Sebastian Thrun says: It is truly an attempt to understand human intelligence and cognition. “
Generative AI could fundamentally change the way many people work. Some may get excited about this concept. What this does for others may be a concern. There is no doubt that in industries where automation is possible, this technology has the potential to significantly improve productivity and reduce costs. As a result, jobs may be lost or less likely to create new jobs, at least in some regions. On the other hand, it’s important to consider that using generative AI can drive prices down. This could enable customers from all walks of life in various industries, such as education and healthcare, to access relevant goods and services. , which could lead to increased production and ultimately more employment opportunities. The way we work will continue to evolve as a result of our creativity and imagination, with many current jobs flourishing and new ones being developed.
To understand whether generative AI will disrupt the job market, what its impact has been, and what it might be in the future, we held a roundtable discussion with industry leaders to get a fresher view. provided a perspective.Moderator of the session annies merchantEVP – Global Growth and Client Success and Panelists at Course5i Karthik RameshVP – Client Partner – US Provider of Emids and Life Sciences, Rahul TotaFounder and CEO of Akaike Technologies, Rasnakumar UdayakumarNetradyne Product Lead, Deepika KaushalVice President of Piramal Capital & Housing Finance, Ravi NigamLead Data Scientist in the Google Cloud AI Ecosystem.
Are you ready?
Technology is growing. Although sophisticated, the use cases and situations in which it is implemented are very different. Few companies are advanced, but most companies are in a basic state. Some people are in the very early stages of just consuming AI. How do you really consume the basics, master the basics, and get into good analytical stuff that delivers results? Is not it. Technology is advancing so fast that few companies can take their eyes off it in terms of its pace and people ready to adopt.
—Deepika Kaushal, Vice President, Piramal Capital & Housing Finance
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It depends on which store we sell to. Educating our clients is what we always have to do. But ultimately it depends on who you talk to and how you package it. Closing the new client is less work than closing the principal data scientist. Finding the right people with experience working with data from different modalities, especially the latest technologies, is a challenge. For one good talent we have to sift through a lot and it’s not a great experience. For our business the rate limiting bottleneck is getting the right talent. Otherwise, in terms of market readiness for such solutions, customers are ready to accept them.
—Rahul Thota, Founder and CEO of Akaike Technologies
Employer Perspectives on Skills
A perfect analogy was parallel triangles ten years ago. We have the people companies are looking for and we can find those talents across the board. Now there are different places you can go and organize them. Indeed, in the last few years, technology has leapt forward at such a rapid pace that it has tipped things in ways that many of us never expected would work. Suddenly, skill sets became niche, and demand and supply were out of balance. What’s really happening is finding very talented people, but there are even more opportunities for those with the right skill sets. It’s getting harder. At this point, there are very few talents that we would consider to have the right skill set, and plenty of choice on their doorstep.
—Rathnakumar Udayakumar, Product Lead at Netradyne
Can your organization keep up with skills?
The problem is magnified in terms of talent opportunities and availability. If I were asked to address this issue today, the rapid growth of technology threatens to outstrip human talent in scale and speed. Focused on internal investment from an early stage. When it comes to innovation, we work closely not only with Next Horizon Technology, but also with academia. Not only do we go to campus and do events, but we are very active working with them to tackle the latest issues in AI/ML. As the market and possibilities continue to grow, this will always be a niche area and innovative ways of sourcing should be considered.
—Karthik Ramesh, Vice President – Client Partner – US Provider of Emids and Life Sciences
Scale: Skill Challenge
There are two sides to scaling. One is human scaling and the other is scaling value. This may be due to scaling of certain tools or some IP. On the second side, a lot of work has been done with the many no-code tools available. Individual productivity is scaling. For example: Work is being done to automate prompt engineering, provide data, generate optimal prompts through algorithms, and provide the type of output you need. It may not be the case now, but in the future it is possible that we only want people who can speak English.
—Rahul Thota, Founder and CEO of Akaike Technologies
When we look at scaling we are looking at both vertical scaling and horizontal scaling. Right now, scaling towards the technology side rather than the human resources is the way to go. That’s exactly how technology is scaling, too. With the help of a co-pilot, there is a way to do things much faster than most people can. A co-pilot is just an assistant, but you can speed things up even more by working with someone who can do things on their own without the need for a large team.
—Rathnakumar Udayakumar, Product Lead at Netradyne
employee perspective
One of the biggest myths in the data science, or AI, journey is the missing piece, the engineering culture. What we don’t do, and I think it’s important, is we don’t instill an engineering culture and data science. To become a better data scientist or machine learning engineer, you need to inculcate these fundamental foundational engineering practices. Because when you go into the industry and work on these models, foundational models, deep learning, ultimately you have to be part of the stack. , is already software engineering driven. It is also important to accommodate the evolution of teaching methods to students. That way, you teach students less in stacks of notebooks and more in an engineering sense.
—Lavi Nigam, Lead Data Scientist, Google Cloud AI Ecosystem
How to build a career today?
There is always a need to build innovative learning methods, and learning does not have to come from various online institutions. Learning is also possible internally. You have to continuously build your skill set. Today you have so many online ways to do it.Forget the prestigious institutes that give you formal education or accredited alumni status, but there are so many ways to self-study things. There is. The AI talent problem is not solved by simply hiring AI data scientists. Some of these tech trends talked about generative AI, prompt engineering, cryptocurrency, blockchain, etc., but they’re all there. But they don’t replace jobs or skill sets. You are looking for someone who is passionate about thinking, learning, and letting go of learning. Not just data science, statistics, machine learning, but fundamentally adaptable.
—Karthik Ramesh, Vice President – Client Partner – US Provider of Emids and Life Sciences
Shu Ha Ri – Stages of Learning To Mastery
From an education industry point of view, I think we’re just preparing for jobs. Because we do MBA in the same way. It doesn’t actually go anywhere. Ten years ago, when we were building predictive models, it was just a hypothesis test, a null hypothesis to build predictive models for these models. ML is now the new buzzword. So everyone started to fall in love with learning, but now we see that it’s a black box. is needed. Education systems need to focus on building aptitudes rather than problem solving. Technology evolves as we speak, so keep learning in a tech environment and include a business element in it.
—Deepika Kaushal, Vice President, Piramal Capital & Housing Finance
Since 2017, and especially in the last decade, creative human intelligence has enhanced the state of generative AI. Human intelligence has used extensive online “literature” to create new deep learning architectures, text statistical analysis techniques, and training methodologies for AI models. Thus, the Lovelace effect shows that artificial intelligence cannot contain human creativity and imagination.
