February 03, 2021 | 26 min 52 sec
Tech Trends & Predictions 2021 – AI & ML
Podcast Host – Madhura Gaikwad, Synerzip
Podcast Guest – Vinayak Joglekar, CTO at Synerzip | Gaurav Gupta, AI Expert and Senior Engineering Manager at Synerzip
Artificial Intelligence and Machine Learning are becoming mainstream in the post-pandemic age. Businesses are leveraging them to resolve unimaginable life and business challenges.
In this episode, we bring in a seasoned AI professional as a guest along with our CTO, Vinayak Joglekar, to unravel the advancements in the implementation of AI and ML that include :
- AI democritization
- Availability of Structured Data
- Active learning
- Reinforcement learning
- A lot more…
Listen on these platforms:
Madhura Gaikwad: (00:08)
Hello, and welcome to Zipradio podcasts powered by Synerzip. I’m your host Madhura Gaikwad, and I’m joined today by Vinayak Joglekar, CTO at Synerzip, and Gaurav Gupta, Senior Engineering Manager at Synerzip. Gaurav is an AI ML expert and has agreed to join us today to discuss the top technology trends and predictions for 2021. In the last episode we discuss cloud as one of the top technology trends and predictions for 2021. Vinayak shared some great insights on the acceleration of cloud adoption, hybrid cloud, edge computing, and more. Today, we will deep dive into the second most trending technology that will take center stage in 2021, which is artificial intelligence and machine learning. And this, we have identified based on our survey of 170 plus CXOs and technology leaders, as well as our observations from client interviews and research documents. For listeners wanting more information on these trending technologies, we have also curated an ebook that covers the nine technology trends and predictions for 2021 in detail. You can find the link to download this ebook in the description. So welcome onboard Vinayak and Gaurav.
So my first question to both of you is that there is a lot of progress in artificial intelligence and machine learning algorithms. These algorithms are easily available on public platforms, such as TensorFlow and AWS. So previously very few had access to such ready algorithms, but there has been a very big shift in 2020, and AI democratization is one of the top contenders in 2021 predictions. So how will all of this pan out in 2021?
Vinayak Joglekar: (01:44)
Hey, yeah. So this is we going to see more of it. If you see the number of people who were not actually data scientists, they’re just developers who have picked up, you know, by doing a solid of courses on Udemy and Coursera and stuff like that, they pretty much up to speed on how to use the algorithms that are available in the public domain, as you rightly pointed out. So AWS has now SageMaker. So another thing that has got democratized is the availability to computing. So there are some of these algorithms that it’s not only the algorithm that was hard to find or hard to get at, but it was also the computing power that is needed to support it that wasn’t so easily available. But today you have, uh, on the cloud, all the computing power and very quickly you can put together the needed computing as well as the algorithm.
So that is going to move to the next level. So you have a lot of GPU’s available on the cloud, and that is going to change the landscape where it’s not something which is reserved for a few people sitting in the ivory towers. This is going to be completely democratized. So it is just like, uh, going to be a part of the standard development and programming, just like what you have on the UI or database machine learning, artificial intelligence is going to, so the bigger problem, and I’ll come to that problem later, is that data, I mean the processing and learning and algorithms, and that part is pretty much available. What is the problem that we are going to have is the availability of data and secondly, experience in putting these things in production and putting them to actual use and not just having these as laboratory experiments, where you’re just proving that these things work. And I let Gaurav speak of that a little bit as to what he sees this, where he sees these things going, but in my opinion it’s not going to be the algorithms or the computing that is the bottleneck. It could be the data and ability and experience of people in deploying these models in production. That’s where we are going to see the bottleneck, but I let Gaurav speak what his opinion is. So what do you think, Gaurav?
Gaurav Gupta: (04:07)
As you said, rightly this is going to be majorly on the data side and also on the, how we productionize these models and monitor those models. So I think 2021, the major focus is going to be the MLOps, the area where we are going to manage the models, build the models, and monitor the models, how to build that infrastructure, because that is the major pinpoint in any machine learning work which has to be done. And as these algorithms are getting democratized, one major benefit, which is going to come down the line is that domain specific understanding, which is required for machine learning slowly that may move out, where you may use any domain data and algorithms are good enough to find out the features for your domain instead of you defining the features. So that kind of democratization for domain agnostic machine learning algorithms is going to happen. And, uh, MLOps is going to be a big deal in 2021.
