today is none
other than Akmal Chaudhry, a tech titan in the realm of database technology. With a career that beautifully blends roles as a developer, consultant, A product strategist, ACMO has been instrumental in driving numerous tech ventures forward since 1988. Well, he's an acclaimed tech expert, holds degrees in computing and information systems.
Business Systems Analysis and Design, and a PhD in Computer Science, specializing in database performance. A member of the British Computer Society, his charted IT professional title reflects his deep expertise and commitment to ethical IT practices. For today, I'm excited to dive into his wealth of knowledge and explore how he sees these powerful technologies.
playing a role in enhancing the lives of our aging population. Well, so buckle up and get ready for an exciting conversation. So, Akmal, welcome to the show.
Thank you very much, Hanh. And I feel almost red in the cheeks, if you can see the color. Thank you very much for that very glowing introduction. I feel a little bit embarrassed, but it's great. Thank you. Um, I mean, I, I worked throughout my career, not for personal recognition.
I mean, where I've done things that it's, it's a challenge that I take on myself and then something that I want to do, hopefully it's beneficial to other people as well, um, and certainly I've focused on a lot on that in terms of the technology outreach that I do, because, I like people to use stuff and, you know, make practical use of it, not, uh, just for the sake of, uh, you know,
exploring technology, but, uh, have it do something very, very useful for people.
Thank you. Thank you. I'm honored that you're here to shed light on very important topics. And as we discussed previously with the advancement of AI data science. NoSQL and so forth, it's very important to be part of the conversations with experts like yourself so that we all can shape the future of technology
and how it's going to impact. Everyone in all regards. So thank you for your time.
Um, my pleasure. Thank you.
Yeah. So, your journey in the field of database technology has been over several decades starting in 1988. So this period has seen remarkable changes in technology and has shaped the way we approach data today. So can you share a bit about your adventure in the database tech world since 1988?
And what major tech moments have you experienced along the way?
Oh, absolutely, Hanh. So, um, I started life as a developer and, um, you know, when I graduated, I, I worked for an awesome company, uh, which was Reuters in London. And I, and I think looking back, uh, at that time, uh, I worked with probably some of the smartest people that I've ever worked with in, in my entire IT career. And I stuck to them like glue, so I
tried to learn everything from them, because I'm a newbie, and I'm a fresh graduate, fresh face, and, uh, it was quite an eye opener as well, because, uh, some of the initial technology that I worked with was ancient, even when, when I was there, I mean, I worked with PDP 11s and Macro 11 assembler. RTL2, these were, uh, you know, you know, PDP 11s were the ancient stuff
even I enjoyed, but I had to learn it. And the reason for this, and this is a very kind of important point that I realized later was that often a lot of organizations, companies and businesses, enterprises have existing systems that they use. These may be called heritage systems or legacy systems. But there is great business
value in them still. They have to be cared for, maintained and loved, and enhanced. And, you know, someone has to do that, okay? They're not just going to go away. And therefore, gaining that experience actually taught me a great deal throughout the rest of my IT career. So, although it was a very painful
period in my life, with hindsight, I realized that I actually benefited very, very much from it. Um, and I think throughout the intervening decades, Uh, I've seen several waves of technology come and go. And so after my time at Reuters, I mean, an opportunity came up to do research. And it's one of those things in life that you, you decide, well, you know,
maybe I don't get this chance again. And funding was available, so I went back into academia to do research on performance benchmarking. And at that time, the hot technology of the day was something called object oriented databases. And, uh, um, 10 years later, then XML was the hot technology, 10 years after that, NoSQL came along and
the hot technology today is vectors. Everyone's talking about vectors and embeddings because of ChatGPD, uh, you know, uh, OpenAI, HuggingFace, these, uh, uh, types of transformers as they're called. Um, and so these technologies come out and come in waves. Sometimes they are successful. So if I look back at object
oriented databases, very few of those vendors survive to today. Uh, the majority of them are acquired. Some went out of business. The technology didn't cut it. And that's, you know, the way things are sometimes. But one thing I've noticed during this time is that relational is still king. Uh, or queen, you know, in the sense
that it dominates the world today. Uh, and the, the market for the technology, it far supersedes and, you know, is much, much greater than anything else currently available. So it's one of these things that I think is going to be around with us for a long time, just like SQL. So, uh, that's my key observation that, uh, things come and go.
Sometimes stuff stays around for a while, but ultimately, you know, there is a dominant technology today that was still dominant in the time when I started my IT career. I mean, that's where I essentially cut my teeth if it, as it were, gained my skills.
Mm hmm. Mm hmm. Great. So, since you started your tech journey, the world has seen a lot of changes and, and I bet you've seen things evolve. in areas, let's say, big data and data science. Can you share more specifics about these areas?
Yes. So I think my biggest observation is the growth in open source software during this time. And specifically, if you look at the likes of Hadoop, for example, Apache Spark, these were open source projects. And, uh, you know, they were donated by companies that were building these systems and then gave them
to the So the community at large, and then they became very popular. And in the early days, it was very unusual because when I started my career in IT, I practically heard of nobody using open source software. Today, it's almost a requirement, you know, and mandated. Uh, the quality of the software, I mean, it varies of course, as does commercial
software, but it gains so much traction and so much widespread use that I think that's my biggest observation that the, the, the choice is mind boggling. You've got so many different packages, so many different tools available and people give their time and effort for this. And then, you know, it's possible for the rest of us to take advantage of these technologies as well.
