Understanding the Recent Relevance of Generative AI
The growing popularity of generative AI programs such as ChatGPT and DALL-E comes with equal measures of confidence and caution.
No matter your particular perspective on intelligent tools, however, one thing remains certain: GenAI software is here to stay. Here’s what you need to know about how generative AI works, how it’s already being used, and what comes next.
What Is GenAI?
Generative AI tools can generate content based on user input. This content goes beyond simple “yes” or “no” answers — with access to massive amounts of data and all its previous answers for context, GenAI solutions can now answer complex, multi-part questions in a way that’s nearly indistinguishable from human responses.
Two of the most popular tools in the generative space are ChatGPT and DALL-E. ChatGPT is a chatbot that can answer virtually any question a user asks. The newest version of the tool can access data across the Internet at large to provide even more in-depth answers.
DALL-E, meanwhile, is a generative digital art program capable of generating some stunning images. Users can ask DALL-E to create art in a certain style or allow it the freedom to take on an abstract concept.
Not surprisingly, these tools come with questions: Is art generated in the style of a human artist truly art, or is it simply an artificial knock-off? Can essays or books written by ChatGPT and other tools actually explore the human condition, or are they just convincing fakes?
Current Use Cases for Generative AI
While valid concerns around GenAI exist, the potential power of these tools makes them a must-have for companies that want to stay competitive.
Predictive artificial intelligence has already carved a niche into areas such as content streaming; machine learning (ML) algorithms help AI tools suggest content that users may enjoy based on their current and previous watch history.
GenAI is taking things to the next level. Companies are now using AI to help write and edit business emails, generate new marketing copy based on predicted trends, and respond to staff and customer questions without the need for human agents. If queries are beyond the scope of AI tools, they’re trained to escalate concerns and bring in trained staff.
Next Steps for Digital Smarts
The adoption of generative AI isn’t slowing down. Advancements in natural language processing (NLP) and large language models (LLMs) have created a functional feedback loop — as they make AI tools better, NLP and LLMs are made better in turn, continually reinforcing the cycle.
This rapid evolution sets the stage for new AI applications. One possible use case is marketing lead identification. Using a combination of publicly available demographic data and information provided by customers, companies are looking to use GenAI to help pinpoint leads with the highest chance of conversion.
Software development is another area on track for GenAI adoption. Equipped with the right learning models and large enough datasets, AI tools can help automate labor-intensive development tasks and free time for developers to focus on larger business objectives.
There’s also an opportunity for GenAI to shine in creating legal documents such as contracts or compliance agreements. The detail-driven complexity of these documents makes them an ideal use case for AI tools capable of understanding context and pinpointing possible mistakes or omissions.
The big takeaway? Generative AI is here to stay. While valid concerns exist around adoption and use cases, the potential benefit of these tools can’t be ignored. New use cases are constantly being developed and implemented; paired with the evolution of guidelines for responsible AI use, GenAI is poised to change the world.
Generative AI Models & Their Applications this infographic was contributed by Web Age Solutions, an organization providing machine learning training courses
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