As technological advancements continue to shape our world, Artificial Intelligence (AI) has been making news lately with capabilities that can easily be integrated into everyday life. If you’re not familiar with this phenomenon, Artificial intelligence is the simulation of human intelligence processes by machines. One specific form of AI that is becoming increasingly common is generative AI. You may recognize this term from current headlines referencing ChatGPT, a model designed to interact in a conversational manner. This dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
In this blog post, we’re going to look at some basics that every association professional should know about Generative AI. We’ll also dive deeper into the underlying technology of ChatGPT and DALL-E, two leading models currently taking over the AI space.
An Overview of Generative AI
Generative AI is a type of artificial intelligence that uses algorithms to create new data or content. It can be used to generate anything from text, images, videos, and music to complex 3D objects. Generative AI differs from basic Artificial Intelligence in that it can create brand new, unique content based on input data rather than simply recognizing patterns in the data. This makes it possible for generative AI to produce seemingly original creative content. However, this content varies in quality and can depend heavily on the model being used. Some AI models can produce content that can be easily mistaken as human-generated. On the flip side, outputs are not always accurate and can come across as repetitive, inaccurate, or unnatural in tone.
While the idea of artificial intelligence may lead to some skepticism, it has proven useful in offering real value to organizations and associations. First, generative AI is easily able to produce a variety of credible writing projects in a matter of seconds. It can also respond to criticism, making responses more personalized. Because generative uses a massive amount of data, it has resources at its disposal that the average human couldn't imagine. This makes producing written materials easier, saving both time and money. What’s even better is that it’s possible to fine-tune generative AI models to perform specific tasks. For instance, you can ask a model to “learn” how to paint like Claude Monet and it would review a massive amount of data a produce a result based on that. More practically, generative AI can crank out freshly written content on demand. From product summaries to blog posts, the possibilities are great.
As with any new technology, there are risks involved with artificial intelligence. One being the possibility of a model producing incorrect or biased information. Therefore, associations who intend to rely on generative AI models should be on alert for offensive, biased, or copyrighted content being published unintentionally. There are ways to reduce such risks including being careful in selecting the initial data used to train the model. It’s also a good idea to consider using smaller models that are more specialized. This will help with problematic content. It’s always a good idea to keep a human involved by always reviewing AI-generated content before it’s published or used in any way.
ChatGPT and DALL-E
You may have already heard about ChatGPT as it has been gaining popularity recently. ChatGPT is a generative AI tool that is designed to answer questions and interact in a way that mimics human dialogue. While chatbots aren’t a new concept, ChatGPT is more advanced in that it can write poetry, debug code, and generate marketing copy. The model was trained by OpenAI using both supervised and reinforcement learning. Basically, a human demonstrated desired behavior and supervised the output produced by the model. The learning is then reinforced by ranking output based on their quality. After lots of repetition, a set of high-quality outputs were produced.
ChatGPT was created to serve as a chatbot function, and while these capabilities already exist, this technology is particularly adept at engaging in dialogue with its users. Responding to feedback, seeking clarification, and iterating on its answers based on how the user responds is how ChatGPT stands out. This can be a great addition to AI tools that are already being used to provide enterprise support, customer interaction, and assist in new product development. From improving the customer journey to generating marketing copy and summarizing long documents, ChatGPT shows promise in the generative AI space.
Another generative AI learning model developed by OpenAI is DALL-E, which generates images based on language descriptions, also referred to as “prompts”. More recently, DALL-E 2 was designed and can produce even more realistic images at higher resolutions. Perhaps the most impressive thing about this learning model is its ability to combine concepts, attributes, and styles to produce uncanny results. Generated images can be produced in several different styles including paintings, emojis, and realistic photos.
While the general public has been enjoying DALL-E and its capabilities for entertainment purposes, there are opportunities for organizations to incorporate DALL-E as well. The most obvious may be the opportunity to generate new ideas and spark creativity. From creating logos to producing web layouts, this is an avenue to easily take an idea and turn it into a visual. You can also edit images on DALL-E offering more personalization for your brand or product.
It’s important to note that generative AI and the models mentioned above are a new field with lots of opportunities to learn, advance, and discover. Both risks and opportunities will be everchanging over time. We can also expect to see regulations proposed as generative AI becomes more integrated into society.
Wrapping It Up
Generative AI can offer associations and non-profits powerful insights through collecting, monitoring, and reweighing datasets. However, organizations must be aware of the potential pitfalls associated with using AI and machine learning technologies, such as incorrect or biased data leading to inaccurate results. It is essential that datasets are continuously monitored, adjusted, and checked for accuracy and consistency to achieve desired outcomes with predictive models. Taking these steps can help organizations maximize the benefits of using Generative AI while minimizing any risks associated with its implementation.
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