Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?
As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.
While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.
When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.
One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.
To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?
Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.
The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.
Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.
For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.
To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/
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