AI and the Future of Work
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Episode Number : 146
Special episode this week! We recorded two live discussions from Turing’s BOUNDARYLESS “Future of Work” event in San Francisco. In the first, Rani Mavram , Complete.so CEO, discusses using data to transform compensation policies from being a liability to an asset for high-growth companies. In the second, Ankit Jain , Aviator CEO, discusses using automation to improve developer productivity for remote-first engineering teams.
Show moreShow lessListen and learn…
- From Rani Mavram:
- Why compensation policies have an outsize impact on employee engagement
- What’s required to make compensation plans transparent
- The difference between compensation plans and “total reward” packages
- Where innovation is happening in the field of employee compensation
- From Ankit Jain:
- How to make remote-first engineering teams successful
- Using automation to improve developer productivity
- How startups can replicate the developer experience at Google and Facebook
- The future of generative AI and GitHub Copilot in assisting human developers
References in today’s show:
- Turing’s BOUNDARYLESS event
- Complete.so for compensation transparency
- Aviator to improve developer productivity
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Episode Number : 145
Merve Hickok is one of the most recognized thought leaders in the emerging field of AI ethics. Merve is the founder of AIethicist.org and Lighthouse Career Consulting. Her work is at the intersection of AI and data ethics along with social justice and DEI policy and regulation.
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Merve was recently listed among the top 100 most brilliant women in AI ethics and in the past she lectured at the University of Michigan’s School of Information on Data Science ethics. Merve’s at the forefront of this emerging field that will define how we live and work for the next several decades. This is an important conversation. Enjoy!Show lessListen and learn…
- What led to Merve founding AIEthicist.org
- How the AI ethics conversation has evolved over the past year
- What the White House got right (and wrong) in the blueprint for an AI Bill of Rights
- What responsible AI means to Merve
- Why regulation doesn’t necessarily constrain innovation
- How AI policy and regulation are different around the world
References in this episode…
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Episode Number : 144
Emmanuel Turlay spent more than a decade in engineering roles at tech-first companies like Instacart and Cruise before realizing machine learning engineers need a better solution. Emmanuel started Sematic earlier this year and was part of the YC summer 2022 batch. He recently raised a $3M seed round from investors including Race Capital and Soma Capital. Thanks to friend of the podcast and former guest Hina Dixit from Samsung NEXT for the intro to Emmanuel.
Show moreShow lessI’ve been involved with the AutoML space for five years and, for full disclosure, I’m on the board of Auger which is in a related space. I’ve seen the space evolve and know how much room there is for innovation. This one’s a great education about what’s broken and what’s ahead from a true machine learning pioneer.
Listen and learn…
- How to turn every software engineer into a machine learning engineer
- How AutoML platforms are automating tasks performed in traditional ML tools
- How Emmanuel translated learning from Cruise, the self-driving car company, into an open source platform available to all data engineering teams
- How to move from building an ML model locally to deploying it to the cloud and creating a data pipeline… in hours
- What you should know about self-driving cars… from one of the experts who developed the brains that power them
- Why 80% of AI and ML projects fail
References in this episode:
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Episode Number : 143
Kevin Mulcahy, co-author of the Future Workplace Experience, has been thinking and writing about the future of work since 2016. Six years ago the future of work was dramatically different. Reading Kevin’s book makes him seem like a clairvoyant who predicted the future.
Show moreShow lessIn addition to being a successful author Kevin is a sought after speaker on all topics related to the future of work and workplace trends. In the past, he also lectured on entrepreneurship at Babson College.