The other thing I've noticed is the growth in what we could term as kind of off the shelf hardware. Essentially, now you don't need a lot of specialist hardware to run stuff. You can run stuff on standard kind of computer, uh, hardware. You can build your own cluster if you like. And the growth in kind of distributed
processing as well, which is very useful because in the world today, the ability to Kind of distribute your data, bring the processing, um, uh, sorry, bring the data to the processing. Whereas in the past, it was kind of the other way around, you know, that is much cheaper. Uh, and again, gives you a lot of flexibility.
So, you know, corporates have these global systems now where the data distributed all over the world. And, uh, it, it provides some level of redundancy as well, you know, parts of the network go down, it's okay, you know, you, you still have the data available and I think that's the, uh, other observation in terms of the growth of distributed systems.
And I think coupled with that, or the, the thing that's really made that possible is the cloud. That has really enabled a lot of these things to, to happen much faster as well.
Mm hmm. So, now, for those perhaps not as techie, elaborate on open source, closed source, and give some of the more renowned ones that we need to stay close.
Yes. So I think with open source software, uh, the licensing tends to be very generous, uh, usually. And so if you look at, say, the Apache Foundation, which is, uh, one of the better known, Uh, sources in terms of, if you go there, you will see a lot of the top level projects that are available. So it's communities that
develop around these products. They are developed in an open environment. So I could go and look at the source code, for example. I could have a look at how, you know, the algorithms that are used, how things actually work. And that is something that a lot of people give their time and effort to. Um, in the past, for example, when
I worked at IBM, IBM was a great contributor to open source software. Many things that they were proprietary and they owned, they actually gave away to the open source community as well. And that's very unusual, and that is not just pieces of software, but the time of the people that work for them as well. So, it's a, it's a great alternative to closed source software where it's
proprietary, something is developed by a company and they keep the, the algorithms, uh, the tools, technologies they use to develop that and how they build that, that is entirely for them. And because that is part of the, the, uh, the, the, the knowledge and the efforts has gone in to build something. And it makes sense, you know, that these things are not done free.
Um, but if you look at and compare that against the open source space, the quality of the software, you know, is as good in the open source world as it is in the closed source world. And as I said, there are many places where you can find great open source software. I mean, I'm a great fan of, so actually Spark, for example, which is one of the big data
technologies that I frequently use. And again, there is a company that's behind that, Databricks, that I work for. And they offer something beyond just the open source technology. I mean, that is part of how they make money. Um, but if I don't want to, uh, uh, use their services, I can simply go to the Apache Software Foundation.
I can download the latest build, I can install it on my laptop, and I can start using it. Um, and I can do so to solve some business problem that I have. And as I said, because the licensing tends to be very generous, typically I don't have to pay, uh, for, for the privilege of using that software. And developers love free stuff.
They don't want to pay for anything. And so, you know, putting my developer's hat on, it's awesome for people just to try it out and use it for business problems.
Yeah. Well, that's great. That's great. Now, which one of these have shaped your career? And the industry as a whole?
Um, I think that, uh, um, the, the growth in the cloud, I think has been the biggest thing, uh, that, uh, as perhaps, uh, kind of shaped the way I do things today. So in the past, for example, I might build an environment on a computer system or a laptop or somewhere. And that would be very specific to me. I mean, I still do that from time to time,
but things like a virtual machine helps. My machine tends to be customized for my use. But when I need to show people how to replicate the steps that I've done, typically all of the set of instructions that I provide, If I have a cloud based environment, they can go away simply creating an account at no cost or no risk. They can do this now in a space that is
not on their local machine, for example. And that's great because it means, again, they feel much more comfortable running this somewhere else. Essentially, you're using somebody else's computing resources to do that, and you can do that for pennies these days. I mean, you know, the cost of running, say, some big data problem or some machine learning algorithm over large quantities
of data, you're paying very, very small amounts for the privilege of doing that. You're renting some resources from somebody else for that privilege. Of course, it's not yours. But. In terms of what you pay for it, I mean, it's very, very economic and a great way to do it. Uh, I, uh, there's been instances in
the past where I've wanted to run... Uh, a machine learning algorithm, for example, to add, do some analysis of a larger quantity of data, no way do I have the resources at home to be able to do that. I, it's just not possible, but in the cloud I can do that. And again, the cost is very, very minimal. So I think that's the biggest thing that
I've seen is the, the economy of scale. I mean, it just makes things so much more productive. Um, the risk is so, you know, reduced significantly. And again, the steps can be replicated. So if I want to share what I've done with somebody else. And give them all of the, the code, the instructions, the scripts, the data.
Now they can go away and do that. And again, no risk to them.
Mm hmm. I echo that. That's great. So, beyond your hands on work in the technology field, you've also made significant. Contributions to the world of literature in this domain, editing and co editing 10 books. So can you elaborate the key technical
concepts discussed in these books and what inspired you to present these ideas?
Uh, okay. So I think the, initially the very first publishing project that I got involved with Hanh was, uh, Just object oriented databases. In those days, I was a researcher and, uh, I was looking at this technology. Um, and I wanted to get case studies with industry to understand how they were using this technology.
