Podcast appearances and mentions of drew conway

Trends in open-source AI: Join Jon Krohn and a panel of data science icons as they discuss the most exciting and concerning developments in open-source AI. Hear insights from Drew Conway, Jared Lander, Emily Zabor, and JD Long on the transformative potential of AI and its future impact. Additional materials: www.superdatascience.com/794 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

The experts reveal their top open-source R libraries with us live from the New York R Conference! This Super Data Science Podcast episode features an exclusive panel with data science trailblazers Drew Conway, Jared Lander, Emily Zabor, and JD Long. They share their favorite R libraries and valuable insights to enhance your data science practice. Additional materials: www.superdatascience.com/790 Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

Ist Data Science nur ein hipper Begriff für Statistik? In dieser Episode sprechen Amit und Sebastian über den Werdegang bei inwt von der Statistik hin zu Full Stack Data Science. Wir decken auf was hinter den Begriffen "Statistik" und "Data Science" steckt und klären wer im Kampf der Daten um Relevanz eigentlich die Nase vorne hat.. Schaut euch unbedingt das Video von Baba Brinkman auf YouTube an! Links: Data Science - Baba Brinkman Music Video auf YouTube Data Science Mengendiagramm von Drew Conway: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

Drew Conway joins us on the first live podcast to discuss his work in private investing and how data science figures into and improves his work. In this episode you will learn: • The R Conference and NYHackR [6:33] • Machine Learning for Hackers [20:17] • Two Sigma and Drew's work [28:27] • Drew's team structure at Two Sigma [35:12] • Audience Q&A [46:27] Additional materials: www.superdatascience.com/511

In episode 2 of "The Orthogonals" Shaun and David spoke to Peter Ellis. Peter is a Director and Chief Data Scientist at Nous Group, an international management consultancy operating in 10 locations across the UK, Australia and Canada. He also writes the Free Range Statistics blog, a great place to see cutting-edge techniques applied to real-world problems, all with reproducible code. But you'll never guess what his undergraduate degree was (hint: requires long fingernails on one hand only!) We spoke about scraping emojis from tweets, why Drew Conway's Venn diagram is still relevant in 2020, and becoming a data scientist to argue with economists. You can find Peter on Twitter @ellis2013nz or on his Free Range Statistics blog.

AI and in particular, deep learning, is a powerful tool for uncovering useful relationships within data; but once found, can't explain what they mean. Contrast this with humans, armed with tribal knowledge and more traditional analytics, who understand the data relationships but just can't find as many of them. In this episode of the IoT show, I speak with Drew Conway about how to find the balance between man and machine when looking for data value. Read the rest of the show analysis notes including the transcripts at: http://bit.ly/IoTPodcast108notes This show is brought to you by DIGITAL OPERATING PARTNERS Related links you may find useful: Season 1: Episodes and show notes Season 1 book: IoT Inc Season 2: Episodes and show notes Season 2 book: The Private Equity Digital Operating Partner Training: Digital transformation certification

When you think about the Internet of Things (IoT), maybe you think of your Fitbit or your Nest Thermostat. But, of course, the IoT has many applications that extend beyond our personal use. In this episode of the Impact Podcast, Jon Prial talks with Drew Conway, CEO and Founder of Alluvium, a company that delivers real-time collective intelligence to expert-driven industrial operations. Find out how Alluvium is tapping into the Industrial Internet of Things, and about streaming data, where machine learning adds value, and where we'll need humans and what skills they'll bring.

In this episode we discuss "What do we mean by Data Science?", where we discuss definitions such as Drew Conway's Venn Diagram: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram. Stephanie discusses her definition in her preprint "Elements and Principles of Data Analysis"(https://arxiv.org/abs/1903.07639).

Drew Conway is a world-renowned data scientist, entrepreneur, author, and speaker, perhaps most well-known for his infamous 2010 “Data Science Venn Diagram”. Today, Drew is the Founder & CEO of Alluvium: a company using machine learning and AI to turn massive streams of data produced by industrial operations companies into insights that bridge the gap between big data and human expertise. Designed with the goal of helping industrial operations become safer, more efficient and more profitable, the Alluvium platform makes industrial machine data meaningful and useful to the people who rely on it to make decisions that affect the stability of their operation. Before starting Alluvium in 2015, Drew helped start: Data Gotham: an organization focused on supporting the NYC data community, with an annual conference bringing together people from all industries DataKind: a non-profit that brings high-impact organizations together with leading data scientists to use data science in the service of humanity. They enable data scientists and social changemakers to address tough humanitarian challenges together, ranging from education to poverty, health to human rights, and the environment to cities. After starting the conversation by exploring Drew’s early years, we focused most of the dialogue around his (quite frankly, brilliant) thought process around identifying the highest-impact, most-needed applications for data science across problem spaces. Some of my favorite talking points included: Why “force of will” and a “tendency toward combativeness” were key to Drew’s early development and overcoming imposter syndrome Lessons learned from his 4th grade teacher who told him he was bad at math and an AAU basketball coach who made his team find their way home from the outskirts of Las Vegas The questions Drew asks executives who tell him they want to hire a data science team, how he recommends they avoid being “seduced by the industry” and “return back to first principles” Drew’s process for determining new applications for data science within various industries The three-question mental model Drew used to identify Alluvium’s first major product offering: business problem → data available → human support Alluvium’s team-building and hiring philosophy, how it’s evolved from day one until today The story behind DataKind, how he and his team decided what nonprofits to start by working with, and the step-by-step process they took to testing their assumptions   Enjoy the show!   Show Notes: https://ajgoldstein.com/podcast/ep14/ Drew’s LinkedIn: https://www.linkedin.com/in/drew-conway-13b5b013/ AJ’s Twitter: https://twitter.com/ajgoldstein393/

