Section + Lightboxes

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What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Heading

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Role: Digital Explorer

Innovation Strategists (Digital Explorers)

Someone who can see different business models and participates in creating hypotheses without being intimidated by a blank canvas.

Digital explorers look at the broader picture of what is possible where user need meets current (or potential) business capability.

"Holistic performance and engagement solutions for today's HR leaders"

Holistic performance and engagement solutions for today's HR leaders

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A human capital management and resource planning suite for emphasizing payroll and HR in enterprise-level businesses.
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Tools for artificial intelligence and machine learning
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Tools and services of various scales which help developers and lay users to connect APIs to each other.
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"A secure, highly scalable, managed source control service that hosts private Git repositories. CodeCommit eliminates the need for you to manage your own source control system or worry about scaling its infrastructure."

"AWS CodeCommit is a secure, highly scalable, managed source control service that hosts private Git repositories. CodeCommit eliminates the need for you to manage your own source control system or worry about scaling its infrastructure."

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"AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications. You can use AWS CodeDeploy to automate software deployments, eliminating the need for error-prone manual operations."

AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications. You can use AWS CodeDeploy to automate software deployments, eliminating the need for error-prone manual operations.

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"Automate continuous delivery pipelines for fast and reliable updates"

Automate continuous delivery pipelines for fast and reliable updates

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A versatile webpage design tool for desktops, often used for the visual 'front-end' design of sites.

Build beautiful sites for any browser or device. Quickly create and publish web pages almost anywhere with web design software that supports HTML, CSS, JavaScript, and more.

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Adobe's suite of marketing, user experience, CMS, and website analytics tools.
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An increasingly-useful tool in Adobe's Creative Cloud offering. It helps prototype websites and apps before streamlining the export of User Interface elements.

Design the incredible. Lifelike in every sense. Create stunningly real UI/UX designs and stand out from the rest.

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"Product management made easy with a flexible platform that helps you manage strategy, understand user needs, prioritize, and align your teams around clear roadmaps."

Product management made easy with a flexible platform that helps you manage strategy, understand user needs, prioritize, and align your teams around clear roadmaps.

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A highly-configurable, no-code-required database for small to mid-sized teams. Popular with startups, developers, and prototypers for its flexibility.

Connect everything. Achieve anything.Accelerate work and unlock potential with powerful apps that connect your data, workflows and teams.

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An end-to-end solution (and services) for managing data supply chains, data rivers, and data lakes. Includes discovery, hygiene, taxonomy, processing, analysis, and machine learning.

Data-Driven. People Powered.With Analytics for All, anyone can solve problems by turning data into breakthrough insights.

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A massive tech company offering a marketplace for all variety of goods, Amazon Web Services cloud tools and hosting, shipping and warehouse logistics, hardware devices like the Echo, software services like Alexa, and digital goods like movies.

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking.

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Amazon Web Services is the leading cloud platform for websites, apps, data processing, and various other uses. It began life as an 'operating system for the web.'

The leading cloud platform.

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"Give your teams self-service product data to understand your users, drive conversions, and increase engagement, growth and revenue."

Give your teams self-service product data to understand your users, drive conversions, and increase engagement, growth and revenue.

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Tools for processing data into useful information. Data may come from many sources, including websites, transactions, or app usage.
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The mobile operating system backed by Google and running on 2.5 billion devices.

Navigate a connected world.

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IT operations automation tools.

Red Hat® Ansible® Automation Platform on Microsoft Azure offers all the benefits of Ansible automation, with the convenience and support of a managed application. This offering is fully supported by Red Hat and deployed in your Azure cloud.

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A library and tool used to build applications in Java and other languages.

Apache Ant is a Java library and command-line tool whose mission is to drive processes described in build files as targets and extension points dependent upon each other. The main known usage of Ant is the build of Java applications. Ant supplies a number of built-in tasks allowing to compile, assemble, test and run Java applications. Ant can also be used effectively to build non Java applications, for instance C or C++ applications. More generally, Ant can be used to pilot any type of process which can be described in terms of targets and tasks.

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Software project management tool for Java-based projects.

Apache Maven is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.

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A widely-used open source software version control system.

"Enterprise-class centralized version control for the masses"

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Create onboarding flows for new users (without touching code).

Boost product adoption with no-code onboarding flowsDesign, deploy, and test captivating onboarding experiences in minutes, not weeks.

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Monitors cloud app performance.

Transform your applications and business with AppDynamics real-time performance monitoring.

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The app store for iOS and Mac devices. Launched the app economy and millions of apps.

The apps you love.From a place you can trust.

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Apple's suite of apps and services for secure aggregation of personal health information.

The Health app was created to help organize your important health information and make it easy to access in a central and secure place.

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A project management tool for creative and cross-disciplinary teams with continuously expanding features for enterprise clients.

From the small stuff to the big picture, Asana organizes work so teams know what to do, why it matters, and how to get it done.

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Computer-aided design software for engineering, architecture, and 3D worlds.

Design it.Build it.Autodesk it.

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An enterprise cloud platform for software development, hosting, and data analysis.

Achieve your goals with the freedom and flexibility to build, manage, and deploy your applications anywhere. Use your preferred languages, frameworks, and infrastructure—even your own datacenter and other clouds—to solve challenges large and small.

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Machine learning and basic AI services for Microsoft's cloud computing users.

Add cognitive capabilities to apps with APIs and AI services. Azure Cognitive Services bring AI within reach of every developer and data scientist.

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Services and features within Microsoft's Azure cloud platform focused on software development.

Overcome challenges at every stage of remote engineering and learn how Microsoft engineering teams have enabled remote development.

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Sketch-style wireframing for user interfaces.
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"Continuous integration, deployment, and release management."

Continuous integration, deployment, and release management.

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"Collaborate on code with inline comments and pull requests. Manage and share your Git repositories to build and ship software, as a team."

Collaborate on code with inline comments and pull requests. Manage and share your Git repositories to build and ship software, as a team.

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"Build powerful, automated workflows: automate your code from test to production with Bitbucket Pipelines, [Atlassian's] CI/CD tool that's integrated into Bitbucket Cloud. "

Build powerful, automated workflows: automate your code from test to production with Bitbucket Pipelines, our CI/CD tool that's integrated into Bitbucket Cloud.

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A social listening tool to find out what the web is discussing.

Use our content insights to generate ideas, create high-performing content, monitor your performance and identify influencers.

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"Cascading Style Sheets (CSS) is a simple mechanism for adding style (e.g., fonts, colors, spacing) to Web documents."

Cascading Style Sheets (CSS) is a simple mechanism for adding style (e.g., fonts, colors, spacing) to Web documents.

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Scheduling software that makes it easy for people to pick the best appointment slot with each other.
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Helps users practice meditation with guided audio and other experiences.
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A place to manage reusable assets like images, logos and videos consistently across a large or diverse set of users, such as an enterprise.
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A template-based graphic design tool that helps social marketers create consistent and attractive digital content.
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Automation for software builds and IT operations.

Automation Software for Continuous Delivery of Secure Applications and Infrastructure

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Automates builds (CI/CD) across multiple environments in the cloud or on private infrastructure for speed and reliability.
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A data broker and aggregation tool for enriching contacts and company information.

Discover, engage and convert your most valuable customers — all from one flexible go-to-market foundation.

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Tools designed to make approximations of working products with the ability to click or tap buttons and other navigation to check the path a user might take through the offering.
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A global hosting and security provider for websites, apps, and services.

Cloudflare is designed to run every service on every server in every data center across our global network. It also gives your developers a flexible, Internet-scale platform to deploy serverless code instantly across the globe.

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Tools and datasets that enable teams to work together, includes communication and project management.
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Tools for a company to engage with its customers, user communities, and other stakeholders.
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A knowledge-management and wiki system favored by software developers and technical writers.

Spend less time hunting things down and more time getting things done. Organize your work, create documents, and discuss everything in one place.

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A service which distributes content such as blog posts and news articles across many publication channels at once.
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A developer-friendly CMS. Able to handle very complex content libraries and big sites while minimizing custom development.

Craft empowers the entire creative process.

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"The all-in-one market intelligence platform to discover, harvest and share insights."

The all-in-one market intelligence platform to discover, harvest and share insights.

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"Find bugs and improve code quality through peer code review."

Find bugs and improve code quality through peer code review.

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Software for managing contact information, activity, needs and other profile data for customers and users.
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"Fast, easy and reliable testing for anything that runs in a browser. Install Cypress in seconds and take the pain out of front-end testing."

Fast, easy and reliable testing for anything that runs in a browser. Install Cypress in seconds and take the pain out of front-end testing.

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D3.js is a JavaScript library for manipulating documents (like HTML and scalable vector graphics/SVGs) based on data. Widely used for data visualization.
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A medical diagnostic tool which helps healthcare providers narrow done symptoms to potential causes.
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An image-synthesizing API which can generate photo-like composites based on a few text keywords.
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Supporting various stages of the data supply chain. Advanced data stacks are connected to artificial intelligence and machine learning stacks.
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"Modern monitoring & security: see inside any stack, any app, at any scale, anywhere."

Modern monitoring & security: see inside any stack, any app, at any scale, anywhere.

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A learning experience platform (otherwise known as a social learning management system).

Changing the world through learning: empower lifelong learners and career growth to drive innovation at your company.

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Software development and information technology tools (increasingly unified in digital organizations as DevOps).
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Tools for the DevOps method of software development and IT operations management.
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A commercial tool for 'containerizing' applications and code, which popularized containers in modern development.

Accelerate how you build, share, and run modern applications.

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A document signing and management system.
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Collaborative scheduling for groups trying to find common times.
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"Simplify cloud complexity with Software Intelligence — observability, automation, AI, and cloud-native application security in one platform."
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A fast set of services for search and data analysis, especially for websites.

Search your way. Analyze at scale.

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Unified solutions to coordinated a variety of functions such as finance, HR, governance, and scheduling, to name a few.
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A mobile-centric expense management system for professionals and employees.

Corporate Cards. Reimbursments. Receipt Scanning. One App, All Free.

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A user experience design tool with additional functionality for collaborative whiteboarding.

Figma connects everyone in the design process so teams can deliver better products, faster.

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Supporting the smooth function of companies' essential logistics.
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"Search, monitor, and track across SVN, Git, and Perforce repositories."

Search, monitor, and track across SVN, Git, and Perforce repositories.

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GPT-3 (Generative Pre-trained Transformer 3) is a language model that uses deep machine learning to synthesize human-like text from data and commands.

OpenAI’s API provides access to GPT-3, which performs a wide variety of natural language tasks, and Codex, which translates natural language to code.

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A market research firm with assessment and information offerings useful to trendcasters and enterprise decision-makers.

Insights to drive stronger performance

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Desktop and mobile operating systems, file storage, and office software.
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Customer experience feedback with on-site, in-app, SMS, and social listening features.

Excel at customer experience in a world of changeㅤQuickly pivot based on your customers' feedback and drive more value for them—and your business.

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Tools for every stage of software development, used by 83 million developers across four million organizations.

Millions of developers and companies build, ship, and maintain their software on GitHub—the largest and most advanced development platform in the world.

