Blast to the Past - Past Drives Future Growth (Notes from June 17 to June 23, 2019)

I've included a few repeat guests but from different podcasts. Fun to see how different the discussion can be with a different host. So, if you've been following along, hope to hear that you recognize Eric Topol and Joseph Jaffe's names.I was quite intrigued by Archit Bhargava's work at Niantic in marketing their games further, along with the creative process behind how they started. Niantic did Pokemon Go - and what went in that from starting with the maps and what game may have worked (some... ~10 years later). The Crew communication app also had a fascinating introduction story to get to where they were - from sitting in a tequila bar coming up with the name to finally developing an enterprise communications app.Then, there was a number of data science-centered episodes (of course). A16z had a discussion in ML and AI for medicine - how we see it, where it stands, where it's weak and should improve. Back to the future was also a method for the Endangered Language Project where 2 contributors were on Data Skeptic discussing their research while at USC going through NLP on languages that are losing speakers/writers/people to pass it on.One of the most exciting and energetic guests were the co-founders of Bulletin Space, though. Two women who eventually decided to make their brand a woman-centric platform focused on products by females for females, and providing them space to do great work.Hope everyone enjoys!

• Archit Bhargava, Head of Global P/Ming at Niantic Inc (Work of Tomorrow)

• Discussion of Harry Potter game after Pokemon Go
• Social aspect of exploration
• Partnering with cities and maps points of interests
• Revenue model - in-app purchases vs sponsored placement of gems / pokemon
• In Japan, partnered with McDonald's
• US - Starbucks and gyms
• Joseph Jaffe (@jaffejuice), author of Built to Suck (Wharton XM)
• AI and Your Doctor, Today and Future (a16z 6/13/19)

• With Eric Topol (@erictopol) (author of Deep Medicine) - cardiologist and chair of innovative med at Scripps Research, Vijay Pande (@vijaypande)
• Didn’t expect AI and medicine to be such back to the future - outsourcing so many things that could get us back to the 70s and before
• Doctors spent more time with humans, patients due to big business, EHR, admin wants more "efficiency"
• On Twitter - kid drew a drawing of going to doctor - it was a doctor with his back turned typing on his computer
• More tech is less computer - fundamental problem, not even drawing eye contact
• NLP can already liberate time, some UK emergency rooms, as well - eliminate the distraction and data clerking
• Encouraging to have the speed/accuracy for transcriptions, ontology and organized data
• Google AIÂ on improving the voice processing
• Min discussion / comm b/w doctors for treatment/diagnoses
• In his book, he went through his knee replacement surgery - orthopedist wasn't in touch with his congenital condition Logistics and coordination for computers - for thyroid cancer, maybe would need endocrinologist with oncologist
• How do computers know things that we don't know now? Complementary - big data has appetite for it (humans - contextual)
• Radiologists have a false negative rate of 32% - ground truths for x-rays / scans that it won't miss - basis for litigation (missing some) Best use of time for doctors would be understanding and discussion with patients
• Diagnosis in general - once trained, doctors are wedged into their diagnostic performance for their career
• Kahneman's System 1 + System 2: if doctor doesn't think of the diagnosis in first 5 minutes, they have an error rate of 70%+
• ML is reflecting system 2 since it's trained off of doctors doing system 2 - but with an aggregation of 1000s
• Up to 12mil errors in medicine a year - can improve upon this, easily
• Negative components potentially:
• Can't trust unilaterally, need oversight
• If FDA approved, have to watch for cohorts - deep liabilities, ethics, privacy issues - have to be tradeoffs and considered
• Rolling out AI - NHS is the leader in genomics (ER rooms without keyboards), China with scale and advantages - data on each person
• Limitation of data in the US and otherwise, no strategy here as well - other countries have a lot of resources spent / proposed
• Education and training has a full wing for AI in the NHS
• Professional organizations have not been forward thinking - maintaining the reimbursement for their members
• Worst outlier, outcomes and mortality - worst, especially due to spending ($11k per capita)
• Naivete of diet and having the same diet - not just glucose responses, but triglyceride tracking - non-normative responses to same thing
• Wiseman Institute in Israel cracked code on promoting health - glucose, lipids in blood - eventually see outcomes / prevention by diet
• Numbers of level and data for each individual - specific bacteria and the sequences, physical activity, sensors for stress, sleep
• Need hundreds of Ks of people to learn more and on a broader spectrum
• Can give retina picture to Intl retina expert and it's 50-50, but an algorithm is 97% Polips in colonoscopy, K level in blood thru smart watch w/o blood
• Why did people go into the medical profession in the first place? Care, helping people and seeing people - but now have highest suicide, burn out and everything More depressed which leads to more errors and cycles
• Could do remote monitoring, for instance, for all of non-ICU patients
• Hospitals won't allow it, they'd be gutted. Hospitals are problematic - 1 in 4 get injured or sick.
• Only quick adoptions are enhancement of revenue (think: robotic surgery rooms)
• Comfort of own home - can sleep, see loved ones, clearly cheaper (average is $5k / night)
• Cardiologists thought you'd have to look at the QT interval - only 1 part
• Flunked with the algorithm - Mayo Clinic wanted all data and use the full cardiogram
• AI / ML already have a great driver detector
• Easier for machines to do new things with no regulation - rare cell detections, genomes
• Imagination is our limit / machine limit (unsupervised learning limited by annotation, for instance)
• Prediction has not done as well as classification, clustering (uses his stepdad as an example, who was resurrected)
• Not there yet for multimodal algorithms - doctor doesn't have to do typing - AI figures out diagnosis
• When you go to see a doctor, you want to be touched - the 'experience'
• He doesn't use a stethoscope anymore, he uses a smartphone ultrasound for EKG - shows the patient in real-time Tools of the exam may change, but interaction will be intimate still - have to get back to this
• The Death of a Language, Endangered Language Project (Data Skeptic 6/1/19)

