The Banana Data Podcast

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we explored data strategy and how humanization can level it up. This week, we discuss  the ethical implications of humanizing your data with very special guest and past host Triveni Gandhi. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading:To Be Seen We Must Be Measured: Data Visualization and InequalityPerspectives on Data EthicsBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode,  we explored data visualization with Nathan Mannheimer from Tableau. This week, we discuss the ins and outs of data strategy from the individual level all the way up to the enterprise and how humanization can level up your strategy. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading:Avoid Getting “Facebooked” — Adopt a Conscious Data StrategyAI: From Moonshot to RealityBuilding an Inclusive AI Strategy for Data DemocratizationMaking Open Source a Sustainable Part of AI StrategyBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we explored the importance of storytelling with data. This week, we discuss data visualization with Nathan Mannheimer, Director of Data Science and Machine Learning at Tableau. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading: Humanizing AI: A Case For Cognitive Design Thinking And Custom AIHuman-Looking Data Visualizations Don't Boost Empathy — YetBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we explored the importance of human in the loop within the ML pipeline with Christina Hsiao, Senior Product Marketing Manager at Dataiku. This week, we discuss storytelling with data. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading and watching: Using Visualization to share the human impact of NumbersThe Power in Effective Data StorytellingMaking Data Mean More Through StorytellingBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we discussed what happens when humanization fails and consequences that can occur with Jeremie Harris Towards Data Science. This week, we are joined by Christina Hsiao, Senior Product Manager at Dataiku to discuss the importance of having a human in the loop within the ML pipeline. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading: What's MLOps? Managing complex ML systems at ScaleMLOps as a Critical, Emerging RoleBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we discussed differing methodologies and functionalities within the data science field with Emma Irwin, Solutions Engineer at Dataiku. This week, we are joined by Jeremie Harris of Towards Data Science to discuss what happens when humanization fails and consequences that can occur. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading and watching: Becoming an Intelligent Organization in the Age of AI: Firsthand Strategies From the CDO of Morgan StanleyArtificial Intelligence and Ethics: Sixteen Challenges and OpportunitiesBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we discussed how data scientists & citizen data scientists collaborate to deliver value to the non technical audience. This week, we are joined by Emma Irwin to discuss differing methodologies and functionalities within the data science field and her role as a Solutions Engineer at Dataiku. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading: Building and Leading Your Organization's Data CapabilityBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we discussed why technology's long-term goal shouldn't solely be efficiency, but actually emotional intimacy. This week, we were joined by Matt Dorros to discuss the rise of the citizen data scientist and his role as a data analyst manager and data citizen at Wayfair. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading and watching: The Rise of the Citizen Data ScientistCitizen Data Scientists: Where Do They Belong?Why the Concept of “Citizen Data Scientist” Terrifies MeBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome back to our bi-weekly episodes of Season 5 of the Banana Data Podcast! This season, we're ushering in the notion of humanizing data science. In last week's episode, we discussed how data insights can be humanized in an easily digestible manner. This week, we'll guide you through why technology's long-term goal shouldn't solely be efficiency, but actually emotional intimacy. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts and stay up to date on the big conversation in data and AI. Check out what we've been reading and watching: Weeks Of My Life PosterDon't Just Digitize, Humanize Humanize Data with Creative IntelligenceBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

We're kicking off Season 5 of the Banana Data Podcast today. This season, we're ushering in the notion of humanizing data science. We'll guide you through issues such as why trends and technologies matter beyond your centralized data team and beyond the tech industry and what it truly takes to humanize technology. Subscribe to the Banana Data Podcast on Apple or Spotify to receive alerts & easy access to the latest data science content!Check out what we've been reading and watching: The Case for Humanizing DataTED Talk: Lack of Data Is an Issue of Global InjusticeBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

Welcome to part 2 of “In English Please!” This recap episode is summary of our "In English Please" segments which are quick explanations of complex data science terms, processes, or phenomena distilled into easy to understand concepts. Think of this as your data science encyclopedia. Here is the second part of the two part series on “In English Please."Check out our In English Please Glossary!Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

