The Banana Data Podcast 22282v

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. Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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 '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? Learn more about the articles referenced in this episode: CAPTCHA: Hard for Humans, Easy for Bots by Liel Strauch and Hadas Weinrib (Perimeterx) AI is making CAPTCHA increasingly cruel for disabled s by Robin.Christopherson (Ability Net) Why CAPTCHAS Have Gotten So Difficult by Josh Dzieza (The Verge) Amy J. Ko (Bio) Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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?  Check out what we've been reading:  Why Bleach, Disinfectants And Other Antibacterial Products Kill Only 99.9% Of Germs Margin of Error - Twitter This is one of our Banana Byte series-  which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, or check this one out on Linkedin, and stay-up-to-data with the Banana Data Podcast!

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? Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! =

When people generally think of AI they think in futuristic 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. This tweet of course is a funny, but apt, metaphor on the immediate challenges we face with AI that need to be addressed now as opposed to the future. This is one of our Banana Byte series-  which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, or check this one out on Linkedin, and stay-up-to-data with the Banana Data Podcast!

In this episode, we take a look at a number of international Artificial Intelligence initiatives and evaluate what countries with burgeoning data science ecosystems can take away. How are lesser known Artificial Intelligence powerhouses like Sweden, Vietnam, and Kenya are ing innovation both intra and internationally?  Learn more about the articles referenced in this episode:  AI Kenya Phase 1 of Konza Technopolis Data Center Complete Vietnam’s Artificial Intelligence Scenario is Evolving How different countries view artificial intelligence

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. Learn more about the articles referenced in this Byte:  How Much Math Do You Need to Know to Get Started with Data Science? Ritobrata Ghosh (Towards Data Science) How Much Math Do I need in Data Science? by Benjamin Obi Tayo, Ph.D. (Medium) Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! This is one of our Banana Byte series-  which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, or check this one out on Linkedin, and stay-up-to-data with the Banana Data Podcast!

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. Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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! Learn more about the articles referenced in this episode:  Why Deepfakes are a Net Positive For Humanity by Simon Chandler (Forbes)  Inside LGTBQ Vloggers' Class-Action 'Censorship' Suit Against YouTube by EJ Dickson (Rolling Stone)  LAPD changing controversial program that uses data to predict where crime will occur by Mark Puente, Cindy Chang (LA Times) Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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. During our off-season break, we'll be releasing more of these Banana Bytes - which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, and stay-up-to-data with the Banana Data Podcast!

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? During our off-season break, we'll be releasing more of these Banana Bytes - which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, and stay-up-to-data with the Banana Data Podcast!

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 experience. During our off-season break, we'll be releasing more of these Banana Bytes - which are short, bi-weekly segments we run live on LinkedIn and Twitter, where we discuss the latest headlines and topics in the data science space. Be sure to tune in for our next live session, and stay-up-to-data with the Banana Data Podcast!

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?  We also take a peek into how the NYT applies Machine Learning to their comment moderation, and how human-in-the-loop monitoring works behind the scenes, especially in fast-paced and ethically questioning environments. This is also our final episode with Will on the team - and we'd like to thank him for all of the hard work, great ideas, and many laughs he's provided with us along the way. He's been an invaluable team member, but do not fear! Season 4 will bring many new and fresh surprises to the Banana Data Team. Stay tuned.....  Banana Riddle Answer: 49  All models are wrong, but some are completely wrong (Royal Statistical Society)  To Apply Machine Learning Responsibly, We Use It in Moderation by By Matthew J. Salganik and Robin C. Lee (NYT Open)

AI is meant to help us expedite processes and get to the conclusions quicker. But, what happens when the process that AI takes to get to the end goal is erroneous? In this episode we discuss how you can prevent your AI from cheating and define what it means to be a successful AI company in today’s tech-saturated world.  Specification Gaming: The Flip Side of AI Ingenuity (DeepMind Blog) The New Business of AI (and How It’s Different From Traditional Software) by Martin Casado and Matt Bornstein (Adreessen Horowitz)