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"From planning to production, bring teams together in one application. Ship secure code faster, deploy to any cloud, and drive business results."

From planning to production, bring teams together in one application. Ship secure code faster, deploy to any cloud, and drive business results.

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"[[Google's]] mission is to organize the world’s information and make it universally accessible and useful."

Our mission is to organize the world’s information and make it universally accessible and useful.

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A solution to "build apps faster, make smarter business decisions, and connect people anywhere" with 150+ sub-products.
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The first major cloud document and collaboration platform. Google Docs is also the common name for the larger Google Workplace suite of cloud collaboration and file storage tools.

Use Google Docs to create, and collaborate on online documents. Edit together with secure sharing in real-time and from any device.

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A simple tool for tracking notes and lists across devices.
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The world's largest search engine.
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An exploration and analytics tool focused on Google Search's traffic patterns, famously known for predicting flu outbreaks due to symptom searches.

Explore what the world is searching

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Google's productivity and collaboration suite, including Gmail, Drive, Docs, and Meet.
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Writing support software which correct spelling and grammar, but also assists with tone, style, and brand compliance.
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Tools focused on the logistics of human 'resources' in companies and can include work scheduling software, coaching/learning & development tools, and performance review functions.
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Basic Hypertext Markup Language (HTML) is the standard markup language for documents designed to be displayed in a web browser. Often coupled with CSS for formatting and JavaScript for functionality.
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Markup language used for structuring and presenting content on the web.The fifth and final version of HTML. It encompasses significant interactivity and styling functions compared to basic HTML.
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"Social is your superpower. Easily manage all your social media and get results with Hootsuite."

Social is your superpower. Easily manage all your social media and get results with Hootsuite.

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"Your monitoring data displayed on beautiful graphs and dashboards - with alerting."

Your monitoring data displayed on beautiful graphs and dashboards - with alerting.

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Records how users behave on your site to help understand them bettergr

Understand how users behave on your site, what they need, and how they feel, fast.

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A CRM, communication, and marketing automation platform with a rich integration ecosystem.
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A category of tools focused on supporting traditional human resources functions inside companies.
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A crowdsourcing tool for innovation and ideation.

IdeaScale is an innovation management solution that links organizations to people with ideas. Collaborative innovation can change the world—not just your business. Find ideas to aid in digital transformation, to face the age of automation, and to fight climate change, inequality and beyond. Connect to the ideas that matter and start co-creating the future.

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A popular social media network centered on sharing images and short videos in an activity feed.
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"The modern customer communications platform that unifies every aspect of the customer journey, from conversion to engagement to support."

The modern customer communications platform that unifies every aspect of the customer journey, from conversion to engagement to support.

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A subset of the Intercom suite of chat tools focused on guiding users to explore and learn new software.
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A collection of tools focused on internet of things devices, like smart home/office/factory, connected car and wearable technologies.
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Weak Signals

The number of points of information in a forecasting or trendcasting network

Hypotheses

Assumptions and possibilities documented; testing these leads to higher likelihood of offerings that are genuinely useful and feasible

On-Paper Value Propositions

Number of specific value propositions articulated as 'on-paper-tested' models

On-paper Business Models

Number of complete business ideas articulated as 'on-paper-tested' business models

Third-Party Apps

The number of third-party apps or other digital offerings on a Multi-Sided Platform or marketplace

Passed Unit Tests

Number of tests passed at a unit level

Offerings or Initiatives to be Sunset, Retired, or Overhauled

Number or financial amount of projects, offerings, initiatives or other resource drains which need to be retired or overhauled

Private APIs

Number of APIs available to strategic partners and clients

Network Quality and Engagement

Category of metrics for the utility and attractiveness of a network

Customer Validation (Users Willing to Pay for X)

Number of users explicitly willing to pay for something, even if they have not paid for it yet (can be measured by interest forms, deposits, etc)

Algorithms (Beta)

Number of successful algorithm sets (or models) developed by a data science team

Time on Site

Measure of network quality on content sites

Registered Users

Number of users who have completed a sign-up process

Time On Page

A measure of network quality on content sites

Public APIs

Number of APIs available to the public (a specific measure is needed)

Core Action User Retention

Improvement of retention for new cohorts (users taking a core action in/for the product)

Network Growth Rate

Category of metrics for the rate of growth (or attrition) for a network

Rage Clicks

Instances of users repeatedly engaging in ineffective clicks because an app or site is malfunctioning

Beta Integrations

Number of integrations of apps and services that reached a beta state

Market-Tested Business Models and Value Propositions

Number of business models and value propositions tested with the marketplace to a uniform stage (such as customer identification interest, or validation)

App Downloads

Number of app downloads (total, or in a given time period)

Number of Marketplace Buyers

Number of (potential) buyers in a marketplace

Number of Marketplace Sellers

Number of (potential) sellers or providers in a marketplace

Alpha Releases

Number of initial, partially-functional digital releases.

Capabilities (Servers, Datasets, Skills)

Category of metrics used to measure the ability to serve current and future demand

Number of Marketplace "Prosumers"

Number of (potential) producer-consumers in a marketplace (who both can contribute to and take from the market or community, like eBay users)

Number of App Downloads

Number of times an app is downloaded and installed to a user's device

Data Points

Number of data points (total) across data sets

Total Addressable Market

Potential network or market size

Network Size

Category of metrics for the number of members in a network (such as people, devices, organizations, or data points)

User Stories

Fully-written user stories (demonstrates listening to users which will lead to increased quality)

Internal APIs

Number of APIs for internal stakeholders (measures the quality and utility of network of systems)

Patents

Number of successful patents filed, which can increase network size or network uniqueness

Data Models (Alpha)

Number of preliminary algorithms or data models; can be affected by quality and quantity of data sets

Experiments Designed

Number of experiments (technical, market, etc.) designed 'on paper'

Delivery Velocity Score

Average of an organizations' percentile rank across Delivery Frequency and Delivery Lead Time

Customer Discovery (Could Be A Customer for X)

How many potential target customers have been identified (eg from an existing customer list or prospect list)

Throughput (DevOps)

Category of metrics measuring the amount of work produced by the DevOps team(s)

Load Average

Average number of system processes, servers, instructions, etc. in waiting for access to the servers' processors in a given period

Memory Percentage Usage

Percent of a server's active memory (RAM) in use at a given time (measures application efficiency and health)

Support Requests

Number of support requests made

Mean Time to Recovery (MTTR)

Time needed to recover or restore from a failure

Defect Metrics

Category of metrics for catching errors that escape various stages of development

CPU Percentage Usage

How hard a server is working relative to its total capacity to run given code (measures server capacity and/or application efficiency)

Gross Merchandise Volume (GMV)

Total amount of money spent on a platform (an indirect measure of network size and quality)

Social Objects

How many social objects get shared (may be divided per user)

Social Object Shares

Number of social objects (like a video or status update) shared from or in a network

Frequency of Engagement

Amount of engagement per user over a period of time (usually 7 or 30 consecutive days); can indicate 'power users'

Unique Visitors

Number of unique individuals who visit a digital property (couple with network quality metrics to avoid vanity measures)

Social Object Engagement

Amount of engagement a given social object, or average social object, receives

Dollar Retention and Paid User Retention

Percent of users paying and average amount paid, indicating if users are getting strong value from a network

Daily Active Users

Number of users interacting with a service, app or offering per day

Utilization Rate

Percentage of time that time-sellers on the platform or marketplace are without satisfactory results (eg, drivers who have empty cars vs. those providing a ride)

Repeat Visits

Number of returning visits

Switching or Multi-Homing Costs

Measurable value to users from switching to a new network (measures network attractiveness)

Success Rate

Frequency of successful user matches (with another user or inventory) in a network

Network Internal Traffic Source

Amount of traffic generated inside a network vs. coming from outside it

Exit Rate

Percent of users who leave a service on a given screen, stage or functio. Indicates barriers or unattractive experiences

Active Users

Number of users actively using your platform (define 'active' specifically; use the same definition throughout).

Sell-Through Rate

Number of units sold in a period divided by the number of items at the beginning of the period

Prevalence of Multi-Tenanting

Number or percentage of users who are on more than one competing platform (Amazon and Alibaba, or Lyft and Uber)

Organic New User Percentage or Organic Share

For one-sided networks, organic new user share measures the number of users invited by their friends to improve experience; multi-sided networks, it can also measure desire for increased supply or demand.

The percentage of new users who are referrals from other satisfied users (on one-sided networks like social media) or users wanting additional inventory (like 'guests' seeking new 'hosts' on multi-sided networks airbnb)

Energy Needed for Product to Become Effective for User

Amount of effort needed to reach the minimum threshold for a product to be useful (eg, Facebook's 'magic number' of 10 friends)

Retention by Location or Geography

For businesses with local network effects like ride-sharing services, how user retention in established markets compares to users in new markets

Power Users

Amount of activity by users who have logged in for a number of consecutive days (7 or 30 days, usually), often represented in a histogram

Marketplace Close Rate

Number of units sold in a period divided by the number of items available at the beginning of the period

Search Cost for Marketplaces or Communities

Financial cost of searching a network for matches

Match Rate

Ease with which two sides of a marketplace or community can find each other

Time to Find a Match (Inventory Turnover)

Time cost of searching a network for matches

Uptime

Amount of time a service remains operational (usually expressed as a percentage over a given time period)

Homogeneous Market Depth

Heterogeneous Market Depth

Customer Tickets

Number of customer tickets opened in a given time period (expresses either dissatisfaction or occasionally positive engagement)

Apdex

A compound metric to measure overall performance as a function of app users's satisfaction

Application performance index expressed as a weighted average of users satisifed, tolerating a service, or frustrated.

Service-Level Objectives (SLOs)

Category of metrics tracking specific performance relative to promised or targeted service levels

Error Rates

Number of mistakes made by a system, which may or may not be due to a defect in programming

Mean Time to Detection (MTTD)

Time needed to detect a failure, error or defect

Average Response Time

How long an application takes to perform a triggered transaction

Cohort Improvement

Percentage of a cohort's new users who are (satisfied, properly onboard, converted, etc) in a set period of time vs. percentage of prior cohorts of new users

Beta Releases

Number of functional early software releases

GA Releases

Number of General Availability (GA) releases (public releases which are not alpha or beta stage)

Minimum Viable Products (MVPs)

Number of MVPs released to market (pre-testing offerings for user alignment both improves quality and helps create an early network of potential users)

Time From Feature Request to Deployment

Time between a feature being requested and it being deployed

Time From Bug Report to Bug Fix

Measures responsiveness of a software team to users, supports a high-quality experience and builds trust

Experiments Completed

Number of experiments completed

Learnings

Number of learnings generated from experiments or other tests, measured against number of hypotheses

Experiments Built

Number of experiments (technical, market, etc.) built

Cost per Initiative/Deployment

Average cost of digital initiatives of comparable result size

Customer Creation

Number of paying customers

API Calls

Times a given API has been accessed (may be divided by the number of unique users making the calls)

Total Revenue

Total revenue created

Marketplace Revenue

Revenue that the marketplace host receives, as a portion of the gross merchandise value passed through a marketplace (eg app store commissions)

Profitability

Amount of revenue retained after costs

Average Revenue per User

Total revenue divided by number of users in a given time period

Annual Recurring Revenue per Customer (ARRpC)

Amount of recurring annual revenue per customer

Deployment Frequency

How often a DevOps team successfully releases software to production

How often a DevOps team successfully releases software to production

Annual Recurring Revenue

Total amount of revenue on a subscription or similar basis

Cycle Time

How long it takes for a code commit to be fully deployed into production

Lead Time for Changes (Code Commit to Code Deploy)

The amount of time it takes a DevOps team' commit to get into production

Duration between code committed to code deploy-able (not necessarily live in production yet)

Mean Time to Restore (MTTR)

Time needed to recover or restore from a failure

Section + Lightboxes

section + card/tabs text

Title

Body Text

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What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

This is some text inside of a div block.
This is some text inside of a div block.

