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 &amp; 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 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:<ul><li><a href="https://www.adalovelaceinstitute.org/blog/to-be-seen-we-must-be-measured-data-visualisation-and-inequality/" rel="nofollow" target="_blank">To Be Seen We Must Be Measured: Data Visualization and Inequality</a></li><li><a href="https://annadangela.medium.com/perspectives-on-data-ethics-a8c8c3e03d49" rel="nofollow" target="_blank">Perspectives on Data Ethics</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Ethical Implications of Humanizing Your Data

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 &amp; 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:<ul><li><a href="https://blog.dataiku.com/avoid-getting-facebooked-adopt-a-conscious-data-strategy" rel="nofollow" target="_blank">Avoid Getting “Facebooked” — Adopt a Conscious Data Strategy</a></li><li><a href="https://pages.dataiku.com/operationalization-ssa-2" rel="nofollow" target="_blank">AI: From Moonshot to Reality</a></li><li><a href="https://www.dataiku.com/stories/inclusive-analytics/" rel="nofollow" target="_blank">Building an Inclusive AI Strategy for Data Democratization</a></li><li><a href="https://www.dataiku.com/stories/open-source/" rel="nofollow" target="_blank">Making Open Source a Sustainable Part of AI Strategy</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Floor to Ceiling Data Strategy

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 &amp; 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: <ul><li><a href="https://www.forbes.com/sites/forbestechcouncil/2021/05/25/humanizing-ai-a-case-for-cognitive-design-thinking-and-custom-ai/?sh=2ab90a5b4a24" rel="nofollow" target="_blank">Humanizing AI: A Case For Cognitive Design Thinking And Custom AI</a></li><li><a href="https://builtin.com/data-science/anthropographics-visualization-empathy" rel="nofollow" target="_blank">Human-Looking Data Visualizations Don’t Boost Empathy — Yet</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Data Visualization w/ Nathan Mannheimer, Director of Data Science & ML at Tableau

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 &amp; 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: <ul><li><a href="https://www.tableau.com/learn/whitepapers/data-storytelling-using-visualization-share-human-impact-numbers" rel="nofollow" target="_blank">Using Visualization to share the human impact of Numbers</a></li><li><a href="https://youtu.be/0e52QfQngrM" rel="nofollow" target="_blank">The Power in Effective Data Storytelling</a></li><li><a href="https://youtu.be/6xsvGYIxJok" rel="nofollow" target="_blank">Making Data Mean More Through Storytelling</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Leverage Storytelling With Data

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 &amp; 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: <ul><li><a href="https://towardsdatascience.com/whats-mlops-5bf60dd693dd" rel="nofollow" target="_blank">What's MLOps? Managing complex ML systems at Scale</a></li><li><a href="https://www.dataiku.com/stories/mlops/" rel="nofollow" target="_blank">MLOps as a Critical, Emerging Role</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

The Importance of Human in the Loop AI With Christina Hsiao

Jeremie Harris

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 &amp; 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: <ul><li><a href="https://egg.dataiku.com/jeff-mcmillan-episode/" rel="nofollow" target="_blank">Becoming an Intelligent Organization in the Age of AI: Firsthand Strategies From the CDO of Morgan Stanley</a></li><li><a href="https://www.scu.edu/ethics/all-about-ethics/artificial-intelligence-and-ethics-sixteen-challenges-and-opportunities/" rel="nofollow" target="_blank">Artificial Intelligence and Ethics: Sixteen Challenges and Opportunities</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

What Happens When Humanization Fails? A Conversation With Jeremie Harris of Towards Data Science

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 &amp; 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 &amp; 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 &amp; 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: <ul><li><a href="https://knowledge.insead.edu/blog/insead-blog/building-and-leading-your-organisations-data-capability-16631" rel="nofollow" target="_blank">Building and Leading Your Organization's Data Capability</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Methodology & Functionality in Differing Data Science Roles

