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If you’ve been following the growth of Artificial Intelligence, it’s likely that you’ve interacted with, or at least seen, Generative AI. Systems like DALL-E and ChatGPT fall into this category, making it an incredibly hot topic in the world of technological advancement, but what does Generative AI even mean?
In short, this type of AI generates things like text, images, and videos. It can respond to questions, build our recipes, or blog posts and make images using input from users. The ever-popular fake ‘90s photos that people have uploaded to their Instagram profiles are an example of Generative AI.
Here, we will explain Generative AI for anyone unfamiliar with it so you can get up to speed and begin implementing helpful tools as needed.
Generative AI is meant to produce content. Models like GPT-4 work by collecting input from thousands of users and consolidating that into specific learnings that can be responses to prompts. For example, one user might ask GPT-4 about what they should make for dinner. GPT-4 would then search through its database of recipes, and its responses about food, and generate a popular recipe for the user to try.
Similarly, image generators like DALL-E receive input of images and code them based on their description. The system might receive hundreds of images of teens from the 1990s, and then an input of a few photographs of a specific user’s face, so that it can generate mock images to look as though the user was a teenager in that time period.
Alongside texts and images, Generative AI models can now generate audio, video, and other multimedia content, which helps automate content creation for things like social media posts and advertisements. Some Generative AI systems might have pre-recorded audio files that they can form into new words; for instance, they could encode thousands of speeches from Barack Obama and then generate a new sentence in his voice, even if he did not actually say that sentence.
MORE: AI for Business: How to Capitalize on Generative AI to Enhance Decision-Making
Many companies and individuals are already implementing Generative AI in their daily lives and regular content protocols. It might surprise some readers to learn just how much of the world is already leaning on AI tools, so here are some examples of places you’ve likely already experienced Generative AI without even realizing.
As previously mentioned, average social media users are already using Generative AI to create fun and engaging posts that put their image and likeness into new scenarios. Another example of this is that some people have used models to generate professional headshots.
A simple search of “AI for headshots” shows thousands of results, as new systems make it possible for users to submit a few high-quality photographs of themselves and will then create professional photos that can be uploaded to sites like LinkedIn.
Many companies want the ROI of these advertising channels without the heavy lift it can put on staff. In that case, they might use Generative AI tools to create the text for these assets, like an AI-written newsletter that discusses a new product.
These tools allow an employee to input general prompts into an AI system and get an output of an email they can circulate to subscribers. It’s often recommended that employees review the content they’ve been delivered for any errors or overly robotic language, so that it has a certain “human touch,” but the process would still save a lot of time.
If you’ve ever gone to a customer service page and looked for solutions, you might have seen a pop-up chat-box with an AI agent asking you how they can help. In these simple cases, the AI bot could direct users to the proper page, so someone saying “my vacuum is broken” gets a message with links to various articles about fixing a broken vacuum.
According to IBM, tech employees are using Generative AI for coding purposes due to its ability to generate accurate and useful code. Coding AI tools often use open-source material to understand the language of code, and then can create a simple code based on what the employees have asked it to do.
“Generative AI can also translate code from one language to another, streamlining code conversion or modernization projects, such as updating legacy applications by transforming COBOL to Java,” IBM reports.
The healthcare industry is also already seeing uses for Generative AI. These tools can assist in drug discovery and other medical research by using data from patient files like genetic information and past diagnoses, AI can develop treatment plans and connect the dots on drug efficacy.
“By accelerating the identification of potential drug candidates, optimizing molecular structures, and even predicting side effects and drug interactions, the speed and efficiency enabled by genAI holds the promise of bringing novel and safer medications to patients,” LeewayHertz reported.
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Of course, with every exciting technological advancement comes plenty of warnings about misuse and ethics. Generative AI is often helpful for content creation, but it can sometimes generate incorrect or nonsensical output. It’s not entirely possible to simply take the AI response and publish it directly; copyediting and fact checking is a key part of implementing AI in the workplace.
Generative AI can also present bias and harmful content. Taylor Swift recently filed a lawsuit against Generative AI that created fake, pornographic images of her without consent. When AI tools are able to create realistic videos and images, it can be terrifying to log onto the internet and not know what is real versus fake.
Similarly, Generative AI can pose copyright concerns. Because they use audio and visual files to generate output, AI tools might be stealing from artists and other creators, using their work as a basis for the generated response. This also gets to the concern of displacing human creativity: many creatives have spent their lives making art and telling stories, and AI threatens to replace those jobs.
It’s therefore an ethical consideration to use Generative AI versus hiring a human to do creative work and is something companies should consider when investing in these tools.
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Generative AI is expected to become more ubiquitous in the years ahead, but it might not be as black and white as one might think. Human jobs could shift from copywriting to copyediting, for example, and it’s likely that many workplaces will avoid over-investing in AI as to not pose ethical concerns to society at large.
As Generative AI tools grow and are used more, they will collect more input and be able to create improved images, text, and videos throughout the years. It’s possible that workplaces will find very specific uses of AI, and it is possible that some forms of AI will be frowned upon or even illegal (for instance, using deep fakes of celebrities in advertisements.)
For more information on the future of Generative AI, we invite you to view our LexisNexis® Future of Work Report 2024: How Generative AI is Shaping the Future of Work to explore more of ways Generative AI is changing the landscape.