Vinayak Joglekar: (05:07)
Yeah. So let’s talk about that a bit later. Domain agnostic is something which kind of makes it very, very interesting because particularly for people who are in a country, such as India, and we are trying to support our clients who are all over, mainly in the Western countries like United States and Europe. So we don’t have the context or the domain knowledge that is required to support modeling. So for building that model domain knowledge is needed today. And what you’re saying is even that may not be required going forward. So we talk about that a little, but Madhura, you had conducted some sort of survey. And what are people saying?
Yes Vinayak. So based on the survey, one thing that we’ve observed is that there is so much progress that is expected in AI ML. And that is very much a, you know, visible through the survey. And the survey also observed that 37% of the respondents, which were mostly technology leaders, and CXOs said that AI ML were among the top skill gaps that they foresee in 2021 that will affect their product roadmap.
Vinayak Joglekar: (06:17)
Oh, that is interesting. Yeah. Skills, availability of skills is also another issue that we will have to overcome. So, uh, you know, in my opinion, it’s not rocket science, so it’s just the way it happens with every skill that is on the horizon. You know, initially to start with, there is always the skill gap and for which people have to scramble to hire. But very soon you will find that a lot of new talent is attracted to this hot field, which is very promising, which is very remunerative and people will pick up. So there will be a lot of availability also very quickly. So by the time we start productionizing and doing MLops and, you know, meeting the, overcoming their data famine in various ways, I think talent availability is also by, uh, we’ll keep pace with that is going to improve. But yes, for now I think people have to buckle up and start learning these skills. So that is what I think. So Gaurav, what do you think, how are we going to build with the current shortage of talent and, uh, how long do you think it takes for someone to pick up these skills?
Gaurav Gupta: (07:33)
I think there are two things mean, so the people who have the experience in software development or coming from some other backgrounds, generally learning one time and just applying that knowledge becomes a straightforward thing for people, but in machine learning is somewhat different that you have to, whenever a new problem comes, you have to think through, from the scratch, you have to understand the domain, you have to understand the business and the outcomes. So I think that kind of understanding few people are not very much capable of doing that sometimes. But yeah, I understand. So if people are interested, I think it may take three to six months to acquire this skill at a good scale from where they can start working on some new projects or something, and maybe a one to two years of long-term roadmap where they can become the expert in the area.
Vinayak Joglekar: (08:25)
Yeah. So the problem, you know, Madhura that I, at least in my experience with software engineers, is that they think that every problem has a solution. And in machine learning, there are several problems which don’t have a single solution and some of them don’t have a solution at all. So it’s a journey as you rightly pointed out, and you have to to apply several different approaches and you can never reach a hundred percent. You are always, you know, if you measure in terms of accuracy or in terms of recall, you are somewhere near 80% 90%, but you can, and it becomes harder and harder as you approach 99%, right? I mean, you can never reach a hundred percent. That is one change that software engineers who are used to the precise way to define things and get the right outcome and output using the right coding. I think that it’s a change of experience for them. So as Gaurav rightly pointed out, it’s not the same, it’s slightly different.
Gaurav Gupta: (09:31)
And also one more thing to add over here, that experience of multiple fields and domains like maths, physics, chemistry, maybe images, 3d, those interests. If you have multiple domains then it becomes easier to do the things. For example, a software engineer may not know very well how audio works, but if you want to do machine learning for audio, like speech to text, you have to have the knowledge of how audio works like some audio knowledge. So that kind of multidisciplinary knowledge is also required a good amount of times to become an expert in this kind of machine learning area.
Vinayak Joglekar: (10:10)
And I can’t agree more with you, you know, even, uh, somebody who is doing text processing needs to know English grammar, right?
