Generative Artificial Intelligence (AI) has made quite impressive strides in recent years, completely changing the face of everything as we know it. This AI can generate text, images, and even entire videos following the instructions and prompts you feed it with.
I saw a recent demo of invideo, and AI that can practically create a video for social media from scratch. From the scripts to images to even voiceover, invideo does everything. All you need do is feed it with prompts, and the prompt could be asking it to create a 30-second social media reel from an article or simply a video topic to create a 2-minute YouTube video. There are already questions about whether this AI can replace content creators. I honestly think it can’t, but that’s not the focus of this article.
As AI continues to captivate the world with its evolving abilities, from automation in various industries to creative content generation, the question arises: have we seen the best of generative AI, or are there more groundbreaking advancements yet to come?
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Well, Billionaire philanthropist Bill Gates believes that Generative AI has plateaued. Gates said there were many reasons to believe that Generative Pre-trained Transformer (GPT) technology reached a plateau. To quote him, the leap from GPT-2 to GPT-4 has been incredible, but “GPT-5 will not be any better”.
Do you agree with him?
Well, a study suggests that GPT-4 may not even be more accurate than GPT -3.5, so one might say that Gates has a point when he says the technology may have plateaued.
However, GPT technology is just one part of Generative AI and may not be sufficient to conclude that all Generative AI has reached its ceiling.
And even if Generative AI technology has reached its peak in capabilities (which I don’t think is the case), there is still a world of possibilities to be explored with applications in work scenarios. With models like the GPT, DALL-E, and OpenAI’s CLIP that can generate human-like text and images, perform language translation, and even create art and design elements, the possibilities are endless.
Amazon is already beta testing AI image generation tools for its advertisers, offering an easy way to create backgrounds or scenes around whatever product ad buyers are hoping to sell.
“This solution is helpful for advertisers of all sizes — enabling those that do not have in-house capabilities or agency support to more easily create brand-themed imagery. ” “The image generation capability is easy to use and requires no technical expertise.”
Like any other image generator tool, you enter a prompt, and away you go with multiple results to choose from. The vendor can test the various versions to optimize performance before running an ad with the best option. With this, vendors and brands can put their products in a lifestyle scene that shows their usage instead of just showing the product on a white background. Research suggests that products in a lifestyle scene can lead to 40 percent higher click-through rates.
As I was saying, this is another example of how we can continue to make advancements in the applications of Generative AI capabilities within our workspace.
Researchers are continually fine-tuning pre-trained models, improving their capabilities, and tailoring them for specific tasks. This fine-tuning allows for better control over the output, making generative AI more reliable and safer in certain tasks.
The integration of vision and language models like CLIP has expanded generative AI’s ability to understand and generate content based on both text and images. This has immense potential for applications in image generation, content recommendation, and accessibility.
There is a growing focus on addressing ethical concerns, such as bias and misinformation. Research is ongoing to reduce biases in AI models and improve their fact-checking abilities.
There are also efforts being made to make generative AI more customizable. Users can adapt models to generate content that aligns with their needs, ensuring it can assist in various tasks and domains.
With moves like this, it is not easy to agree that Generative AI has peaked already?
Generative AI has already found applications with several Python-based coding projects using OpenAI, LangChain, Matplotlib, SQLAlchemy, Gradio, Streamlit, and more. If you’d like to run your chatbot powered by something other than OpenAI’s GPT-3.5 or GPT-4, you could efficiently run the Meta’s Llama 2 model in the Streamlit web framework.
There are still so many gaps that generative AI could fill. In Healthcare Advancements, for instance, Generative AI could play a crucial role in drug discovery, medical imaging analysis, and personalized treatment recommendations. I’m not sure if there is such an AI yet.
Generative AI also has the potential to create entirely new forms of art, literature, and media that were previously unimaginable.
The possibilities for generative AI are limitless, and it is an exciting field to watch as it shapes the future of technology and creativity.