Now, generally, um, organizations, enterprises tend to pick a technology because it gives them a competitive edge. So they aren't necessarily very, you know, want to share with anybody else what they're doing or how they're using that technology simply because it, you know, the world is such that. Um, organizations look for some advantage, you know, business opportunities, they,
they look for threats, uh, challenges, you know, new products and services that they want to bring to market much faster than their competitors. And therefore it was a real struggle for me to find companies that would be willing to, you know, share with me how they were using this technology. So, uh, the idea came to me that maybe I would like to publish a book.
And, uh, uh, one way to do that is as. Rather than writing it myself, which can be quite a time consuming task, work with others, uh, collaborate with others, let them write the chapters. And then I have the opportunity to put that together into a publication. And so that was my initial thinking. I, again, an opportunity to, uh, showcase some of the benefits of this technology,
object oriented databases, as I said. And so that's how it really started. I mean, that was my first publishing project. And of course, when I did the first one, it was not enough. You know, I had to do some more. So the thing is, uh, what I, what I realized with something like this, that if you are working collaboratively
with colleagues who are distributed around the world, you can only go as fast as the slowest person. Now, if that person has got lots of other things that he or she is working on. And they aren't necessarily finishing off a chapter that you asked them to do. I mean, there's not much you can do. I mean, you just have to be patient with them, and just steer
them in the right direction. Hope they'll get it finished. Um, and the other observation I think that I made in those early years as well, that publishing is very time consuming in the sense that if I have an idea today, um, uh, it might take two years before I see that actually published in a physical format. Now...
Of course, looking at 2023, no way could you do that because the world moves so much faster today. You know, it, it, people might have been able to wait two years in the past today. You cannot wait two months or two weeks. You know, the world changes that fast in a, in a blink of an eye. And so publishing, I think, uh, was served a great purpose.
I really enjoyed it. I, I enjoyed working with all the collaborators globally. And, and I felt that, uh, there came a sort of point in time where it's an exhausting process and I just felt, well, I need to stop, okay, because I'm losing hair, I'm getting old, so I need a bit of time for myself. Um, and so as a project manager,
essentially, that's what you are when you're doing, you know, this editing work, it is something that, uh, you know, you make a decision at some point and say, well, Uh, that enough, all right, but I mean, that's not to say I haven't worked at other projects. They even today, for example, at single star, my current employer, we are working on a project uh, for a book of which I've
contributed a couple of chapters myself. Other colleagues will do that as well, but that's slightly different from taking on the project entirely myself.
And I think that's the biggest observation, Hanh, that, uh, you know, it's time consuming. It's great, I mean, it's a great reward to see the published book in your hands when it's finally out, but... Like I said, today you cannot afford to do that because the world just moves so fast. You have to do things online. You have to get things out much, much
faster simply because the pace and the rate of change of technology is. I have in my, in my entire ID career, I think the last six months is unprecedented. I have never seen things moving this fast ever before.
And that's, that's the reason why I enjoy these kind of conversations so that we can put it out there. What we know, don't know our own story, our own history, and what we bring to the table so that we all can be a part and shape. The future of technology and AI, how it's going to impact everybody. Okay.
So I think it's so important and thank you. So, you've not only worked with technology hands on and contributed to academic literature, but you also have been a part of various major conference committees. And these events are like a mashup of ideas from industry experts worldwide. So can you share how being part of these committees has shaped your
understanding of the industry and maybe given you some deeper insight?
Yes. So I think, um, one of the nice things about organizing events, workshops, uh, being part of conference, uh, organizations is that you get to see some great proposals for ideas in terms of, you know, the papers that people want to submit. Or, uh, the presentations that want to, they want to do, and you
see, you know, sometimes that they really are stretching the boundaries of technologies to the limit. Um, and they come up with some great ideas, which, you know, you as an individual may never have thought of in a hundred years. And, and so I think that's the great aspect of it. The other thing that I've really enjoyed
is giving opportunities to others to be able to come to these events and present. It's very, very difficult, uh, ultimately, for example, if you have a conference or a workshop that you're running and let's say you can only accept, say, 30 presentations, for example, and then you've got 300 to choose from is a very, very difficult decision. Well, what I've always tried to
do is to balance this with looking particularly at those who are up and coming, young researchers, for example. So for particularly for academic conferences. Try to find, you know, great papers and give these researchers their first opportunity to come to an event and be able to talk about their research. I know how much I struggled
when I was a researcher in terms of getting my papers accepted. And so what I always tried to do was to give others the chance to come and do exactly that. Because, you know, getting your foot into a conference or getting published into a journal, that's a great first step. And it's a way to share your knowledge. So...
Um, one of my, uh, thesis examiners, he, he told me when I was, you know, being tested for my, uh, doctoral research, he, he reminded me, he said, look, it's a bit like you have put down a brick. You walk, you turn the other way, you walk away from the brick. A while later, you look back over your shoulder and you see the brick is now become higher.
