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Are AI-generated models NEGATIVE for brands?

Are AI-generated models NEGATIVE for brands?

PLUS: AI at work needs more guidance

Shubhangi Mittal
March 01, 2024

As AI makes inroads into our work and our lives, there is only fascination with what it can accomplish. The new shiny toy that can execute at command.

However, some research reports are advising caution with using AI involving sensitive topics. One such example is using visually diverse models in ad campaigns.

This issue highlights one such research report.

Let’s dive in.

IN THIS ISSUE

Brands and advertisers cautiously use AI-generated digital models to avoid negative brand impact.
New Slack research shows accelerating AI use and quantifies the “work of work”
From the AI community
Events
Growth: Director of IBM’s new 15-day course on Gen AI. Learn for just 5 mins/day

TOP PICKS

Latest Research: Brands and advertisers cautiously use AI-generated digital models to avoid negative brand impact

Brands trying to show diversity using AI-generated models might face problems.

When brands use artificial techniques, like AI, to create models that represent different types of people, it can make those people feel bad about the brand.

Nowadays, brands use AI to make digital models that look like humans. They customize these models to have different body types, ages, sizes, and skin colors.

But we don't know how people who are not often represented in these models feel about it. This research study discovered:

We found that when a brand plans to use AI-generated models instead of real people, it makes consumers feel not so good about the brand.
This happens because people feel like their identity is being threatened, and they feel like they don't belong.
This leads to a negative opinion about the brand.
But, if people think the brand genuinely wants to represent diversity using AI, it can lessen these negative effects.
When people believe the brand truly cares about diversity, they feel less threatened and more connected to the brand.

In the future, technologies like deepfakes and generative AI will be powerful tools in marketing. However, they can bring both opportunities and threats as they become more common in advertising.

Read the research report here. Ungated.

New Slack research shows accelerating AI use and quantifies the “work of work”

AI offers the most value to workers in various areas such as writing assistance, summarization, workflow automation, and research on new topics.

Employees who spend a lot of time on repetitive tasks find AI automation and quicker work delivery particularly exciting.

However, many workers lack guidance on how to effectively use AI tools in their jobs.

On the other hand, over 80% of senior executives are eager to implement AI in their organizations to achieve increased employee productivity, data-driven decision-making, significant cost reductions, and innovations in products and services.

To make the most of AI, senior leaders must provide clear and open guidance to their employees on how to integrate AI into their workplace.

This guidance is crucial for employees to harness the benefits of AI in improving their work efficiency and decision-making processes.

Read the full report.

FROM THE AI COMMUNITY

How I Create a Video Storyboard in 10 Minutes with ChatGPT + Canva
Don’t Let Gen AI Limit Your Team’s Creativity, HBR

EVENTS

1. AI For Writers Summit Presented by Jasper, March 6 12pm-5pm EDT, Online

2. B2B Performance Marketing in 2024, Wednesday, March 13 from 11:00-11:45am PST, Online

GROWTH CORNER

Director of IBM’s new 15-day course on Gen AI. Learn for just 5 mins/day

Director of IBM, Armand Ruiz’s new 15-day course is designed to help you learn all the core concepts of Generative AI, in just 5 minutes a day. Here’s what he covers:

𝗗𝗮𝘆 𝟭: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: Overview of generative AI and its importance in business.

𝗗𝗮𝘆 𝟮: 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀: Exploring different generative AI models like GANs, VAEs, and transformers.

𝗗𝗮𝘆 𝟯: 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 𝘃𝘀 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: Comparing traditional machine learning with generative AI methods.

𝗗𝗮𝘆 𝟰: 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗚𝗣𝗨𝘀: Understanding the role of GPUs in AI and machine learning tasks.

𝗗𝗮𝘆 𝟱: 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗧𝗮𝗸𝗲𝘀 𝘁𝗼 𝗧𝗿𝗮𝗶𝗻 𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹: Insights into the resources and processes for training large foundation models.

𝗗𝗮𝘆 𝟲: 𝗛𝗼𝘄 𝘁𝗼 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀: Discussing techniques for customizing foundation models for specific uses.

𝗗𝗮𝘆 𝟳: 𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗟𝗟𝗠𝘀 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲: Overview of the most widely-used large language models and their features.

𝗗𝗮𝘆 𝟴: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Exploring practical applications of generative AI across business sectors.

𝗗𝗮𝘆 𝟵: 𝗧𝗵𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗦𝘁𝗮𝗰𝗸: Understanding the components and architecture of the generative AI tech stack.

𝗗𝗮𝘆 𝟭𝟬: 𝗧𝗵𝗲 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗼𝗳 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Discussing the rise and importance of small language models in AI.

𝗗𝗮𝘆 𝟭𝟭: 𝗧𝗵𝗲 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀: Exploring the role, responsibilities, and required skills of AI engineers.

𝗗𝗮𝘆 𝟭𝟮: 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗔𝗜: Discussing the ethical challenges in AI development and deployment.

𝗗𝗮𝘆 𝟭𝟯: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: How to develop a strategic AI integration roadmap for businesses.

𝗗𝗮𝘆 𝟭𝟰: 𝗙𝘂𝘁𝘂𝗿𝗲 𝗧𝗿𝗲𝗻𝗱𝘀 𝗶𝗻 𝗔𝗜: Exploring future developments and trends in AI.

𝗗𝗮𝘆 𝟭𝟱: 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: Providing resources and advice for continued AI learning and exploration.