Heading

This is some text inside of a div block.

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

This site is in beta. Tell us what you think.
Chapter # | Guidebook name Guidebook

The AI Explorer's Forum

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New Concept/Section Header

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Reflection

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Recap

  • recap bullet points

Read More:

  • read more links

Digital leaders and teams are fluent in five key pillars:

Thinking

The mental models of the 20th century won’t allow us to see the future clearly. We need to upgrade our thinking in order to realize the opportunities of the digital age.

Data

In order to create digital value, we have to understand how data is structured, how it moves from system to system, and how it can be monetized in an ethical way.

Business Models

Digital business models and value propositions require new thinking about who creates value and how it is delivered.

Tools

Selecting and implementing tools for digital value creation is not as easy as it may seem. The right tool can save a lot of work—while the wrong tool can distract you from your goals.

Skills

A new set of skills is required for digital value to be created. Technical, intellectual, interpersonal and leadership skills need to be acquired and evolved.

How Exponential is Your Organization?

The higher your digital fluency, the more likely it is that you or your team can create digital value.

A graph demonstrating five stages of organizational digitization. 
1X: Analog
2X: Digital Operations for an Analog Organization
3-4X: Digitizing Value Despite the Circumstances
5-7X Significant Digital Momentum
8-10X Exponentially Digital
A hand drawn illustration of a old style horse drawn carriage holding two people  but without the horse.

New Thinking is Needed for New Technologies

Shifts in thinking about technology tend to follow a pattern. When cars first came into being around 1900, people had no frame of reference for what designers were calling 'automobiles.' So they became known as “horseless carriages.” Why? When people needed transportation, they thought of horses. The concept of a world full of automobiles as commonplace as horses, as visionaries like Ford and Daimler imagined, was so far beyond comprehension that it seemed liked a fantasy. So we had to view the future through the lens of the past—as a horseless carriage. Only much later, with widespread understanding of automobiles, did the network of roads of the modern age come about.

This is an example of 'unlearning.' Before the exponential value of a new way of doing things with advanced technology can be realized, we usually need some time to identify and let go of existing mindsets and practices. It's not just enough to learn new knowledge about digital technologies—we have unlearn our old practices.

Advanced technologies may not look impressive at first, while our thinking catches up with their potential. The language of “more, better, faster” lets us know when we are approaching technology with a limiting, incremental, analog mindset—and indicates where we need to raise the minimum level of fluency so we can see what’s really possible.

For an example of analog vs. digital thinking, consider the internet. Early users thought of the internet as an incremental improvement on analog ways of doing things: more information (online newspapers), a better way to promote products (the banner advertisement), and a faster postal system (e-mail). Most people couldn’t foresee what paradigms the internet makes possible today (like social networks, massive multiplayer gaming and remote robotic surgery, to name but a few) until a critical mass of people—from many disciplines—became fluent in how it worked and discovered new digital ways of thinking about value, information and community.

Analog, linear thinking: more, better, faster

Building a Network of Digitally-Fluent Thinkers

Digital transformation is partly a function of harnessing the power of network effects to connect thinkers.

Here, we mean network effects to be the exponentially-increasing value of a network as more nodes are added to them. For example, a telephone network with three members is exponentially more valuable than a telephone network with only two members; the same can be said for online social networks or transit networks. Similarly, a network of thinkers who understand digital possibilities becomes exponentially more valuable as more members are added to it.

When raising the digital fluency of an organization, attend to the size, quality and growth (or attrition) rate of your network. Some organizations find it helpful to determine key groups. For example…

A trophy icon

Digital “Champions,” business leaders who bring resources and remit

Binoculars icon

Digital “Explorers,” who discover new opportunities and share about new models of value creation

Personal electronics icon

Digital “Makers,” who have the technical know-how to make prototypes

Measure the number of people in these groups, how well-connected they are to each other, and if you are gaining or losing members.

As someone bringing digital fluency to your organization, it might be helpful to think of yourself as a matchmaker or outfitter to these various members.

Thinking For A Digital Era

Transform your mindsets for the digital era.
Read the Guidebook

Data Supply Chain

Infographic:
This representation of a data supply chain is segmented into three columns with two to three corresponding segments underneath each larger theme. 
Disclosure contains Acquire and Store. 
Manipulate contains Aggregate and Analyze. 
Consume contains
Use, Share/Sell, and Dispose.

To understand how data works, we need to understand the data supply chain, so we're going to apply some computational thinking to help us think through how all of these pieces fit together. There are three stages of the data supply chain:

1) Disclosure, whether by a person or a sensor or a system;

2) Manipulation, which is where we process data and understand what's possible with it or analyze it in some way; and

3) Consumption, where data is used by a stakeholder or fed back to users as insight about themselves.

At each phase from acquisition to storage, to aggregation, to analysis, to use, to sale and disposal, there are key implications and handoffs that have to happen that make sure that ethics are preserved and the efficiencies occur and that the data is still accurate.

Acquire

The first stage is data acquisition—data is collected from sensors, systems, and humans.

For the purposes of this article, let’s use the example of a driverless car or autonomous vehicle as the context for data’s motion—its journey—through the supply chain. In the acquisition stage of data, the car captures raw data from its on-board sensors, like cameras or speed sensors. It's just bits and bytes, and no work has been applied in terms of processing or thinking about it.

Assets & Things

An asset and things model asks the question, “what are our assets and how do we best protect and leverage them?” That might be tangible assets like real estate or money, or products a company acquired or manufactured.

It is estimated that every person online produced 1.7 MB of data every second in 2020.

That's 1.145 billion gigabytes of data a day.

The world produced twice as much data in 2021 as 2019, with prediction for the next five and ten years increasing exponentially.

Computational Thinking

Four puzzle pieces interlocking.

Decomposition:

breaking down a complex problem into several simpler problems

A collection of five sided shapes forming an abstracted icon.

Abstraction:

a model of a system which leaves out unnecessary parts

A stylized illustration of a gear.

Patterns:

using reusable components to minimize error and work

A stylized illustration of a gear rests in the center of a infographic of rectangles connected by lines that multiply in quantity from left to to right.

Algorithms:

a series of unambiguous instructions to process data, make decisions and/or solve problems

A graphic of a one to many series of rectangles indicating multiple choices is contained within a blue box.

Programs:

algorithms converted to programming languages; sometimes called applications

Exercise

Thinking Like a Computer

Think of a problem or process you're working with that could benefit from computational thinking, i.e., being broken down into individual steps. What key steps can you identify? When you look at the list of steps, notice how using computational thinking may change the way you see the problem.

Metadata

Metadata is data about another piece of data. We use it to understand, sort, and validate datasets to increase their usefulness. For example, the data in an MP3 is a recording of music, but the information about the artist and song name is metadata—additional data about the core data.

Other common examples of metadata include the send and receive dates of emails, the unique address of a server, or info about which app was used to post a particular message to Twitter. For example, a recent president was found to be using an unsecured phone to post things to Twitter—because Twitter shows which app posted a tweet.

Additional examples of metadata include when a computer file is created or modified, the number of times a post has been viewed on social media, or the number of times a song has been played on Spotify.

Explore the many attributes of data.

There are many, many more factors to consider when analyzing a data set. For a more comprehensive list of attributes, explore the "Attributes of Data" gallery.

Read More

Example

The backwards bicycle

Unlearning deeply embedded mental models is tough—but it can be done.

Check out this video for a great example of how deeply ingrained mental models can be.

Watch on YouTube

You’re not going to get exponential results with a “bike” (mental model) that’s a little better and a little faster. You're going to have to learn how to ride a backwards bicycle.

The good news is that it can be done, and it doesn't necessarily take eight months.

It takes rewiring your automatic responses, which means going through the awkward and frustrating phase where you don’t feel like you're good at what you’re doing.

In this stage, even 'knowing' what you need to do differently is not enough. As the narrator says, knowledge is not equal to understanding.

Why unlearning?

We think about learning as adding to what we already know. But sometimes what we already know gets in the way of learning something new.

When ways of thinking that used to be effective don’t work as well anymore, we need to find new ones. This often requires as much unlearning as learning.

Trying to learn new information without changing the underlying thinking is like trying to paint over peeling paint. You have to strip off the old paint first, otherwise the new paint won’t stick.

“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”
— attributed to Mark Twain

Example

The Big Dig

When the landscape changes, we need new maps.

This was the situation 15 years ago in Boston thanks to a project called the Big Dig. All the highways were moved from above-ground to below the city.

The navigation devices at the time were of little use because their internal maps became obsolete.

This is the situation we find ourselves in today: the landscape of business has changed, but we haven't yet updated our mental maps for how to succeed.

Example

Introduction of the iPhone

The 2007 announcement by Steve Jobs of the original iPhone is a great example of a horseless carriage.

He began by talking about how Apple was announcing three new products: a touch-screen music player, a mobile phone and an Internet communicator. Then he showed how this wasn’t three products but one.

By doing this, he ensured that people understood the iPhone wasn’t just a phone but had all three of these capabilities.

Exponential thinking is hard for humans

Our brains evolved in an incremental world.

Imagine someone taking 30 steps. These are incremental steps of about two feet each. You have a pretty good sense that it’s about the length of a very large room.

Now imagine someone taking 30 exponential steps, meaning each step doubles in length from the one before. How far would that be?

It turns out it’s nearly the distance to the moon.

An image of the Moon.

Example

Nupedia and Wikipedia

It takes exponential thinking to deliver exponential results.

Wikipedia was originally called Nupedia. It had the same management, technology and mission: to be the world's largest international peer-reviewed encyclopedia.

But Nupedia had a central team that reviewed articles using a seven-step approval process. The result? Only 21 articles got approved in the first year.

Then they reinvented themselves as Wikipedia, which is based on a very different mental model: one that sees the audience not as consumers, but as producers.

With the community editing the articles in alignment with a shared purpose and set of principles, Wikipedia posted 18,000 articles in the first year.

Network effects drive 10x change

The key is unleashing network effects.

Network effects occur when value increases for all members of a network as each new member joins. With network effects, a single interaction can ripple out to generate multiple subsequent interactions.

“Kids Building Tall Tower with Kapla Blocks”

Consider building a tower of blocks or lining up a chain of dominos. Each block or domino can only impact the one right next to it. This kind of linear relationship can only deliver incremental results.