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 &amp; 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: <ul><li><a href="https://www.datanami.com/2019/05/20/the-rise-of-the-citizen-data-scientist-how-humanized-machine-learning-is-augmenting-human-intelligence/#:~:text=May%2020%2C%202019-,The%20Rise%20of%20the%20Citizen%20Data%20Scientist%3A%20How%20Humanized,Learning%20Is%20Augmenting%20Human%20Intelligence&amp;text=IDC%20estimates%20worldwide%20data%20volume,of%20this%20generated%20by%20businesses." rel="nofollow" target="_blank">The Rise of the Citizen Data Scientist</a></li><li><a href="https://www.dataversity.net/citizen-data-scientists-where-do-they-belong/" rel="nofollow" target="_blank">Citizen Data Scientists: Where Do They Belong?</a></li><li><a href="https://www.linkedin.com/pulse/why-concept-citizen-data-scientist-terrifies-me-bill-schmarzo/" rel="nofollow" target="_blank">Why the Concept of “Citizen Data Scientist” Terrifies Me</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Rise of the Data Citizen

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 &amp; 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: <ul><li><a href="https://weeksofmylife.com/products/weeksofmylife" rel="nofollow" target="_blank">Weeks Of My Life Poster</a></li><li><a href="https://www.oliverwyman.com/our-expertise/insights/2016/jun/ten-digital-ideas/humanize-digital.html" rel="nofollow" target="_blank">Don't Just Digitize, Humanize </a></li><li><a href="https://smartpandalabs.com/blog/humanizing-data-creative-intelligence/" rel="nofollow" target="_blank">Humanize Data with Creative Intelligence</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

Create Technological Emotional Bonds w/ Creative Intelligence

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 &amp; 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 &amp; 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 &amp; easy access to the latest data science content! Check out what we've been reading and watching: <ul><li><a href="https://medium.com/elbarrad-design/the-case-for-humanizing-data-a051a9772d20" rel="nofollow" target="_blank">The Case for Humanizing Data</a></li><li><a href="https://www.data4sdgs.org/news/ted-talk-lack-data-issue-global-injustice" rel="nofollow" target="_blank">TED Talk: Lack of Data Is an Issue of Global Injustice</a></li></ul>Be sure to subscribe to our <a href="https://banana-data.com/" rel="nofollow" target="_blank">weekly newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

What Does It Mean to Humanize Your Data?

Welcome to the Banana Data Podcast! We're a data science podcast focused on the latest &amp; greatest of the DS ecosystem, sprinkled in with our musings &amp; data science expertise. With topics ranging from ethical AI and transparency to robot pets, our hosts, Christopher Peter Makris &amp; Corey Strausman, are here to keep you up to date on the latest trends, news, and big convos in data. If you're looking to keep the knowledge up, be sure to also subscribe to our weekly Banana Data Newsletter! Register here: https://banana-data.com/

Welcome to the Banana Data Podcast! We're a data science podcast focused on the latest &amp; greatest of the DS ecosystem, sprinkled in with our musings &amp; data science expertise. With topics ranging from ethical AI and transparency to robot pets, our hosts, Christopher Peter Makris &amp; Corey Strausman, are here to keep you up to date on the latest trends, news, and big convos in data. If you're looking to keep the knowledge up, be sure to also subscribe to our weekly Banana Data Newsletter! Register here: <a href="https://banana-data.com/" rel="nofollow" target="_blank">https://banana-data.com/</a>

Banana Data Podcast

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 &amp; 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) 

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<a href="https://banana-data.com/" rel="nofollow" target="_blank"> newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!  Learn more about the articles referenced in this episode below: <a href="https://xkcd.com/927/" rel="nofollow" target="_blank">Standards comic </a>(xcd)  <a href="https://towardsdatascience.com/we-are-living-in-the-era-of-python-bc032d595f6a" rel="nofollow" target="_blank">We are Living in “The Era of Python” </a>by Rinu Gour (Towards Data Science)  <a href="https://gist.github.com/markrendle/26e423b6597685757732" rel="nofollow" target="_blank">Is Kafka Overrated? thread</a> (GitHub Gist)

The Essentials (and not-so essentials) of Data Science Pipelines

When we release our AI into the world, its impact extends far beyond the business and tech we’re working on. On this episode, we’re diving into the consequences of AI on consumers, housing, and the environment through the lens of GDPR, the supposed “AI job apocalypse” and some controversial takes on models’ carbon emissions. As always, be sure to rate and subscribe! Be sure to check out the articles we mentioned this week: How is the GDPR Doing? By Josephine Wolff (Slate) The Answer to the A.I. Jobs Apocalypse Is All About Geography by Carl Benedikt Frey (OneZero)Training A single AI Model Can Emit As Much Carbon as Five Cars by Karen Hao (Technology Review)

When we release our AI into the world, its impact extends far beyond the business and tech we’re working on. On this episode, we’re diving into the consequences of AI on consumers, housing, and the environment through the lens of GDPR, the supposed “AI job apocalypse” and some controversial takes on models’ carbon emissions.  As always, be sure to rate and subscribe! Be sure to check out the articles we mentioned this week: <a href="https://slate.com/technology/2019/03/gdpr-one-year-anniversary-breach-notification-fines.html" rel="nofollow" target="_blank">How is the GDPR Doing?</a> By Josephine Wolff (Slate) <a href="https://onezero.medium.com/housing-policy-is-the-answer-to-the-a-i-job-apocalypse-ae70e7dda093" rel="nofollow" target="_blank">The Answer to the A.I. Jobs Apocalypse Is All About Geography</a> by Carl Benedikt Frey (OneZero) <a href="https://www.technologyreview.com/s/613630/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/?utm_campaign=the_download.unpaid.engagement&amp;utm_source=hs_email&amp;utm_medium=email&amp;utm_content=73419330&amp;_hsenc=p2ANqtz-_dbBBeNzw0SNETezRNgRbrKuEj7YoeeFnP0nuWbj8RPQ4p9eatB2otj4HuD_8_vlVezSazuf71bBdCsXjhuUaXo8vF0g&amp;_hsmi=73419330" rel="nofollow" target="_blank">Training A single AI Model Can Emit As Much Carbon as Five Cars</a> by Karen Hao (Technology Review)

AI Meets World: GDPR, the AI Job Apocalypse, and AI’s carbon footprint

As AI continues its embedding into our lives, humans will have to start evaluating how we as humans interact and build relationships with our artificial intelligence applications. On this episode of Banana Data, we’re taking a look at what AI means for the individual - from emergent Robopet friendships to AI art as a medium, to what emotional intelligence looks like in AI - and how we can produce better, more human AI systems.  The Second Coming of the Robot Pet by Arielle Pardes (WIRED) The Past, Present, and Future of AI Art by Fabian Offert (The Gradient) Is Your Product’s AI Annoying People? by James Glasnapp (TechCrunch)

As AI continues its embedding into our lives, humans will have to start evaluating how we as humans interact and build relationships with our artificial intelligence applications. On this episode of Banana Data, we’re taking a look at what AI means for the individual - from emergent Robopet friendships to AI art as a medium, to what emotional intelligence looks like in AI - and how we can produce better, more human AI systems.  <a href="https://www.wired.com/story/the-second-coming-of-the-robot-pet/" rel="nofollow" target="_blank">The Second Coming of the Robot Pet</a> by Arielle Pardes (WIRED) <a href="https://thegradient.pub/the-past-present-and-future-of-ai-art/" rel="nofollow" target="_blank">The Past, Present, and Future of AI Art</a> by Fabian Offert (The Gradient) <a href="https://techcrunch.com/2019/06/17/is-your-products-ai-annoying-people/" rel="nofollow" target="_blank">Is Your Product’s AI Annoying People?</a> by James Glasnapp (TechCrunch)

A New Kind of Relationship with AI: Robopets, AI Art, and AI EQ

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)

