DA Dialogues - "AI will replace humans, but...", says Sagar Ghonge, co-founder, Konverge.ai

Welcome to DA Dialogues. Konverge.ai is an AI services start-up firm that is helping organizations across the globe converge on better, faster and efficient solutions using ML and AI. Three young entrepreneurs - Prateek Chandrayan, Ketan Paithankar and Sagar Ghonge founded this firm in 2018 and it is already making waves in the AI industry. Today, we are in conversation with one of the founders, Sagar,  who is also the COO of the organization.

* Welcome to DA Dialogues, Sagar. Thanks for giving us the time today.

Thanks for inviting me. I am glad to be a part of DA Dialogues.

* You have done a variety of roles in the industry over the years. What made you start Konverge.AI?

The technological landscape has seen a drastic change in a short time span of the last 5-8 years. The advent of deep learning architectures boosted using GPUs has opened new avenues of efficiency improvement and value creation for organizations across different industry domains.

We saw this as a great opportunity to help companies take a leap and infuse artificial intelligence (AI) and machine learning (ML) into their technology road-map. Konverge.AI was conceived with the idea of enabling organizations to unlock the next levels of innovation and growth from their data using AI.

* Tell us more about your products/services.

Konverge.AI is focused on providing end-to-end AI/ML related services. We help our clients tackle high impact problems with innovative and state-of-art AI-based solutions. We work on three main verticals - natural language processing (NLP), computer vision and advanced analytics.

We are industry agnostic and are working with a wide range of customers in the fields of media & entertainment, healthcare, construction, oil & gas, etc.

* What do you think is the future of AI? Do you think it will replace humans going forward?

Ultimately, yes! AI will replace humans in most of the existing tasks in the future. However, with current state-of-art results, I believe that we are still a long way from the so-called artificial general intelligence (AGI). At least for the next few decades, we are going to see solutions penetrating very specialized tasks using applied AI or artificial narrow intelligence (ANI). This would lead to tremendous improvement in overall human efficiency and productivity. 

But, in the long run, as Elon Musk says, “The last job that will remain will be writing the AI software.” As for the artificial super-intelligence (ASI) - an AI overlord overpowering humans, I have my doubts.

* What do you think about the moral aspects of using AI in life and death scenarios? Can such scenarios be tackled based on numbers?

It all depends on what error rate the AI system is able to achieve. If the AI can achieve an error rate better than human level (or in the case of diagnosis, a team of human doctors) and close to the Bayes/irreducible error, I believe, it would be wise to involve AI in such decision making. 

Take the example of self-driving cars. By avoiding diversity and unpredictability in human behavior and skills, we will surely be able to reduce the number of accidents and save lives. However, in scenarios such as saving a human over an animal or saving one human over another, it's still tricky. One thing we can do is take human perspective on such moral decisions and try to program the same in a machine. MIT has been doing a similar study (Moral Machine). The results have been insightful showing certain general preferences as well as some variation in regional preferences. 

On the other hand, a greater challenge in such cases would be the legal implications and how are we going to tackle that. By the way, legal is another industry, I believe, is going to remain AI-proof (at least parts of it) for a long time.

* Delivery models are now focusing more on speed. How does Enterprise architecture fit in such an environment? Do you think it adds value?

We have worked with start-ups & enterprise customers as well. Both types of customers look for speedy iterative product build models. The idea always is to build the first MVP as fast as we can & deploy it in the market. Enterprise customers have their own enterprise architecture. There are two angles to it. EA implementation always helps enterprise customers to have set processes in place. This helps in smooth deliveries. The business use case data can be gathered across hierarchies very easily due to EA. 

On the other hand, EA can slow down the process also. For e.g. if an enterprise customer, due to EA, demands compliance of certain rules, it can add time and cost repercussions. For start-up customers, generally having smaller teams, compliance issues on the use of tools is mostly not a factor. So, it varies from customer to customer & also depends upon how deeply rooted is EA in customer’s organizational entity.  

* Which technology trend, other than AI, do you see as most promising for the future? How are you planning to use it?

With everything going digital today coupled with increasing volume and value of data, cyber-security is one field to look after. There are some interesting use cases where AI can be very effectively used to identify certain types of cyber-attacks. One of my co-founding partners being a networking expert, we have a keen interest in developing solutions in this field. 

IoT (and IoRT) is another promising tech trend which has huge application potential in industries. One interesting aspect of this is the interdisciplinary nature where surprising insights can be extracted using AI/ML systems - another area of interest for Konverge.AI.

* Is economic slowdown affecting your business? How are you dealing with it?

We haven’t seen a significant impact on business due to the economic slowdown yet. One reason for that is high positive impact potential of AI solutions in an organization.

* How easy/difficult is it to convince potential customers about the use of AI in their organization?

Depends on the industry. But, convincing potential customers about the use of AI is comparatively easy. Customers are usually aware of the general trends and applications of AI. 

An interesting part is that by understanding the customer’s business and analyzing their data in more detail, we find a lot more applications of AI in their organization other than the problem statement the customer has identified. 

One of the challenging things is setting the right expectations about the results and its impacts once an AI system is deployed. We are in the stage of applied AI (or ANI) and the current AI solutions are targeted to address a specific problem. Transfer learning helps but requires a lot of fine-tuning on case to case basis. Take an example of information extraction using NLP. The general pipeline for the extraction could be similar but a lot of fine-tuning is required depending on what needs to be extracted and what the output will be used for.

* What is your idea of a good leader?

Three main characteristics - 

- Helping the team reach its potential and go beyond

- Adaptability

- Unrelenting commitment towards achieving the goal

* What has been your toughest decision as a leader?