“Guinness World Record - Most dominoes toppled in a spiral”

Sometimes we try to transform large systems as if they were a row of perfectly-aligned dominoes, hoping we can change everything in perfect synchronicity. But real life rarely works this way.

“Exponential Growth”

Now imagine a contraption made up of mouse traps and ping pong balls, like the one in the video. Dropping a ball into the box leads to a chain reaction in which each ball can set off multiple other traps.

Where blocks and dominos are linear and connect one-to-one or one-to-many, ping pong balls generate a network effect because their pattern of connection is non-linear and many-to-many.

Example

Google Maps / Waze

Two different exponential strategies for navigation.

Consider the different strategies of Google Maps and Waze. Both are owned by Google and use a traditional advertising-based business model. Both also have the goal of helping people navigate and avoid traffic. But they have quite different exponential strategies.

Google Maps works by creating a network effect between devices and data. Google can tell where people’s mobile phones are and how fast they are moving at any given time, a passive source of data. That enables them to know where the traffic is in real time. Google Maps is what people use to navigate in unknown circumstances.

By contrast, Waze focuses on creating a network effect between drivers. Waze has cultivated a community where people share what they are seeing as they are driving, so much so that they use Waze to report on their daily commutes, not just to navigate to new destinations.

With Google Maps, devices do the reporting; with Waze, it’s people. They offer different kinds of value, and many people use both.
This is some text inside of a div block.

Example

Uber / Airbnb

Most people don’t realize that the source of their growth comes from a network effect.

Both Uber and Airbnb have platform-based business models. They don’t turn inputs into outputs like most companies. Instead, they are multi-sided platforms that connect supply and demand.

Uber doesn’t employ drivers or own vehicles. Airbnb doesn’t own hotels or employ housekeepers. Instead, Uber’s platform connects people who have cars with people who want rides, and Airbnb connects people who have rooms with people who want a place to stay.

Take a look at these charts for Uber and Airbnb. You can see how the number of drivers and rooms have grown exponentially. This would never have been possible for a taxi company or hotel chain with a linear business model.

Uber and Airbnb aren’t alone in using multi-sided platforms as business models. YouTube, Paypal and Amazon have all done the same: connecting creators and viewers, senders and receivers, and retailers and consumers.

A graph showing the number of new driver-partners starting at Uber each month between July 2012 and January 2015. It is an exponential upward curve.A graph showing the number of guests staying with AirBnB Hosts from 2010 to 2015. It is an exponential upward curve.

The vision gap

How can you help people see the direction more clearly?

A graph illustrating the 'vision gap', which occurs in the 'Launch' and 'Build' phases. A caption reads "Help people get comfortable with moving in a compelling direction towards and undefined destination."

When you are on an exponential path, you don’t have a line of sight to the goal. It’s like sending a rocket into space. You know it’s going up, but you can’t see where it is ultimately going to go.

This poses a challenge to the incremental mindset which wants to know exactly where you are going, how you will get there, how long it will take, and how much it will cost.

To overcome the vision gap, you need to help people get comfortable with moving in a compelling direction towards an undefined destination.

A strong Shared Purpose and Strategic Narrative help create a compelling direction. You can also find a “horseless carriage” to help people make a mindshift that makes the new direction clearer.

"But this is Day 1 for the internet and, if we execute well, for Amazon.com. Today, online commerce saves customers money and precious time. Tomorrow, through personalization, online commerce will accelerate the very process of discovery. "

-Jeffrey P. Bezos: 1997 Letter to Shareholders

Incremental Metrics

Track progress against pre-defined goals, like profit or revenue.

Exponential Metrics

Measure network effects of progress towards creating them, like network size, quality, or growth rate.

Purpose WITH is about co-creation. It’s the Shared Purpose at the heart of your Narrative.

This is what you are creating with others. It is the Shared Purpose to which all your stakeholders are contributing.

It’s important to remember that the purpose you share with your customers and other stakeholders is not just philanthropic. It is not separate from your business—rather, it’s the context for your business.

It is why you do what you do—and why others would want to do it with you.

Your Purpose WITH is something you don’t have to defend or persuade people about. It’s a truth that you hold to be self-evident. For instance, “life, liberty and the pursuit of happiness” is the Shared Purpose of the United States.

How do Thinking Styles work?

The idea is that thinking tends to focus on Ideas, Process, Action or Relationships, and to be oriented towards the big picture or details.

By knowing how members of your team and organization think—and by others knowing how you think—everyone can be more energized, more engaged, more creative and more productive.

Remember that most people are comfortable with more than one Thinking Style, and styles can be fluid and change in different settings. However, most people have one or more dominant styles, just as they are either left- or right-handed.

A mountaintop icon
Explorer thinking is about generating creative ideas
A gps map icon
Planner thinking is about designing effective systems
A lightning bolt icon
Energizer thinking is about mobilizing people into action
Two linked circles icon
Connector thinking is about building and strengthening relationships
A dictionary icon
Expert thinking is about achieving objectivity and insight
A graph and gear icon
Optimizer thinking is about improving productivity and efficiency
A checklist icon
Producer thinking is about achieving completion and momentum
A speech bubble icon
Coach thinking is about cultivating people and potential

Exercise

What’s your Thinking Style?

When you know your predominant Thinking Styles, you know what naturally energizes you, why certain types of problems are challenging or boring, and what you can do to improve in areas that are important to reaching your goals.

Once you know your default styles, it helps to share them with others, and to have others share theirs with you. In this way, your Thinking Style becomes a useful tool—a kind of social currency—for the team.

iPad with images of quizzes

Shift from E-Mail to Chat

In the e-mail approach, messages are drafted and edited until close to perfect and then sent, just like a paper letter might have been. Text is used to encapsulate (hopefully) complete thinking and convey a thought process from start to finish.

In chat, communication is immediate and bi-directional, like a conversation. This can be a challenging transition for leaders and employees who are used to having time to compose their thoughts. For this reason, and because of unconscious bias towards existing ways of communicating, new users of modern chat systems often use them like an abbreviated e-mail system at first.

Common reasons to perform analysis of data:

Describe

Describe what happened in the past or what is happening in the present.

Example: determine last quarter's profitability or today's air quality level.

Predict

Predict trends or potential outcomes with some degree of certainty, based on past and present data.

Example: forecast next quarter's sales or tomorrow's weather.

Recommend

Recommend options to humans or machine systems based on a variety of inputs.

Example: suggesting movies or news stories to users based on what their peers have watched or read, or proposing changes to text based on the tone a reader might expect.

Decide

Decide the next action without ambiguity—and without additional human input.

Example: accept or reject a user's request for credit.

Diagnose

Diagnose the underlying cause(s) of a result.

Example: identify which part is broken in a car's motor, or find the source of new sales activity.

Discover

Discover new opportunities or combinations within a particular domain.

Example: find unidentified features in photographs, trending discussions on social media, or commonalities between successful investment strategies.

Three key stages of data management:

Data Disclosure

Data at Rest

Data may be sourced from archives or other backups

Guideline: Ensure the context of original consent is known and respected; data security practices should be revisited regularly to minimize risk of accidental disclosure. Aggregating data from multiple sources often represents a new context for disclosure; have the responsible parties made a meaningful effort to renew informed consent agreements for this new context?

Data in Motion

Data is collected in real-time from machine sensors, automated processes, or human input; while in motion, data may or may not be retained, reshaped, corrupted, disclosed, etc.

Guideline: Be respectful of data disclosers and the individuals behind the data. Protect the integrity and security of data throughout networks and supply chains. Only collect the minimum amount of data needed for a specific application. Avoid collecting personally identifiable information or any associated meta-data whenever possible. Maximize preservation of provenance (or lineage).

Data Manipulation

Data at Rest

Data is stored locally without widespread distribution channels; all transformations happen locally.

Guideline: Set up a secure environment for handling static data to minimize the risk of security breaches; ensure data is not mistakenly shared with external networks. Data movement and transformation should be fully auditable.

Data in Motion

Data is actively being moved or aggregated; data transformations use multiple datasets or API calls which might be from various parties; the public Internet may be used for data access or transformation.

Guideline: Ensure that data moving between networks and cloud service providers is encrypted; shared datasets should strive to minimize the amount of data transferred and anonymize as much as possible. Be sure to destroy any temporary databases that contain aggregated data. Are research outcomes consistent with the discloser’s original intentions?

Data Consumption

Personal electronics icon

Data at Rest

Data analytics processes do not rely on live or real-time updates.

Guideline: Consider how comfortable data disclosers would be with how the derived insights are being applied. Gain consent, preferably informed consent, from data disclosers for application-specific uses of data.

Data in Motion

Data insights could be context-aware, informed by sensors, or benefit from streamed data or API calls.

Guideline: The data at rest guidelines for data consumption are equally important here. In addition, adhere to any license agreements associated with the APIs being used. Encrypt data. Be conscious of the lack of control over streamed data once it is broadcast. Streaming data also has a unique range of potential harms—the ability to track individuals, deciphering network vulnerabilities, etc.

100-Day Plan:

Over the next three months, these are the actions you can take to improve your informed consent practices and minimize potential harm:

  1. Evaluate existing ethics codes that your business has agreed to follow. Consider whether they have sufficient guidance for data ethics. If not, host a design session to draft your own Code of Data Ethics. Coordinate with partners and suppliers to ensure their future ability to honor your new Code.
  2. Build an operations plan for communicating and implementing your Code of Data Ethics by charting the roles that furnish, store, anonymize, access, and transform data on behalf of your customers.
  3. Evaluate any informed consent agreements your organization offers for language that may be unclear and could lead to misunderstandings between your business and your customers. Begin to develop a plan to address these inconsistencies by simplifying language and clarifying intent around data use.
  4. Pilot a Data Fluency training program for data scientists, technical architects, and marketing professionals. Use their feedback to refine a larger program for all employees.
  5. Implement regular reviews of data-gathering techniques. Involve a diverse group of stakeholders and maximize transparency of the proceedings.
  6. Perform a gap analysis of your company’s current cybersecurity strategies that provide threat intelligence and other ways of discovering and automatically mitigating potential data breaches. Enumerate the potential harms that could impact your customers if your company mishandles or discloses data about them. Identify the organizations responsible for safeguarding against these missteps and communicate your findings with them.
  7. Develop a training toolkit to teach your employees who interface with customers how to identify harms that occur through the use of your products. Priority-rank the groups within your company who should receive the training with the group that responds to the greatest variety of situations as the highest priority.
  8. Draft and launch a Data Fluency plan for ensuring a shared understanding of data usage and potential harms throughout your organization, including partners and vendors.

Traditional Data

Directly describes an asset’s market position or fundamentals

Broadly accessible, obvious, usually from within financial markets

Tends to be ‘now’ or ‘after the fact’

Tends to be free or low-cost

Often has a long, consistent history

Alternative Data

Can be used to infer fundamentals or something a/effecting fundamentals

Is ‘discovered’ or ‘mapped,’ sometimes not obvious—usually from outside financial markets

May be used to predict the future

Tends to be expensive

May be shorter or less consistent

Within the financial industry, there are many different APIs available. Here are some examples that are connected and recombined regularly in everything from disruptive personal budget apps to enormous multinational banks.

