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6. Disruptive Strategy – Terms to Know

This is a term that describes when an incumbent gladly gives up a lower-end part of the market because the company is motivated to move up market. The motivation to flee is most often fueled by higher gross margins. This is often called “flying under the radar”. For more on this term, read here. http://hbswk.hbs.edu/item/the-innovators-battle-plan.

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“…most of all: nothing without skin in the game.” ~ Nassim Nicholas Taleb

This."No muscles without strength, friendship without trust, opinion without consequence, change without aesthetics, age without values, life without effort, water without thirst, food without nourishment, love without sacrifice, power without fairness, facts without rigor, statistics without logic, mathematics without proof, teaching without experience, politeness without warmth, values without embodiment, degrees without erudition, militarism without fortitude, progress without civilization, friendship without investment, virtue without risk, probability without ergodicity, wealth without exposure, complication without depth, fluency without content, decision without asymmetry, science without skepticism, religion without tolerance, and, most of all: nothing without skin in the game." ~ Nassim Nicholas Taleb

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Innovation Strategy Questions

When seeking to develop an innovation strategy, here are some questions you should get answered.How do I see emerging trends before they become problematic?How do I generate a robust pipeline of new growth ideas to consider?How do I identify and focus on the highest-potential opportunities in areas like blockchain and AI?How can I motivate traditional company management to realize the need for digital transformation?How do I evaluate competitive signals in a noisy, buzzword-filled market?How do I get the middle layer of my company to embrace change?How do I bring outside ideas into my organization?When does it make sense to be a fast-follower? And when does it not?How do I decide whether to build, buy or partner?Should I start a venture capital fund?

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4: AI for Everyone – AI and Society – Notes

IntroductionHypeLimitationsBiasAdversarial attacksImpact on developing economics and jobsA realistic viewGoldilocks rule for AI:Too optimistic: Sentient/AGI, killer robotsToo pessimistic: AI cannot do everything, so an AI winter is comingas opposed to the past, AI is creating value today.Just right: Can't do everything, but will transform industriesLimitations of AIperformance limitations. (limited data issues)Explainability is hard (instructible)Biased AI through biased dataAdversarial attacksDiscrimination/Bias    BiasesBias against women and minorities in hiringBias against dark skinned peoplebanks offering hiring interest rates to minoritiesreinforcing unhealthy stereotypesTechnical solutions"Zero out" the bias in wordsUse more inclusive dataMore transparency and auditing processesMore Diverse workforceAdversarial attacksMinor perturbation to pixels can lead and AI to have a different B output.Adversarial defensesDefenses exist; incur some performance costThere are some applications that will remain in an arms race.Adverse uses of AIDeepFakes, fakes can move faster than the truth can catch upUndermining of democracy and privacy, oppressive surveillanceGenerating fake commentsspam vs. anti-spam, fraud vs. anti fraudAI and developing economiesAI will eliminate lower rung opportunities. The development of leapfrog opportunities will be required. Think how countries jumped to mobile phones, mobile payments, online education, etc.US and china leading, but still a very immature space.Use AI to strengthen country's vertical industries.More public-private partnershipsinvest in educationAI and JobsAI is automation on steroids.SolutionsConditional basic income: provide a safety net but incentivize learningLifelong learning societyPolitical solutionsConclusionWhat is AI?Building AI projectsBuilding AI in your companyAI and society

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2: AI for Everyone – Building AI Projects – Notes

IntroductionStarting an AI projectWorkflow of projectsSelecting AI projectsOrganizing data and team for the projectsWorkflow of a machine learning projectHow do you build, say a speech recognition engineKey Steps:Collect Data: people saying "Alexa", and other wordsTrain model: learns A to B mapping... audio clip to "word"many iterationsDeploy the model: implement in to a smart speakerWill collect new data (get data back), to  maintain /update the modelHow do you build, say a self driving carKey steps:Collect Data: images - > positions of other cars, draw rectangles around carsTrain model: need to iterate and precisely identify carsDeploy model: may learn that golf carts are identified and positions well. keep iterating.Workflow of a data science projectoutput: actionable insightsOptimize a sales funnelKey steps:Collect Data: where are people coming from, time of day, machines type, etsc...Analyze the data: Iterate many time to get good insights insights from the data collected.Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.Optimizing a manufacturing lineKey steps:Collect Data: clay supplier, mixing time, ingredients, lead times, relative humidity, temperature, kiln duration, etc...Analyze the data: Iterate many time to get good insights insights from the data collected. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.Every job function needs to learn how to use dataUse data to optimize workflows through data science based analysis, and to take on tasks with machine learning (remember less than a second), Inputs (A) to Output (B).From Sales, recruiting, marketing, to agriculture, and beyond DS and ML are having huge impactsHow to choose an AI projectBring together a cross-functional team knowledgeable in AI, plus domain experts.Brainstorming framework:Think about automating "tasks," vs automating "jobs."what are the main drivers of business values?What are the main pain point in your business?Note: you can make progress without big dataHaving more data almost never hurtsData makes some business [Google, Facebook, Netflix, Amazon] defensible.With small datasets, you can still make progress. The amount of data you need is problem dependant.Due diligence on projectWhat AI can do + Valuable for your businessTechnical diligenceCan AI system meet desired performance (e.g. accuracy, speed, etc)How much data is need to meet performance goalsEngineering timlineBusiness diligenceCurrent business: Lower costsCurrent business: Increase revenue ( getting more people to check out)New business: New product or business*Ethical diligence*money vs impact on societyBuild vs. buyML projects can be in-house or outsourcedDS projects are more commonly in-houseSome things will be industry standard, avoid building those."Don't sprint in front of a train."some times it makes sense to adopt another's platform or approach than to build your own. resource constraints, capability constraints...Working with an AI teamSpecify your acceptance criteriaGoal: defects with 95% accuracy...How do you measure accuracyTest Set (n1000): labelled training dataset to measure performance. Training Set: Pictures with labelsLearn mapping from A to BTest Set: Another data set to test the mappings. Often more than 1 test set will be requested.Pitfall of expecting 100% accuracy. Discuss with AI engineers what's reasonable.Limitations of MLInsufficient dataMislabeled dataAmbiguous labelsTechnical Tools for AI teamsCPU vs. GPU [Great for deep Learning/Neural Networks] NvidiaCloud vs. On-prem, ....Edge [Processor, where data is collected.]

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