Listen and learn:
- What HR teams need to know about delivering great employee experiences
- How Airbnb created a culture of measuring and improving the employee experience
- What are progressive employers doing to make the transition back to office work easier
- The three “soft leadership” questions every manager should get great at asking
- How to measure the quality of employee experiences
- How AI can be used to detect changes in tone in employee engagement
- Where to start when using AI to improve the employee experience
- How the metaverse will improve remote work
References in this episode:
- Twitter boss Elon Musk fires the entire ethics team as one of his first acts of “leadership”
- Charlene Li on AI and the Future of Work
- Gary Bolles on AI and the Future of Work
- Mark van Rijmenam on AI and the Future of Work
- Burn In: A Novel of the Real Robotic Revolution by P.W. Singer and August Cole
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Episode Number : 142
Today’s guest is the co-founder and CEO of vAIsual, the company pioneering the use of generative AI to create synthetic stock media. All of those photos you see online and in print publications of people promoting products usually are human models posing in generic ways. Their pictures are sold by companies like Getty Images in marketplaces that are inefficient and limited in scope.
Show moreShow lessMichael Osterrieder and his partner Nico are legends in the world of stock media who realized there’s a better way. They created what they call an algorithmic camera and launched vAIsual last year to scratch their own catch. Michael is a serial entrepreneur and photographer based in Budapest and he’s out to test the limits of generative AI.
Listen and learn:
- How growing up listening to heavy metal inspired Michael’s career in visual media
- What are the challenges of using generative AI to create synthetic stock images of people
- How visual media content creation has evolved
- The ethics of generative AI
- What Michael describes as “the biggest art heist in history”
- How vAIsual extends human photos using machine vision and human labeling
- Can an AI be the owner of copyrighted material it produces?
- What is the definition of consciousness?
References in this episode…
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Episode Number : 141
Otto Soderlund co-founded Speechly in 2016 with Hannes Heikinheimo in their hometown of Helsinki. He believes voice should be a first-class citizen for all apps and making it easy for developers to add voice support from any platform will unlock new innovation.
Show moreShow lessSpeechly is a member of the YC Winter 22 batch. Otto and I recently co-presented at the VOICE22 event in Washington DC although I presented remote so this is the first time we’re actually meeting. I heard good things about his talk so I was eager for this discussion. It didn’t disappoint.
Listen and learn…
- Why voice is the new app and what it means to develop “voice-first” apps
- How RAIN Agency uses Speechly to help auto technicians use voice assistants to fix cars
- How to accurately detect and transcribe speech when dealing with common challenges like background noise and accents
- When speech detection achieved “superhuman” levels of accuracy
- How Speechly combines speech recognition with natural language understanding (NLU) on the local device
- How Otto thinks about exercising responsible AI
- Why “voice technology won’t exist as a separate field in a decade”
References in this episode…
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Episode Number : 140
Jonathan Frankle Jonathan Frankle, incoming Harvard Professor and Chief Scientist at MosaicML, is focused on reducing the cost of training neural nets. He received his PhD at MIT and his BSE and MSE from Princeton.
Show moreShow lessJonathan has also been instrumental in shaping technology policy related to AI. He worked on a landmark facial recognition report while working as a Staff Technologist at the Center on Privacy and Technology at Georgetown Law.
Thanks to great guest Hina Dixit from Samsung NEXT for the introduction to Jonathan!
Listen and learn…
- Why we can’t understand deep neural nets like we can understand biology or physics.
- Jonathan’s “lottery hypothesis” that neural nets are 50-90% bigger than they need to be…but it’s hard to find which parts aren’t necessary.
- How researchers are finding ways to reduce the cost and complexity of training neural nets.
- Why we shouldn’t expect another AI winter because “it’s now a fundamental substrate of research”.
- Which AI problems are a good fit for deep learning… and which ones aren’t.
- What’s the role for regulation in enforcing responsible use of AI.
- How Jonathan and his CTO Hanlin Tang at MosaicML create a culture that fosters responsible use of AI.
- Why Jonathan says “…We’re building a ladder to the moon if we think today’s neural nets will lead to AGI.”
References in this episode…
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Episode Number : 139
Eric Olson , CEO and co-founder of Consensus, is a collegiate athlete turned data scientist turned entrepreneur who needed faster access to reliable data while working at DraftKings. Consensus is a search engine that uses a large language model to find answers in peer-reviewed research articles. Eric’s living proof that the best entrepreneurs start by solving a problem they’ve encountered. Hear how Eric’s scratching his own itch.