There are other people have put their bricks on top of that, you know, it's like a wall that's being built up. And so your contribution to human knowledge may be very small, but it may be helping others to do the research. And that is the part of contributing to the body of human knowledge. And so I've always tried to do my best to help others to get that, you know,
that foot in the door kind of thing. Get them to their first event, give them a chance to talk about what they're doing. So that's been great. I know on the industrial side, again, I've been very fortunate. So I've been involved in a number of events over the years. Uh, a few years back, I was, uh, invited by the government of Malaysia,
for example, to travel out there. As part of, uh, uh, you know, giving them some advice and guidance on big data. And that was wonderful because, uh, uh, it was simply because I was a contributor to a Facebook group, the moderators, um, happened to be based in Malaysia and they knew this, uh, particular event was coming up and they put my name forward to the Malaysian government who very graciously.
Invited me, paid for my travel costs, you know, awesome trip and great opportunity to discuss some of the, uh, topics of the time. I mean, we are quite a bit, uh, you know, it wasn't yesterday as a few years ago, you know, people talk about big data. That was kind of a fresh thing and data science was a fresh thing at the time. Um, and you know, I, I teamed
up with other colleagues who came from all over the world. So there was a gentleman from MIT, for example, who was there. There was a gentleman who works for the Australian government who was there, you know, a panel of us. And we contributed our ideas, discussed them. And all of these were taken on board.
This is the suggestions. Um, and again, it was great, you know, because it's a chance to share your life experience and your knowledge with others. And hopefully. As a result of that, you know, it will help, um, people at large as well.
So are there upcoming events like that with emphasis on AI, data science and so forth?
Yeah. So, um, for, for that type of event, uh, there's nothing immediate. Um, and as you know, COVID has been a bit of a, you know, it's been a bit of a bummer. Let's put it that way. I mean, it's caused a lot of grief, especially if you are in a role. Where you go out often and travel and do
meetups and conferences, which is what I was very privileged to do in the past. Um, so a lot of, uh, conferences, events switch to kind of a virtual mode. So this year, for example, I've done mostly virtual events. Uh, I am very privileged, though, that next week, um, it is, I think, 2nd of August here in London, I will be doing a session for the British Computer
Society at the offices in London, where we will be talking about generative AI. It'll be a hands on lab as well, and that'll be great. I mean, it's one of the few opportunities I've had in the recent past to actually do an in person event, get the chance to talk to people and learn their pain points and do a workshop with them with hands on experience, you know, show them.
Some examples, get them to run the examples and then customize those examples by themselves, you know, try them out with their own data, or, you know, just change the code or whatever they want to do. It's their opportunity to, to, to learn. Um, and so I think those kinds of things would be great to do, but they are. Uh, sometimes few and far between, um, the world is a small place, of
course we can travel anywhere but you know, that's a physical event. That's a physical event. Yes. Uh, but the thing is that I will be there physically in the room, about 50 people will be there physically in the room, but it's also online as well for those that cannot attend. So, you don't have to be in
London to come and attend this. You could be on the other side of the world. You know, it could be midnight or so in Australia. You might be able to join from there. Absolutely fine.
That's awesome. Okay. Would it be all right in the show notes? May I include a link so others can join?
Yes, I can give you the link for that answer. Absolutely fine.
Yeah, that would be awesome. So I think, and I like the word customize that you said, because as you know, anyone that's used ChatGPT, you know, and you see a lot of advice, tips of what various prompts that one can try. And I really believe those are good starters. Okay.
But what's most valuable for you is what is it that you're trying to solve? What is that pain point? What solutions are you looking for? And then after you try other people's prompts and then you realize, okay, I got to swing up this, even though it's not the output that I'm looking for. And I understand the logic, the creativity,
the prompts, the way, how you put it into words and so forth, then you start solving your own problems. And that's where customization is key. And I appreciate what you just said that because like when people ask, like, Hey, can you show me how to do this? The first thing I say is what are you looking to solve? Then I can really hone in
to solve your pain points.
As opposed to everybody else's prompts that may, no, it may not be helpful to you.
Yes, absolutely. And I think The way to start, I mean, it's so, it seems a bit daunting. I mean, you, you see this chat GBT. I mean, I ignored it for a couple of months, but there came a point in time where I saw everybody else was using it. I was like, okay, let me try it. And it's been very helpful. I would say, uh, I mean, it, it has
these things called hallucinations. Sometimes it makes things out, which is very strange because it's like almost its knowledge base is so vast. Sometimes it tries to join the dots and it gets the dots wrong. You know what I mean? It comes to assumptions, which you as a person would think, well, how did it arrive at this?
You know, tell me you're thinking, well, why did you come with this suggestion? Um, I did a bit of research on it not so long ago, asking it for some questions. For example, I was interested in fraud detection, you know, how, what does it know about fraud detection? And could it give me some references for, uh, you know, links to videos and articles that might help me with a
particular problem that I was focusing on. And of course, it made some suggestions. Every one of the links that I looked at got no way.
It, it was just something that it made up. And even when I search for the names of the authors for the links that it was providing with it, they'll, they don't exist.
I, I agree.
And I think that's why they took Bing browser down from ChatGPT because I think at that time it was not good. You know, but I still believe from my own personal experience, although I recognize the hallucinations because first of all, you got to be an expert in your field to recognize that you can't just go in blindly because you wouldn't know what's right or wrong.
