Wondering what’s real about artificial intelligence? Today on BrainTrust LIVE, we’re fortunate to have Cynthia Holcomb, founder/CEO of Prefeye, and Shawn Harris, Customer Partnerships & Strategy, SmartLens — two retail practitioners who are working with their clients on real A.I. solutions. They’ll give us the lowdown — more specifically, on how retailers can currently use AI for personalization, the limitations that are frustrating them at present, and what does the future holds.
Module 6 of 6 of MIT CSAIL AI Implications & Strategy: The future of artificial intelligence – Notes
Module 6: The future of artificial intelligence
Module 5 of 6 of MIT CSAIL AI Implications & Strategy: Artificial intelligence in business and society – Notes
Module 5: Artificial intelligence in business and society
Module 3 of 6 of MIT CSAIL AI Implications & Strategy: Natural language processing in business – Notes
Module 2: Machine learning in businessUnit 1: Key features of machine learning
Module 1 of 6 of MIT CSAIL AI Implications & Strategy: An introduction to artificial intelligence – Notes
Unit 1: Outcomes of this program
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
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.]
#010: This Game is Not One-Dimensional, You Have to Face the Customer with Shawn Harris, Global Innovation Strategy Lead, Zebra Technologies.
In this episode of The IoClothes Podcast, we speak with Shawn Harris, Global Innovation Strategy Lead for Zebra Technologies. The reality is, innovative products don’t just sell themselves and companies aren’t composed of just designers, developers and engineers. Someone has to interface with the customer, and keep the ship sailing along a strategic path, which includes profitability (that’s if you want to stay in business). Today, we shift gears and talk a bit about the struggles of retail, the importance of differentiating yourself in the marketplace and how are current relationship with MS Excel may be a sign of the future!
Highlights from a recent article in the South China Morning Post:
1. Classification (probability estimation or scoring): binary or categorical.Attempt to predict, for each individual in the population, which of a (small) set of classes this individual belongs to. Classification will bucket individuals, and scoring will provide quantification of likelihood of being in a particular bucket.