Show moreShow lessListen and learn…
- Why Google isn’t the answer for scientists seeking evidence-based answers online
- Why a business model that relies on ads can’t solve the “unbiased answer” problem for researchers
- How Consensus addresses the problem of conflicting information online from credible resources
- How to use labels to improve search retrieval accuracy… without introducing bias into results
- How to use extractive large language models (LLMs), to extract relevant portions of documents and match them to NLP questions
- Why generative AI like GPT-3 can’t answer “what’s the consensus opinion out there” when multiple potential answers exist
- Who is responsible if Consensus delivers answers that lead to harmful outcomes
- What Eric learned as a division I NCAA athlete (Go Wildcats!) that has helped him as a high-tech entrepreneur
References in this episode:
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Episode Number : 138
Mona Akmal , CEO of sales intelligence platform Falkon, is the outspoken co-founder behind an emerging leader in a hot space. Mona migrated to the United States at age 20 with a CS degree and little else. She had an impressive 12-year run as a product leader at Microsoft where she helped scale OneDrive and Office. She subsequently led product and technology organizations at places like Code.org and Amperity. Two decades later, Mona’s the CEO of Falkon AI, an intelligence platform for go to market teams. Falkon recently raised $16M from a group of A-list investors that includes Greylock and Madera among others.
Show moreShow lessListen and learn…
- Why Mona’s philosophy revolves around two words: “efficiency” and “excellence”
- What makes a standout sales rep great.
- How do find signal in noisy sales and marketing data
- How many touches are required from stage one to closing a B2B deal
- How to fix the CRM data hygiene problem
- Why econometrics approaches perform better than machine learning to solve the “small data problem”
- Why “everyone needs to be coached and nobody needs to be managed”
- Mona’s (legendary) mental health advice to entrepreneurs
References in this episode…
- Barr Moses from Monte Carlo on AI and the Future of Work
- Derek Steer from Mode on AI and the Future of Work
- Peter Fishman from Mozart Data on AI and the Future of Work
- Stephen Messer from Collective[i] on AI and the Future of Work
- Kamal Ahluwalia on AI and the Future of Work
- Leading scientists fear AI could lead to nuclear war by the end of the century
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Episode Number : 137
Hina Dixitl , venture capitalist leading AI investing at Samsung NEXT, grew up in a small town in India from humble beginnings. She couldn’t afford a Starbucks coffee and graduated with significant student debt… which fueled her passion for mentoring and coaching as she became financially independent.
Show moreShow lessPrior to Samsung NEXT, Hina was an Apple engineering leader who helped launch two-factor authentication and other core iOS technologies. Hina’s a reluctant venture investor having always been a builder. A mentor from Homebrew encouraged her to pursue investing and she’s now passionate about finding and funding the next generation of AI and web3 entrepreneurs.
Listen and learn…
- How Hina overcame institutional biases to achieve success in engineering leadership roles and venture investing
- How being trusted with money at a young age by her father helped Hina become independent and confident in her career
- The challenges Hina faced transitioning from a builder at Apple to an investor at Samsung NEXT
- What Hina looks for when investing in AI and web3 startups
- Where there are opportunities for innovation in web3 and metaverse infrastructure
- What will prevent Big Tech from centralizing the decentralized web
- How Hina thinks about responsible AI when evaluating new investments
- How and when entrepreneurs should engage corporate venture capital (CVC) firms
- The AR/VR technology Hina wants to invest in… her inbox is open 🙂
References in this episode:
- Paul Lee, Synesis One CEO, discusses AI, web3 and crypto for gaming on AI and the Future of Work
- Krishna Gade, Fiddler CEO, discusses AI explainability on AI and the Future of Work
- Barr Moses, Monte Carlo CEO, discusses data pipeline monitoring on AI and the Future of Work
- Bindu Reddy, Abacus AI CEO, discusses training and managing data models on AI and the Future of Work
- How Jack Clark is incorporating AI ethics into new AGI research
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Episode Number : 136
Rana Gujral , CEO of Behavioral Signals since 2018, joined the company after a distinguished tech career growing companies like Logitech, TiZE, and Cricut. Behavioral Signals uses emotion and behavioral science to help contact center agents deliver better service. Rana and the team are on a mission to improve customer interactions by using signals other than the spoken word to understand exactly what they need based on indicators like voice tone and pitch.