And the fact that you are an expert, you'll be able to discern when it's hallucinating. And from my personal experience, it's not enough for me to disregard it. It's been very few, but I recognize it right away. But then what I do instead is I challenge myself. I said, well, could you have asked?
better questions and I think that's key, at least for me. It's like, what questions are you asking? How can you do better and be more concise in, in conveying what you want? So it's been a great learning lesson every day. It's not perfect, but I think I've become more productive, more thought invoking, more thoughtful in everything that I do.
And I've learned not to anybody else's prompts. Ah yes. I feel like I have a target, an objective, a solution that I'm after and I'm going to hone in. It's like a journey. You may not. get it right the first time. And it's a journey.
But what I love about it is once you arrive in that journey, it remembers.
It does. And I think this is going to be one of the important skills going forward, Hanh, this prompt engineering, knowing how to use the technology effectively, asking it exactly what you want, and then giving it enough information in the context so that you can go away. And be able to retrieve and give you answers that are meaningful and
correct, or as correct as possible, given the vast wealth of knowledge that it's kind of trying to draw upon. Um, so this is going to be the future, I think. So anyone looking for a career, I think Prompt Engineering is going to be. The hot, uh, job that everyone wants to do.
I agree. I agree. And another thing I want to add with regards to younger first to 12th grade college kids, even, is that education should not be frowning on chat GPT or AI. Somehow we need to package it in a constructive way and integrate that into the curriculum and not look at it as copy and paste because you're, you're never
going to learn if you copy and paste, it's really, it's a thought process where you start, where you end and what did you do along the way to achieve the solution that you're looking for because along the way, from my experience, it actually invokes you to become a better critical thinker. So, we have to turn that paradigm around and teach the kids to use it intelligently and how to grow themselves, right?
And not frown upon it, because I still think we're, we're in, we're in a day and age that it's still frowned upon and forbidden in some school system.
I think you make a very important point and if I could use the analogy and the example from the distant past. So, in the days when, when I was at high school, Um, at that time, calculators were first allowed into examinations and people thought, you know, these will be things that will, you know, we will forget how to do math, you know, because now we're using a machine to be able to do stuff for us.
But in fact, it was just an aid. It's a tool. It's something that just makes us more efficient. We still need to learn the theory. We need, still need to understand how to do the math. And then the tool just makes, uh, the task, uh, uh, you know, much faster for us, of course, go in
because it can, you can enter your equations and your calculations and return the result much faster, but you know, have to know how to use it. And you, and by doing so you understand the theory behind that as well. And I think chatGTD will be much the same. It will become an intelligent assistant. It will be something that you can use to help you do your job better, or,
you know, help you in terms of the research that you're doing or whatever. It isn't something that's going to replace everything, but as over time it improves and the quality of its answers improve as well, as it learns more. I think it will become something that we make great use of and will really make a big difference in our lives.
I agree. I absolutely. I always tell people, you know, approach it with caution and excitement and focus on the excitement. There's so much to learn and you really have to adapt the paradigm of a lifelong learner. Right? Because you may not get it right the first
time, or even the hundredth time, and it's an ongoing evolution of, of PROMs, of getting more accurate PROMs, let's say.
So, that is so important. And, and don't look at it as a crutch, something cheating and so forth. It's not. So that's just my personal take. So, with the evolution of technology, new paradigms emerge that challenge the status quo. One such paradigm shift is in the database technology landscape
is the emergence of NoSQL. So could you provide the compare, compare, comparative, insight into how NoSQL contrasts with traditional database technology in terms of technical challenges, architectural differences, and potential benefits?
Yes. So, I think if we again go back in time a little bit Hanh, so as I said, these things tend to come in waves, you know, roughly every 10 years or so, something new comes along. And so, the NoSQL databases, uh, grew out of an event that I think was held quite some time ago. And it was just used as a hashtag, and
it's something that actually stuck. Um, but it's not a kind of single technology. There are a multitude of different products in this space. So, you have products that can support, like, documents. They are very good for storing JSON documents, for example. Or you have products that are very
good for key value, um, or graph structures, for example, you know, so there are a few major categories. These are designed for different types of problems. But what's happened over time is this, what's called multi model. Um, so a single product can support a number of different capabilities. So it might be able to do, uh, you know,
because now you're dealing with pretty much the same thing all the way through. And as a developer, that makes your life easier rather than you having now to map whatever it is you're trying to model. onto a relational database system. But again, I think what's happened and this is again my observation seeing these different waves of technology come and sometimes go is that the
relational guys don't, are not idle. What they say is they typically tell their customers, okay, wait a little bit, we'll add these capabilities for you. So with the advent of NoSQL some great things happened as well. Distributed processing was something that came to the fore. So many of these products could allow you to, you know, uh, create
a cluster with three, four, five different nodes and then, you know, distribute your data amongst this, uh, or even larger if you want, and then it could be cloud based as well. And that's something that the relational databases didn't generally do in the past. They tend to be very monolithic, you know, they're installed on a single machine, you want to process more
data, get a bigger box, all right? And so the, the distributed processing is something that emerged out of that. And the, the, the development of now what's termed as distributed SQL. So there are a range of vendors now that do the stuff that NoSQL crowd originally kind of came up with, if you like the principles behind it, and now they provide those capabilities with
relational database systems as well. In addition to saying, okay, we can also store JSON documents, you can query that using SQL or, you know, we can store graph structures and now you can query that using a graph based query language as well, in addition to, say, SQL. Um, so in a sense, it's become a bit more, how to say, you know, fuzzy, you know, there's no clear kind of black and white.
