AI & Research,And Industrial Tracks - all videos, Also inviting registrations for Signal In Bangalore This update is for participants only
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Bumpy Roads, High Speeds: My Unexpected Journey from PhD to Tech Entrepreneurship
In 2015, I sold the intellectual property (IP) of my Silicon Valley company, Perceptive Code LLC, to Mercedes Benz. Subsequently, I was tasked with meeting certain milestones as part of the handover process. I chose to complete this task in India, where I aways wanted to be. I successfully downsized our research model, initially consuming 6GB of GPU memory, to a mere 300KB of weights. I then implemented this model on a 2W FPGA with 112 DSP blocks. This model is now included as part of the MBUX option in all Mercedes cars. Using cameras installed in the car, it enables gesture control for various functions such as turning on passenger side lamps when a hand is extended to retrieve something, or choosing which rearview mirror to adjust simply by looking at it, among other features.
This talk will be a personal reflection on the past decade of my life. I’ll discuss my journey from leaving my job at Yahoo! in Bangalore and moving to Italy, then Germany for my PhD. I will share how I didn’t secure the jobs I initially aimed for post-PhD and subsequently “settled” for a post-doctoral position with Chris Bregler at the Courant Institute at NYU. Intriguingly, I unintentionally found myself working with Yann LeCun, which led to the publication of four papers alongside this Turing Award winner.
I’ll touch upon the pre-AlexNet era and provide insights into the academic environment at NYU during that time. I’ll also delve into my experience with Apple’s self-driving team and discuss my decision to leave Apple for a riskier venture with Mercedes, striving to meet milestones and deliver a product. I’ll discuss how, in hindsight, this all unfolded.
Additionally, I plan to share my experiences in academia. Despite having offers and spending time at prestigious institutions like IIT Bombay and IISc, I made the choice not to pursue academia full-time. Instead, I continued to explore new ventures such as UAVIO Labs and Fastcode AI.
- My PhD journey: Machine Learning for Computer Graphics, spending time at Weta, earning credits for Adventures of Tintin, 2011
- The jobs I desired but didn’t secure after my PhD
- My tenure at NYU: people at NYU at that time, working with Yann, working on Theano, and projects I undertook
- How and when Jonathan and I started Perceptive Code LLC
- Life after my post-doc: why I chose Apple
- My conversations with Mercedes and the ensuing deal, coming back to India
- The efforts behind building the product: technology, papers, and patents produced
- My current aspirations and what I look forward to