Compare and contrast generative AI and machine learning
Generative AI was the hottest topic at Shoptalk in Las Vegas a few weeks ago. His 44% of participants surveyed wanted to see examples of generative AI and learn more about its potential to improve performance.
An interesting follow-up question might be, “What benefits do you expect from generative AI?” Next, “Do you know the difference between generative AI and machine learning?”
Can you see the difference? This article was co-authored by Ascendant Loyalty founder David Slavick and Netail CEO Dr. Mark Crystal, and provides a solid overview of what you need to know about both generative AI and machine learning.
Share an excerpt from the full article. Learn from here.
Generative artificial intelligence (AI): Describes algorithms (such as ChatGPT) that can be used to create new content such as audio, code, images, text, simulations, and video. The most commonly used generative AI tool is ChatGPT. For us laymen, this stands for Generative pretrained Transformer. Another common generative AI tool is the image generator DALL-E (an ode to surrealist artist Salvador Dali and Pixar's robot WALL-E).
While all the talk is about generative AI, the more “practical” and practical tools and solutions used by marketers today primarily leverage machine learning. If you've evolved from basic modeling and segmentation to machine learning over the past decade, you might be surprised to learn that this is made possible by supervised machine learning models that both predict and classify. In fact, you may have even used unsupervised machine learning models to segment and cluster your data. Supervised learning models use labeled training data and adopt hypotheses about what the correct output should be. Unsupervised learning models do not.
The best way to decide whether to use a machine learning algorithm or a generative AI algorithm is to identify whether your task involves working with language, images, or numerical data. For language-based tasks, generative AI may provide the best results. Conversely, numerical analysis requires machine learning techniques.
In fact, generative AI is not good at dealing with numerical data. Generating a new image from a text prompt (language) is a generative AI task. Currently, analysis of existing images (computer vision) is actually a numerical task, which is best performed by machine learning models.
Steps to bring AI to customer loyalty include:
- Identify quick-hit methods
- Try it with an experienced partner
- Build organizational knowledge
- Develop and communicate your AI strategy
- Addressing barriers to progress
- learn and repeat
In the future, generative AI will play a key role in loyalty and affinity programs by enabling personalized interactions and member experiences through chatbots and website content. By customizing what members see, how they interact, and the offers they are presented with in a given session, Generative AI can deliver the one-on-one, personalized experience that members “expect” from today’s digital channels. Masu.
The challenge in 2024 is to explore analytic partners and generative AI for opportunities to improve customer interactions at scale at every touch point. Better customer service (faster, more consistent, more courteous, more accessible – no wait times) and making your valued customers say, “You showed me that you know me.” Creating truly personalized content.
To learn more, read the full article here and look for David Slavick at the CRMC event in Chicago, May 20-22, 2024.
