Several weeks ago, we announced becoming an EU-accredited college with our Founding Dean, Dr. Tadas Jucikas. Dr. Jucikas is an excellent match as our Founding Dean of Turing College as he is himself an AI practitioner with over 17 years of experience in AI & Data Science, and a successful entrepreneur being the CEO & Founder of generative AI company Genus.AI. Dr. Jucikas holds a Ph.D. in Computational Neuroscience from the University of Cambridge, so he understands how traditional advanced degrees look from the inside and how they can be shared globally.
We sat down with him to learn more about why he decided it was crucial to join Turing College and to ask him why he still believes a master’s degree is still relevant in today's world of AI.
1) Why learn Data Science & AI?
Today, Data Science and AI are transforming the world at an incredible pace. The most recent advancements of Generative AI will undoubtedly transform the global economy and workforce, increasing the global GDP by nearly $7 trillion. Goldman Sachs Research estimates the total addressable market for Generative AI software to be $150 billion - which I find a conservative estimate. I agree with other investors and entrepreneurs, such as Mark Cuban, who predicts that the world’s first trillionaire will be an Artificial Intelligence entrepreneur.
Now is the very best time to earn a degree in Data Science and study the application of Artificial Intelligence. As AI automates tasks and optimizes business workflows, obtaining AI expertise will enhance your professional value, making you a key player in industries where up to 300 million full-time jobs may be impacted by automation. So there has never been a better time to enter the AI field! We need students and creators to understand the fundamentals and to be the next generation to grow out of the limitless possibilities of how AI can transform the world.
2) Is it too late to get into the AI wave?
No, it's not too late to get into the wave of AI - we are at the very beginning. The current AI revolution is analogous to the internet revolution of the 1990s in many ways. If you draw a parallel to the story of Marc Andreessen, the co-founder of Netscape and pioneer of the early internet, you'll see why.
Marc Andreessen co-founded Netscape in 1994, well after the invention of the internet, which began as a government project in the 1960s and became more publicly accessible in the late 80s and early 90s. He was not part of the first generation of Internet inventors, however, he was one of the first to recognize its transformative potential as a platform for the World Wide Web. Despite entering what some might have considered a mature market, Andreessen's timing was impeccable. The general public was just starting to understand the potential of the internet, and Netscape rode that initial wave of public interest.
The same scenario is playing out in AI right now - and I believe will continue to do so for many decades. AI has been a concept and an area of research for decades, but only in recent years have we seen practical, widespread applications for its use. Just as with the internet, the public is starting to understand and appreciate the potential of AI, from its uses in everyday technology like smartphones and voice assistants to more specialized applications like drug discovery and autonomous vehicles. We are truly on the cusp of understanding where it can fit into our everyday lives - akin to the invention of the printing press in the 15th century.
Despite AI’s great progress in recent years, we're still in the early days of understanding the full extent of the possibilities of applying this technology. As Marc Andreessen has famously said about the internet, "We're still in the early days." The same can be said about AI. It's a vast field with numerous possibilities for innovation, creativity, and connection.
3) What is the most interesting technical development in AI today?
New technology is being released every day - I can’t even keep up with it! It has been particularly exciting to watch Large Language Models (LLMs) being developed and improved at an incredible pace. The most recent LLMs now boast using training datasets of over one trillion words. As a reference, the common crawl dataset of all words on the internet is around 100 trillion, so we are at over 1% of all data understanding. It is possible to imagine when this number will increase to 5%, 10%, and beyond. The creation of the Transformer neural network architecture is also an incredible innovation powering how knowledge is acquired by LLMs. The size of the models has grown as well, reaching over 1 trillion parameters in the most recent models. That is an incredible size of the neural network, which takes many resources to train. This is why you are currently seeing large tech companies releasing the models instead of universities or startups - they simply do not have the monetary resources or computing resources to handle this load. What is very encouraging to see is the emergence of open-source LLMs that make available various degrees of commercial licenses. What is a remarkable opportunity right now is the ability to build on top of these large LLMs and further fine-tune them for specific applications, business use cases, and industry verticals. Being able to understand these models and to be able to learn how to finetune them further is, in my opinion, the most desirable technical skill set globally.
4) What was your master’s degree experience?
After completing my Undergraduate degree in Bioinformatics and Computational Biology I was very fortunate to be able to join the University of Cambridge MPhil program in Computational Biology. I remember arriving at the Department of Applied Mathematics and Theoretical Physics and working in the same department as Dr. Stephen Hawkings and others and just being starstruck and motivated more than ever to work hard. For a kid from a farm in the middle of Lithuania, that was a truly life-changing and formative experience. I was very excited about the application of computer vision and machine learning in life sciences and continued with a PhD in Computational Neuroscience, where I studied genetic and molecular mechanisms of behavior in a nematode C.Elegans, with colleagues building a system for automatic tracking and quantification of different C.Elegans phenotypes. My Master's degree was intense, but it prepared me well for the scientific rigor and challenges in the lab as well as outside in more applied settings.
However, joining traditional universities might not work or be possible for everyone. Today’s world is moving very fast, and there is a significant need for flexibility and emphasis on practical applications of the most cutting-edge technology. A Master’s program functions as a bridge between different stages of one’s career, and the difference between fundamental research and practical applications is decreasing. That is why I am excited about what Turing College has created because we can offer an extremely high standard of degree packaged in a way that fits an individual’s current needs.
5) Why did you join Turing College?
We need more people who are well-versed in Data Science and AI. There is so much that needs to be built, and I was very impressed by Turing College’s novel approach, commitment to quality education, and leadership team. I have known Lukas & Benas since 2017, and I followed their journey - they were accepted into Y Combinator and have built one of the biggest online data science schools in the Nordics in just a few years. I am excited to support the Turing College team with my expertise and insights, and I think we have a chance to create a truly revolutionary way to learn online with exceptional quality, foundations of practical applications of Data Science and AI, and the highest levels of interaction and engagement with peers. All of which are skills to build on in future careers or entrepreneurial endeavors
In a world where it is possible to transform information in ways that we couldn’t even imagine, Turing College invites everyone to participate. I am excited that this Master's degree will create a very positive impact on the world, and open up access to better, more flexible options to learn online with a project-based curriculum and with 1on1 professional support. The whole idea of being able to get one’s hands dirty as soon as possible, to me, is the best way to learn.