And the NoSQL guys, they've not been idle as well. So they've added SQL support. You know, it's become a blurred distinction. And that kind of tends to happen because if you look at what enterprises tend to do, they've committed a lot of, um, their time resources and money to building systems, your
transactional or analytical systems based around relational technology. That is not something they're going to get rid of overnight. Um, and therefore anything that can be kind of augment that, perhaps there's some specific problem that they need the NoSQL product for, or vice versa, you know, maybe there are companies that have gone down the NoSQL route, they've
used that as their primary database, but today they have a need for analytics, for example, and they find that that particular NoSQL product can't support it very well, they go down the SQL route. It's much harder to kind of, uh, you know, draw lines in the sand, as it were, you know, nothing is quite as clear as it seems. Um, but as I said, you know, the
NoSQL crowd that they've some great things that they brought with them. I said the distributed processing, one thing, uh, that I think has really helped and pushed the relational guys again to do something similar. You know, it's a distributed SQL products have taken a little bit of time to develop. It's a complex thing to do.
But now we have a range of these products on the market as well.
So, choosing between SQL or NoSQL, it's not a one size fits all decision. It depends on the unique needs of the project. And from what you're saying, traditional SQL databases bring benefits like transaction reliability and strong performance,
for complex queries, right?
Yes. Yes. Absolutely, Hanh. You've kind of hit it on, hit the nail on the head, as they say. And so the thing is, I think that You know, if you think about it, things like, uh, optimizers. Because if you look at SQL, it's declarative, in the sense
that you tell the system, I want you to go away and get me this. And then the system figures out the best way to retrieve that. So it will use all of the knowledge it has about the data to figure out the optimal, the lowest cost, if you like, if it's a cost based optimizer, in order to retrieve that data for you. So, you as a developer, You
don't, there's less for you to do. I mean, the system does the hard work for you. In a sense, with some of these NoSQL products, they turned it around its head. So the developer has to think very carefully. He or she now has to decide, okay, what's the best way I need to retrieve this data? Um, And so it's having this kind of
marriage of these two technologies, I think, helps in ultimately, because again, you have the choice now of utilizing the power of SQL, which has been around for a long time, optimizes a very well understood technology. They've been researched to death, you know, over the years, look at all the academic papers that have been published about them.
So something that's very efficient, it will do the job for you. But at the same time, the ability to take advantage of some of these new data formats such as, say, JSON for documents that you might want to store, and then... Be able to, uh, you know, have your database system manage those for you as well and be able to query those for you efficiently as well.
I think that is, uh, something that we see today as well. Again, this kind of marriage of the two worlds, if you like, which, uh, from a developer's perspective is great because now you as a developer, you focus much more on the business problem, have to focus far less on the intricacies of how, oh, how do I retrieve the data? Do I need to do it this way?
Do I need to do it that way? Let the system do the hard work, okay? And again, work with the data types that you want to work with to solve that business problem.
Mm hmm. Mm hmm. Very, very interesting. Okay, so we're going to just hone in a little bit on the topic of the aging population.
So, as someone with a wealth of experience and expertise in database technology, big data, Your insights have served valuable guidance to those starting in the journey. I guess first, before we go into the topic of aging population, so I want to know, based on your extensive experience, technical advice would you give to novice entering the field
of database technology and big data?
Um, okay. I think, um, it's really, um, you know, learn as much as you can. We are very fortunate today in that there is so much free knowledge, free training available Uh, you've got the biggest encyclopedia in the world at your fingertips. Yes, understanding how to search effectively and finding, uh, that's,
uh, you know, a little bit of an art. You do need to understand how to do that. And as we discussed a little bit earlier on, you know, using technologies such as CheckGPT or BARD, for example, doing that prompt engineering, figuring out what it is that you're after and then asking the right questions. That is something I think that, that will really help in terms of finding
things that you need, um, getting the information that you need. And so, for anyone starting out in the industry today, um, I would draw upon the lessons that I learned with my very first job, which I still look back on today. And I think to myself, well, that, you know, I'm glad I did this and I'm glad I did that. And so I think the key
things that I would say. Um, look at your peers. Don't be afraid to ask questions, things that you don't understand. That's absolutely fine. You know, nobody has got all the answers. Talk to friends, colleagues, peers. If you were starting, you know, your, your work environment, reach out to people. The vast majority of people
are very, very helpful. They will guide you or steer you. Or if they don't know, they will tell you where to go in order to find the information that you need. That's very important. Um, you know, be very proactive, uh, get to know the, the, you know, where to find the sources of information. That's very important as
well within an organization. I, I had to learn that with my very first IT job because I was given a system to learn that I knew absolutely nothing about. It was ancient, it was creaking, and, and, uh, some of the people that had built it had long gone from the company. And therefore, the, the, the knowledge that was contained within
all that is very, very, very small. So I had to seek out those people who had that knowledge and get their time and then learn from them. And the other thing I would say is, again, take us all the training that you can get, whether that's, uh, on the job training or whether it's formal training, or if there's a training that you can find on the internet,
there is, like I said, you know, free code camp, for example, is awesome. Coursera is awesome. EDX is awesome. You know, all of these places. There are some really, really great courses. A lot of them, uh, costs very small amount or they are free. Take the training, learn from them, get
as much hands on as experience as you can. You know, if there's particular programming languages you want to learn, you know, get some code experience and under your fingertips and you'll learn how to do stuff, look at examples. Um, and then there are still great places despite the growth in ChatGPT and BARD and so on. I mean places like Stack Overflow, for
example, where if you think about it, people just go there and they share their knowledge, so they give it away for free. You know, you can go there, you can find it, and you'll find awesome answers from people all over the world that, you know, literally they're giving their time and their knowledge for free. Search these places, you know, find the answers.
