This talk was delivered as part of an Intel TedXOcotillo event —please find the recording of it here.
The number of times I relied on smart algorithms to think for me in the past few minutes alone is intriguing. From using facial recognition to log-in, to my Teams’ microphone filtering out background noise and Microsoft Word correcting sentence grammar, AI is there to help think and act for me so I can present the best version of myself. This isn’t just for administrative tasks — smart algorithms are ubiquitous, be it in self-driving cars, healthcare, robotics, policy, and education.
Starting a data science research project can be challenging, whether you’re a novice or a seasoned engineer — you want your project to be meaningful, accessible, and valuable to the data science community and your portfolio. In this post, I’ll introduce two frameworks you can use as a guide for your data science research projects. Please note this guide isn’t mean to be exhaustive — it’s based on my experiences working with data science and machine learning that I think can be helpful for beginner data scientists.
In this blog post, we will briefly cover a few use cases of differential privacy (DP) ranging from biomedical dataset analysis to geolocation.
If you’re interested in learning the basics of differential privacy before diving into additional use cases, check out my blog post A High-level Introduction to Differential Privacy.
The notes in this post were created for the SG OpenMined Explorers Study Group — for the slide deck associated with this post, please see Use cases of Differential Privacy and Federated Learning.
Let’s start with the application of differential privacy for genomics!
Machine learning has important implications for genomics…
In the eyes of a data scientist, every moment of your life is a data point. From the brand of your toothpaste to the number of times you wave your hand, details that we often take for granted are crucial factors that can be used to infer our behavior and intentions. These insights, mined by multinational organizations, can be used to make aspects of our collective lives more convenient and interesting at the price of our private information being exposed or even exploited.
This brief tutorial covers instructions for:
The primary benefit of Anaconda Distribution is that it makes installation and maintenance of packages convenient and quick; in addition, it also already contains over 150 packages that are automatically installed.
Before continuing with this tutorial, make sure that your system is/has the following in order to confirm that the Anaconda Distribution can be installed: Windows, macOS or Linux x86 or POWER8, 32- or 64‑bit, 3GB HD available.
Navigate to https://www.anaconda.com/download/, click on the Windows option, and download…
The challenging task of fabricating a pristine environment to nurture a growing, powerful learning entity capable of translating data into insights is one that cannot be avoided. It is necessary to realize that automation forces us to lose control and management over overwhelming amounts of data that A.I. mines. The source of the above relevant question is due to the volatility of data, as definitions of bias and corruption transform in accordance with circumstance and environmental conditions.
We observe the same ramifications the question addresses in the upbringing of a human child, an entity capable of active participation in an…
AI Research Engineer at Intel with a master’s degree in data science and a bachelor’s degree in CS at Harvard. Opinions are my own.