AI and the Future of Work
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Episode Number : 100
Rob May, serial AI entrepreneur and investor, started as a hardware engineer but realized he could have more of an impact as an entrepreneur and investor. Since then, he has started companies including Backupify (acquired by Datto) and Talla (conversational AI) and invested in over 100 startups. Rob’s a deep thinker and the author of the popular Inside AI weekly newsletter and Investing in AI podcast.
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- What’s holding back AI adoption in the enterprise
- New approaches to address the “small data” AI problem
- About the ethics training we should require for AI algorithm developers
- Why those who fear bots taking over are the modern equivalent of Luddites
- What it means to be human when machines are sentient
- The moonshot AI idea Rob’s most excited about
References in today’s episode:
- Krishna Gade from Fiddler on AI and the Future of Work
- AdeptID for matching helping employees upskill
- Mythic for edge AI
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Episode Number : 99
Elliot Shmukler, CEO and founder of Anomalo, needed a better way to monitor data quality at scale. He previously led growth teams at Wealthfront, Instacart, and LinkedIn and experienced firsthand the impact of incomplete or inaccurate data. Anomalo has now raised nearly $40M from amazing investors including Norwest, Two Sigma, and Foundation to make data problems a thing of the past.
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- What Elliot means when citing Jeff Weiner from Linkedin: “If you’re launching a rocket even a one degree course change can mean you won’t land on the moon.”
- About the data quality issue nobody noticed at Instacart that impacted millions of users.
- How the role of the data scientist will change as AI platforms automate data quality monitoring.
- When there’s a need for humans in the loop to override AI systems.
- Why every product will soon be as good at personalization as Spotify and Netflix.
- The number one skill every student needs to learn that will never be replaced by machines.
Past episodes referenced in today’s discussion:
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Episode Number : 98
Luke Arrigoni started Arricor in 2012 to help large companies make sense of their data. Since then, he and the team have taught organizations like Goldman Sachs, AT&T, and Thomson Reuters about the principles of AI. His secret? Focus on the business problem and the right technology approach becomes obvious.
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- How UPS uses AI to automatically assign the right tax code for packages
- What responsibility AI developers have for the decisions their algorithms make
- How to clean dirty data to make it ready for AI model training
- When to use neural nets vs. gradient-boosted trees
- Which tasks are good candidates for classifier models vs. NLP
- Which job skills are future-proof… and which are likely to be replaced by automation
References in this episode:
- Fish from Mozart Data on AI and the Future of Work
- Airflow for data pipeline automation
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Episode Number : 97
Peter Fishman (“Fish”), co-founder and CEO of Mozart Data, had a vision for making it easy for any business to unlock the value of their data via a modern data stack. He and his co-founder believe rote data engineering work shouldn’t require teams of in-house data engineers. Fish turned his PhD in Economics and passion for statistics into a successful, venture-backed YC company that is defining the future of data analytics.
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- Why Fish believes “not every business gets value out of their data… but every business can.”
- The role of data pipelines in automating the cleaning and transforming of data.
- Fish’s prediction for where humans will be needed for data analysis in a decade.
- What Fish learned working with David Sacks at Yammer.
- How bacon hot sauce inspired the founding of Mozart Data.
References in this episode:
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Episode Number : 96
Shawn Merani, entrepreneur and venture investor, has started two venture funds and been an operator at early stage companies including Liquidnet and ReachLocal. Shawn has invested in some amazing companies including Clubhouse, Dollar Shave Club, and Stance. He shares his definition of “hustle” and the challenges of raising money for a venture fund vs. raising money for a company.
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- How to raise your first venture fund.
- Why the goal of Parade Ventures is “to be the first call great founders make when raising money.”
- Shawn’s secret to getting access to over-subscribed deals with high-profile investors.
- Why Shawn makes it a priority to meet every one of Parade’s founders every other week.
- The biggest mistake founders make when pitching investors.
- What one entrepreneur did to convince Shawn to invest in a first meeting.
References in this episode:
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Episode Number : 95
Mel Engle, executive and cancer survivor, has spent more than 30 years in and around the intersection of healthcare and technology. He’s now the CEO of Predictive Oncology using AI and patient data to develop personalized cancer treatments. Mel discusses how research from Carnegie Mellon is being commercialized to accelerate the future of healthcare.