Um, and, uh, but again, be careful in terms of using some of those, you know, give credit where credit is due. So for example, when I'd use Stack Overflow to find answers, if I write an article or I write a piece of code, I'd always put a reference in there to say where I got this from. So that's, you know, someone wants to follow the, the, you
know, the, uh, um, the breadcrumbs go back to the original source. They know exactly how to get there. Um, and so I think that's the, those are the key lessons. You know, we just be proactive. You know, be effective and learn as much as you can take all the free training that you can get. And then really, you know, reach
out to friends, colleagues. You know your workmates and don't be afraid to ask questions, you know, and that's the key thing You know be be like that and you will pick up so much Versus if you don't ask anything, I mean you will be in in a very difficult situation then.
Absolutely It's a paradigm shift. Thank you so much You know, it's a paradigm shift. Everything that you're saying, it's really independent of age, whether you are 35 or 55, be a lifelong learner. Yes. That's key because with everything changing daily and continue to change in many years to come,
that's the key is just be a lifelong learner, independent of your age. So, yeah. So artificial intelligence has been a game changer across various sectors and its impact on senior care is significant and growing and by offering potential solutions ranging from predictive healthcare to social engagement tools, AI is emerging as
a powerful ally in addressing the complex needs of the aging population. So can you tell us more what you think as far as how AI is assisting us in better understanding? And addressing the needs of our aging population.
Yeah, I think, Hanh, one of the greatest things that is, is, I've seen and I've read about just recently, I came across a couple of stories about this, is this development of, you know, personalized medicine, as they call it. And then being able to Do greater analysis of demographics down to the level of, uh, uh, you know, you know, the, the kind of, uh, where people live
in the streets around them in terms of, so I'll give you a quick example. So, I live in a part of London where, um, it's Wimbledon and then literally a couple of streets away from me is the, uh, local doctor surgery where it's a practice. There are a number of doctors that, uh, uh, run this practice, but there are other medical services available there as well. Now for them, uh, I'm sure having a lot
of detailed demographic information about the population is something that would be extremely useful because then they know, in terms of what sort of health care would be necessary in terms of targeting particular types of population. So if it's a slightly more aged population, people like myself, for example, we may need more care and attention than if it's a lot
of younger people that live in their younger families with kids. And that's helpful because it makes this almost like a neighborhood thing. You know, you can literally offer services then that are very much customized to the local neighborhood. And that means that money well spent and services better targeted. And that I think is very, very important.
Um, so using artificial intelligence, because we have so much data available today, helping us mine that data, looking for patterns, looking for demographic information. Offering this, uh, uh, kind of, you know, local services. I think this is going to be the key to be able to improve the quality of life. And we are, we live in such
great times that medicine has improved our living standard. Uh, you know, in terms of the aging population, that's great that we live longer. Uh, uh, my mum, uh, was 80 years old, uh, before she passed away a few years ago. And essentially, um, she was sustained by medication for about 20 years of, of, of, you know, the last 20 years of her life.
In, uh, another country where we came from, uh, originally that wouldn't have been possible. Mum wouldn't have received that level of, uh, care and attention or the medication to help her survive for another 20 years. Um, and so I think. This is the kind of thing that we need. Much more targeted approach. Better use of the technology to help us.
Using AI in a very effective way here to be able to look for these interesting patterns that we as human beings may not necessarily see since because The quantity of information that we have to look at is just so vast, you know, it can find those interesting trends and patterns for us and allow us to target the services, um, and for example, healthcare, other services as well in a much more tailored fashion.
And I think that is going to be very, very important going forward.
Mm hmm. I echo that. And what you're describing is precision medicine and how I relate that to me personally is that when I'm sick, when I have any type of health issues, it isn't just that. local problem that I have. It's the entire body. It's what could be attributing
to that is my mental, physical, spiritual, in all regards. So it's very unique to me. It's very precise to my own issues, my own health conditions. And that just underscores the importance of precision medicine. And even my mom who has, you know, in the late dementia, my mother in law is even later.
in her dementia journey. It's so personal, personalized. So, I agree with you. I think with AI and with all the data available, we all have to get more precise in how to use that constructively and to provide precision medicine. So, thank you. Now, data science, with its predictive modeling capabilities and pattern
recognition, This potentially allow us to anticipate the needs of our seniors, even before they arise. And by analyzing trends and patterns like you described, we can effectively plan for a future that caters to this demographic because it's growing so rapidly. So can you offer some insight how you think a data science could help us foresee?
the future requirements of the, um, aging population?