Show moreShow lessListen and learn…
- How to train AI models on past service interactions and outcomes to determine which agents should speak to which customers
- How to use deep learning and NLP to process non-speech behavior signals like intonation, pitch, and tonal variance
- How behavior signals can be used to predict stress, duress, and propensity to buy or pay
- How to achieve high levels of prediction accuracy without processing “the spoken word”
- Why tone and pitch are better indicators of sentiment than actual words across any language
- How to compete with Google/Microsoft/Amazon for data when building an AI-first conversational intelligence product
- The biggest opportunity Rana sees to use AI to help humans live better lives
References in this episode:
- Mahesh Ram from Solvvy (now Zoom) on AI and the Future of Work
- Gadi Shamia from Replicant on AI and the Future of Work
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Episode Number : 135
Ahmed Elsamadisi built the data infrastructure at WeWork before realizing every company could benefit from his team’s innovation. Traditional star schemas aren’t the best way to manage data. Ahmed instead pioneered a new approach using a single-table column model better suited for real questions people ask. He launched Narrator in 2017 to make it easier to turn data questions into answers and has since raised $6.2M from Initialized Capital, Flybridge Capital Partners, and Y Combinator. Ahmed received his BS in Robotics from Cornell. Hear from a pioneer (and tech provocateur) how new data wrangling techniques are making it easier for mere mortals to get more value out of their data.
Show moreShow lessListen and learn…
- How a roboticist who got his start building self-driving cars and designing missile defense systems ended up redefining how data is stored
- Why traditional approaches that require SQL to access data are broken
- How a single-column schema eliminates the complexity of joining systems and tables
- Why it’s easier to tell better stories with data using temporal relationships extracted from customer journeys
- Why Snowflake, Redshift, and BigQuery are really all the same… and data modeling is the place to innovate
- What it means to replace traditional tables with activities… and why they’ll eliminate the need for specialized data analysts
- How to reduce data storage costs by 90% and time to generate data insights from weeks to minutes
- Why data management vendors are responsible for bad decisions made using your data
- What is data cleaning and how you should do it
- What is a racist algorithm
- Why querying data with natural language will never work
- Is the WeCrashed version of Adam Neumann’s neuroticism accurate? Hear from someone who lived it…
References in this episode:
- Google’s LaMDA isn’t sentient
- Chandra Khatri from Got It AI on AI and the Future of Work
- Derek Steer from Mode on AI and the Future of Work
- Barr Moses from Monte Carlo on AI and the Future of Work
- Peter Fishman from Mozart Data on AI and the Future of Work
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Episode Number : 135
Seth Earley is a Chemist by training and an expert on AI. Specifically, how AI is used to improve knowledge management. In fact, he wrote the book on the topic titled “The AI-Powered Enterprise” in which he explains the importance of ontologies when applying AI. Seth is the CEO of Earley Information Science. He has been advising companies on technology strategy since 1994 and is currently focused on AI and knowledge engineering.
Show moreShow lessListen and learn:
- Seth’s contribution to AI history… including the term he coined that was co-opted by former IBM CEO Ginni Rometty
- Why all AI is a data (and information architecture) problem
- How the Applied Materials field services team reduced time spent finding information by 50% with knowledge engineering and ontologies
- Why proper information architecture is required for virtual agents to reduce call volume and help live agents
- What has changed since Seth first published his AI book in 2020
- The benefits of semantic search vs. traditional keyword search
- Where to start with a knowledge management strategy
- Why “data scientists spend more time being data janitors”
- How to mitigate the impact of bias in AI training data
References in this episode:
- How AI can detect employee burnout
- The Innovation Delusion on Amazon
- Earley Information Science
- The AI-Powered Enterprise on Amazon
- Kevin Dewalt, Prolego CEO, on AI and the Future of Work
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Episode Number : 134
Peter Scott , author, TedX speaker, and futurist, worked at NASA’s JPL laboratory after receiving his Masters Degree in Computer Science from Cambridge. Raising kids made him realize the potential impact of AI to do both good and harm. He left NASA and switched careers to feel confident he was doing all he could to secure their future. He recently published Artificial Intelligence and You after publishing Crisis of Control five years back.
