AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics

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. In 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 experiencesHow Airbnb created a culture of measuring and improving the employee experienceWhat are progressive employers doing to make the transition back to office work easierThe three "soft leadership" questions every manager should get great at askingHow to measure the quality of employee experiencesHow AI can be used to detect changes in tone in employee engagementWhere to start when using AI to improve the employee experienceHow the metaverse will improve remote workReferences 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 WorkGary Bolles on AI and the Future of WorkMark van Rijmenam on AI and the Future of WorkBurn In: A Novel of the Real Robotic Revolution by P.W.  Singer and August Cole

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. Michael 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 mediaWhat are the challenges of using generative AI to create synthetic stock images of peopleHow visual media content creation has evolvedThe ethics of generative AIWhat Michael describes as "the biggest art heist in history"How vAIsual extends human photos using machine vision and human labelingCan an AI be the owner of copyrighted material it produces?What is the definition of consciousness?References in this episode...AI has a burnout problemEric Olson from Consensus on AI and the Future of WorkJonathan Frankle on AI and the Future of WorkMichael's whitepaper about vAIsual

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.Speechly 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" appsHow 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 accentsWhen speech detection achieved "superhuman" levels of accuracyHow Speechly combines speech recognition with natural language understanding (NLU) on the local deviceHow Otto thinks about exercising responsible AIWhy "voice technology won't exist as a separate field in a decade"References in this episode...Responsible AI has a burnout problemAlex Capecelatro from Josh.ai on AI and the Future of WorkKrish Ramineni from Fireflies on AI and the Future of WorkThe Speechly demo site

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.Jonathan 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...The AI Bill of RightsMosaicMLJonathan's personal site

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.Listen and learn...Why Google isn't the answer for scientists seeking evidence-based answers onlineWhy a business model that relies on ads can't solve the "unbiased answer" problem for researchersHow Consensus addresses the problem of conflicting information online from credible resourcesHow to use labels to improve search retrieval accuracy... without introducing bias into resultsHow 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 existWho is responsible if Consensus delivers answers that lead to harmful outcomesWhat Eric learned as a division I NCAA athlete (Go Wildcats!) that has helped him as a high-tech entrepreneurReferences in this episode:Elon Musk launches the Optimus bi-pedal robot at AI dayDan Grunfeld, Stanford athlete and Lightspeed partner, on AI and the Future of WorkConsensus

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.Listen 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 dataHow many touches are required from stage one to closing a B2B dealHow to fix the CRM data hygiene problemWhy 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 entrepreneursReferences in this episode...Barr Moses from Monte Carlo on AI and the Future of WorkDerek Steer from Mode on AI and the Future of WorkPeter Fishman from Mozart Data on AI and the Future of WorkStephen Messer from  Collective[i] on AI and the Future of Work Kamal Ahluwalia on AI and the Future of WorkLeading scientists fear AI could lead to nuclear war by the end of the century

Hina Dixit, 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. Prior 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

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.Listen and learn...How to train AI models on past service interactions and outcomes to determine which agents should speak to which customersHow to use deep learning and NLP to process non-speech behavior signals like intonation, pitch, and tonal varianceHow behavior signals can be used to predict stress, duress, and propensity to buy or payHow 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 productThe biggest opportunity Rana sees to use AI to help humans live better livesReferences in this episode:Mahesh Ram from Solvvy (now Zoom) on AI and the Future of WorkGadi Shamia from Replicant on AI and the Future of WorkHow personalization algorithms work in your social feedsBehavioral Signals

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.Listen and learn…How a roboticist who got his start building self-driving cars and designing missile defense systems ended up redefining how data is storedWhy traditional approaches that require SQL to access data are brokenHow a single-column schema eliminates the complexity of joining systems and tablesWhy it’s easier to tell better stories with data using temporal relationships extracted from customer journeysWhy 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 sentientChandra 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 Ahmed on Twitter 

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. Listen 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 burnoutThe Innovation Delusion on Amazon Earley Information ScienceThe AI-Powered Enterprise on Amazon Kevin Dewalt, Prolego CEO, on AI and the Future of Work