What's Going On With GoCharlie And AI?

What a week it has been for all of generative AI. Hugging Face, EleutherAI, CarperAI, and Stable Diffusion all reported significant advancements, improvements to Stable Diffusion, and of course, significant funding announcements. I'd want to congratulate Stability. Our pals at AI raised $101 million in their seed round, while Jasper raised $125 million in their Series A round. This comes after Cohere's $125 million Series B round and AI21's $64 million raise from earlier this year. And I think the remainder of this year is still far from over.

These questions confirm that, when effectively applied to the proper issue, the generative AI domain has the potential to provide enormous benefit. GoCharlie wanted to take a moment after these announcements to talk about the enormous potential that is still ahead of them, their strategy for the future, and how GoCharlie is different from Jasper.

What Does Generative AI Mean?

This will be the next big buzzword for the next ten years. But what precisely is it for people without a PhD in computer science? In the emerging topic of "generative AI," robots create new material rather than classifying or analyzing already existing information. This might alter how we engage with technology and make it more believable and human-like. For instance, much like humans, multimodal generative AI systems may analyze data and produce works from several modalities (text, pictures, audio, and video). They become more adaptable and effective as a result.

The $1 Trillion Possibilities of Generative AI

Sequoia reports that "Generative AI has the potential to generate trillions of dollars of economic value," implying that the first trillion dollar AI companies will be in Generative AI, and the CEO of HuggingFace agrees: "There's going to be a bunch of trillion-dollar companies — a whole generation of startups who are going to build on this new way of doing technology." 

The era of generative AI is only getting started. Literally everything in your mind may become a reality in the digital world. Since the capabilities of generative AI are still in their infancy, we wanted to emphasize the prospects we saw.

Integrated Mode Models

Both GPT-3 and more current picture production (and even video creation!) models have changed the game. Nevertheless, the majority of these models, which concentrate on a single kind of creativity, are not accessible to the general public. We see huge potential for developing multimodal training sets and models that can take into account and produce many media simultaneously. The future is multimodal, in our opinion.

The On Ramp Issue: Models Based on Instruction

Even while AI is improving, many applications of generative AI suffer from a on ramp issue analogous to that of Web 3.0. We don't see the typical user becoming a prompt engineer, where they must provide several samples of the output they wish to create in the prompt. Many believe there is a big potential in prompt improvement or prompt removal altogether in favor of functionality that is driven by instructions and yet fully utilizes generative AI models. A user asks a model a question in plain English, much as you would ask a buddy to help you out, and the AI assistant responds with the initial draft. The potential for straightforward technical editing is much more intriguing. Abhor Photoshop? Just let the AI do the editing.

Models with Goals

Given user-specific inputs, current Generative AI systems are capable of creating tailored content by either quick engineering in the backend or fine-tuning. We see more potential in the automated adaptation of these models to a user's profile, objectives, brand voice, and brand playbook. Some think that this is both doable in the near future and that our own users really want this feature.

Completing the Picture: Hyper Personalization

What do goal-driven, multimodal, instruct-based models all together mean? Hyperpersonalization. With hyperpersonalization, material produced by models might be customized to the interests of each individual end user. This would no longer be limited to simply the title but could be applied to the whole piece of information, taking into account your preferences, goals, personality, etc. Hate Game of Thrones Season 8? We do too. Use AI to recreate it to your precise preferences. Do you prefer plain-language explanations over academic texts that are too technical? AI will make it simpler to understand, no issue. Generative AI makes it feasible for hyperpersonalization in a manner that wasn't before achievable and that caters to the preferences of the end user. This is very thrilling.

Strengthen Multimodal

GoCharlie wants to be the platform that creates content for several media simultaneously, in contrast to their rivals who see visual and text material as mutually incompatible offerings. GoCharlie was the first to introduce Image 2 Ad, which allows customers to utilize an image as an input when producing Facebook ad language that is optimized. You were able to make photographs based on your copy when GoCharlie made 4K image production available (another first). Additionally, their 1-click blog features an integrated picture recommendation function that takes your content into account. You may quickly go from a video to a blog using our content repurposing tool.

The future of providing value to clients, in our opinion, is in the creation of visual material that considers text and vice versa, and their product experience will continue to add new modalities to the experience.

Natural Instruction & Quick Improvement

AI for everyone is one of GoCharlie's corporate beliefs. This implies that they create AI that everybody may access and utilize. But for many users, prompt engineering stands in the way of fully using AI's potential as a collaborator in the creative process. In two ways, GoCharlie wants to make it as simple as asking a buddy for a favor:

• Enhancing Prompts: To better guide the AI, we will improve the prompts that the average user sees. When a dog types on a keyboard, the prompt becomes significantly richer.
• Innate instruction: There are instruct models, but many of them are still made to sound like a robot. By including iteration functionality, we are rethinking how instructions may operate and making it a more collaborative experience. dislike the hairdo in a picture? Charlie, please change it.

The entire potential of AI creation and iteration at a pace not feasible with traditional mouse-based creative editing tools will be completely unlocked by natural text instruct configurations.

Personalized and goal-driven product experiences via customization

Up until now, using AI to personalize content, the main emphasis has been on descriptions and details about your company or product. GoCharlie thinks there are two excellent chances to increase this customization:

• User customization: Do you provide links to your most interesting content? 's website? Your product collection? You want to employ certain keywords for SEO, right? A colorway or a brand logo library? Bring everything, and Charlie will reduce it to a unique product that speaks in your brand's voice.
• Goal-driven: The material you produce to boost brand recognition differs greatly from that which you employ to boost website traffic. Therefore, shouldn't the material the AI produces also adapt to those objectives? How about the people you want to reach? To include content and audience objectives into the platform, we are expanding Charlie.

Customization is a key differentiator because it helps a tool better understand a user's needs and preferences as they use it more frequently. Users will want to remain where the product gets to know them and adjusts to their needs, much like a Spotify playlist.