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- How Mel and his team have turned cancer detection and prevention into a data problem.
- How to use AI to figure out which tumor types can be healed by which chemical compounds to reduce the time required to develop new cancer treatments.
- How to mitigate the impact of bias in patient data when using AI to make high-stakes decisions.
- How long before AI will be more accurate at detecting cancer than human doctors.
- What’s ahead for AI in healthcare for the next decade.
References in this episode:
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Episode Number : 94
Matt Cowellstarted QuantHub in 2018 to help upskill and reskill employees to prepare for careers in data and AI. The QuantHub data skills platform exists to satisfy the growing demand for data-fluent team members. Amazing organizations like Uber and Southern Company use QuantHub to improve data literacy for employees. Plus, Matt’s the first Alabamian on the podcast. Roll tide!
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- How a Chemist landed in IT and eventually became a venture-backed entrepreneur
- Why there’s a need for a “Duolingo for data literacy”
- What are the skills required for “data citizens” vs. “data storytellers”
- Why “every employee will soon be a knowledge worker”
- Why data literacy will be part of general education requirements in the future
References in this episode…
- Gary Bolles on AI and the Future of Work
- Panos Siozos on AI and the Future of Work
- Bryan Talebi on AI and the Future of Work
- Derek Steer on AI and the Future of Work
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Episode Number : 93
Dan Grunfeld, former professional basketball player and operator at Lightspeed Venture Partners, discusses the parallels between sports and entrepreneurship. His grandma’s escape from the Holocaust inspired him to share her story, one he has been writing for five years. Dan’s family history is inspirational. His advice for entrepreneurs is timeless.
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- Why professional basketball is a lot like entrepreneurship
- Dan’s lessons from experiencing hardship on the court… and how to recover from setbacks
- How the best entrepreneurs find product-market fit
- How his 96 year-old grandma was saved twice by Raoul WallenbergÂ
- Why Dan says “…basketball was sent from heaven for my family”
- Dan’s advice about discipline for aspiring authors: “if you snooze once, you can snooze every day.”
References in this episode:
- Rene Morkos on AI and the Future of Work
- Panos Siozos on AI and the Future of Work
- Bernie and Ernie on ESPN 30 for 30
- Dan on Twitter: @dan_grunfeld
- Dan’s personal site: DanGrunfeld.com
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Episode Number : 92
Matt Compton‘s a restless tinkerer from Indianapolis who started Filo.co to solve a problem he had. He needed something better than Zoom to be able to spend more time with his family without being on the road three weeks a month. He and his co-founders joined a venture studio and built the prototype for Filo.co in four weeks. Now it powers virtual employee events for an impressive list of companies.
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- What’s really required to make virtual events productive
- How Filo’s better than Zoom
- How Anaplan crushed sales kickoff using Filo
- The future of virtual spaces
- Why we don’t need a metaverse
References in this episode:
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Episode Number : 91
Phil Heltewig, co-founder and CEO of Cognigy, the low-code conversational AI platform for managing customer service bots, discusses how new AI technologies are improving the support experience. Phil started Cognigy in 2016 with two co-founders and has since raised $55M from Insight Partners among others. The team is now about 100 employees and boasts an amazing customer list including Lufthansa and Bosch.
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- What Teddy Ruxpin has to do with the future of conversational AI.
- About the highs and lows of the entrepreneurial journey: “…there are days when you’re wondering if you can make payroll…”
- How customer service bots “are designed to help humans in call centers, not replace them.”
- What are the biggest technical challenges when applying NLP in narrow enterprise domains with limited training data.
- How Cognigy thinks about mitigating the impact of bias in AI models.
- Why your experience re-booking your flight will be much better in the future.
References in this episode:
- Cognigy on Twitter
- Cognigy
- AI and the Future of Work with Panos Siozos
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Episode Number : 90
Dr. Panos Siozos, LearnWorlds CEO, went from academic to high-tech CEO and raised $32M from Insight Partners to help creators monetize e-learning courses. Dr. Siozos is our first guest from Cyprus and his story about building a remote-first, global team should inspire international entrepreneurs everywhere. As Dr. Siozos says, “everyone has something to teach… that someone else wants to learn.” This is a great opportunity to understand the future of learning… in 30 minutes.