Show moreShow lessListen and learn:
- When will we achieve artificial general intelligence (AGI)… and is that the right goal for the AI community?
- Why we weight the potential of AI doing harm about five times as much as the potential for it doing good.
- What’s the biggest global problem AI might solve in the near term.
- How DeepMind’s AlphaFold protein folding technology could change humanity.
- What does it mean to be human in an era when machines can do more tasks historically reserved for humans?
- Why Peter blames Big Tech for “breaking” democracy.
- What Peter expects will be AI’s greatest achievement in the next decade.
- Why the evolution of a digital race hinges on global economic incentives.
References in this episode:
References in this episode:
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Episode Number : 133
Kia Kokalitcheva, Axios tech reporter, is a Silicon Valley native who writes about tech news and culture. Among other things, she co-authors the popular Pro Rata newsletter (over 200k subscribers) with Dan Primack. Kia has covered many of the most iconic tech stories of the past decade as a writer at Fortune and VentureBeat prior to Axios which was just acquired by Cox Enterprises. Kia recently wrote about Adam Neumann’s new company, Flow. Hear Kia’s perspectives on how Flow could transform living like WeWork transformed working… and why she’s not scared that bots may take her job.
Show moreShow lessListen and learn…
- How Adam Neumann of WeWork fame raised $350M at a $1B valuation from A16Z for his new company Flow… before launching
- Kia’s proudest moment as a journalist
- What the acquisition of Axios by Cox Enterprises means for journalism
- How Flow may be more than the reincarnation of WeWork’s failed WeLive experiment
- As a culture, are we ready for communal living?
- What is the future of company perks… are the days of on-site dry cleaning numbered?
- How the generational shift is impacting cultural norms in the workplace
- What tasks bots will never do better than live journalists
References in this episode:
- How AI is transforming journalism according to The Knight Foundation
- Gary A. Bolles discusses the WorkNet on AI and the Future of Work
- Mark van Rijmenam, The Digital Speaker, on AI and the Future of Work
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Episode Number : 132
Deon Nicholas , Forethought Co-Founder CEO, grew up in inner-city Toronto stocking shelves in a pharmacy before learning to code at an early age. He started Forethought in 2017 after learning the value of answering customer questions working for companies like Facebook and Pure Storage.
Show moreShow lessDeon has since raised $92M from an exceptional group of investors including funds like Steadfast Capital and NEA plus celebrities including Gwyneth Paltrow, Ashton Kutcher, and Robert Downey Jr. Deon won the TechCrunch Disrupt Battlefield startup competition in 2018 and is a member of the Forbes 30 under 30. He’s also a mentor and advisor to founders of color.
Listen and learn…
- How AI connects customers to the right agents then indicates the likelihood of a support interaction escalating
- How to use historical data to help live agents fix problems faster
- The evolution of chatbots from decision trees to AI
- How to combine generic language models with domain-specific data to increase the accuracy of NLP
- How to solve the problem of bias encoded in data
- How GANs, generative adversarial networks, work
- Why ML pipelines need to be monitored like web apps
References in this episode…
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Episode Number : 131
Jim Lawton , VP and GM of Robotics Automation at Zebra Technologies, met the founder of Roomba, Rodney Brooks , at MIT nearly three decades ago. It inspired a lifetime passion for robots that help humans. Since then, he has influenced generations of robotic automation technology at companies from Rethink Robotics to Zebra Technologies. This is a fascinating discussion that will make you reconsider what robots can do and why humans shouldn’t feel threatened by them.