Gaurav Gupta: (10:18)
I mean, yeah, you have to be a linguist kind of guy.
Vinayak Joglekar: (10:21)
It’s not very straight forward. You need to have that domain in the context without which it’s not the same as, you know, picking up, uh, say a front end tech skill like Angular or React. And, you know, uh, and if you have a wireframe, you can just build it. So Madhura, uh, you know, this data thing is it goes to the natural language processing. I mean, that is where I think plenty of availability of data is there. So structured data, which is, uh, you know, where a lot of the machine learning used to happen in the past. You know, that is hard to come by. At one thing, companies who have a lot of data, they are not completely digitally transformed and they are sitting on the data and they don’t have the wherewithal to utilize it in the right fashion. And there are companies who are further advanced in machine learning, but they are not having their data.
I mean, there are a lot of companies they are like are dressed up and nowhere to go because they have the algorithm, they have the know-how, but they don’t have the data. So to come out of this chicken and egg situation, I think natural language processing seems to be the natural choice because there’s lots of data that is available to be scraped from the web that like whether it is Twitter streams or social media streams and other social media, or even images and videos, and there’s plenty of available public domain where you can use it for natural language processing in combination with machine learning. So Gaurav, I think in 2021, do you think you’ll see a lot of this natural language processing taking lead or over pureplay machine learning where you’re crunching, just numbers.
Gaurav Gupta: (12:04)
True. I think, uh, natural language processing, robotics and IOT, these kinds of areas are going to pick up like anything in 2021. And one more thing, which I have observed, which is going to happen in 2021. People and companies are going to get educated, what data has to be captured, even if they want to do machine learning in the future, because you’ll need historical data sets. Many of the companies I’ve worked with we have found that they have the data, but data was half-baked. It was not the complete data. And that data becomes useless as a historical data for the machine learning. So companies knowing, even if they want do the machine learning down the line a year or so, they need historical data. And what data has to be captured, that is very important. If you don’t have the proper data, then full data capturing becomes, you know, useless all that data.
Vinayak Joglekar: (12:56)
Yeah. In old times we used to say garbage in, garbage out!
Gaurav Gupta: (12:59)
Yeah. So, uh, one customer, they were capturing the data from the global scale, but they were not keeping from where requests are coming. They were having a timestamp and everything, no, everything was translated to GMT timestamp, but don’t not knowing from where these requests are coming. Now you can’t train a model which can take the time as the major feature. Right. And their product required major features at the time. Now, that data is useless because we don’t know from where these requests were coming from.
Vinayak Joglekar: (13:29)
Yeah. So, uh, you know, who knew at that point of time that, uh, you would be using for machine learning? So another topic you alluded to was IOT and edge computing. So that is another way of overcoming data famine, is you’re going to have millions of these devices that are going to be spread all over and they will be sensor data that will be gathered from these devices. And already, I think we have more of these devices than cell phones, and it’s going to be like a hundred times the number of cellphones that we have. So it will be close to a trillion of these devices. Very soon, we will, that those devices will be sending so much data. Now, one trend that I foresee that will happen is that instead of sending all the data to the cloud and then doing the decision making on the cloud, and doing the decision making on the cloud.
So a lot of the decisions also need to be deployed at the edge because the business context exists at the edge and that’s where the decision has to be implemented. So it’s far easier when you reach the decision where it is to be implemented at the place where it is to be brought into action. So that is what is going to, you know, bring the new paradigm, which is on the cloud, you will do the learning and creation of models, which would be periodic, if not one time exercise. And then once this model is ready, that model will be pushed to the edge. And on the edge, it could be very, very low computing. Maybe your cell phone, maybe a raspberry PI, or maybe an Arduino board in which the model will reside. The inference will be drawn at the edge. And outcome of the inference would be displayed to the human expert or the executioner and the person who is actually were to take action on that decision, that you will be able to reach based on the inference. So this type of working is going to bring in what we call as more of active learning wherein you know, the human at the end may say that, okay, his particular inference is right or wrong or needs to improve. And that same would in turn, go back into training the model further. So with every use, the model will become smarter and smarter. So I’ll let Gaurav talk a little bit more about this phenomenon that we are going to see of active learning. And he sees as the future and going forward in 2021.