With the 2020 presidential election, there's a lot for data scientists and analysts to learn from the political realm and its unending streams of messy data. Will and Triveni sit down with seasoned political data expert, Grace Turk Martinez, Analytics Director at The Messina Group to understand how political data professionals extrapolate insights from messy data, work around human indecision, and forecast using imperfect data sets. Why You should Care about the Nate Silver v. Nassim Taleb Twitter War by Isaac Faber (Towards Data Science) Solution to Riddle #2:  Question:  I bought a baseball and a bat for a combined cost of $1.10. The baseball bat cost $1 more than the ball. So how much does the ball cost? Answer: The answer is the baseball bat costs. $1 dollar and five cents. And the ball itself is five cents. Be sure to subscribe to our biweekly newsletter to get more of the latest and greatest in your inbox! 

This episode, in honor of draft season, we’re discussing the NFL’s newest tactics to quantify and predict players’ success, and diving into Spotify’s case for data discovery. Leaving behind the problems of “not enough data,” Will and Triveni ask new questions: when we have so much data, where do we start, how do we organize it, and how can we use it? Catch up on what we’re reading:  How We Improved Data Discovery for Data Scientists at Spotify - https://labs.spotify.com/2020/02/27/how-we-improved-data-discovery-for-data-scientists-at-spotify/ The NFL’s Quest to Quantify Quarterback Evaluation - https://www.theringer.com/2020/4/17/21224389/nfl-draft-quantifying-quarterback-evaluation Solution to Riddle #1:  Question: Write an equation to make two 2s equal the value of 5. You can only use the number 2 twice.  Answer: Square root of 0.2 to the power of minus 2.

In our season 3 kickoff, we’re challenging ourselves to ask --who grants authority to those in charge of validating content? How do we remain cognizant of big tech and corporations that shape our content and decisions? In a landscape filled with big, competitive players - we explore how data scientists should focus their learnings.  Check out what we’ve been reading:  Attestive CEO on Using DLT to Fight Fake News, Insurance Fraud, and Deep Fakes by Samuel Haig (CoinTelegraph) Expanding at-home learning with 30 days of training at no cost by Rochana Golani (Director, Google Cloud Learning and Enablement) Be sure to subscribe to the Banana Data Newsletter to stay up-to-date on the latest data news.

In our season 2 finale, we’re asking about the business impact and ROI of data science - what are our measures of success, who calls the shots, when should we see returns, and how do we know this is all worth it? From ROI To RAI (Revenue From Artificial Intelligence) by AJ Abdallat (Forbes) What’s the Best Approach to Data Analytics? by Tom O’Toole (Harvard Business Review) Making Data Science Useful by Cassie Kozyrkov (Strata Data Conference) BI and Analytics Delivering over 1300% ROI according to Nucleus Research: Do you believe it? By Lach James (YellowfinBI) Measuring AI’s ROI in Retail: Thinking Big and Small by Nikki Baird (Forbes)

With Tristan Handy, CEO & Founder of Fishtown Analytics, we ask -- who should be part of the data science process? Bearing both technical requirements and business objectives, the data scientist cannot run the show on her own. We ask what it means to collaborate intra-, inter-, and out of teams, when to do bring heads together, and how to do it successfully.\r\nTo DBT, be sure to check out https://www.getdbt.com/. You can also learn more about Fishtown Analytics and Tristan Handy at https://www.fishtownanalytics.com/.\r\n

On this week’s episode, Karen Hao, Senior AI Reporter at the MIT Technology Review, shares what it’s like to cover AI in the peak of the hype cycle. We’ll walk through the dangers of inaccurate AI reporting, striking the delicate balance between realistic and exciting, and the what, where, and how we should be reading about AI in the news.\r\nKaren Hao is the artificial intelligence reporter for MIT Technology Review. In particular she covers the ethics and social impact of the technology as well as its applications for social good. She also writes the AI newsletter, the Algorithm, which thoughtfully examines the field’s latest news and research. Prior to ing the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.\r\nBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! \r\n