Service

Type

Records

Used for

Benzinga

News

Companies, stories, authors

Market intel

Yodlee, Plaid

Financial data portal for individuals

Accounts, investments, transactions, identity, etc.

Quickly creating 'consumer' fintech apps integrated with banks/others

Stripe, PayPal

Merchant services and know-your-customer

Users, payments, transactions, invoices

Enabling e-commerce

Apple Pay, Google Pay, authorize.net

Payment method/wallet

User, card numbers, authorizations

Making digital payments in person or online

Coinbase

Blockchain market/pricing data

Currencies

Understanding crypto positions

Ripple

Currency exchange

Currencies, Ripple's own currency

Reducing payments friction

Bloomberg

Pricing, market data, news

Price, news, etc.

Market intel

Crunchbase, Angellist

Company/startup info

Companies, investors, founders, developers

Tracking and understanding startups

Clearbit

Professional contacts

Individuals, companies, employment history, contact info

Updating CRMs

Recombination is key

Calls:
Verbs

Records/Results:
Nouns

...create mashups

Recombination is a key mindset for working with APIs—the mixing and matching of various elements of data and processing functions in different systems to create new value.

In the music world, 'mashups' refers to carefully layering and timing multiple tracks (and 'samples') to create a unique sound. Unlike remixes which might layer a new beat or add a guest appearance by another artist, mashups are significantly different from their original track.

In the same way, recombining bits of data and processing them in new ways can go beyond additive, incremental value to creating something entirely new—or vastly faster or cheaper than a human-mediated approach.

Recombination of APIs usually happens around a couple of key elements.

First, 'calls' are commands passed through an API, like 'get,' 'put' or 'delete.' These calls, which are always verbs, tell a remote system to do things like search, update or delete. These actions are often performed on 'records, or 'objects' which are entries in a database or other system; you can think of these as nouns.

Get

Search for contact records matching "apple" company with name "Johnny Appleseed"

Post

If a record exists, then update the record with new phone number "855-2222222"

If a record doesn't exist, then create a new record with that company, name and phone number

Delete

Delete contact

Recombination: Mashup

Let's go through a hypothetical example of a very simple algorithm (a script of precise actions for a computer to follow):

RECORD

CALL

API

IF a STORY about ACME is posted on BENZINGA

AND we hold stock in ACME

CREATE a COMPANY in SALESFORCE

THEN GET the STORY AND PUT it in SALESFORCE

In this example, a bank's systems are directed to send a search request (or 'query') to a financial news company, Benzinga, searching for any stories about Acme Incorporated. If there are any stories, the script checks to see if the bank holds stock in that company. If it does, the system will create a new company record in Salesforce (the bank's customer relationship management software), and then get the entire story text from the news service and attach it to that company record.

This used to be a manual operation—the cost of which might have been prohibitive to do at scale. But with APIs and the simplest of algorithms, these three different companies (the bank, Benzinga, and Salesforce) can be connected quickly and pass data and commands between them. In this case, the bank is a 'consumer' for the API 'provider' of Benzinga.

By recombining key functions from several systems, creative individuals inside the bank can automate simple actions that used to take humans a lot of time. They don't necessarily need to know how to manipulate code, either, with modern tools that provide human-friendly interfaces to machine systems.

Far more complex datasets, commands, and instruction sets can be applied here as part of various strategies for machine learning, app development, and other topics out of the scope of this guide. Nearly all modern technology strategies use APIs to connect different datasets and systems to each other.

Platforms don’t have to be digital.

Any resource that enables network effects can be a platform.

Example

Encyclopedias

Things:

Encyclopedia Britannica’s model focused on things and people: hiring writers and editors to produce books.

People:

Nupedia shifted to the right with a focus on people and technology, but it was still distributing knowledge through a digital pipe.

Ideas:

Encarta streamlined the pipes for information delivery by going totally digital with traditional encyclopedia content.

Connections:

Wikipedia moved to technology and relationships as a platform connecting a community of co-creators.

Elements of a platform thinking model

To design your platform thinking model, define these fundamental elements.  

In order to advance Shared Purpose, a platform needs to create a mindshift and connect available resources and social currency so it can generate network effects and achieve 10X results.

Shared Purpose

What is the goal shared by everyone connected by this platform?

Platform

What is the vehicle that creates exponential value through network effects?

Mindshift

What is the shift from one mental model to another required to use this platform?

Available Resources

What resources (things, people, ideas, connections) are connected to create network effects?

Network Effects

How is value increased as each new member
joins and participates?

10x Results

What exponential outcomes will this platform generate?

How to design principles

1. Find the fork

2. Develop a principle to direct the choice

3. Test and refine

1. Find the fork

A “fork in the road” is a decision that requires Decision Principles, because either path could be viable. You can imagine reasons to take either one, depending on circumstances, and you can’t know in advance what the circumstances will be.

“Be safe” isn’t a Principle because it’s so vague that there’s no way to base a decision on it, but also because its alternative, “Risk injury,” isn’t reasonable for most people.

One large company developed a Principle that called for employees to “be all in.” That may seem like a value at first, but you can tell that it’s a decision principle because there’s a viable alternative: “Change incrementally to hedge your bets.” And, in fact, there are situations in which it makes sense to do just that.

If you’re developing Principles around the value of collaboration, your choice isn’t whether or not to collaborate; it’s how to handle the inevitable disagreements that come up between people who are collaborating.

Similarly, if you’re developing Principles around the value of innovation, you’ve already chosen to take risks; now you need help deciding which particular risks are worth taking and how much risk to take at any given moment.

2. Develop a principle to direct the choice

This step bridges the gap between your values and goals and the Decision Principles that helps you achieve them.

For example, let’s say your organization has a value of maintaining focus and agility. To understand how that might translate into Decision Principles, think of how you would coach a tennis player to stay focused and agile while returning a serve: stay on your toes, keep your eye on the other player, be prepared to let the serve go by, etc.

Here are three ways to turn a goal or value into a decision principle:

  1. Start with an existing value statement and make it more specific. “Be customer-centric” is a good value, but it doesn’t tell you how to make choices that result in customer-centricity. To create guidelines that direct your choices, make it more specific—like Amazon’s “Start from the customer and work backwards” or “Consider competitors but obsess about customers.”
  2. Start with an existing rule or policy that’s too rigid, and loosen it to provide more autonomy based on context. “All processes must be open”
    is a rigid rule. “Default to an open process unless there’s a good reason not to, in which case a closed process is OK” leaves room for judgment at the fork in the road.
  3. Look for places where no policy or rule exists to guide decisions, and base Decision Principles on general values. This is how IBM approached the challenge of creating social media guidelines for employees. The fork in the road was “How do we manage the way employees present themselves in public so that it reflects well (or at least not poorly) on the company?”

IBM’s Principles give employees autonomy to behave as they wish on social media as long as they stay within the boundaries of the organization’s corporate values. In an example of nested Principles, the first item in IBM’s social media policy is “Know and follow IBM's Business Conduct Guidelines.”

3. Test and refine

A new Decision Principle is successful if it meets these criteria:

It’s neither too broad nor too specific. It doesn’t lapse into vague value statements that give employees too much autonomy, but it also doesn’t overprescribe what to do to the point of eliminating all flexibility.

It points out available, viable options and leaves the choice between them open. For example, IBM could have created a rule requiring all of its employees to announce their IBM affiliation every time they post anything anywhere online. However, in some situations, that’s neither relevant nor appropriate, so IBM made Decision Principles that allows employees to decide whether to identify their employer.

It’s phrased in a way that is easy to remember and fits your corporate culture. Ideally, decision principles are conversational phrases that people can recall and use in the moment, rather than having to look them up every time they have to make a decision.

IBM’s social media Decision Principles includes “Be yourself,” “Try to add value,” and “Respect your audience.” Another company turned its value of being ethical into the principle “Would your mother approve?”

Explorer thinking is about generating creative ideas.

Explorer

Details
Big Picture

Planner thinking is about designing effective systems.

Planner

Energizer thinking is about mobilizing people into action.

Energizer

Connector thinking is about 
building & strengthening relationships.

Connector

Expert thinking is about achieving objectivity and insight

Expert

Optimizer thinking is about improving productivity & efficiency.

Optimizer

Producer thinking is about achieving completion & momentum.

Producer

Coach thinking is about cultivating people & potential.

Coach

Orientation
Ideas
Relationships
Action
Process
Focus

Click on a Thinking Styles tile to flip to the definition

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Time to Walk: Bernice A. King

ChatGPT is suddenly everywhere. Are we ready? | Engadget

This article explores the potential impact machine coworkers like robots, low-code tools and plug-and-play automation systems are just beginning to have on jobs.

Working With Robots in a Post-Pandemic World

The EU is participating in an international data ethics process as it proposes new bills that would "allow consumers to sue companies for damages—if they can prove that a company’s AI harmed them." This could cause a stifling impact on innovation—but it also could be a major tool to prevent algorithmic bias and other downsides of poor AI.

The EU wants to put companies on the hook for harmful AI | MIT Technology Review

When self-driving cars cause harm, who is responsible? This problem exploration looks into the ethics, data and complexity of manufacturing AI.

Why courts need 'explainable AI' when self-driving cars crash

The text-processing engine GPT-3 (by OpenAI) learned the worst biases of humans amplified by the internet. See how quickly things went wrong to see the importance of future AIs growing up right.

GPT-3’s bigotry is exactly why devs shouldn’t use the internet to train AI

Network effects—network size, quality and growth rate—are critical to track for exponential projects and predict future paradigm changes. How do companies actually do it? VC firm Andreessen Horowitz explains.

How To Measure Network Effects | Future

Get a nine-minute quick take on what NFTs are and why they could transform economies around content creators.

Kayvon Tehranian: How NFTs are building the internet of the future | TED Talk

Keynes was wrong. Gen Z will have it worse.

Uploading one's mind to a computer, also known as whole brain emulation or brain uploading, is a theoretical concept in transhumanism and futurism that proposes to transfer the entirety of a person's consciousness, memories, and personality into a digital substrate, such as a computer or a robotic body. What could go right? What could go wrong? Kurzgesagt's thinkers and animators help us conceive of what some see as nirvana and others see as insanity.

Can You Upload Your Mind & Live Forever? - YouTube

In their prescient TED talk "We Are All Cyborgs Now," Amber Case, a cyborg anthropologist, argues that integration of technology into our daily lives has made us all cyborgs. She defines a cyborg as an organic being that uses technology to extend its physical and mental capabilities, and believes that our smartphones, computers, and other devices have become integral parts of our identity.

Amber Case: We are all cyborgs now - YouTube

As AI becomes integrated into society, there is growing concern about how these technologies may affect individual and human privacy, human rights, and societal values. But what about the rights of the machines? This rich visual journey explores various facets of the idea.

Do Robots Deserve Rights? What if Machines Become Conscious? - YouTube

Nanotechnology is often heralded as the answer to scarcity. Nanotech could mean abundant food, shelter, water, and the like. Diamond Age explores how old mental models of hierarchy and scarcity could still shape a world of abundant resources like AI and nanotech—and how tech could be appropriated by the poor to turn the tables.