Show moreShow lessListen and learn…
- How Jim cultivated a passion for robots… and why that makes him “the cool dad”
- How innovation in robotic technology is helping AMRs, autonomous mobile robots, perform more human-like tasks with less training
- Which “dirty, dull, dangerous” tasks are the best candidates for robotic automation
- How new training techniques are reducing the time required to train a robot from 300 hours to a fraction of that which “democratizes automation”
- What’s required to keep humans safe from robots
- How supplementing humans with robots for a task like picking items from warehouse shelves using machine vision saves 12-15 miles of walking per day while increasing accuracy
- How techniques like SLAM and machine learning are making it easier to program robots to do more complex tasks more accurately with zero or minimal coding
- Which new careers will be created by industrial robots… and which will be eliminated
- Two quick ways to know if a factory using robots and humans is safe
- Why Jim’s passion is using robots to help people be their best selves
References in this episode:
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Episode Number : 130
Kamal Ahluwalia and the Eightfold team set out to find the right career for everyone in the world. Six years later after having raised more than $200M from a legendary group of investors and built a talented 600-person team, they’re well on their way. Kamal joined Eightfold as President in 2018 from a successful tech career at companies like Model N and Selectica. Hear Kamal share his vision for how to use data and AI to help employees upskill, reskill, and ultimately find careers they love.
Show moreShow lessListen and learn…
- How Eightfold operationalizes the bold vision to find “the right career for everyone in the world”
- What has helped Eightfold scale to support customers in 140 countries and 19 languages
- How an AI HR platform helps with upskilling for internal mobility but also with hiring and talent-skill matching
- Why legacy HR tech software failed by focusing on “compliance vs. employee needs”
- Why automation won’t eliminate jobs… but every job will change as a result of AI
- How understanding human potential starts with understanding data stored outside HCM in “systems of work” like CRM and ITSM
- How to mitigate the impact of biased data to use AI to achieve inclusion and diversity goals
- How AI can identify roles where employees are likely to succeed… even when they have no experience performing skills they require
- What are the ethical implications of using AI to hire and promote employees
References in this episode:
- OneTen.org and the role of Eightfold in its formation
- Renee Steenvorden from Randstad on AI and the Future of Work
- Giselle Mota from ADP on AI and the Future of Work
- Ashu Garg from Foundation Capital on AI and the Future of Work
- The BigScience BLOOM LLM
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Episode Number : 129
Ben Brennan , former guest, accomplished author, and QSTAC CEO, guest hosts today’s “turn the tables” episode… and interviews Dan Turchin, PeopleReign CEO. Learn about Dan’s vision for augmenting human intelligence with machine intelligence and how AI will be used to give the next billion employees back an hour a day.
Show moreShow lessListen and learn…
- The origin story behind this podcast
- What’s required to use AI to improve employee experiences
- How many new jobs will be created by AI in the next five years according to The World Economic Forum
- The right way for investors to identify talent and catalyze innovation
- How Ben learned the value of human-centric AI from his days at Yahoo, Box, and Twitter
References in this episode:
- Mark Settle, serial CIO, on AI and the Future of Work
- Mark van Rijmenam, The Digital Speaker, on AI and the Future of Work
- Rory O’Driscoll from Scale Ventures Partners on AI and the Future of Work
- Ashu Garg from Foundation Capital on AI and the Future of Work
- Glenn Solomon from GGV on AI and the Future of Work
- The great Katie Stanton, PeopleReign investor from Moxxie Ventures
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Episode Number : 128
Listen and learn: Why we hate calling customer support… and how AI is making the experience better Why […]
Show moreShow lessListen and learn:
- Why we hate calling customer support… and how AI is making the experience better
- Why automation beyond IVR is saving contact centers
- What happens when AI makes bad decisions
- When it’s ok to “nudge” users to work with the bot… even when they ask for a human
- The ethical implicatio ns of bots pretending to be human
- What new careers will be created when call center agents are replaced by bots
References in this episode:
