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From General to Customized AI: How Businesses Can Leverage ChatGPT Upgrades

Poster - From General to Customized AI: How Businesses Can Leverage ChatGPT Upgrades
Ivan Popov

Ivan Popov

July 7, 2024

Conversational AI that enables machines to understand, process and respond to human language naturally is gaining momentum these days. The most well-known example of such AI is probably ChatGPT. With the recent update, this tool provides a number of benefits for businesses and public organizations.

In this article, we explain how companies in various industries can leverage ChatGPT and shed light on its possible evolution in 2024 and 2025.

What GPT means

But first, let’s clarify some terms and context.

Recently, OpenAI, the company developing ChatGPT, failed to trademark the abbreviation GPT (Generative Pre-trained Transformer) for their flagship product. The U.S. Patent and Trademark Office rejected the application, citing that GPT is too widely used to be trademarked. This could hinder competitors from accurately describing their GPT-based products. As a result, GPT has become a general term for a class of Large Language Model (LLM) solutions.

LLMs are closely related to GPT but are not interchangeable. LLM refers to a broad class of AI trained on vast amounts of textual data to understand, generate and translate text, among other language-related tasks. GPT is a specific implementation of LLM. Therefore, while all GPTs are LLMs, not all LLMs are GPTs, as there are other architectures and training approaches for large language models.

What ChatGPT gained from recent upgrade

The latest ChatGPT-4 Turbo version has promoted Conversational AI to a new level.

Enhanced relevance. First of all, ChatGPT is now trained on datasets up to April 2023, which makes it more relevant in a fast changing business landscape.

Increased power and reduced cost. This is achieved due to the enlargement of the context window up to 128,000 tokens (a unit of text or code read by AI), which is equivalent to 300 pages of text. Now, a full-length novella can be submitted as an input query. In previous versions, the maximum requests were 8,000 and 32,000 tokens.

The limited capacity of current LLM models increases the growing context load. This remains one of the biggest obstacles to achieving AI singularity—the threshold at which artificial intelligence clearly surpasses human intelligence. One may say that there are impressive LLM models, like the Anthropic Claude 2.1 with the context window of 200,000 tokens. However, in practice, only half of this volume is effectively utilized.

Possibility of creating specialized GPTs. There are thematic variations of the general ChatGPT, a sort of mini-versions tailored to solve specific user tasks. They are compiled into lines of ready-made AI tools based on the main engine for all major application areas. For example, for working with text, images, audio and video, writing and analyzing code, and for effective planning and data analysis.

Apart from choosing from the ready-made solutions, there is also the possibility to create your own GPT tools, maximally customized to the specific tasks of the industry, company, specific department or individual specialists.

Regular updates like these pave the way to the future where multifunctional AI becomes a norm, becoming something like «AI tools store for all occasions.»

How businesses benefit

Indeed, businesses have started developing customized versions of GPT to meet their specific needs, without waiting for what the chatbot vendor can offer them. Custom GPTs created by various companies can even compete with OpenAI’s native tools and occupy niche market segments.

By creating their own GPTs, either entirely proprietary or based on OpenAI, businesses cover tasks that require specialized knowledge or unique functions that are not available in the standard GPT version. Some typical scenarios include:

  • Automation and optimization of internal processes, such as automatic responses to frequently asked employee questions;
  • Specialized tools for data analysis and decision making in specialized fields like finance or healthcare;
  • Personalized customer service that requires deep understanding of industry or product features;
  • Unique marketing and advertising tools tailored to specific industries or brands;
  • Niche application tools, for example in programming or content creation.

Imagine, you need to identify products in images in an updated catalogue using ChatGPT Vision. The general ChatGPT does not know all the product lines and new items, nor does it have built-in functionality to categorize products into groups, and so on. Therefore, it has to be trained each time, which can be tedious and time-consuming. With a customizable GPT, it becomes possible to create a «mini-GPT» with preloaded product data, allowing you to immediately start identifying new items in the catalogue. Thus, your task can be solved quickly, without the need to write code and develop a new solution from scratch.

Apart from meeting their own needs in the most efficient way, businesses can potentially benefit from sharing such specialized GPTs publicly if other companies find their functionality useful.

However, it is important to remember that the successful implementation of GPT models depends on the quality of the initial training data and the ability to integrate such solutions into business processes.

What’s next?

In 2024 and 2025, we expect GPT customization opportunities will enhance and very likely lead to:

  • Deepening specialization. We’ll see the emergence of new specialized GPT versions targeted at specific industries and capable of performing increasingly complex and specific tasks.
  • Integration and interoperability. Improved GPT capabilities to integrate with other systems and platforms will make this tool even more effective within corporate ecosystems.
  • Accessibility and scalability. The availability of customized GPTs will expand to a broader range of users, including small and medium-sized enterprises.
  • Ethical and legal issues. The world will continue to focus on ethics and legal responsibility in AI use, particularly concerning copyright and data privacy.

Integration, not invasion

AI in its new interpretation will not seek to replace humans or forcibly change established business processes. The focus, given the recent updates to ChatGPT and the activation of its corporate customization, shifts to the harmonious combination of AI capabilities with current technical and software resources of companies.

The goal of such integration is not only to improve the efficiency and speed of existing systems but also to enrich the user experience with intuitive and adaptive tools. AI will complement and expand human capabilities, helping solve complex tasks and providing personalized support in real-time.

For example, in a corporate setting, customized GPT versions can be integrated into existing CRM systems, analytical platforms and other corporate tools to enhance functionality without the need for a complete overhaul or replacement of current solutions.

AI should not be seen as a threat or competitor to employees. It is a powerful tool that can enhance the value and productivity of your team by providing new levels of analysis, insights and automation. Thus, AI becomes a valuable addition to the team, not a replacement.

The Middle East leveraging AI

The latest forecast from IDC shows AI spending in the META region (the Middle East, Türkiye and Africa) soaring at a five-year compound annual growth rate of 37%, with investments set to reach $7.2 billion in 2026.

In absolute terms, the largest gains are expected in Saudi Arabia, where AI is forecasted to contribute over US$135 billion to the economy in 2030, equivalent to 12.4% of GDP. In relative terms, the UAE is expected to see the largest impact, with AI contributing close to 14% of 2030 GDP.

As for LLMs, Gartner predicts that by 2024, 40% of enterprise applications in MENA (the Middle East and North Africa) will have embedded conversational AI, up from less than 5% in 2020.

The UAE is currently developing its own LLM, Falcon 180B. With a staggering 180 billion parameters and trained on 3.5 trillion tokens, this LLM soars to the top of the Hugging Face Leaderboard for pretrained open LLMs. It outperforms most notable competitors in various benchmarks, including reasoning, coding, proficiency and knowledge tests.

Ivan Popov

Ivan Popov

Ivan Popov is Applied Intelligence FS-G2M lead at Axellect. He has more than 20 years' experience in financial and banking industry, as well as in data governance, data engineering and AI, keeping special focus on customers’ data and analytics capabilities boost.
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