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 <a href="https://banana-data.com/" rel="nofollow" target="_blank">Banana Data Newsletter</a>, and rate our podcast!Articles mentioned: <a href="https://www.alistdaily.com/technology/location-based-advertising-performance-study/" rel="nofollow" target="_blank">Poor Quality Data, Fraud in GPS Signals Undermine Geotargeted Ad Campaigns</a> by Nina Aghadjanian (A.list)<a href="https://medium.com/fast-company/the-problem-with-autonomous-cars-that-no-ones-talking-about-e34d617c338b" rel="nofollow" target="_blank">The Problem With Autonomous Cars That No One’s Talking About</a> by Jasper Dekker (Fast Company)

What Makes a Good Data Science Practice

Dan Shiebler

Twitter Eng

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 &amp; a host of new and exciting data-happenings in your inbox!  Learn more about Dan Shiebler, Senior Machine Learning Engineer at http://danshiebler.com/.

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<a href="https://banana-data.com/" rel="nofollow" target="_blank"> newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox! Learn more about Dan Shiebler, Senior Machine Learning Engineer at <a href="http://danshiebler.com/" rel="nofollow" target="_blank">http://danshiebler.com/</a>.

Life after Production, a Tale of Technical Debt with Dan Shiebler, Twitter Eng

Accessibility, by definition, is about making tasks more achievable. In episode 7 of the podcast, Triveni and Will explore how AI is shaping our world to become more accessible, and how we as data scientists can help it get there, diving into Salesforce’s new “unstructured” querying tool, the various physical manifestations of AI, and even questioning some of their previous takes on ethics. They’ll also walk us through the contributions of BERT to the NLP space, and the how and why it’s been so revolutionary. As always, be sure to rate and subscribe! Be sure to check out the articles we mentioned this week: Salesforce creates AI tool for talking to databases by Blair Hanley Frank (VentureBeat) Ethical codes v. ethical code by Schaun Wheeler (Towards Data Science) Brain signals translated into speech using artificial intelligence (Nature)

Accessibility, by definition, is about making tasks more achievable. In episode 7 of the podcast, Triveni and Will explore how AI is shaping our world to become more accessible, and how we as data scientists can help it get there, diving into Salesforce’s new “unstructured” querying tool, the various physical manifestations of AI, and even questioning some of their previous takes on ethics. They’ll also walk us through the contributions of BERT to the NLP space, and the how and why it’s been so revolutionary. As always, be sure to rate and subscribe! Be sure to check out the articles we mentioned this week: <a href="https://venturebeat.com/2017/08/29/salesforce-creates-ai-tool-for-talking-to-databases/" rel="nofollow" target="_blank">Salesforce creates AI tool for talking to databases</a> by Blair Hanley Frank (VentureBeat)<a href="https://towardsdatascience.com/ethical-codes-vs-ethical-code-fea118987a5" rel="nofollow" target="_blank"> Ethical codes v. ethical code by Schaun Wheeler </a>(Towards Data Science) <a href="https://www.nature.com/articles/d41586-019-01328-x?utm_source=ActiveCampaign&amp;utm_medium=email&amp;utm_content=BDN+%23153%3A+AI+products%2C+brain+waves%2C+design+thinking%2C+++a+new+AI+diversity+section%21&amp;utm_campaign=Banana+Data+%23153#highlighted" rel="nofollow" target="_blank">Brain signals translated into speech using artificial intelligence</a> (Nature)

Building accessible queries, codes, and speech using AI

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&amp;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 &amp; Society at Harvard University on Medium)

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. - <a href="https://www.zdnet.com/article/keras-creator-chollets-new-direction-for-ai-a-q-a/" rel="nofollow" target="_blank">Keras inventor Chollet charts a new direction for AI: a Q&amp;A</a> by Tiernan Ray (ZDNet) - <a href="https://arxiv.org/abs/1911.01547" rel="nofollow" target="_blank">On the Measure of Intelligence</a> by François Chollet (Cornell University) - <a href="https://medium.com/syncedreview/federated-learning-the-future-of-distributed-machine-learning-eec95242d897" rel="nofollow" target="_blank">Federated Learning: The Future of Distributed Machine Learning</a> by Synced (Medium) - <a href="https://medium.com/berkman-klein-center/optimization-over-explanation-41ecb135763d" rel="nofollow" target="_blank">Optimization over Explanation</a> by David Weinberger (Berkman Klein Center for Internet &amp; Society at Harvard University on Medium)