• USC students research Zane and Leena, CS / App Math and CS / Cog Sci
• Using unsupervised learning to assist classifying pho (basic unit of sound in speech) names of endangered languages with PyTorch
• Last living speaker of a language dies - globalization has rid the world of a number of languages (Latin, inc)
• Helping linguists and a sociological effort to carry on the language - Zane's father speaks a dying Italian dialect (Venetian, so not living) Very similar to Italian just from listening to audio but has different conjugation / words for some
• 3.5 hours of audio from 4 speakers collected by their professor in an area near the Northern Italy
• Most valuable for research - more speakers to improve dataset
• Output as recommended start and stop times, unsupervised labels for the times - rec time for pho name
• Slicing audio file into many small segments, labeling them and then combining the adjacent segments
• Built classifier with an NN - series of vectors (condensed, auto-encoding of audio data)
• 7000 spoken languages but estimated that half will go extinct by the end of the century
• Manual transcription is tedious, so hoping it will assist linguists in proscription
• Three-Legged Stool, Chuck Akre - Akre Capital founder (Invest Like the Best, 6/18/19)

• Managing $10bn after forming firm in 1989Being in "one-stop town" quality of life, not being disturbed by outside events or people
• Distracted or curious by friends, their thoughts - he spends a lot of time reading vs screens
• Thing that disappointed his son, a tenured professor without luxury of being highly select best students (those that got A's) wanted to only know what would get them an A, rather than have curiosity
• 3-legged stool as more stable than 4 legged - can deal with uneven surfaces
• Rates of return in common stocks were higher than any other asset class in unlevered case
• "Once a guy sticks his hand in your pocket, he'll do it again" - human behavior happens to be antithetical sometimes
• Legs: quality of business enterprise, quality and integrity of people running it, what is their record for reinvestment and opportunity for it
• Own exceptional businesses, don't sell them
• Bandag - "tire business" but looked at returns on capital and they were 3-4x everyone else
• Wanted to figure out what business it was in - Marty Carver (founded by his father) was a different feel Retreading bus and truck tires, early 70s had tanked oil + petroleum so dealer's had skyrocketed costs
• Bandag passed on savings to their dealers in the evidence that they had to reinvest the money in their stores - new ones, franchises
• Created huge dealer loyalty - trucks/buses' tires are constructed so that they retread 2-3 times
• Weren't smart or quick enough to get into FANGs or otherwise - couldn't figure out the value creation in quick span
• Mastercard March 2010 - during Dodd-Frank, Congress act, Durbin amendment - Mastercard/VISA selling at 10-11x
• Discovered operating margin on returns over capital "not enough words that were superlative enough" - still above average after cutting 50% twice
• Everybody wants some of that - jamming every expense into the income statement to reduce how good margin is What's causing that? How do they have it?
• Wall Street - wants transactions in general (his biz model - compound capital) - create false expectations on earnings estimates
• "Miss by a penny, beat by a penny" - gives them opportunities since markets behave irrationally
• Dollar Tree as 3 major players and that was it
• How do you measure whether you've been a success at running this business? Or we hit our earnings target or board goals, etc.
• Impact of compounding economic value per share. Not trained to do that.
• O'Reilly as duopoly and buying and deleveraging - capital allocation change / International Speedway
• Growth of antennae - American Tower Corp (buying in Africa recently) - 5G won't be here soon, but they're acting as gatekeeper Microsoft as the OS toll
• Collecting datapoints and making judgments on them in general, whether you're an English major, pre-med, investment management Reading business biography learning behaviors