Gaurav Gupta: (15:59)
I feel that active learning is going to be a major player in the market because most of the behaviors which we are seeing on mobiles and mobile applications with many things can be done by artificial intelligence on the cloud and models can be deployed on the device. For example, the speech to text kind of thing, which requires the web. Now Google is pushing the models on the device itself. So many Android devices, new versions are having those, these speech to text functionality which is on the device, not on the cloud. So I think that is going to change the way companies are using machine learning for many scenarios. Yeah.
Vinayak Joglekar: (16:37)
You mean you can be dictating notes to my Android phone, even though it is not connected to the internet. Yes.
Gaurav Gupta: (16:44)
You can do today also. Today they have the application for the speech to text and write on the Google application that doesn’t use the web that does it offline to my knowledge, they’ve done it a few months back.
Vinayak Joglekar: (16:57)
Yeah. And you know, I had recently worked on one project where this is specific to the pandemic era where no, you want the camera to be smart enough to know whether someone is wearing a mask or not. So that needs to happen in a very quick way. There is no way to process that, the inference, by sending the image on the cloud. So the person who was waiting at the gate, he or she can’t wait for that long just to check the temperature and whether he is wearing a mask or not. Such image data can be processed right there at the camera is, with very, very low computing power. So there are many, many applications like that we are going to see in 2021. And, you know, active learning is one thing, but you know, this data famine, in which we don’t have enough data, and I have heard about this. And Gaurav this is your area of expertise that you can actually borrow an existing proven model or, you know, and use that and improve upon that. So can you talk a little bit, I think what I’m talking about is transfer learning. If I’m not wrong?
Gaurav Gupta: (18:05)
Yeah Vinayak. What you are talking about is exactly transfer learning is. So the idea is very similar that if some kid knows one language and you want to teach them another language, they can pick up very fast because they already know the concepts of what is the languages. So it’s coming from the same thought process that how humans learn, transfer learning that if they have a skill similar kind of skill learning is not a big deal. It can be done very fast, not putting that much effort. So in transfer learning, for example, it means, so you take the images and you train a model which can reduce the dimension of the image, to few variables and recreate that similar image from those variables. So we know we named as a latent variables. So this is like an encoder decoder architecture, where you, encode the whole image to few variables from these variables, you decode the image properly.
You train this kind of model a lot. Now the problem, and based on this latent variables, you remove the decoder and then you do the prediction, whether what is the category of the image? Now, the thing is your domain images may be very less. For example, I want to classify between two basic images, let’s say, suppose the cycle and the bike, I want to segregate two things. I may have maybe 50, a hundred, 200 images, let’s say, but it’s very difficult to train a model only with a hundred, 200 images. You need millions of images. For example, now here we can use the transfer learning approach. What we can do. We can take free hand images from the web. A lot of images, let’s say millions of images, train a model with the encoder decoder, which doesn’t learn anything except to encode the image and decode the image.
Take that once that model is trained, take that model, take your a hundred, 200 images of cycles and bikes and tune the model with the very low learning that so that it can adopt to new data because already it has understood the concepts as to how an image has to be encoded and decoded. Once this model learns your bikes and cycles with a very few learning rate. And maybe in a very small time after that this latent variables of the encoder can be directly used for the classification. So you can apply from one domain to another domain. Similar thing. Now cutting edge is going into the NLP side also where people are doing similar things on NLP. They take the Wikipedia data, train it, train the language model, which is now the whole English. Yeah.
Vinayak Joglekar: (20:43)
I think you have done it, right?
Gaurav Gupta: (20:45)
Yeah, we haven’t been both things, different customers. We have used the transfer learning for images also for the customer for segregating the images between multiple classes, finding out the similar images, basically that if I give a image, can you find a similar image from the dataset? We have used a similar technology task for learning for that customer and for the NLP. Also, we have used a similar technology where customer had data from the API data. And, uh, we are using the API data in the language model, which was trained on the Wikipedia to retrain the our API data and, uh, finding out, doing the classification of the API data sequences, whether they are, uh, in this direction, customers are doing that application more or less or what. So yeah, transfer learning is a very powerful concept. Uh, and, uh, I think in other areas like video processing, robotics and robotics and car driving, I think, uh, people are using from long time in these areas.