Yes, Hanh, so I think that, uh, if you look at some of the research that's going on today, uh, and I, I, of the last couple of months, uh, I mean, I'm sure you've heard of DeepMind, for example. which is part of Google now. Uh, they've made some very interesting discoveries. So now, you know, this kind of, uh, technology, it goes way beyond what
perhaps its original intent was, you know, it can make these incredible discoveries and that's going to help us with tackling a lot of these, uh, common illnesses and diseases that the aging population suffer from, you know, and so things like Alzheimer's, for example, or, being able to, uh, find a cure for cancer, you know, because these technologies can sift through vast quantities of
data much faster than we ever could. The processing power, the cost of it has come down so much. That we can now, whereas in the past, you know, organizations may have dealt with sort of, uh, you know, tens or hundreds of gigabytes. Now we're talking about sort of petabytes and larger in terms of the quantities of data that we can work at.
And the cost of storage, the cost of processing, everything is so much cheaper. It makes it possible for artificial intelligence, machine learning, um, being able to really... Analyze the vast quantities of data much more effectively, looking for these interesting discoveries that are waiting there to be done. And I'll just draw your attention
to one sort of quick thing. So many years ago, during my time as a researcher. Um, I was very fortunate in that I was invited to go to CERN, uh, which straddles the French and Swiss border. There, you know, they do the particle physics. They smash atoms and find out what matter is made of.
So in those days, they were just thinking about experiments, uh, about how they would store the vast quantities of data for this, uh, Large Hadron Collider, as it's called. And so, uh, you know, over time, um, they, of course, this Higgs boson is being discovered, you know, but each experiment, I was told at the time, generates 40 petabytes of data.
I mean, a mind boggling number. Can't even imagine it. But the thing is, they've only so far just mined a tiny amount of that. So just imagine all the other wonderful discoveries, the scientific discoveries are waiting there to be found, if they could find the means to be able to look at that data. And I think it's very much the same
case with us because fortunately medicine has come a long way. We, we are also fortunate that we live longer care that we receive is great as well. But there are there are certain kind of, uh, no, I don't want to use the word burden, but I think, uh, costs associated with that, because if you look at some, uh, of the, uh, country, say in the Far
East here, I'm thinking particularly thinking of Japan and South Korea, where the population is aging, they are already thinking about, you know, how will This be sustained in the long run because the younger generation are paying for those now as we age and live longer. Fortunately, that's great. You know, we have more time to enjoy ourselves.
How will this be supported? So there's a whole range of both economical, scientific, uh, other things that surround this. Uh, I, I think it's a, it's a very difficult problem to address one that I think governments grapple with at the time. Here in the UK it's the same, you know, we are for very fortunate in terms of
people living longer because of the. The level of medical care and, um, that's great, you know, early retirement may be a solution, but then how to fund, you know, how to fund this. This is a, this is the, the dilemma that we face today. Um, lots of things I think around that to think about. Very difficult to know and to
suggest any particular answers there. You know, I'm constantly thinking about these things. I'm an old guy. It worries me as well, you know. Who's going to pay for my, uh, my support and my pension when I retire?
Yeah, I echo that. I'm the youngest of 10 and I'm seeing it in my older siblings, ranging from 57 to 78. So, that's the age span of our family. It's real, you know, it's real. Wow, thank you so much. I enjoy our conversation. I really appreciate your time. Do you have anything else
that you would like to add?
Oh, no, thank you very much, Hanh. It was a great pleasure. Thank you very much for inviting me. Um, just a chance for me to share some of my thoughts and ideas. Uh, as I said before, I don't have all the answers. I mean, I look at problems from time to time. I use technology, solve stuff,
and, uh, and I share what I learn. Uh, and I publish a lot simply because I think to myself, well, if I'm thinking this, maybe somebody else has got the same problem. Let me write about this. Perhaps it might help them too. So I do enjoy that aspect of it. Um, But i've been very privileged as I said, I've worked with some super people
over the years Very smart people and I kind of stuck to them like look to learn everything that they could teach me and uh, so, you know, I I've I feel that I've Being in a good place and I still feel that I'm in a good place today.
Thank you. Thank you so much to share your wealth of knowledge. Yeah. So as we reach the end of our captivating conversation, I'm sure that all of us have a greater understanding of how technology is transforming care for our aging population. Yeah.
And our expert insights have given us a lot to think about. And we're incredibly grateful for your time and knowledge. And as we continue to see the world evolve. We can only imagine the possibilities and positive changes that AI, data science, big data, and NoSQL technologies could bring to industries
such as healthcare, education, and environment, revolutionizing the way we analyze and interpret information to make informed decisions. And it's clear that the potential for innovation and improvement is immense. The future is undoubtedly promising and remember, understanding these technologies is not just about staying updated. It's about ensuring that we can provide
the best possible care to our aging population, making their lives more comfortable, safe, and fulfilling. Technology is an important enabler of change and a bridge to a future where our elders are not only taken care of, but are also empowered to lead lives of dignity and respect. So we'd like to extend our sincerest thanks to Akhmal.
Thank you for joining us today and sharing his invaluable insights. And of course, a big thing to the audience who's tuning in and we look forward to exploring more interesting topics with you in the future episodes. I am Hanh Brown, the host of the Boomer Living Broadcast signing off until next time. So let's continue to learn, grow
and embrace the beauty of aging in this ever changing world. Take care.