Predicting AI Trends for 2020

On episode 4 of Banana Data, we’re taking a look at how our data is changing. With models in the wild skewing our future data sets, the impending shift to Python 3.0, and navigating a public distrust of Machine Learning, Triveni and Will talk through how our current decisions in AI will heavily influence its future. They’ll also take a stab at explaining GANs - in English. Learn more about the articles referenced in this episode below: Here’s a prediction: In the future, predictions will only get worse by Alison Schrager (Quartz) Python’s Caduceus Syndrome: What happens when a programming language grows up by Normcore Tech (Substack) How a Feel-Good AI Story Went Wrong in Flint by Alexis C. Madrigal (The Atlantic)

On episode 4 of Banana Data, we’re taking a look at how our data is changing. With models in the wild skewing our future data sets, the impending shift to Python 3.0, and navigating a public distrust of Machine Learning, Triveni and Will talk through how our current decisions in AI will heavily influence its future. They’ll also take a stab at explaining GANs - in English. Learn more about the articles referenced in this episode below: <a href="https://qz.com/1623596/what-if-all-the-data-we-use-is-wrong/?utm_medium=email&amp;utm_source=topic+optin&amp;utm_campaign=awareness&amp;utm_content=20190605+data+nl&amp;mkt_tok=eyJpIjoiTlRGbU9EZ3lPR0kxT1RreSIsInQiOiIxbklcL3Vlc0lYYW8wb2ZlMWNHMitMRzdXbHZrOEpNTnlkdmNZcmpESUhiVUNVdlhFXC8za0xaalwvM1M2TG9DcEJsdHB5WGlnSlhhVUd5QjB3NDhkWWI4MkZTSFBEcjVcLzlsczlKOGtqbWo0cEY1RzVhMU1wT3NDYWRRU016UzJZT1IifQ%3D%3D" rel="nofollow" target="_blank">Here’s a prediction: In the future, predictions will only get worse</a> by Alison Schrager (Quartz) <a href="https://vicki.substack.com/p/pythons-caduceus-syndrome?mkt_tok=eyJpIjoiTXpaak5tSXdNR1JqTlRjdyIsInQiOiJLaVBcL0gxOUNFV1ZkNXIybTJkUWRKQ2hoR085QTA0QXBRdHFYZmN4dm1cL3lRUk9Sc2FlYnlZbmdCOFlWV3RcL3EzdGQ1REZyd2h6WFRyTW1RWDV4T2JRODREUUxJb1RYNGtkbHIyaThuTGFnMlFYZHNhRTZtWnVRTHpZa1NOak5MMSJ9" rel="nofollow" target="_blank">Python’s Caduceus Syndrome: What happens when a programming language grows up</a> by Normcore Tech (Substack) <a href="https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/" rel="nofollow" target="_blank">How a Feel-Good AI Story Went Wrong in Flint</a> by Alexis C. Madrigal (The Atlantic)

The future of data according to predictions, Python 3.0, and people.

