AI for Biodiversity Research – Google, in collaboration with Global Biodiversity Information Facility (GBIF), iNaturalist, and Visipedia is making a push to bring AI to biodiversity research. While ML is prevalent in biodiversity research, proper attribution and oversight is a hit or miss. Google is hoping to bridge the gap and raise the academic bar. With the growing importance and awareness of fairness,ethics, and transparency, the innovative uses of machine learning for biodiversity stands to be a challenging, exciting and rewarding task.
A Beginners’ Guide to Machine Learning– This article serves as an interesting machine learning 101. The article breaks down machine learning into three types; classification, regression and unsupervised learning with examples in Python using the library scikit-learn. While there is a lot more to delve into, this is a quick intro to the ML world, or a nice refresher if you have been away for a while.
Polynote by Netflix – Improve notebook execution, code quality in an IDE environment, meet Netflix Polynote! Open-source software that empowers data science and machine learning; super exciting experiments lie ahead. “Plenty of exciting work lies ahead, we are very optimistic about the potential of Polynote, and we hope to learn from the community just as much as we hope they will find value from Polynote.” Check it out on their Github.
AI and Health – The article sheds light on an interesting perspective with the use of AI to improve life spans. Imagine the possibility of knowing our risk profiles when we are born, the types of diseases we could be prone to and preventive measures taken years in advance. However, this will be no easy feat by any means, it will require social inclusiveness, strict governance of AI and data and diversity across the spectrum of devices, code, and human forms. Nonetheless, the idea itself and the nascent research is an exciting take on how AI can improve our lives and make the world a better place.