How to engage customers and speed up the sales process? How to get a sale out of a production plant fire? How should you price SaaS so that it encourages engagement and referrals? How to identify who isn’t pulling their weight on a power grid system? Drew explains his sales philosophy and lessons learnt. We also explore a few case studies that makes use of Alluvium Primer to increase data processing and decision making speed from months to minutes.   Drew Conway is the CEO and founder of Alluvium. Alluvium is a data company. Alluvium uses machine learning and artificial intelligence to turn massive data streams produced by industrial operations into insights that help you, the experts, focus on the anomalies that affect your team’s safety, productivity and bottom line. http://www.alluvium.io    IoT ONE is an online platform devoted to accelerating adoption of Industrial Internet solutions. We are mapping the global ecosystem of IoT vendors, use cases, case studies, and technologies. We leverage this data to help companies source technology, research competitors, and enter new markets. Learn more about IoT One: https://www.iotone.com Twitter: @IotoneHQ

Where within the chain of operation in an industrial company is the best place to insert a piece of software? Is a bus system just a bunch of big computers moving around on wheels? Does machine learning hold the answer to maximising the value of each interaction between humans and software? Are we all data engineers regardless of what our name card states? Drew explains how to take heterogenous data streams and distill it into a stability score that is an index of operations and produce high value alerts. We also explore human machine interaction to build a model more effectively than a fully unsupervised method.   Drew Conway is the CEO and founder of Alluvium. Alluvium is a data company. Alluvium uses machine learning and artificial intelligence to turn massive data streams produced by industrial operations into insights that help you, the experts, focus on the anomalies that affect your team’s safety, productivity and bottom line. http://www.alluvium.io    IoT ONE is an online platform devoted to accelerating adoption of Industrial Internet solutions. We are mapping the global ecosystem of IoT vendors, use cases, case studies, and technologies. We leverage this data to help companies source technology, research competitors, and enter new markets. Learn more about IoT One: https://www.iotone.com Twitter: @IotoneHQ  

What is the similarity between stability and the S&P 500 index? How can we use data to identify fire risks and where to best open a coffee shop in New York City?  From using regression to identify fire risks in New York City to managing complex process control, it all comes down to getting the right information to the right person at the right time to make a decision. Drew explains what stability means, and the tension between  machines and people as it pertains to data.    Drew Conway is the CEO and founder of Alluvium. Alluvium is a data company. Alluvium uses machine learning and artificial intelligence to turn massive data streams produced by industrial operations into insights that help you, the experts, focus on the anomalies that affect your team’s safety, productivity and bottom line. http://www.alluvium.io    IoT ONE is an online platform devoted to accelerating adoption of Industrial Internet solutions. We are mapping the global ecosystem of IoT vendors, use cases, case studies, and technologies. We leverage this data to help companies source technology, research competitors, and enter new markets. Learn more about IoT One: https://www.iotone.com Twitter: @IotoneHQ

Drew Conway, world-renowned data scientist, entrepreneur, author, speaker and creator of the Data Science Venn Diagram speaks with Hugo about how to build data science teams, along with the unique challenges of building data science products for industrial users. How does Drew now view the Venn circles he created, those of hacking skills, mathematical and statistical knowledge and substantive expertise, when building out data science teams?

In this episode of the ARCHITECHT Show, Alluvium co-founder and CEO Drew Conway shares his thoughts on the state of AI in industrial settings, and how his company is tackling that opportunity. Conway, who was also an early voice in the data science movement after starting his career in the intelligence community, also discusses: the the importance of utilizing experts and build trust in AI; the growth of data science as a profession; and the evolution of data technologies over the past decade.

This show features my interview with Drew Conway, whose Wrangle keynote could have been called “Confessions of a CIA Data Scientist.” The focus of our interview, and of Drew’s presentation, is an interesting set of observations he makes about the role of cognitive biases in data science. If your work involves making decisions or influencing behavior based on data-driven analysis--and it probably does or will--you’re going to want to hear what he has to say. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/39