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16 AI Terms Every Brand Executive Must Know

Posted 29 Apr 2024

Chad Reynolds

AI terms brand leaders need to know

I was fortunate enough to be in Chicago recently for a Google event. Some of the most influential technology leaders in the world were there discussing the rapid adoption of AI among the world’s top brands, and how many of the top companies like Vurvey are pushing AI forward.

You might be surprised by how fast AI technology is moving, but I want to share something with you today. The top executives are pushing their teams even faster to adopt new models, agents, and wrappers. That was a big reason why we recently announced vTeam at Google Next in Las Vegas and why I decided to write this post today.

If those words aren’t commonplace in your business yet, they should be. At Vurvey we work with some of the world’s largest brands across the globe. And we always start every deployment by making sure everyone on our team and our customers have a deep understanding of AI.

If you are a leader, AI is no longer optional. You must be grounded in sound principles and a solid understanding of the terminology and technology. So, I wanted to provide you with a sneak peek into some of the terms we use with our customers every day.

These terms will empower you and your team with the knowledge and tools necessary to lead in this new era.

1. Artificial Intelligence (AI)

AI is technology that helps computers act like humans, making smart tools that transform businesses. AI can perform many advanced tasks like research and recommendations.

Example: Netflix uses AI to recommend shows and movies based on what you’ve previously watched, enhancing user experience and engagement.

2. Generative AI

Generative AI creates new content, from pictures to text. Brands use it to design products and create marketing materials. This type of AI learns from the training data it is given and then creates content similar to that of a human.

Example: A footwear brand trains AI on data like customer feedback, then uses Generative AI to create new designs for sneakers, speeding up the design process and introducing innovative styles.

3. Natural Language Processing (NLP)

Natural Language Processing helps computers understand and respond to human language. It’s used in chatbots and helps improve customer service. Other examples include search engines, predictive text, virtual assistants, and autocomplete.

Example: American Express uses NLP to power its chatbot, which assists millions of customers with inquiries about their accounts, enhancing customer service speed and efficiency.

4. Machine Learning (ML)

Machine Learning is one way to use AI. Machine Learning allows computers to learn from data without direct instructions. The most common use case is to predict customer behaviors.

Example: Amazon uses Machine Learning to predict what products customers are likely to buy next, which helps them manage inventory and personalize marketing efforts. The more times that customers that use Amazon, the better it’s personalization becomes.

5. Deep Learning (DL)

Deep Learning helps computers recognize patterns and make decisions, similar to human brain functions. In a way, it’s similar to machine learning, but much more complex. While Machine Learning (ML) mostly uses algorithms, Deep Learning (DL) structures those same algorithms in multiple layers, similar to how a human brain would function.

Example: Google Photos uses deep learning to automatically tag and organize photos based on the content and faces in images. It might require multiple layers of learning algorithms to detect subtle nuances in photos, hence the “deep” learning.

6. Reinforcement Learning

Reinforcement learning teaches computers to make decisions by trying different things and learning what works best. It’s useful for improving how things are done in a business. Reinforcement learning is very similar to how most people use trial-and-error to learn and improve their own decision making.

Example: Alibaba uses reinforcement learning for its logistics routing algorithms, optimizing delivery routes to reduce costs and delivery times. It is constantly feeding what works and what doesn’t work back into the routing algorithm, getting better each time.

7. Computer Vision

Computer Vision allows computers to see and understand images and videos. With trillions of images and videos across the internet, the ability for AI to analyze and understand media (images, videos, and audio) is just as important as the ability to understand text.

Walmart using AI to scan shopping carts

Example: Walmart uses computer vision in its stores to detect when shelves are empty or when items are wrongly placed, improving store management.

8. AI Ethics

AI Ethics is about using AI in ways that are fair and safe. It’s important for companies to use AI without harming customers’ trust. Using AI responsibly is one of the most important issues of our time.

Example: Vurvey has established AI ethics guidelines to ensure its AI solutions are developed responsibly, focusing on fairness, reliability, and safety.

9. Federated Learning

Federated Learning is a way for computers to learn from data without sharing it. This keeps customer information private and secure. A common use case for Federated Learning is when AI processes data directly where it’s collected, like on phones or in cars.

Example: Google uses federated learning to improve the predictive capabilities of its Gboard keyboard features without transmitting user typing data back to the servers.

10. Agent, Copilot, or GPTs

These AI helpers automate tasks and answer questions. They’re like having an extra team member who’s always ready to help. Some companies call these “copilots” or custom GPTs, but the concept is the same.

Example: Shopify uses AI agents like Magic to help merchants manage their online stores by providing recommendations on stock levels and pricing strategies.

11. AI Wrappers

AI Wrappers are companies or technology that makes it easier for businesses to use AI. They adjust AI technology to fit different industries, or for specific use cases.

Example: Runway specializes in creating AI that companies in the entertainment industry use to create videos from simple text or even other video prompts.

12. Large Language Models (LLM)

Large Language Models understand and generate human-like text. They help businesses talk to customers and provide helpful information. And LLM makes it easy to interact with AI, ask questions, and gather information.

Example: OpenAI’s ChatGPT is used by firms to automate customer support, providing quick and accurate responses to customer inquiries.

13. RAG (Retrieval-Augmented Generation)

This technology helps AI answer questions more accurately by looking up information. It’s like teaching AI to do research by connecting it to external knowledge sources. This might be a database, a website, or customer data.

Example: Facebook uses RAG to enhance its conversational agents, making them more accurate and contextually aware when answering user queries.

14. Context Window

The context window is what AI looks at when it’s trying to understand something. Getting this right helps AI make more sense of the information. This allows AI not just to understand what words mean, but the subtle nuances behind how words relate to each other.

Example: A search engine uses a context window approach in its algorithms to better understand the relevance of search results and how they relate to user interests.

15. Multimodal Models

Multimodal Models use different types of data, like text and images, to understand the world better. They help AI get a fuller picture of what’s going on. For example, one multimodal model might use a combination of audio data and visual data to analyze of video clip.

Example: Pinterest uses multimodal models to analyze both the text and images in pins to recommend more relevant content to users.

16. Data Models

Data Models organize information so that AI can use it effectively. They help companies make better decisions by understanding their data better. Think of it like an org chart for your data, keeping everything organized in a logical hierarchy.

Example: Credit Karma uses data models to analyze user financial data, providing personalized advice and product recommendations based on credit scores and spending habits.

Final Thoughts

Hopefully this gives you a better idea of the important terms related to AI. And more importantly, you can hopefully see how understanding these technologies is not just advantageous—it’s essential for any brand looking to thrive in the future.

As executives, it’s our responsibility to not only keep up with this rapid evolution but to be ahead of it, ensuring our teams are knowledgeable and our strategies are cutting-edge. To truly leverage the full potential of AI, continuous learning and adaptation are key.

Interested in how the world’s leading brands are leveraging AI for marketing, insights, and product creation? Join the waitlist for vTeam today.