##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
What can software learn from robots and math
In almost every aspect of life, there is a concept of state (for example, where are you right now?) and a concept of uncertainty (for example, how soon can you reach home?). And whenever there is uncertainty, there is a tool that can help you get a better estimate of that state. It is called a Kalman Filter, named after an American Mathematician, Rudolf Kalman.
Kalman Filters, or, Linear Quadratic Estimator, is an amazing -- almost magical -- tool for estimating state in the presence of unreliable or uncertain data. With applications in a multitude of fields ranging from finance, economics, marketing, sports to vehicle control, Robotics, signal processing and biology, it combines data over time resulting in a much better performance than simply using the data present at the current moment.
It is basically a two-step process:
- PREDICT where it generates estimates of the state variables with some associated uncertainty and,
- UPDATE where the new measurements (or data) are incorporated and the estimates are updated using a weighted average with more weight given to more certain estimates.
If you have no clue about what is going on, don’t worry. In this presentation, I will try to build your intuition with a series of simple examples. Then, with a little bit of math, I will demonstrate how the Kalman filter works its charm. Finally, I will end by giving you a glimpse of its numerous applications in different fields and how you can probably use it in your own project.
Naman is a Robotics Engineer with Masters in Robotics from Robotics Institute, Carnegie Mellon University. After his masters, he spent some time working on Automotive Computer Vision at BOSCH. Then, he worked on Indoor Commerical cleaning robots at Discovery Robotics before moving to California where he worked on Self-driving vehicles at Faraday Future. Last year, he moved back to India and is currently working on Agriculture Robotics at TartanSense in Bangalore, India. He strongly believes in the positive impact AI and Robotics can have in the day-to-day lives and it keeps pushing him to work on it every single day.
When he is not busy with robots, you will find him reading, traveling or indulging himself in adventure activities.