Get Smart with Salesforce Einstein
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Salesforce Einstein Basics
Get Started with Einstein -
Get Started with Einstein7 Topics
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Learning Objectives - Einstein
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AI Basics and Smart Assistants
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I Have AI and Smart Assistants Down. How Does Salesforce Einstein Fit In?
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But How Can Einstein Specifically Benefit My Business?
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So What Makes Einstein Different?
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Do I Have to Be a Genius to Use This? I’m Pretty Sure Einstein Was a Genius
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The Time Is Now for Salesforce Einstein
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Learning Objectives - Einstein
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Learn About Einstein Out-Of-The-Box Applications7 Topics
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Responsible Creation of Artificial IntelligenceUse the Einstein Platform9 Topics
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Understand the Ethical Use of Technology8 Topics
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Learn the Basics of Artificial Intelligence5 Topics
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Recognize Bias in Artificial Intelligence6 Topics
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Einstein Bots BasicsRemove Bias from Your Data and Algorithms6 Topics
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Learn About Einstein Bots6 Topics
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Plan Your Bot Content4 Topics
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Einstein Next Best ActionLearn the Prerequisites and Enable Einstein Bots3 Topics
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Get Started with Einstein Next Best Action9 Topics
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Learning Objectives - Einstein Next Best Action
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Rise of Business Intelligence
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A Wealth of Insights Brings a New Set of Challenges
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Better Recommendations with Einstein Next Best Action
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Unify Sources of Insight
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Connect Recommendations to Automation
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Surface Actionable Intelligence
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Applications for Different Lines of Business
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How Can I Get Einstein Next Best Action?
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Learning Objectives - Einstein Next Best Action
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Sales Cloud EinsteinUnderstand How Einstein Next Best Action Works7 Topics
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Increase Sales Productivity5 Topics
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Automate Sales Activities5 Topics
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Target the Best Leads3 Topics
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Close More Deals6 Topics
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Connect with Your Customers and Create New Business4 Topics
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Sales Cloud Einstein Rollout StrategiesImprove Sales Predictions4 Topics
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Use AI to Improve Sales
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Start with a Plan
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Set Goals and Priorities
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Get Ready for Einstein
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Quick Start: Einstein Prediction BuilderStart Using Sales Cloud Einstein
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Sign Up for an Einstein Prediction Builder Trailhead Playground
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Create a Formula Field to Predict
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Enrich Your Prediction
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Build a Prediction
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Quick Start: Einstein Image ClassificationCreate a List View for Your Predictions
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Get an Einstein Platform Services Account
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Get the Code
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Create a Remote Site
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Create the Apex Classes
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Einstein Intent API BasicsCreate the Visualforce Page
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Get Started with Einstein Language
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Set Up Your Environment
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Create the Dataset
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Train the Dataset and Create a Model
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Put Predictions into Action with Next Best ActionUse the Model to Make a Prediction
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Learn the Basics and Set Up a Custom Playground
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Define and Build a Prediction
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Customize Your Contact and List Displays
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Create Recommendations for Einstein Next Best Action
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Create a Next Best Action Strategy
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Add Next Best Action to Your Contacts
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Research shows that diverse teams (across the spectrum of experience, race, gender, and ability) are more creative, diligent, and hardworking. An organization that includes more women at all levels, especially top management, typically has higher profits. To learn more, check out the Resources section at the end of this unit.
Everything we create represents our values, experiences, and biases. For example, facial recognition systems often have more difficulty identifying black or brown faces than white faces. If the teams creating such technology had been more diverse, they would have been more likely to have recognized and addressed this bias.
Development teams should strive toward diversity in every area, from age and race to culture, education, and ability. Lack of diversity can create an echo chamber that results in biased products and feature gaps. If you are unable to hire diverse team members, consider seeking feedback from underrepresented groups across your company and user base.
A sense of community is also part of the ethical groundwork at a company. No one person should be solely responsible for acting ethically or promoting ethics. Instead, the company as a whole should be mindful and conscious of ethics. Employees should feel comfortable challenging the status quo and speaking up, which can identify risks for your business. Team members should ask ethical questions specific to their domains, such as:
- Product managers: What is the business impact of a false positive or false negative in our algorithm?
- Researchers: Who is impacted by our system and how? How can it be abused? How can people try to break the product or use it in unintended ways? What is the social context in which this is used?
- Designers: What defaults or assumptions am I building into the product? Am I designing this for transparency and equality?
- Data scientists: What are the implications for users when I optimize my model this way?
- Content writers: Can I explain why the system made a prediction, recommendation, or decision in terms the user can understand?
- Engineers: What notifications, processes, checks, or failsafes can we build into the system to mitigate harm?
Notice that these questions involve the perspectives of multiple roles. Involving stakeholders and team members at every stage of the product development lifecycle helps correct the impact of systemic social inequalities in your system. If you find yourself on a team that’s missing any of these roles, or where you play multiple roles, you may need to wear multiple hats to ensure each of these perspectives is included—and that may involve seeking out external expertise or advice. When employees are dissatisfied with the answers they receive, there should be a clear process for resolving the problem areas, like a review board. We go into more detail on that later.