The Diamond Age: Or, a Young Lady's Illustrated Primer (Bantam Spectra Book)

In A.I., we see what might happen if humanoid robots (androids) were to encounter a lost child in need of help. What would their initial programming guide them to do—and how might they evolve in response to the very human experiences they are all having?

A.I Artificial Intelligence

What if your Siri, Google Assistant, or Alexa became sentient—and became your friend? What if you fell in love with them—and they with you? If they had the ability to become exponentially intelligent, and you didn't, what might happen? This film explores what happens when an everyday person and an AI develop feelings for each other.

Her

I, Robot, loosely based on a classic Isaac Asimov sci-fi short story, asks the question of how we would investigate crimes committed by machines.Asimov's original story forwarded the idea of the 'three laws of robotics:'"First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law."What problems do you see with those laws? How could harm 'sneak through the cracks?'

I, Robot

Are you a nerd for cyborg anthropology? Read a discussion of the main points of Donna Haraway's classic 'Cyborg Manifesto!' (Might be a little densely academic).

A Cyborg Manifesto - Summary/Discussion on Wikipedia

Many of us are digitally fluent in the basic types of AI in today's headlines about ChatGPT and DALL-E, but want to know "why now?" This piece by Haomiao Huang dives into the… not-too-deep end? of why these 'generative' AI models have reached an inflection point. Unpacking the recent history and major network effects of the underlying models, datasets, and computing power, it's a great read on the trends in the field and why certain breakthroughs all seem to be happening at once.

The generative AI revolution has begun—how did we get here?

What if you were already a cyborg - a combination of human and machine? The Cyborg Manifesto explores the interlocking relationships between technology, power, and culture and is considered a fundamental text in futurist literature. (Note: dense academic text).

A Cyborg Manifesto

What would it mean if we could project a simulacrum of our dead loved ones? A new tech field is emerging, with major implications for how we process grief, retain generational knowledge, and ethically navigate our concept of those who have passed.

Technology that lets us speak to our dead relatives has arrived. Are we ready? | MIT Technology Review

Technology shapes, and is shaped by us. Developing ethical frameworks for the use and development of technologies is critical in establishing futures that are equitable, and kind, and thwart fascism.

When Futurism Led to Fascism—and Why It Could Happen Again

Eli Pariser, author of Filter Bubble, talks about the importance of being mindful of the incentives of commercial algorithms, which are biased towards attracting users to spend more and more time on platforms—but are not necessarily designed to have a balanced variety of viewpoints.

What obligation do social media platforms have to the greater good? | Eli Pariser - YouTube

Dollarstreet is a project of the myth-busting data site Gapminder. The site makes wealth disparity clearer by posing a set of uniform questions (and photo prompts) for households around the world, rich and poor. Explore the site to unlearn some of your assumptions about what poverty (and wealth) look like in different contexts.

Dollarstreet

Are machines coming for you and your jobs? Distinguish between automation of the industrial era and now to better understand our trajectory and the future of human (and machine) work.

The Rise of the Machines – Why Automation is Different this Time - YouTube

Using Digital Storytelling to Win Customers and Grow Your Business

What are containers and why do you need them?

Web scraping and purchasing data sets — the easiest way to get your hands on the world’s most valuable commodity | TechRadar

DevOps Monitoring

CI/CD & DevOps Pipeline Analytics: A Primer

Git Source Control Management

Comparing Workflows

Git Branching and Forking in the Enterprise: Why Fork?

VIDEO: Punch Card Programming for Mainframe Computers

How to get started with Continuous Integration

Atlassian Agile Coach: A Mind-Shift from Hours to Storypoints

CompTia: DevOps and DevSecOps

Virtual Reality for Rehab Specialists.

What is Innersource?

Causeit: Cybersecurity as an Enabler of Innovation

The Importance of Team Structure in DevOps

Team Topologies and DevOps

Don’t Choose Between Design Thinking, Lean Startup, and Agile When Focusing on Customer Value

The Agile Manifesto

Agile Manifesto Principles

Ideo: Design Thinking

Vanity Metrics: Definition, How To Identify Them, And Examples

Microservices vs SOA: What’s the Difference?

OKR vs. KPI: Which Goal-Setting Framework is Better? • Asana

OKRs 101 by What Matters

Team Structure: 10 Effective Ways to Organize Your Team

Measure What Matters

Asana Playbook to OKRs

T-shirt Sizing: An Agile Hack for Project Estimation

GitHub's Opensource Guide

An Introduction to Innersource

Commit (version control)

Wikipedia: Lean Startup

How to write a software requirement document (with template)

Atlassian Guide to Agile

Atlassian Guide to Continuous Integration

Stripe Atlas: the first five years and 20,000 startups - YouTube

Software Design Decoded: 66 Ways Experts Think

Amazon AWS: Continuous Integration

Code Repository Managers: A Comparison

Code Repositories

Automated Ticket Systems

Agile Manifesto

AI and machine learning in finance: use cases in banking, insurance, investment, and CX - Fintech News

Apple (and its visionary Steve Jobs) used very intentional language to introduce their revolutionary new iPhone in 2007—bridging familiar and unfamiliar concepts by using a kind of 'horseless carriage' concept that led to powerful unlearnings about the limits of mobile tech.

Apple Reinvents the Phone

Data Marketplaces: What, Why, How, Types, Benefits, Vendors

A great TED talk showing how quantum computing works in terms accessible to those of use who aren't quantum physicists.

TED: A beginner's guide to quantum computing | Shohini Ghose

Zapier report: Marketers lead the pack in automation at work

The API Product Mindset

Gartner Magic Quadrant for Full Life Cycle API Management 2021

Making API Decisions: Are You Connecting Business and Technical Interests? | ProgrammableWeb

API Directory | ProgrammableWeb

API product mindset

How Apple Is Organized for Innovation

This crowdsourced list of ways to unlearn things is a great (and diverse) starting point to find everyday strategies to intentionally adjust your biases and counteract social media's 'filter bubble.'

Resources to Unlearn Things

Before you begin a journey to "unlearn racism" you must first learn about it's history and development as a concept and a tool of political oppression. This article explores these histories while also examining the mindsets and motivations why individuals and groups would take on this task.

How to Unlearn Racism - Scientific American

[[Unlearning learned helplessness]]

In Doha, Qatar, at a TED conference sponsored largely by the Queen of Qatar, I saw this great talk delivered by expert statistician (and storyteller) Hans Rosling. He started with a provocative question—what is the relationship between fertility rates and religions? It was clear that nearly everyone in the audience thought they knew the answer. But did they?

Religion and Babies

[Unlearning Gender Roles]

[[Unlearning old ways of working]]

The 2007 announcement by Steve Jobs of the original iPhone is a great example of a horseless carriage.He began by talking about how Apple was announcing three new products: a touch-screen music player, a mobile phone and an Internet communicator. Then he showed how this wasn’t three products but one.By doing this, he ensured that people understood the iPhone wasn’t just a phone but had all three of these capabilities.

Apple Reinvents the iPhone (video)

[[Unlearning heteronormative things]]

We know things. But we don't always know how we know. In this whirlwind tour of surprising statistics, expert statisticians help us see how our personal experiences, education, and media consumption all result in our flawed understandings of the world—that we take to be truths.

How not to be ignorant about the world

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

People systematically overlook subtractive changes

The authors lay out four stages people pass through when learning any new skill. People are:1. Unconsciously unskilled 2. Consciously unskilled 3. Consciously skilled 4. Unconsciously skilled. It is the first and fourth stages where unlearning is vital. Our 'unconscious unskilled-ness' and also our 'unconscous skilled-ness' are both times when we are operating on autopilot, with data-sorting and decision-making happening out of our conscious view. This is where our biases and set ways of thinking are invisible to us.

Learning a New Skill is Easier Said Than Done - Gordon Training International

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

Less is more: Why our brains struggle to subtract

Unlearning deeply embedded mental models is tough—but it can be done. Check out this video for a great example of how deeply ingrained mental models can be. You’re not going to get exponential results with a “bike” (mental model) that’s a little better and a little faster. You're going to have to learn how to ride a backwards bicycle. The good news is that it can be done, and it doesn't necessarily take eight months. It takes rewiring your automatic responses, which means going through the awkward and frustrating phase where you don’t feel like you're good at what you’re doing. In this stage, even 'knowing' what you need to do differently is not enough. As the narrator says, knowledge is not equal to understanding.

The backwards bicycle

Walgreens created a prescription refill API to help providers and other stakeholders coordinate actions and data related to the refill process, with the aim of improving treatment plan adherence and reducing costs and errors.

Walgreens Pharmacy Prescription Refill API | ProgrammableWeb

Algorithmic decision-making in healthcare settings promises to provide better, more equitable and efficient care—but can only do so if we shift mindsets and provide good data into those systems. The qqual rights watchdog American Civil Liberties Union lays out the risks of both action and inaction.

Algorithms Are Making Decisions About Health Care, Which May Only Worsen Medical Racism | News & Commentary | American Civil Liberties Union

Want to go to the river, but unsure if you'll be swarmed by a cloud of mosquitos? Fear not, friend—data scientists and the bug repellant brand Off have come together to provide a tool that predicts mosquito populations via machine algorithm and live weather data.

Google and Off Launch Mosquito Forecast Tool - CNET

AR/VR technology can transform the practice of surgery and medical care. Fraunhofer's suite of software serve as machine coworkers to provide data-backed decision support about the best strategies for surgical interventions and risk reduction.

Liver Surgery - Fraunhofer MEVIS

Is it the new Marvel movie with heroic doctors? No, it's real-world AR/VR technology giving surgeons superpowers.

This tech uses augmented reality to give surgeons 'superpowers' - CNN

Getting your lab tests done shouldn't be painful or frustrating. AccuVein is an augmented reality tool that uses near-infrared technology to help practitioners find a vein with ease.

Augmented Reality | AccuVein

Figs captured the attention of the healthcare industry by offering scrubs as a lifestyle brand with more in common with fashion than with stiff and scratchy uniforms. Figs see the value in self-expression and empower their customers to be "Awesome Humans" who take pride in their profession and appearance.

This Company is Fast Becoming the Warby Parker of Scrubs - WSJ

This "Dear Apple" video shows real users of the Apple Watch who have written to Apple to share how the device has changed their lives. Each user had a positive experience based on little data—the data about them as an individual. Watch it to experience what little data feels like versus the more generic strategies of big data.

Dear Apple [Apple Watch]

Procedures live on even after they’ve been proved ineffective. It can lead to harms and wasted resources. This piece unpacks what it means to unlearn stuck ways of operating amongst professionals used to being the 'smartest ones in the room.'

It’s Hard for Doctors to Unlearn Things. That’s Costly for All of Us.

Analog, linear thinking: more, better, faster

Advanced technologies may not look impressive at first, while our thinking catches up with their potential. The language of “more, better, faster” lets us know when we are approaching technology with a limiting, incremental, analog mindset—and indicates where we need to raise the minimum level of fluency so we can see what’s really possible.

