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Get Smart with Salesforce Einstein

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  1. Salesforce Einstein Basics

    Get Started with Einstein
  2. Get Started with Einstein
    7 Topics
  3. Learn About Einstein Out-Of-The-Box Applications
    7 Topics
  4. Responsible Creation of Artificial Intelligence
    Use the Einstein Platform
    9 Topics
  5. Understand the Ethical Use of Technology
    8 Topics
  6. Learn the Basics of Artificial Intelligence
    5 Topics
  7. Recognize Bias in Artificial Intelligence
    6 Topics
  8. Einstein Bots Basics
    Remove Bias from Your Data and Algorithms
    6 Topics
  9. Learn About Einstein Bots
    6 Topics
  10. Plan Your Bot Content
    4 Topics
  11. Einstein Next Best Action
    Learn the Prerequisites and Enable Einstein Bots
    3 Topics
  12. Get Started with Einstein Next Best Action
    9 Topics
  13. Sales Cloud Einstein
    Understand How Einstein Next Best Action Works
    7 Topics
  14. Increase Sales Productivity
    5 Topics
  15. Automate Sales Activities
    5 Topics
  16. Target the Best Leads
    3 Topics
  17. Close More Deals
    6 Topics
  18. Connect with Your Customers and Create New Business
    4 Topics
  19. Sales Cloud Einstein Rollout Strategies
    Improve Sales Predictions
    4 Topics
  20. Use AI to Improve Sales
  21. Start with a Plan
  22. Set Goals and Priorities
  23. Get Ready for Einstein
  24. Quick Start: Einstein Prediction Builder
    Start Using Sales Cloud Einstein
  25. Sign Up for an Einstein Prediction Builder Trailhead Playground
  26. Create a Formula Field to Predict
  27. Enrich Your Prediction
  28. Build a Prediction
  29. Quick Start: Einstein Image Classification
    Create a List View for Your Predictions
  30. Get an Einstein Platform Services Account
  31. Get the Code
  32. Create a Remote Site
  33. Create the Apex Classes
  34. Einstein Intent API Basics
    Create the Visualforce Page
  35. Get Started with Einstein Language
  36. Set Up Your Environment
  37. Create the Dataset
  38. Train the Dataset and Create a Model
  39. Put Predictions into Action with Next Best Action
    Use the Model to Make a Prediction
  40. Learn the Basics and Set Up a Custom Playground
  41. Define and Build a Prediction
  42. Customize Your Contact and List Displays
  43. Create Recommendations for Einstein Next Best Action
  44. Create a Next Best Action Strategy
  45. Add Next Best Action to Your Contacts

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Not familiar with AI? Before completing this module, check out the Artificial Intelligence for Business  module https://trailhead.salesforce.com/en/content/learn/modules/artificial-intelligence-for-business (part of the Get Smart with Salesforce Einstein trail) to learn what it is and how it can transform your relationship with your customers.

The terms machine learning and artificial intelligence are often used interchangeably, but they don’t mean the same thing. Before we get into the nitty-gritty of creating AI responsibly, here is a reminder of what these terms mean.

Machine Learning (ML)

When we talk about machine learning, we’re referring to a specific technique that allows a computer to “learn” from examples without having been explicitly programmed with step-by-step instructions. Currently, machine learning algorithms are geared toward answering a single type of question well. For that reason, machine learning algorithms are at the forefront of efforts to diagnose diseases, predict stock market trends, and recommend music.

Artificial Intelligence (AI)

Artificial intelligence is an umbrella term that refers to efforts to teach computers to perform complex tasks and behave in ways that give the appearance of human agency. Often they do this work by taking cues from the environment they’re embedded in. AI includes everything from robots who play chess to chatbots that can respond to customer support questions to self-driving cars that can intelligently navigate real-world traffic.

AI can be composed of algorithms. An algorithm is a process or set of rules that a computer can execute. AI algorithms can learn from data. They can recognize patterns from the data provided to generate rules or guidelines to follow. Examples of data include historical inputs and outputs (for example, input: all email; output: which emails are spam) or mappings of A to B (for example, a word in English mapped to its equivalent in Spanish). When you have trained an algorithm with training data, you have a model. The data used to train a model is called a training dataset. The data used to test how well a model is performing is call test dataset. Both training datasets and test datasets consist of data with input and expected output. You should evaluate a model with a different but equivalent set of data, the test dataset, to test if it is actually doing what you intended.

Bias Challenges in AI

So far, we've discussed the broad ethical implications of developing technology. Now, let's turn our attention to AI. AI poses unique challenges when it comes to bias and making fair decisions.