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- How Dr. Siozos transitioned from being an academic and researcher to a high-tech CEO
- Why creators need a better platform to share and monetize custom e-learning courses
- How technology is redefining the learning experience and why “the industrial education experience” is antiquated
- How future innovation in the areas of AI, AR, and VR will increase engagement rates for e-learning
- Why Dr. Siozos says “…learning is the only superpower we possess as humans.”
References in this episode:
- Bryan Talebi, Ahura AI CEO, on AI and the Future of Work
- Jordan Husney, Parabol CEO, on AI and the Future of Work
- LearnWorlds
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Episode Number : 89
Scott Zoldi, FICO Chief Analytics Officer and PhD in Theoretical Physics, shares how to use AI responsibly. FICO uses consumer data and machine learning models to make decisions ranging from fraud to credit risk. Hundreds of thousands of signals can be used to make a single decision by comparing new data with historical data. Scott’s team is focused not just on making accurate decisions but also ensuring the signals used and the decision-making process are bias-free.
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- How Scott’s team uses AI to make automated decisions using consumer data
- Why Scott’s priorities are “explainability first and performance second”
- Why the principles of “humble AI” are as important as the principles of ethical AI
- What’s required to increase public trust in AI-based decisions
- What’s the role of data scientists in the future when AutoML is prevalent
- What Scott means when he says “models aren’t biased when they’re built, they’re only biased in production”
References in this episode:
- Scott’s blog posts at FICO
- @ScottZoldi on TwitterÂ
- Facebook’s AI experiment gone awry
- Amazon’s facial recognition failure
Thanks to Benjamin Baer for the intro to Scott!
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Episode Number : 88
Bindu Reddy, CEO and co-founder of Abacus AI, was a product exec at Google and Amazon and an accomplished entrepreneur before setting out to make AI model management accessible to non data scientists. Bindu has since raised more than $40M from investors like Index Ventures, Coatue, Ram Shriram, Deep Nishar, and others. Her team is investing in core AI research and building a platform to make it easy to deploy models trained on deep neural nets with tabular data. Hear Bindu’s vision… then go follow her on Twitter for provocative tweets like the one we discuss in this episode :).
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- How Bindu became an AI enthusiast after an algorithm helped her previous company increase revenue 40%
- What Bindu learned at Google and AWS… and what she has had to un-learn to grow Abacus AI
- How AI is being used to reduce customer churn
- Why Bindu’s vision is to make creating and managing AI models “as easy as creating podcasts or websites”
- Why building bias into AI models isn’t necessarily bad
References in this episode:
- Krishna Gade on AI and the Future of Work
- Jeff Meyerson on AI and the Future of Work
- Abacus.ai
- Bindu on Twitter
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Episode Number : 87
Charity Majors, CTO and co-founder at honeycomb, grew up in rural Idaho and dropped out of college. This is her unlikely journey from pianist to successful high-tech entrepreneur. She’s a pioneer in the monitoring and observability space who turned her learning at Facebook into a company focused on helping developers find and fix bugs faster. Charity’s opinionated, thoughtful, and one of the most outspoken critics of, well, the status quo :).
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- What motivated Charity to start a career in tech having been a “perennial dropout”
- Why “ops has a well-deserved reputation for masochism”
- Why Charity says the “Kool-aid at Facebook is strong and potent”
- Why it’s impossible to troubleshoot software bugs with high cardinality data
- How Charity defines observability
- What it means to practice observability-driven development (ODD) and why it should replace test-driven development (TDD)
References in this episode:
Thanks to Rachel Chalmers for making this episode happen!
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Episode Number : 86
Kai Nunez, VP Research & Insights at Salesforce, didn’t always know she wanted to pursue a career in AI because it didn’t exist as a field. Her dad was a linguist and she became interested in human-computer interaction at a young age. Kai’s deep concern for values-based leadership and operating with integrity led her to become a leading voice in the DEI and AI ethics communities. Today, her teams are creating a culture of awareness about the impact tech has on underserved populations. She’s a passionate advocate for educating teams to do the right thing… even when nobody’s looking.
