In this Banana Byte, our podcast hosts discuss a recent breakthrough in drug discovery catalyzed by AI and Machine Learning. What are the larger implications of this discovery and why is it significant?
In our final episode of season 4, Chris and Triveni discuss looming trends in data science and AI that will lead us into 2021. We'll touch on latency, normalized AI, citizen data scientists, and actualized responsible AI.
With the holidays in full force, we’re taking stock of all that we’re grateful for in our lives. In this Banana Byte, the Banana Data Podcast team shares the top data tools that they are thankful for. These tools make data science easier, quicker, and more understandable thus improving our lives every day.
Today we’re sitting down with a roundtable of data science and machine learning experts from Spotify, PwC, and Google Cloud. What does it truly mean to be steeped in the data science industry and what considerations should be addressed as a practitioner?
In a world of work that is becoming increasingly virtual, the volume of data available to understand and predict employee output is growing at exponential pace. People analytics by virtue of AI and big data is essential to managing and improving organizations’ effectiveness.
On today’s episode, we are speaking to Oscar Wahltinez, Engineer at Google and Board Member at FinMango, about his work on the Covid Monitor Project and the value of data transparency and access for all.
Core algorithms might only take up a few times of code and a few minutes to do so. But, the rest of the program may get messy quickly. In this Banana Byte, we tackle the question of when its worth it to invest your time in code and the trade-offs between developing something accurate vs. something quick.
The field of data science is wrought with many unsolved debates. Is data science nothing more than fancy statistics? What performs better: R or Python? Most crucially, do you need to be a great coder to be a great data scientist? In this episode, Chris and Triveni take these burning questions to the debate stage.
Typically when the average person thinks of bots, it rings with a negative connotation. Bots are immediately associated with spam and fake personas. But, is there a positive flip side to this coin? Listen to our 15-minute Banana Byte to find out.
In this episode, Chris and Triveni take a deeper look at CAPTCHA, a completely automated system that has become a nearly inevitable part of a user's online experience. How did complete automation of this system give rise to complications and exclusion of a smaller subset of the online community? How do you distinguish between pure artificial intelligence and artificial intelligence that's being powered by a human? Finally, what ethical concerns should we be taking into consideration?
In this episode, our Banana Data hosts discuss the many implications that can arise from misinterpreted data. What criteria needs to be established for valid conclusions from data and how can we interpret uncertainty?
In this episode, our hosts Chris and Triveni walk us through commonly overlooked implications of what it means to dole out personal data. What are the downstream effects of sharing your data? What are you benefitting and losing from opting out of data collection?
When people generally think of AI they think in futuristic terms defined by movies like The Terminator. However AI, at least at this moment, is nowhere near Skynet, a fictional artificial neural network-based conscious group mind and artificial general superintelligence system that serves as the antagonist of The Terminator franchise. Instead of worrying about Skynet, maybe we should worry about this bear wielding nunchucks, which seems like more of an immediate problem.
We're talking about one of the most frequently asked questions by people looking to jump start their Data Science career: do you need to have every mathematical formula memorized? What are the true prerequisites you need to be prepared in this field? Tune in and we’ll get you up to speed.
This episode, Chris and Triveni take a look at the most common mistakes in AI, and the misconceptions that plague most data scientists as a result. We'll explore how perceptions of data quality, data quantity, and accuracy can impact data science in practice, and what steps you can take to avoid these pitfalls.
For our season 4 kickoff, we’re taking a look at uses of AI that aren’t so black and white. When it comes to deepfakes, filtering, and predictive policing - when do the risks outweigh the benefits? Are these use-cases inherently bad, or is there a way to combat underlying unfairness? We're also welcoming our new host, Christopher Peter Makris to the show in his inaugural episode!
Deep Learning has become a mainstay in today's data science and AI practices - but what makes it so valuable? On this Banana Byte, we explore when, why, and how to use deep learning, and how it compares to (and might replace!) other common algorithms.
In anticipation of our season 4 launch on August 7th, we’ll be releasing profiles of our wonderful hosts and their hot takes on the data space. Our first profile is our new (!!) host, Christopher Peter Makris.
Many claim that Cloud has stolen the computing show - providing scalability, cost savings, loss prevention, and more - it's taken the world (and the headlines) by storm. So, on this Banana Byte, we ask - is cloud computing inevitable? Or is it just a disruptive buzzword whose negatives outweigh the benefits?
On the Banana Data Podcast we have the In English Please segment. This serves as a way to simply breakdown concepts in plain english. Here is a glossary of all the terms discussed to date. If you like what you hear, please be sure to subscribe!
Zoom conferencing software recently made headlines for its huge leaks in privacy and security, pushing a number of big corporations to block the software and push for new privacy legislation. During this Banana Byte session, we cover the things Zoom overlooked - and what it means for data privacy, usability, and user experience.
For our Season 3 finale, we're taking a look at model accuracy, the threat of generalized results, and how to understand and demonstrate the nuanced results of your models. Is the onus on scientists and journalists to subdue buzzy headlines or should media consumers be more wary of extrapolated statistics?