This week we’re diving into some deeper impacts of AI’s successes and failures- asking where responsibility lies for an algorithm’s failures, and the endless benefits of accessibility and responsibility that come with AI implemented in healthcare. We’re also taking a deep dive into Epsilon greedy multi-armed bandits and how we can more accurately describe our successes (and our failures) in AI.  When algorithms mess up, the nearest human gets the blame by Karen Hao (MIT Technology Review) Google Trained Its AI to Predict Lung Cancer by Christine Fisher (Engadget) No This AI Can’t Finish Your Sentence by Tiernan Ray (ZDNet) Introduction to Multi-Armed Bandits with Applications in Digital Advertising by Dave King (SpotX) Register for EGG NYC, Dataiku's human-centered AI conference on June 20th to hear from leaders in the AI &amp; advanced analytics space, including WIRED, Twitter, Hinge, and more!  https://nyc.egg.dataiku.com/

This week we’re diving into some deeper impacts of AI’s successes and failures- asking where responsibility lies for an algorithm’s failures, and the endless benefits of accessibility and responsibility that come with AI implemented in healthcare. We’re also taking a deep dive into Epsilon greedy multi-armed bandits and how we can more accurately describe our successes (and our failures) in AI. <a href="https://www.technologyreview.com/s/613578/ai-algorithms-liability-human-blame/?utm_campaign=Content%20Curation&amp;utm_content=93375845&amp;utm_medium=social&amp;utm_source=linkedin&amp;hss_channel=lcp-2770554" rel="nofollow" target="_blank">When algorithms mess up, the nearest human gets the blame</a> by Karen Hao (MIT Technology Review) <a href="https://www.engadget.com/2019/05/20/google-ai-lung-cancer-screening/" rel="nofollow" target="_blank">Google Trained Its AI to Predict Lung Cancer</a> by Christine Fisher (Engadget) <a href="https://www.zdnet.com/article/no-this-ai-cant-finish-your-sentence/" rel="nofollow" target="_blank">No This AI Can’t Finish Your Sentence</a> by Tiernan Ray (ZDNet) <a href="https://www.spotx.tv/resources/blog/developer-blog/introduction-to-multi-armed-bandits-with-applications-in-digital-advertising/" rel="nofollow" target="_blank">Introduction to Multi-Armed Bandits with Applications in Digital Advertising</a> by Dave King (SpotX) Register for EGG NYC, Dataiku's human-centered AI conference on June 20th to hear from leaders in the AI &amp; advanced analytics space, including WIRED, Twitter, Hinge, and more!  <a href="https://nyc.egg.dataiku.com/" rel="nofollow" target="_blank">https://nyc.egg.dataiku.com/</a>

Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits

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 &amp; 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) 

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<a href="https://banana-data.com/" rel="nofollow" target="_blank"> newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox! Learn more about the articles referenced in this episode below:<a href="https://biztory.com/2019/06/11/salesforce-buying-tableau-means-standalone-data-visualization-is-dead-and-i-couldnt-be-more-excited/" rel="nofollow" target="_blank">Standalone Data Visualization is Dead...and I Couldn’t Be More Excited</a> by Matthew Miller (Biztory) <a href="https://techcrunch.com/2019/05/20/idc-asia-pacific-spending-on-ai-systems-will-reach-5-5-billion-this-year-up-80-percent-from-2018/" rel="nofollow" target="_blank">IDC: Asia-Pacific spending on AI systems will reach $.5 billion this year, up 80% from 2018</a> by Catherine Shu (TechCrunch) <a href="https://www.wired.com/story/ai-needs-to-be-audited/" rel="nofollow" target="_blank">High-Stakes AI Decisions Need to Be Automatically Audited</a> by Oren Etzioni and Michael Li (WIRED)

The Death of Data Viz, Cross-Cultural AI, and AI Auditing

Andreas Mueller

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.

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: <a href="https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade" rel="nofollow" target="_blank">https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade</a> Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book <a href="http://amueller.github.io/#book" rel="nofollow" target="_blank">“Introduction to Machine Learning with Python”,</a> 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 <a href="http://software-carpentry.org/" rel="nofollow" target="_blank">Software Carpentry</a> 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 <a href="http://amueller.github.io/cv_andreas_mueller.pdf" rel="nofollow" target="_blank">here</a>. 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.