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- About Salesforce’s real “biggest competitor”
- Why DEI matters in technology
- How Kai lives the value of “being a respectful truthteller”
- Why the future of software includes data audits to mitigate the risk of bias
- What happened when Kai presented an unpopular idea to Marc Benioff
References in this episode…
- Krishna Gade from Fiddler on AI and the Future of Work
- AI ethics at Salesforce
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Episode Number : 85
René Morkos, CEO and Founder of ALICE Technologies, grew up wanting to build things. That passion led him to degrees in Construction Management and an adjunct professorship at Stanford. In 2015, he founded ALICE to improve the efficiency of complex construction projects. Rene has since raised nearly $40M from an incredible list of investors including Lightspeed, Merus, and Future Ventures.
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- What René learned from his first job after college working on construction sites in Afghanistan
- Why ConstructionTech has attracted nearly $6B since 2014
- How algorithms figure out what 6,000 people on a construction site should do every day
- What the world will be like in ten years when technology is fully adopted in the construction industry
- Why “startups are really just R&D departments for large companies”
- What’s hard about building startups…that they don’t teach you in CEO school
References in the episode…
- ALICE Technologies
- ALICE on Twitter
- AI and the Future of Work with Rachel Chalmers from Alchemist Accelerator
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Episode Number : 84
Gary Bolles, entrepreneur, venture advisor, and best-selling author, is a deep thinker who established roots in Silicon Valley in the 80s to pursue his joint passions for technology and exploring what he calls the three boxes of life – learning, work, and leisure. He’s the author of The Next Rules of Work which was published August 31. He’s also the chair for the Future of Work at Singularity University and the founder of eParachute among other companies. Oh, and his LinkedIn courses have helped train more than 800,000 students.
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- How the son of a laid off minister became one of the foremost authorities on the future of work
- What opportunities are being created by “The Great Reset”
- How technology is redefining work… and redefining our identity as humans
- What it means that we’re moving from a “workforce” to a “worknet”
- About the $10 million exercise… and why it’s the best way to find your passion
- Why living the acronym “PACE” is the best way to ensure future career success
References in today’s episode…
- Gary’s site
- Singularity University
- AI and the Future of Work with Charlene Li
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Episode Number : 83
Derek Steer cut his teeth as a data analyst at Facebook and Yammer more than a decade ago. He co-founded Mode in 2013 to make it easier to ask questions about data and get better answers faster. Mode has since raised four rounds of funding including a recent $33M round from an exceptional group of investors. 52% of Fortune 500 companies use Mode and Derek has grown the team to more than 300 employees. Derek’s vision: “drive the time to do analysis down to zero.” He’s well on his way!
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- What Mode’s doing to “unlock the power of human reasoning”
- What Derek learned about tools for data analysts from Facebook and Yammer
- Why there will always be a need for analytics frontends even amid competition from data warehouses like Snowflake
- The future of work for data analysts
- The biggest tragedy in BI today
References in this episode:
- Benn Stancil’s “season without bats“
- Fivetran
- ThoughtSpot
- Trifacta
- Tiernan Ray on AI and the Future of Work
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Episode Number : 82
Jeff Meyerson, entrepreneur, musician, technologist, and author of the acclaimed “Move Fast: How Facebook Builds Software”, discusses the stranglehold Big Tech has on developer tools and how the future of software development may be quite different from the present.
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- What Jeff learned about sales from playing poker
- How Facebook builds software… and how it can avoid being evil
- Why React is the “Linux of the frontend of the web”
- The development tools Jeff’s most excited about
- Why Zuck’s not a good leader
- What Jeff will tell Zuck when they finally meet
References in this episode…
- “Move Fast: How Facebook Builds Software” on Amazon
- Jeff on Twitter
- Software Engineering Daily
- Janelle Shane’s great book
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Episode Number : 81
Krishna Gade was an engineering leader at Twitter, Pinterest, and Facebook before realizing AI is eating software… and responsible AI is essential. He turned a passion for “trustworthy AI” into a company that recently raised $32M to make AI explainable.
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- Why AI explainability matters… and how to instrument it in AI models
- What developers need to know to ensure AI models don’t make bad decisions
- What legislators need to know when creating frameworks to regulate “responsible” use of AI
- How Apple fumbled AI explainability when issuing credit cards through Goldman Sachs
- Krishna’s top lesson learned as a first-time CEO
References in this episode:
- AI and The Future of Work with Mark van Rijmenam
- AI and The Future of Work with Tiernan Ray
- The Algorithmic Accountability Act
- fiddler.ai