The Banana Data Podcast team is currently prepping for Season 5. In the meantime, check out a recap of all our In English Please segments. These “In English Please” segments are quick explanations of complex data science terms, processes, or phenomena distilled into easy to understand concepts. Think of this as your data science encyclopedia. Here is part 1 of the two part series on “In English Please."Check out our In English Please Glossary!Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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 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?Check out what we're reading: London A.I. Lab Claims Breakthrough That Could Accelerate Drug DiscoveryHow Machine Learning is Transforming Drug DiscoveryThe US Government Will Pay Doctors to Use These AI AlgorithmsOne of biology's biggest mysteries 'largely solved' by AIThis 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 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.Check out what we've been reading: Data Privacy & SecurityNLP & Conversational Analytics Gartner's Top Technology Trends That Will Define 2021Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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. 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!

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?Roundtable Interviewees: Sanjay Agravat, Machine Learning Specialist at GoogleAlex Simonoff, Senior Data Scientist at SpotifyAbdallah MJ Musmar, Data Science Lead at PwC Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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.Learn more about the article's we're reading: Tech Is Transforming People Analytics. Is That a Good Thing? by Tomas Chamorro-Premuzic and Ian Bailie (Harvard Business Review)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!

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. Here you can find links to Oscar's ongoing work: The COVID MonitorFinMango.orgFlorida COVID ActionBe sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

We're turning our attention towards the new Netflix documentary on the harms and potential of social networking: 'The Social Dilemma.' In this episode, Chris and Triveni comment upon the film's perspective on  data commodification, accountability, and how the we can all be more responsible and effective creators. Take a look at what we're reading: You watched ‘The Social Dilemma.' Read these 11 books next, by Ashley Boyd and Audrey Hingle (Fast Company) Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!

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. 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!

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!

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.   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, 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? 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 users 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 GermsMargin of Error - TwitterThis 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 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.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 supporting innovation both intra and internationally? Learn more about the articles referenced in this episode: AI KenyaPhase 1 of Konza Technopolis Data Center CompleteVietnam's Artificial Intelligence Scenario is EvolvingHow 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 user 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 upcoming 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 Turke-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-evaluationSolution 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.To download 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/.

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

So many pieces of our lives are intertwined with AI - from our phones to our commutes, we're constantly being supported by (and maybe relying on) algorithms to select our next move. On this episode, Will & Triveni take us through the many unexpected places we find AI - and challenge what it means to be a responsible AI consumer.We tried Amazon's bizarre Alexa microwave and weren't convinced by Sarah Perez (TechCrunch)Why Some Cities Have Had Enough of Waze by Tala Salem (U.S. News)This New Gmail Smart Compose Feature is So Accurate That People Area Freaked Out by Amanda Fama (Elite Daily)Are the algorithms that power dating apps racially biased? By Thomas McMullan (WIRED) 

AI constantly promises the cutting edge. So, what's behind the newest, hottest AI trends out there? This episode, Triveni & Will sit down with Azalia Mirhoseini, Senior Researcher at Google Brain, named on Technology Review's 35 Innovators under 35 to explore what's really going on behind the scenes, and what's actually overrated, underrated, and just right in the field. Azalia Mirhoseini, 35 Innovators Under 35: Visionaries (MIT Technology Review)

This AI podcast has been live for two seasons - but we haven't stepped back to ask - what even is AI? In this episode, Triveni & Will work through their definitions of AI, exploring theories, use-cases, and examples of what they think qualifies as AI - and how we measure it.  Do statistics count as AI? Does AI need to include Arnold Schwarzenegger? Who has actually achieved AI? Is it AI or not? A Score Card with 4 Dimensions by Florian Douetteau (Medium) AI Is Not Just for Big Tech Companies -- You Can Use AI to Transform Your Organization, Too by Vishal Gupta (Forbes)