Why Open Source? feat. Andreas Mueller, a Core Contributor of scikit-Learn

Reshama Shaikh

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 &amp; 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

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 &amp; 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: <a href="https://insights.stackoverflow.com/survey/2019#developer-profile-_-contributing-to-open-source" rel="nofollow" target="_blank">https://insights.stackoverflow.com/survey/2019#developer-profile-_-contributing-to-open-source</a>How to Organize a Scikit Learn Spring by Reshama Shaikh: <a href="https://reshamas.github.io/how-to-organize-a-scikit-learn-sprint/" rel="nofollow" target="_blank">https://reshamas.github.io/how-to-organize-a-scikit-learn-sprint/</a>Reshama Shaikh’s Website: <a href="https://reshamas.github.io/" rel="nofollow" target="_blank">https://reshamas.github.io/</a>Contributing to scikit-Learn: <a href="https://scikit-learn.org/stable/developers/contributing.html" rel="nofollow" target="_blank">https://scikit-learn.org/stable/developers/contributing.html</a>Gitter scikit-Learn: <a href="https://gitter.im/scikit-learn/scikit-learn" rel="nofollow" target="_blank">https://gitter.im/scikit-learn/scikit-learn</a>scikitLearn Mailing List: <a href="https://mail.python.org/mailman/listinfo/scikit-learn" rel="nofollow" target="_blank">https://mail.python.org/mailman/listinfo/scikit-learn</a>Join Dataiku’s Paris scikit-Learn sprint this January: <a href="https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade" rel="nofollow" target="_blank">https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade</a>

Finding Community in Data Science with Reshama Shaikh, key scikit-Learn sprint organizer

This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter.  Triveni will also take us through a step-by-step of the decisions made by a Random Forest algorith  As always, be sure to rate and subscribe!  Be sure to check out the articles we mentioned this week: The Side of Machine Learning You’re Undervaluing and How to Fix it by Matt Wilder (LabelBox) The Hidden Costs of Automated Thinking by Jonathan Zittrain (The New Yorker) Another AI Winter Could Usher in a Dark Period for Artificial Intelligence by Eleanor Cummins (PopSci)

This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter.  Triveni will also take us through a step-by-step of the decisions made by a Random Forest algorith   As always, be sure to rate and subscribe!  Be sure to check out the articles we mentioned this week: <a href="https://labelbox.com/blog/the-side-of-machine-learning-youre-undervaluing-and-how-to-fix-it/" rel="nofollow" target="_blank">The Side of Machine Learning You’re Undervaluing and How to Fix it </a>by Matt Wilder (LabelBox) <a href="https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking" rel="nofollow" target="_blank">The Hidden Costs of Automated Thinking</a> by Jonathan Zittrain (The New Yorker) <a href="https://www.popsci.com/ai-winter-artificial-intelligence/" rel="nofollow" target="_blank">Another AI Winter Could Usher in a Dark Period for Artificial Intelligence</a> by Eleanor Cummins (PopSci)

Prioritizing training data, model interpretability, and dodging an AI Winter

Azalia Mirhoseini

AI constantly promises the cutting edge. So, what’s behind the newest, hottest AI trends out there? This episode, Triveni &amp; 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)

AI constantly promises the cutting edge. So, what’s behind the newest, hottest AI trends out there? This episode, Triveni &amp; 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. <a href="https://www.technologyreview.com/lists/innovators-under-35/2019/visionary/azalia-mirhoseini/" rel="nofollow" target="_blank">Azalia Mirhoseini, 35 Innovators Under 35: Visionaries</a> (MIT Technology Review)

The Future (and the now) of AI with Azalia Mirhoseini, Senior Researcher at Google Brain

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 &amp; 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)

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 &amp; 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? <a href="https://medium.com/ai-musings/is-it-ai-or-not-a-score-card-with-4-dimensions-8a5297372a55" rel="nofollow" target="_blank">Is it AI or not? A Score Card with 4 Dimensions</a> by Florian Douetteau (Medium) <a href="https://www.forbes.com/sites/forbestechcouncil/2020/01/06/ai-is-not-just-for-big-tech-companies-you-can-use-ai-to-transform-your-organization-too/#52549fc07d8d" rel="nofollow" target="_blank">AI Is Not Just for Big Tech Companies -- You Can Use AI to Transform Your Organization, Too</a> by Vishal Gupta (Forbes)

Do I do AI?

