Conversion AI May be Key to Avoiding A Technological Plateau

The demand for content is seeing steadfast and steep growth. To get a realistic idea about the growth rate, Adobe conducted a survey encompassing more than 2,600 customer experience and marketing professionals across major global economies. 

The survey results, as published by Adobe in March 2023, indicated a growth in demand for content by at least twofold during the last two years. Around two-thirds were even more optimistic about the future of content. They said they expected the demand to grow by anywhere between five to twenty times in the next twenty hours. 

Catering to this demand has forced companies to opt for new content production models. As per the World Federation of Advertisers, over half of the major international companies have developed in-house content production capabilities. But is that enough? Well, conversion AI has stood up to the challenge. 

What is Conversion AI?

Conversion AI is about using artificial intelligence to help businesses talk to their customers better and make more sales. It does this by helping create, improve, and manage content like writing, pictures, and sounds. This makes it easier for companies to grab their audience's attention, make content that really speaks to them, and improve how they experience things, all through smart AI technology.

Today, companies can use Conversion AI to meet a range of content marketing goals, including Facebook and Google Ad copies, website content, social posts, and more. But how does Conversion AI make it all possible? What is the technology that fuels it? For that, we need to know about Large Language Models or LLMs. 

What is a Large Language Model?

It is a kind of AI that can learn from large amounts of text and data. There are use cases where these AI models could deal with hundreds of billions of data points to come up with meaningful insights, strategies, and projections. 

The Genesis of LLMs in Brief

The modern-day LLMs broke into the scene six years back, in 2017. These LLMs utilized transformer models that could deal with a large number of parameters to understand and generate precise responses rapidly. 

Four years later, in 2021, the Stanford Institute for Human-Centered Artificial Intelligence came up with the term and concept of foundation models. The name originated from the fact that these models, large and impactful, delivered as the foundations for further optimizations and use cases. With the development of LLM, content generation, especially in the written form, went through significant changes. 

The Impact of LLMs

ARK Invest's Research shows that during the past two years, the writing quality of LLMs has experienced significant improvement. It has led to more trust and deployment of LLM-powered solutions to generate content. 

As a result, the cost of authoring written content has come down drastically. It was relatively constant in real terms over the past century, close to US$100 per 1000 words written. In the past two years, however, that rate has come down to US$0.10 and even less than that. 

The performance and productivity are set to further improve as AI training performance rapidly advances. AI researchers are innovating in training and inference, hardware, and model designs, enhancing performance while reducing costs. This is expected to yield over five times the performance gains in 2024, using 2023 as the base year.

There have been several algorithmic innovations that have resulted in superior writing ability and improved productivity and performance of LLMs. Reinforcement learning from human feedback, for instance, has helped a lot. Prompts have been optimized to outperform human prompts by more than 50%. 

Speculative Decoding capabilities have sped up inference two to three times on specific models. Researchers have also been proactive in prioritizing inference costs. And their efforts are showing results on the ground, with inference costs falling at an annual rate of close to 86%, faster than training costs. GPT models have seen nearly 3-times training speedup with Flash Attention 2. 

With all these improvements and enhancements happening at rocket speed, investigations are also underway to ascertain if a potential plateau is imminent in the use of LLMs and if such an instance could be avoided. 

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Potential Plateau of LLMs and its Avoidance

The researchers are looking into a possible scenario where LLMs will run out of data, resulting in limited performance. LLMs need to be fed with large volumes of training data consistently to keep learning and improving. However, computing power and potential lack of training data appear to be a concern. 

According to Epoch AI estimates, high-quality language/data sources, including books and scientific papers, could be exhausted by 2024. And that is when one would have to tap into a larger set of unexplored vision data. These untapped vision data that can help LLMs avoid plateauing include words spoken and some other sorts of physical world data. 

Research shows that annually, thirty quadrillion words are spoken. Speech-to-text generators and similar tools capture more than 80 trillion words spoken daily. Moreover, there are taxis, trucks, drones, and other robotic vehicles that generate large volumes of physical world data and synthetic data that augments primary data. All these unexplored data could act as an unmatchable wealth of resources and make LLM better. 

However, enhancements in performance, productivity, and quality are not only about locating unexplored data mines. Several innovative companies are making all-around efforts to improve the scenario holistically. 

#1. Replit AI

When it comes to boosting productivity, Replit AI has been one of the major success stories. As a coding assistant, it has increased the productivity and job satisfaction levels of software developers. It helps do away with the iterative parts of coding for one to focus on the creative parts of the job. 

Replit AI streamlines coding by integrating an AI code generator that eliminates the need for tab switching, offering auto-complete code suggestions for faster progress. It enhances project understanding with its Code Context functionality for writing relevant next lines and proactively detects issues, suggesting fixes directly within a seamless editor interface.

Altogether, Replit AI is an efficient suite of AI code generation features, including Complete Code, Generate Code, Edit Code, and Explain Code. 

What powers Replit AI are LLMs. It returns results generated from large language models trained on publicly available code and tuned by Replit. It considers the conversational prompt and the programming language one is using to make suggestions and explanations on the codes generated. 

Replit AI's features are available for free to all users. All one needs to have is a Replit account. Replit AI performs best with JavaScript and Python code but supports 16 languages, including Bash, C, C#, C++, CSS, Go, Java, JavaScript, HTML, PHP, Perl, Python, R, Ruby, Rust, and SQL. 