Now that we've covered how open source works, we're looking to pull back the curtain and see who's actually contributing. In part 2/2 of our series on open source, we sat down with Reshama Shaikh, a statistician and key organizer of scikit-Learn sprints, to learn about the ups & downs of open source contributing, as well how a Sprint in Nairobi benefits Fortune 500 companies in the US. Reshama Shaikh is an independent data scientist/statistician and MBA with skills in Python, R and SAS. I worked for over 10 years as a biostatistician in the pharmaceutical industry.Further Reading: Stack Overflow Developer Survey; Open Source Contributors: https://insights.stackoverflow.com/survey/2019#developer-profile-_-contributing-to-open-sourceHow to Organize a Scikit Learn Spring by Reshama Shaikh: https://reshamas.github.io/how-to-organize-a-scikit-learn-sprint/Reshama Shaikh's Website: https://reshamas.github.io/Contributing to scikit-Learn: https://scikit-learn.org/stable/developers/contributing.htmlGitter scikit-Learn: https://gitter.im/scikit-learn/scikit-learnscikitLearn Mailing List: https://mail.python.org/mailman/listinfo/scikit-learnJoin Dataiku's Paris scikit-Learn sprint this January: https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade

Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we're covering the ins and outs of open source - and how this special type of software supports 98% of enterprise-level companies' data science efforts.In part 1, we're chatting with Andreas Mueller, a core contributor of  scikit-Learn aboutthe  value in open source versus corporate software, and what it looks like to run and govern this type of community-written (and driven) project.Join our Paris scikit-Learn sprint this January: https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-DecadeAndreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book “Introduction to Machine Learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv here. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

As we near the end of the decade, Will and Triveni place their bets on the biggest data science trends for 2020- including AutoML, explainable AI, Cloud computing, and federated learning. They'll also reflect on whether or not the trends of 2019 lived up to their hype.- Keras inventor Chollet charts a new direction for AI: a Q&A by Tiernan Ray (ZDNet)- On the Measure of Intelligence by François Chollet (Cornell University)- Federated Learning: The Future of Distributed Machine Learning by Synced (Medium) - Optimization over Explanation by David Weinberger (Berkman Klein Center for Internet & Society at Harvard University on Medium)

Triveni and Will sit down with Dan Shiebler, Senior ML Engineer at Twitter to tackle the final frontier of data science: production. From technical debt to model maintenance, they'll look at what it means to have a model in production, when it's time to take a model out of production, and how challenges of technical debt can affect the entire data science pipeline. Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! Learn more about Dan Shiebler, Senior Machine Learning Engineer at http://danshiebler.com/.

In our season 2 inaugural episode, we're debating how to approach data science pipelines (are they cyclical or linear? How should we test them?) - and how tools like Python and Kafka may not be all they're hyped up to be in AI. Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox!  Learn more about the articles referenced in this episode below: Standards comic (xcd)  We are Living in “The Era of Python” by Rinu Gour (Towards Data Science)  Is Kafka Overrated? thread (GitHub Gist) 

For our season 1 finale, Triveni and Will give their two cents on the most important aspects of a data science practice. From intentional data to getting outside perspectives, they walk us through how to build not only a scalable AI practice, but one that is responsible, ethical, and interpretable.We'll be back for Season 2 in October - but in case you miss us too much, be sure to subscribe to the Banana Data Newsletter, and rate our podcast!Articles mentioned: Poor Quality Data, Fraud in GPS Signals Undermine Geotargeted Ad Campaigns by Nina Aghadjanian (A.list)The Problem With Autonomous Cars That No One's Talking About by Jasper Dekker (Fast Company)

In our second-to-last episode of the season, Triveni and Will explore the data world's shifting attitude toward standalone data visualizations (are they dying? Who are they for?), how to respond to global AI practices (what are global AI standards? How do different countries vary in their AI approaches?), and the feasibility of an AI audit. We'll also see how Spark fits into the infrastructure of our data science systems.Be sure to subscribe to our weekly newsletter to get this podcast & a host of new and exciting data-happenings in your inbox! Learn more about the articles referenced in this episode below:Standalone Data Visualization is Dead...and I Couldn't Be More Excited by Matthew Miller (Biztory) IDC: Asia-Pacific spending on AI systems will reach $.5 billion this year, up 80% from 2018 by Catherine Shu (TechCrunch) High-Stakes AI Decisions Need to Be Automatically Audited by Oren Etzioni and Michael Li (WIRED)