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.

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 - <a href="https://labs.spotify.com/2020/02/27/how-we-improved-data-discovery-for-data-scientists-at-spotify/" rel="nofollow" target="_blank">https://labs.spotify.com/2020/02/27/how-we-improved-data-discovery-for-data-scientists-at-spotify/</a>The NFL’s Quest to Quantify Quarterback Evaluation - <a href="https://www.theringer.com/2020/4/17/21224389/nfl-draft-quantifying-quarterback-evaluation" rel="nofollow" target="_blank">https://www.theringer.com/2020/4/17/21224389/nfl-draft-quantifying-quarterback-evaluation</a>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.

Analytics in the NFL & Revolutions in Data Discovery

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 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: <a href="https://cointelegraph.com/news/attestiv-ceo-on-using-dlt-to-fight-fake-news-insurance-fraud-and-deep-fakes" rel="nofollow" target="_blank">Attestive CEO on Using DLT to Fight Fake News, Insurance Fraud, and Deep Fakes</a> by Samuel Haig (CoinTelegraph)<a href="https://cloud.google.com/blog/topics/training-certifications/expanding-at-home-learning" rel="nofollow" target="_blank">Expanding at-home learning with 30 days of training at no cost</a> 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.

Deepfakes & Data Upskilling

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)

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?<a href="https://www.forbes.com/sites/forbestechcouncil/2020/01/15/from-roi-to-rai-revenue-from-artificial-intelligence/#3989b8d61fcc" rel="nofollow" target="_blank">From ROI To RAI (Revenue From Artificial Intelligence)</a> by AJ Abdallat (Forbes)<a href="https://hbr.org/2020/03/whats-the-best-approach-to-data-analytics" rel="nofollow" target="_blank">What’s the Best Approach to Data Analytics?</a> by Tom O’Toole (Harvard Business Review)<a href="https://conferences.oreilly.com/strata/strata-eu-2019/public/schedule/detail/75135" rel="nofollow" target="_blank">Making Data Science Useful</a> by Cassie Kozyrkov (Strata Data Conference)<a href="https://www.yellowfinbi.com/blog/2014/10/yfcommunitynews-bi-and-analytics-delivering-over-1300-roi-according-to-nucleus-research-do-you-b-175078" rel="nofollow" target="_blank">BI and Analytics Delivering over 1300% ROI according to Nucleus Research: Do you believe it?</a> By Lach James (YellowfinBI)<a href="https://www.forbes.com/sites/nikkibaird/2019/02/28/measuring-ais-roi-in-retail-thinking-big-and-small/#5d19ad7ebdd0" rel="nofollow" target="_blank">Measuring AI’s ROI in Retail: Thinking Big and Small</a> by Nikki Baird (Forbes)

Is AI Worth it?

Tristan Handy

With Tristan Handy, CEO &amp; 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/.

With Tristan Handy, CEO &amp; 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 <a href="https://www.getdbt.com/" rel="nofollow" target="_blank">https://www.getdbt.com/</a>. You can also learn more about Fishtown Analytics and Tristan Handy at <a href="https://www.fishtownanalytics.com/" rel="nofollow" target="_blank">https://www.fishtownanalytics.com/</a>.

The Roles in Data Science, feat. Tristan Handy, CEO & Founder of Fishtown Analytics

Karen Hao

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 &amp; a host of new and exciting data-happenings in your inbox! 

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 <a href="https://forms.technologyreview.com/the-algorithm/" rel="nofollow" target="_blank">Algorithm</a>, 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<a href="https://banana-data.com/" rel="nofollow" target="_blank"> newsletter</a> to get this podcast &amp; a host of new and exciting data-happenings in your inbox!

How We Talk about AI, feat. Karen Hao, MIT Technology Review

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