• Land conservation plan / donations
• What is the source of pricing power for each company?
• Named Entity Recognition (Data Skeptic, 6/8/19)
• Entities as core features of a sentence, idea
• Text file analyzing or software doing a good job of named entity If you give labelings, some are from computer vs English majors (Turing test) Using SpaCy for NER - hard problem, different expectations but not great - just good
• Chatbots seeking NER - flight example, for instance - pull out things that are mentioned City is destination, airline mentioned
• BERT can do NER pretty well - Google Assistance and chat interfaces have been improving
• Semantic web projects can pull entities out of documents and connect them in knowledge graphs
• Transfer learning - pretrained model on generic model and use that as jumping off point
• Carmax: Way Data-Science-powered Car Buying Should Be (BD Beard, 5/28/19)
• Tod Dube, Chief Architect for Data Science at Carmax
• Adding 1 store per month, no. 3 wholesaler (to Barrett Jackson, eg), $18bn in rev, #1 used car
• Determining pricing is through ML, but now omnichannel - looking/exploring cars interactions Customer service buying exposure
• Changing how data scientists go about their job - laptops with minimal compute power, governance issues trying to fit onto laptop
• Analytical leadership to push tech to do things better - how to make availability Security, data losing, privacy, model
• Shift data and move data around but if data moved in 3 weeks - how can you iterate easily
• Architectural changes from laptop / personal side to service and data warehouse pulls / data centers
• Azure service response - pick use case, can't swallow the elephant (replatform rec that were done today - handwritten code)
• 2 week sprints for changes before - different cars, reasons, prices and availability What tools could help? SaaS, subscription to bridge the gap - Had python (Jupyter, Spyder, Anaconda) / R
• Started a data lake because of use case Had to pivot and find data scientists (Type A - analytical, business; Type B - data engineer, why model is necessary for data)
• Consulting partners as unsung heroes to figure out how to build out a team or look at problems
• Spark as a Service, Spark as data lake, DataBricks (Delta Lake), Azure customer Will auto finance almost any cars, call centers - better enabling customers in financial choice
• Walk-on song for conference: I'm Not Afraid by Eminem
• Spends money on tech, iPad Pro new now, MacPowerUsers (how their workflow is) AI, weather on sprinklers for rain predictions
• Ali Kriegsman, Alana Branston, co-founders of Bulletin (Wharton XM)

• Switching from platform with competitors like Etsy, Bazaar to drive sales, initially
• Had creative, original content and hooked brands up with some unused channels/media
• Asked brands/customers after > 100 brands - 'peak' initially
• They talked about how valuable, but expensive, physical space was - pop-ups individually, Brooklyn Flea, etc
• Decided to do pop-ups in big parking lots - ineffective, felt the heat - literally (12k sq ft in parking lot outside)
• Shrunk it and rented out a front space from a bar - charged $300 * 30 brands for pop-up for a weekend Worked for both brands and them - realized they could do that ~10 months
• Grinding for the year, made money equivalent to month of prior sales, but 7 days / wk wasn't scalable
• Finetuned branding for pop-ups to female founders, female products (had men originally) Best products at time were stamped necklaces, ready-to-wear clothing increasing
• Sucharita Kodali (@smulpuru), Retail Analyst - Forrester (Marketing Matters)

• Danny Leffel, CEO, cofounder of Crew (Wharton XM)

• Communications app for everyone on professional page
• Bryan Murphy (@bryanpmurphy), CEO of Breather (Wharton XM)

• Talking about the optionality to get working space

• Dan Widmaier, CEO cofounder at Bolt Threads Biotech (Wharton XM)

• Using spider silk and attempting to synthesize stronger proteins for apparel, clothing
• Tie was the first - needed a quick demonstration
• Have gone on to other materials, solving environmental waste of apparel