According to the latest available information, the company raised US$97.4 million at a US$1.16 billion valuation to expand its cloud services and lead in AI development in April 2023. 

#2. Github Copilot

Another coding assistance service, similar to Replit AI, that has helped improve the productivity of developers on coding tasks by 2.2 times in 2023 is Github Copilot. The Github Copilot offers contextualized assistance throughout the software development lifecycle. 

According to consumer satisfaction survey numbers, Github Copilot user developers report up to 75% higher satisfaction with their jobs than those who do not and are up to 55% more productive at writing code without compromising quality. 

Github Copilot trains itself on natural language text and source code from publicly available sources, including code in public repositories on GitHub. It is trained in all languages that appear in public repositories. However, languages with less representation in public repositories may prove less effective. Github Copilot can be used by individual developers, freelancers, students, educators, and open-source maintainers.

According to publicly available statements of Microsoft's VP of Product Mario Rodriguez, GitHub Copilot is thriving and generating revenue at an annual rate of $100 million.

#3. MosaicML

MosaicML helps train LLMs, positioned as a solution that brings generative AI to all, offering everything needed for training and deploying models on user data. Its MPT Foundation Series facilitates easy LLM integration into applications running on both open-source and commercially licensed models.

MosaicML training empowers developers to pre-train or fine-tune models with full data control, simplifying the process to a single command directed at an S3 bucket. The platform efficiently handles orchestration, node failures, and infrastructure, exemplified by Replit's use of Mosaic ML to train its Ghostwriter LLM, showcasing leading results within a week.

MosaicML's approach to training in any cloud environment emphasizes data privacy, security, and full model ownership, with features supporting seamless transitions between clouds. It offers full data ownership, content filtering according to business needs, and plug-and-play integration with existing data pipelines, being cloud-agnostic and enterprise-proven.

In June of the previous year, Mosaic ML's acquisition by Databricks in a deal valued at $1.3 billion highlighted its significant market impact and value proposition.

#4. Anthropic

Anthropic's flagship product, Claude, runs on two models: Claude and Claude Instant. 

Claude is Antropic's most powerful model that excels at a range of tasks, from sophisticated dialogue and creative content generation to detailed instruction. It works well in scenarios that require complex reasoning, creativity, thoughtful dialogues, coding, and detailed content creation. 

Claude Instant – the faster and cheaper version of Claude – helps with casual dialogue, text analysis, summarization, and document comprehension. It works well where performance at a low cost is a requirement. It does its job with reduced latency. 

Overall, Claude is suitable for processing volumes of text, having natural conversations, getting answers, and automating workflows. From customer service to legal, coaching, search, and back-office related jobs, Claude is suitable for a variety of use case scenarios. 

According to reports published in late December 2023, Anthropic, seen as the OpenAI rival, was in talks to raise SU$ 750 million in funding at a US$18.4 billion valuation. 

#5. Humata

With Humata, one can skip through long technical papers with ease. The Humata PDF AI helps summarize findings, compare documents, and search for answers. It is compatible with unlimited files uploaded with no limits on file size. Its document AI answers come with citations so that one can trace where the insight came from.

Since it is like ChatGPT for PDFs, it can rewrite the summary until satisfaction. However, unlike ChatGPT, Humata can work with files, read every file one upload, and generate answers based on the content of the documents. 

On October 2nd, 2023, Humata AI announced the successful completion of its latest funding round, where it raised US$3.5M from Google's Gradient Ventures, Cathie Wood's ARK Invest, M13, and other prominent angels for their seed round. 

#6. OpenAI

Finally, no discussion around AI, its LLM-learning, and content creation features can be complete without the mention of OpenAI, the company that has been most popularly building generative models using a technology called deep learning that leverages a large volume of data train AI systems. 

Open AI has Chat, Image, and Audio services. Its GPT-3, an autoregressive language model, has been trained with 175 billion parameters. However, it has trained language models that are even better than GPT-3 at following user intentions. 

Among products related to images, OpenAI's research on generative modeling for images has led to representation models like CLIP, which makes a map between text and images for an AI to read, and DALL-E, a tool for creating vivid images from text descriptions. 

Finally, it has made developments in the field of audio with Whisper, Jukebox, and Musenet. Whisper offers robust English speech recognition facilities, while Jukebox helps generate music in a variety of genres and artist styles. MuseNet is a deep neural network that can generate full-fledged musical compositions with a variety of instruments. 

According to reports published in late December 2023, OpenAI was in early discussions to raise a fresh round of funding at a valuation at or above US$100 billion.

Beyond the Plateau: The Road Ahead 

Artificial Intelligence has helped content generation travel millions of miles in a very short span. The future will be brighter and more prosperous, where computational systems and software that grow with data will be solving intractable problems. 

Economic sectors of all kinds will be able to leverage technology better by automating all sorts of knowledge work that is repetitive and time-consuming. While models like GPT-4 will boost productivity, open-source AI models will lead to drastically reduced operation costs and radically enhanced efficiency. Researchers estimate not only the avoidance of a technological plateau but also the quadrupling of knowledge worker productivity by 2030. 

This underscores the significance of Large Language Models in the technological ecosystem, having played a crucial role in all this. It helped products like ChatGPT consolidate the public's understanding of AI while enabling them to offer a simple chat interface that anyone speaking any language can utilize to their benefit.

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