Konverge.AI is focused on developing AI/ML or data analytics related solutions for our clients. But being in the software services sector, we do get some opportunities for working on non-AI/ML projects. It’s tough to say no to such projects but in the long run, I believe that this will lead to sustainable growth and better positioning of the company.

* How do you keep employee morale and motivation level high? 

Project milestone parties, annual celebrations, etc. are a few things that we do at Konverge.AI. However, one thing that helps is to keep working on interesting projects. If the work really excites the team, that also acts as a motivating factor.

* Which book are you currently reading?

1. The Hitchhiker's Guide to the Galaxy, 

2. Deep medicine

Design Thinking - Ayes and Nays

What is Design Thinking?

A buzzword for the last few years, that has grown in stature and importance steadily is "Design Thinking". Industry and academia alike wax eloquent about how amazing it is and how we should have been doing this forever rather than in the last few years. So, what exactly is "Design Thinking"?

Design Thinking is an iterative process in which we seek to understand the user, challenge assumptions, and redefine problems in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding. Design thinking is a solutions based approach rather than problem based thinking to solve a problem. It aims to come up with something constructive to effectively tackle a certain problem, whereas problem based thinking tends to fixate on obstacles and limitations.

There are four key principles of design thinking (Ref - CareerFoundry) -

1. The human rule: No matter what the context, all design activity is social in nature, and any social innovation will bring us back to the “human-centric point of view”.
2. The ambiguity rule: Ambiguity is inevitable, and it cannot be removed or oversimplified. Experimenting at the limits of your knowledge and ability is crucial in being able to see things differently.
3. The redesign rule: All design is redesign. While technology and social circumstances may change and evolve, basic human needs remain unchanged. We essentially only redesign the means of fulfilling these needs or reaching desired outcomes.
4. The tangibility rule: Making ideas tangible in the form of prototypes enables designers to communicate them more effectively.

There are multiple variants of the Design thinking process and they have different phases. However, they all essentially say the same thing. The key phases of the process are -

1. Empathise – with your users
2. Define – your users’ needs, their problem, and your insights
3. Ideate – by challenging assumptions and creating ideas for innovative solutions
4. Prototype – to start creating solutions
5. Test – solutions

The way to traverse these phases is iterative, but is it linear? Not really, you don't necessarily have to go through the phases in sequence all the time, but the first iteration definitely needs to be linear in order to ensure that the key aspects of the processes are covered. After that, the phases can be traversed as needed. (Ref - InteractionDesign.org)

How does it stack up against Lean and Agile?

After reading a bit about Design thinking, especially the iterative aspect of it, it is tempting to think of it as a alternative to Lean or Agile way of working. However, we should understand that all these methodologies are essentially for a faster and more efficient way of coming up with a solution and implementing it. As Jonny Schneider, Product Strategy and Design Principal at ThoughtWorks, explains: “Design Thinking is how we explore and solve problems; Lean is our framework for testing our beliefs and learning our way to the right outcomes; Agile is how we adapt to changing conditions with software.” All three methodologies highlight and work towards different aspects of this process and hence they are different but at the same time there are overlaps across them. No single methodology can be the solution for everything and hence it is essential to take the best out of all and fine tune our core way of working. In that sense, these three can and should be made to work together to achieve the best results.

Benefits

There are a variety of benefits of using Design Thinking process. Here are a few - 

1. Promotes out of the box thinking and fosters innovation
2. Reduces Time to market
3. Increases ROI
4. Improves customer retention and loyalty
5. Can be applied company-wide

Pitfalls

Design thinking certainly works and it is touted as THE way to solve problems. However, we need to be very careful not to over-promise its utility. There are certain things to keep in mind when thinking about or using Design thinking

1. It hides the complexity and completeness of solution - It is quite easy to come up with beautiful solutions that ignore the constraints and seem to solve all the customer problems (Since we have kept the customer at the center), however, the proof is in the pudding. When you bring in the burden of constraints and limitations (which you ignored earlier) the complexities of the final product start coming into view. These things cannot be ignored for "later iterations" always and could well be the key problem to tackle at the start of the solutioning rather than at the end of it. It is not enough to be customer focused first and think about all the harder stuff later.
2. It is good for solving 'wicked problems', not necessarily the normal ones - Design theorist Horst Rittel defined 'wicked problem' as a particularly tricky problem that is highly ambiguous in nature. For such problems, there are many unknowns and variables. They don't have a single right solution. In such cases, Design thinking may help come up with a possible solution. However, even then the possibility of unearthing more problems in unknown areas while solving for one area, needs to be taken into account. At the same time, for the normal problems we may not need this methodology at all.
3. It sometimes kills innovation rather than fostering it - This is a controversial one. Design thinking is supposed to foster innovation, however, because of the focus on customer solution, the other aspects of the problem are ignored and more often than not, possible chance of innovation actually is in those areas that are getting ignored. Innovation in those areas could be the differentiating factor between our products and those of the competitors and hence, focus on them is very important - that Design thinking tends to ignore
4. Out of box is not always good - Design thinking is also called out of the box thinking. We have heard this term so many times in various contexts, that it has now got an aura of being the rebellious, innovative way of thinking that HAS to be right. Just because a solution is out of the box or rebellious doesn't make it the right solution. Sometimes it is. Sometimes it isn't. While using Design thinking, we need to keep in mind this possibility instead of going into it blind.

Conclusion

Is Design thinking useful? Yes. Does it work? Yes. Have people come up with good solutions by using this methodology? Yes. Is it the panacea we are looking for? No. It can't be. No single methodology can be. We have to use the best of multiple processes together as per the organizations need and maturity in order to provide efficient, quality solutions to our end customers, instead of putting all our energy into one.