Vinayak Joglekar: (21:47)
Gaurav, these are driving for example, this is great. Totally. I mean, you know, I’m just concluding the discussion that we have on transfer learning and necessity is the mother of invention. And when we have, we are faced with data famine, we overcome that, uh, even with less data we can make do with, by using this latent variables and encoder decoder model, this is something which is very innovative, but you mentioned this in passing, just in the last minute about driving, right? I mean, that is a very, very different process. I mean, you start from somewhere, you reach the goal, but the actions you take, and let’s say, if you are fitted a camera on a self-driving car and you try to see what all steps happen, give us from time to time. And you know, every time a driver doesn’t reach the destination in the same way, I mean, there could be a hundred different events and actions that happen along the way. So it’s not a straightforward thing. And then how do you train, such models? I mean, this is something which is totally mind boggling.
Gaurav Gupta (22:55)
Yeah. What her thing is true, that this is mind boggling and even understanding how it really works is quite complicated. So in a nutshell, if I want to say the techniques which we use here is not transfer learning, mainly we use reinforcement learning, where we have a simulated environment, for example, a real life car also, but that will met so many accidents. How many cars we can use in the speed is going to be slow. Robot is learning how to change the gear, how to go up and down or how to accelerate how to deaccelerate. It may take maybe months on a real car for a model to train, even to deaccelerate and accelerate, maybe learning. So usually what people are doing this situation, we’ve used reinforcement learning, in a, simulated, and, like you can take the game of like NFS game engine and you have different perspectives. So use every perspective camera as the instrument.
NFS is need for speed game, right? I mean, it’s a video game?
Yes. And need for speed. It’s a game. It supports all the features which cars have. And, uh, it already, it also has different camera angles, which you can use. It has the night mode. It has all the weathers, uh, your car will skid on muddy roads. Everything can happen which happens the in real world. So you can create a simulated environment, train the model on a reinforcement learning techniques, where you are working as a reward and punishment mechanism that your target is to reach this destination. In between if your car gets damaged, then it’s a punishment for you. If you go properly, it’s a reward. If you reach, if you take too much time again, it’s the punishment. So we set up a punishment and reward mechanism over there, learning continuously so that it can achieve the target properly. We do this generally in a simulated environment, like training the robots, how to pick up the objects, how to paint a car, how to assemble a car, everything is done in a simulated environment.
And once models are trained, then again, it’s not exactly transfer learning, but quite a kind of transfer learning that we take that model, put on the real word robot and put on the real word car. And then we ask this model to be retuned on the real world data, which is coming from the cars and robots. So here have used reinforcement learning for training the cars and all that.
So I think this is something that, uh, you know, it’s very easy for us to say that self-driven autonomous cars are good, but we don’t know what was behind. So there’s something which what we want to see maybe 2021, or maybe towards the end, but maybe next year, we are very likely to see extensive use of reinforcement learning. So this is exciting. I can’t wait for these things to pan out very, very quickly so that we can start using these things for our benefits.
So Gaurav thanks a lot for your time. And thanks for joining us. So Madhura, is there something that you wanted to cover today that we didn’t?
Madhura Gaikwad: (26:10)
No, I think Vinayak we’ve covered almost everything that we had discussed, and this was a great session and I would also like to thank Gaurav for joining us and thank you. And thanks Vinayak.
Vinayak Joglekar: (26:23)
You are most welcome. It’s always a pleasure for us.
Madhura Gaikwad: (26:26)
Thanks. And that was a brilliant session and we will continue to discuss these tech trends and predictions for 2021 in the upcoming episodes. Thank you everyone for tuning in. If you are looking to accelerate your product roadmap, visit our website, www.synerzip.com, for more information. Stay tuned for future episodes for more insights on technology and agile trends.
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