For an example of analog vs. digital thinking, consider the internet. Early users thought of the internet as an incremental improvement on analog ways of doing things: more information (online newspapers), a better way to promote products (the banner advertisement), and a faster postal system (e-mail). Most people couldn’t foresee what paradigms the internet makes possible today (like social networks, massive multiplayer gaming and remote robotic surgery, to name but a few) until a critical mass of people—from many disciplines—became fluent in how it worked and discovered new digital ways of thinking about value, information and community.

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Working With Robots in a Post-Pandemic World
Working With Robots in a Post-Pandemic World Plug-and-play automation systems can be rapidly set up to meet sudden surges in demand — and quickly reconfigured when needs change.
MIT
Garrett Pepper

This article explores the potential impact machine coworkers like robots, low-code tools and plug-and-play automation systems are just beginning to have on jobs.

The EU wants to put companies on the hook for harmful AI | MIT Technology Review
MIT Technology Review
Garrett Pepper

The EU is participating in an international data ethics process as it proposes new bills that would "allow consumers to sue companies for damages—if they can prove that a company’s AI harmed them." This could cause a stifling impact on innovation—but it also could be a major tool to prevent algorithmic bias and other downsides of poor AI.

GPT-3’s bigotry is exactly why devs shouldn’t use the internet to train AI
TNW (The Next Web),TNW Neural
MJ Petroni
CEO and Cyborg Anthropologist

The text-processing engine GPT-3 (by OpenAI) learned the worst biases of humans amplified by the internet. See how quickly things went wrong to see the importance of future AIs growing up right.

How To Measure Network Effects | Future
Network effects are one of the most important dynamics in software and marketplace businesses. But they’re often spoken of in a binary way: either you have them, or you don’t. In practice, most companies’ network effects are much more complex, falling along a spectrum of different types and strengths.
Future by a16z
MJ Petroni
CEO and Cyborg Anthropologist

Network effects—network size, quality and growth rate—are critical to track for exponential projects and predict future paradigm changes. How do companies actually do it? VC firm Andreessen Horowitz explains.

Can You Upload Your Mind & Live Forever? - YouTube
The desire to be free from the limits of the human experience is as old as our first stories. We exist in an endless universe, only bound by the laws of physics and yet, our consciousness is trapped in mortal machines made of meat. With the breathtaking explosion of innovation and progress, for the first time the concept of leaving our flesh piles behind and uploading our minds into a digital utopia seems possible. Even like the logical next step on our evolutionary ladder.
Kurzgesagt – In a Nutshell
Garrett Pepper

Uploading one's mind to a computer, also known as whole brain emulation or brain uploading, is a theoretical concept in transhumanism and futurism that proposes to transfer the entirety of a person's consciousness, memories, and personality into a digital substrate, such as a computer or a robotic body. What could go right? What could go wrong? Kurzgesagt's thinkers and animators help us conceive of what some see as nirvana and others see as insanity.

Amber Case: We are all cyborgs now - YouTube
http://www.ted.com Technology is evolving us, says Amber Case, as we become a screen-staring, button-clicking new version of homo sapiens. We now rely on "external brains" (cell phones and computers) to communicate, remember, even live out secondary lives. But will these machines ultimately connect or conquer us? Case offers surprising insight into our cyborg selves. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. Featured speakers have included Al Gore on climate change, Philippe Starck on design, Jill Bolte Taylor on observing her own stroke, Nicholas Negroponte on One Laptop per Child, Jane Goodall on chimpanzees, Bill Gates on malaria and mosquitoes, Pattie Maes on the "Sixth Sense" wearable tech, and "Lost" producer JJ Abrams on the allure of mystery. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate.
TED
Garrett Pepper

In their prescient TED talk "We Are All Cyborgs Now," Amber Case, a cyborg anthropologist, argues that integration of technology into our daily lives has made us all cyborgs. She defines a cyborg as an organic being that uses technology to extend its physical and mental capabilities, and believes that our smartphones, computers, and other devices have become integral parts of our identity.

Do Robots Deserve Rights? What if Machines Become Conscious? - YouTube
Kurzgesagt
Garrett Pepper

As AI becomes integrated into society, there is growing concern about how these technologies may affect individual and human privacy, human rights, and societal values. But what about the rights of the machines? This rich visual journey explores various facets of the idea.

The Diamond Age: Or, a Young Lady's Illustrated Primer (Bantam Spectra Book)
Amazon
Garrett Pepper

Nanotechnology is often heralded as the answer to scarcity. Nanotech could mean abundant food, shelter, water, and the like. Diamond Age explores how old mental models of hierarchy and scarcity could still shape a world of abundant resources like AI and nanotech—and how tech could be appropriated by the poor to turn the tables.

A.I Artificial Intelligence
Amazon
Garrett Pepper

In A.I., we see what might happen if humanoid robots (androids) were to encounter a lost child in need of help. What would their initial programming guide them to do—and how might they evolve in response to the very human experiences they are all having?

Her
Amazon
Garrett Pepper

What if your Siri, Google Assistant, or Alexa became sentient—and became your friend? What if you fell in love with them—and they with you? If they had the ability to become exponentially intelligent, and you didn't, what might happen? This film explores what happens when an everyday person and an AI develop feelings for each other.

I, Robot
Amazon
MJ Petroni
CEO and Cyborg Anthropologist

I, Robot, loosely based on a classic Isaac Asimov sci-fi short story, asks the question of how we would investigate crimes committed by machines.Asimov's original story forwarded the idea of the 'three laws of robotics:'"First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law."What problems do you see with those laws? How could harm 'sneak through the cracks?'

A Cyborg Manifesto - Summary/Discussion on Wikipedia
Wikipedia
MJ Petroni
CEO and Cyborg Anthropologist

Are you a nerd for cyborg anthropology? Read a discussion of the main points of Donna Haraway's classic 'Cyborg Manifesto!' (Might be a little densely academic).

The generative AI revolution has begun—how did we get here?
We’re in another cycle, this time with generative AI. Media headlines are dominated by news about AI art, but there’s also unprecedented progress in many widely disparate fields. Everything from videos to biology, programming, writing, translation, and more is seeing AI progress at the same incredible pace. Why is all this happening now?
Ars Technica
MJ Petroni
CEO and Cyborg Anthropologist

Many of us are digitally fluent in the basic types of AI in today's headlines about ChatGPT and DALL-E, but want to know "why now?" This piece by Haomiao Huang dives into the… not-too-deep end? of why these 'generative' AI models have reached an inflection point. Unpacking the recent history and major network effects of the underlying models, datasets, and computing power, it's a great read on the trends in the field and why certain breakthroughs all seem to be happening at once.

A Cyborg Manifesto
University of Warwick
Garrett Pepper

What if you were already a cyborg - a combination of human and machine? The Cyborg Manifesto explores the interlocking relationships between technology, power, and culture and is considered a fundamental text in futurist literature. (Note: dense academic text).

Technology that lets us speak to our dead relatives has arrived. Are we ready? | MIT Technology Review
Technology that lets us “speak” to our dead relatives has arrived. Are we ready? Digital clones of the people we love could forever change how we grieve. By Charlotte Jeearchive page
Garrett Pepper

What would it mean if we could project a simulacrum of our dead loved ones? A new tech field is emerging, with major implications for how we process grief, retain generational knowledge, and ethically navigate our concept of those who have passed.

When Futurism Led to Fascism—and Why It Could Happen Again
The Italian Futurists praised invention, modernity, speed, and disruption. Sound familiar?
Wired
Garrett Pepper

Technology shapes, and is shaped by us. Developing ethical frameworks for the use and development of technologies is critical in establishing futures that are equitable, and kind, and thwart fascism.

What obligation do social media platforms have to the greater good? | Eli Pariser - YouTube
Social media has become our new home. Can we build it better? Taking design cues from urban planners and social scientists, technologist Eli Pariser shows how the problems we're encountering on digital platforms aren't all that new -- and shares how, by following the model of thriving towns and cities, we can create trustworthy online communities.
TED
MJ Petroni
CEO and Cyborg Anthropologist

Eli Pariser, author of Filter Bubble, talks about the importance of being mindful of the incentives of commercial algorithms, which are biased towards attracting users to spend more and more time on platforms—but are not necessarily designed to have a balanced variety of viewpoints.

Dollarstreet
Imagine the world as a street ordered by income. Everyone lives somewhere on the street. The poorest lives to the left and the richest to the right. Everybody else live somewhere in between.
Dollarstreet (Gapminder project)
MJ Petroni
CEO and Cyborg Anthropologist

Dollarstreet is a project of the myth-busting data site Gapminder. The site makes wealth disparity clearer by posing a set of uniform questions (and photo prompts) for households around the world, rich and poor. Explore the site to unlearn some of your assumptions about what poverty (and wealth) look like in different contexts.

The Rise of the Machines – Why Automation is Different this Time - YouTube
Automation in the Information Age is different.
Kurzgesagt
MJ Petroni
CEO and Cyborg Anthropologist

Are machines coming for you and your jobs? Distinguish between automation of the industrial era and now to better understand our trajectory and the future of human (and machine) work.

Apple Reinvents the Phone
Apple
MJ Petroni
CEO and Cyborg Anthropologist

Apple (and its visionary Steve Jobs) used very intentional language to introduce their revolutionary new iPhone in 2007—bridging familiar and unfamiliar concepts by using a kind of 'horseless carriage' concept that led to powerful unlearnings about the limits of mobile tech.

