In our previous article, we discussed the journey of preparing your business for AI. Now, it's time to discover the most suitable Generative AI models for your business needs.
Generative AI is a branch of Artificial Intelligence that produces new content based on its learning from existing data during training. This training phase involves an iterative training cycle to ingesting and learning from existing content, fine-tuning the parameters, and culminating in the creation of a statistical model. When provided with a prompt, Generative AI leverages this statistical model to generate a probable response.
In this article, we'll dive into three common use cases for Generative AI and explore what they bring to the table.
Text generation is arguably the most prevalent use case today. Thanks to models like ChatGPT, this capability is within everyone's reach. Essentially, text generation pertains to the creation of textual content. The applications of text generation models span various domains:
- Code generation: Individuals with varying degrees of coding proficiency, ranging from business users trying to solve a coding conundrum to seasoned developers aiming to expedite their coding process, are now turning to ChatGPT. If you're contemplating building a code-generation tool for your enterprise and you are looking for a model that can be fully controlled, consider exploring models like the diverse models offered by SEBIS.
- Story Writing: The advent of AI has simplified the narrative process. Several intriguing projects, such as MosaicML's storywriter, concentrate on AI-driven storytelling. While this model excels in composing and deciphering fictional tales, it does necessitate a substantial amount of context to align with your specific vision.
- Online Virtual Assistants: Personally, my experience with virtual assistants was far from satisfactory just three years ago. However, the narrative has changed in recent times. Major tech giants, in a bid to circumvent the escalating costs of human resources amid the pandemic, have funneled significant investments into refining virtual assistants. The results? A dramatic upswing in user experience. Thanks to AI, establishing virtual assistants is no longer the exclusive domain of tech behemoths. With access to bespoke data sets like inventory records and order details, businesses can now train their own virtual assistants. For instance, if you helm an e-commerce venture, your virtual assistant should competently present the apt product when prompted with a description like "a red and black wallet".
The arena of image models, though relatively nascent, has made significant strides. Trained on vast datasets comprising images each paired with concise text descriptors, these models possess the capability to generate or modify visuals based on text prompts. Leading the charge in this domain is Midjourney. You can delve into recent community showcases and innovations here. Businesses that lean heavily on designers for crafting 2D or 3D visuals might find Midjourney an invaluable tool to streamline processes and eliminate repetitive tasks. If you're an artist, it's vital to recognize that AI is an ally, not an adversary. Harnessing AI can elevate your artistic creations and set you apart from your contemporaries.
Two other noteworthy contenders in the image generation domain are Stable Diffusion and DALL-E 2.
Stable Diffusion specializes in refining visuals by eradicating noise and enhancing overall clarity. It excels at rejuvenating low-resolution or compromised images. The main difference between Stable Diffusion and Midjourney is in their creative approach; Stable Diffusion doesn’t focus on major visual changes or distortions, whereas Midjourney does. If your aim is to retain the fundamental structure of an image while enhancing it, Stable Diffusion is your best bet.
DALL-E 2, a deep learning model conceived by OpenAI, churns out intricate images utilizing the CLIP (Contrastive Language-Image Pre-training) method. With a penchant for in-painting and out-painting, DALL-E 2 shines when tasked with image alterations. For instance, businesses that conventionally use tools like Photoshop to replace photo backgrounds — a process that sometimes results in noticeably edited images — can now seamlessly integrate a backdrop like the Statue of Liberty with DALL-E 2's prowess.
Most video models are text-to-video models, which aim to generate a video representation from a text input. Creating voiceovers is the most common use case. Text-to-video models can easily craft AI voiceovers for videos, spanning a wide range from TikTok videos to films. These could help creators produce more content in a more efficient way. Advanced companies have harnessed text-to-video models to fabricate marketing and training videos, not only to reduce costs but also to expedite the process. A significant challenge in video generation is the video's quality; popular models like zeroscope_v2_576w can only generate videos at a 576x320 resolution. If you aim to upscale the resolution, the intrinsic quality of the content could pose a problem.
There are more use cases to be discovered beyond these three. For instance, training AI to perform a specific task or action based on text input is a relatively new use case but has tremendous potential. These tasks can span a wide range of activities, such as answering questions, conducting searches, making predictions, or initiating certain actions. For instance, Acho employs text-to-task models with Data Apps, allowing the creation of business applications without the need for coding. If your business carries out a structured, repeatable task, it should be possible to automate it with AI. The journey of AI in the business world has only just begun, and there is much more awaiting us to explore.