Making API Decisions: Are You Connecting Business and Technical Interests? | ProgrammableWeb
Making API Decisions: Are You Connecting Business and Technical Interests? API UNIVERSITY Analysis, API Strategy, API Education Sep 27, 2017By Mark Boyd, ProgrammableWeb Staff Welcome to the series on Maximizing the ROI on Your API. When a business or enterprise commences its API journey, it has a number of key decisions that need to be made. Not surprisingly, at each decision point, multiple options Branch out and those employees new to API strategy and design can become confused and cautious quickly. How do you make the decisions to embark on a successful API journey? Increasingly, businesses of all sizes are recognizing the API opportunity. The need to move quickly and create new products, to connect disparate data and service systems, to capitalize dormant assets and datasets, and to strengthen (and extend) customer relationships makes APIs the go-to solution for businesses, enterprise, governments, not-for-profits, and startups on a growth trajectory. However, APIs are more than a technical solution; they're borne out of a business need, and as an API strategy is implemented, it needs to align with a company's overall business plan and be leveraged by individual business units, partners, suppliers, and customers. Externally, a growing demand for Integration and faster digital product development means your API strategy must build on this drive. Internally, employees may be resistant to change, they may be confused about the project's purpose, or they may simply dismiss APIs as a technical solution that the dev team should use. While at some point, decisions around an API strategy do become technical concerns, the process starts with business leadership, enters a collaborative stage, moves to technical departments for the completion of foundational systems, and then returns to being a business concern focused on how best to leverage the API strategy. How APIs move through waves of predominantly business to technical discussions and back again, and some of the decisions a company needs to make along the way, does reflect a typical development process: The API Decision Series Flowchart The Modular Enterprise To compete in today's digital, agile marketplace, businesses need to reimagine themselves as a series of re-organizable modular pieces that are connectable in ways that can respond in real time, allow for the movement of data and services along a workflow (and often automate it), and create reusable internal assets in new product and service design. Once that's done, businesses can consider a Platform and ecosystem model that assists consumers and partners in creating their own value as business partners. James Higginbotham James Higginbotham, author of A Practical Approach to API Design and founder of API consultancy LaunchAny, says that becoming API-centric is the first step towards reorienting a business as a modular enterprise. An API strategy is not just about building an API and publishing it, according to Higginbotham. "As technical managers and product managers, it's great to focus on the technology but don't forget the people behind the technology," Higginbotham told an audience of API business and tech leads at the 2016 API Strategy and Practice Conference in November 2016. "Look at the business and technical capabilities and map them into API capabilities. You then turn them into API products and release them to customer segments." Once that's done, he says, an organization can take further steps to decompose those capabilities into microservices. Throughout the API strategy development journey, businesses should need to organize their teams as product teams that create value. "When you think about project-based approaches, you think about one-off, fixed-end-date, date-driven development," cautions Higginbotham. Instead, he says, API strategy teams should include technical and product leads as well as technical writers, QA, scrum masters and others. "When you think about product-based approaches, you think repeatable and reusable systems with a results focus, and you can introduce metrics and evangelize what has been done," says Higginbotham. Higginbotham advocates a lean startup mindset, regardless of the organization's size, which means building an API strategy with a minimum viable product (MVP) trajectory. For example, when Walgreens started its API strategy the company chose to create the QuickPrints APITrack this API for its photo printing service first, because this area needed a new business model to reach customers' digital needs. Once they proved success with that API product line, they could build organizational support for more sensitive areas such as the Walgreens Pharmacy Prescription Refill APITrack this API. But Higginbotham makes clear that an MVP still speaks to all necessary components, meaning it must be based on a functional, reliable, usable, and emotional design. To illustrate, he references the work of Melbourne-based design and innovation expert, Jussi Pasanen: From "APIStrat 2016: Moving Toward a Modular Enterprise," a presentation made by James Higginbotham (Slide #25). Key to this mindset is starting with a clear understanding of your customers, in this case, the API consumers: developers who will be using your APIs. The first decision in an API strategy is to identify the various developer customer segments that will make use of your API. Create customer journey maps for how they'll find your API, test it, integrate with it, and build new products, services, and workflows using your APIs. Eventually, you'll want to add more developer segments, and you may be surprised to discover new customer segments are using your API that you hadn't thought to approach. But having a shortlist of who you expect to use your API and how they will use it is an essential starting point to building an effective API strategy. As we make decisions about the development of an API strategy, we must constantly ask, who benefits from this? What value are we unlocking? Make sure to view the Dixon Carphone case study, which showcases how a company can use APIs to take control of its sales process, revolutionizing how it works with its customers by more closely tying the shopping experience with customer service and support.   Resource List What Are APIs and How Do They Work? A Practical Approach to API Design APIStrat 2016: Moving Toward a Modular Enterprise Self-serve everywhere: Enhancing the Telcos Customer Experience The Hitch Pitch Deck: Building Support for an API Strategy In this series on API Decisions, we explore some of the key considerations you need to account for when answering these two questions and making API strategy decisions. You'll learn about resource lists, discover additional tools, and receive expert advice from leaders in the field during each stage. Be sure to read the next API Strategy article: How To Get the Team and Support in Place for Your API Strategy About the Author: Mark Boyd is a ProgrammableWeb writer covering breaking news, API business strategies and models, open data, and smart cities.
Resources to Unlearn Things
My New Years Resolution for 2018 involves the idea of 'unlearning' a lot of harmful paradigms, traditions, dynamics, etc, that I was exposed to while growing up in WASP-y, middle class,...
MetaFilter
MJ Petroni
CEO and Cyborg Anthropologist

This crowdsourced list of ways to unlearn things is a great (and diverse) starting point to find everyday strategies to intentionally adjust your biases and counteract social media's 'filter bubble.'

How to Unlearn Racism - Scientific American
How to Unlearn Racism Implicit bias training isn't enough. What actually works?
Scientific American
Garrett Pepper

Before you begin a journey to "unlearn racism" you must first learn about it's history and development as a concept and a tool of political oppression. This article explores these histories while also examining the mindsets and motivations why individuals and groups would take on this task.

Religion and Babies
Hans Rosling had a question: Do some religions have a higher birth rate than others -- and how does this affect global population growth? Speaking at the TEDxSummit in Doha, Qatar, he graphs data over time and across religions. With his trademark humor and sharp insight, Hans reaches a surprising conclusion on world fertility rates.
TED
MJ Petroni
CEO and Cyborg Anthropologist

In Doha, Qatar, at a TED conference sponsored largely by the Queen of Qatar, I saw this great talk delivered by expert statistician (and storyteller) Hans Rosling. He started with a provocative question—what is the relationship between fertility rates and religions? It was clear that nearly everyone in the audience thought they knew the answer. But did they?

Apple Reinvents the iPhone (video)
John Schroter
MJ Petroni
CEO and Cyborg Anthropologist

The 2007 announcement by Steve Jobs of the original iPhone is a great example of a horseless carriage.He began by talking about how Apple was announcing three new products: a touch-screen music player, a mobile phone and an Internet communicator. Then he showed how this wasn’t three products but one.By doing this, he ensured that people understood the iPhone wasn’t just a phone but had all three of these capabilities.

How not to be ignorant about the world
How much do you know about the world? Hans Rosling, with his famous charts of global population, health and income data (and an extra-extra-long pointer), demonstrates that you have a high statistical chance of being quite wrong about what you think you know. Play along with his audience quiz — then, from Hans’ son Ola, learn 4 ways to quickly get less ignorant.
TED
MJ Petroni
CEO and Cyborg Anthropologist

We know things. But we don't always know how we know. In this whirlwind tour of surprising statistics, expert statisticians help us see how our personal experiences, education, and media consumption all result in our flawed understandings of the world—that we take to be truths.

People systematically overlook subtractive changes
Improving objects, ideas or situations—whether a designer seeks to advance technology, a writer seeks to strengthen an argument or a manager seeks to encourage desired behaviour—requires a mental search for possible changes1,2,3. We investigated whether people are as likely to consider changes that subtract components from an object, idea or situation as they are to consider changes that add new components. People typically consider a limited number of promising ideas in order to manage the cognitive burden of searching through all possible ideas, but this can lead them to accept adequate solutions without considering potentially superior alternatives4,5,6,7,8,9,10. Here we show that people systematically default to searching for additive transformations, and consequently overlook subtractive transformations. Across eight experiments, participants were less likely to identify advantageous subtractive changes when the task did not (versus did) cue them to consider subtraction, when they had only one opportunity (versus several) to recognize the shortcomings of an additive search strategy or when they were under a higher (versus lower) cognitive load. Defaulting to searches for additive changes may be one reason that people struggle to mitigate overburdened schedules11, institutional red tape12 and damaging effects on the planet13,14.
Nature
MJ Petroni
CEO and Cyborg Anthropologist

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

Learning a New Skill is Easier Said Than Done - Gordon Training International
Learning a New Skill is Easier Said Than Done By Linda Adams, President of GTI Before rolling out specific training or initiatives that are aimed at improving some facet of your business, you need to ensure that your leaders and team members are equipped with fundamental communication and relationship management skills.
Gordon Training International
MJ Petroni
CEO and Cyborg Anthropologist

The authors lay out four stages people pass through when learning any new skill. People are:1. Unconsciously unskilled 2. Consciously unskilled 3. Consciously skilled 4. Unconsciously skilled. It is the first and fourth stages where unlearning is vital. Our 'unconscious unskilled-ness' and also our 'unconscous skilled-ness' are both times when we are operating on autopilot, with data-sorting and decision-making happening out of our conscious view. This is where our biases and set ways of thinking are invisible to us.

Less is more: Why our brains struggle to subtract
When solving problems, humans tend to think about adding something before they think of taking something away - even when subtracting is the better solution. Experiments show that this newly discovered psychological phenomenon applies across a range of situations from improving a physical design to solving an abstract puzzle.
nature video
MJ Petroni
CEO and Cyborg Anthropologist

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

The backwards bicycle
YouTube
MJ Petroni
CEO and Cyborg Anthropologist

Unlearning deeply embedded mental models is tough—but it can be done. Check out this video for a great example of how deeply ingrained mental models can be. You’re not going to get exponential results with a “bike” (mental model) that’s a little better and a little faster. You're going to have to learn how to ride a backwards bicycle. The good news is that it can be done, and it doesn't necessarily take eight months. It takes rewiring your automatic responses, which means going through the awkward and frustrating phase where you don’t feel like you're good at what you’re doing. In this stage, even 'knowing' what you need to do differently is not enough. As the narrator says, knowledge is not equal to understanding.

Algorithms Are Making Decisions About Health Care, Which May Only Worsen Medical Racism | News & Commentary | American Civil Liberties Union
Algorithms Are Making Decisions About Health Care, Which May Only Worsen Medical Racism Unclear regulation and a lack of transparency increase the risk that AI and algorithmic tools that exacerbate racial biases will be used in medical settings.
ACLU
MJ Petroni
CEO and Cyborg Anthropologist

Algorithmic decision-making in healthcare settings promises to provide better, more equitable and efficient care—but can only do so if we shift mindsets and provide good data into those systems. The qqual rights watchdog American Civil Liberties Union lays out the risks of both action and inaction.

Google and Off Launch Mosquito Forecast Tool - CNET
CNET
Garrett Pepper

Want to go to the river, but unsure if you'll be swarmed by a cloud of mosquitos? Fear not, friend—data scientists and the bug repellant brand Off have come together to provide a tool that predicts mosquito populations via machine algorithm and live weather data.

This Company is Fast Becoming the Warby Parker of Scrubs - WSJ
Figs’ slick, brightly lit approach to branding and marketing their scrubs has more in common with fashion brands than it does other medical apparel companies.
The Wall Street Journal
Garrett Pepper

Figs captured the attention of the healthcare industry by offering scrubs as a lifestyle brand with more in common with fashion than with stiff and scratchy uniforms. Figs see the value in self-expression and empower their customers to be "Awesome Humans" who take pride in their profession and appearance.

Dear Apple [Apple Watch]
Apple
MJ Petroni
CEO and Cyborg Anthropologist

This "Dear Apple" video shows real users of the Apple Watch who have written to Apple to share how the device has changed their lives. Each user had a positive experience based on little data—the data about them as an individual. Watch it to experience what little data feels like versus the more generic strategies of big data.

It’s Hard for Doctors to Unlearn Things. That’s Costly for All of Us.
Procedures live on even after they’ve been proved ineffective. It can lead to harms and wasted resources.
The New York Times
MJ Petroni
CEO and Cyborg Anthropologist

Procedures live on even after they’ve been proved ineffective. It can lead to harms and wasted resources. This piece unpacks what it means to unlearn stuck ways of operating amongst professionals used to being the 'smartest ones in the room.'

Chapter # | Guidebook name Guidebook

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