ChatGPT has proven the catalyst for the wide adoption and popularity of modern generative AI services. On the other hand, the system has its limitations and shouldn’t be the only tool considered when implementing genAI-based solutions in your company.
ChatGPT has undoubtedly built worldwide recognition and popularity, reaching its first million users in the first week of operations. Its versatility and flexibility resulted in thousands of manuals and guides on using the system. No matter if one is looking for a personal trainer or a business advisor, there are collections of awesome prompts for ChatGPT people can use to achieve their goals.
Yet the system has its limitations that also need to be considered.
Limitations of ChatGPT
The narratives around Artificial Intelligence Solutions vary from world-saving to world-dooming. The truth is way more complicated, with apps like ChatGPT being both powerful and limited simultaneously.
Generative AI is known to deliver plausible-sounding results that are only meaningless babble – entirely made up with no connection to reality. The reason is obvious – the system provides an answer with no natural way to distinguish between truth and lies since it is only a machine with no real experiences. When tasked with delivering an answer, it does so, no matter if the answer is correct or not. Or, in more complex cases, if the “correctness” applies to the given situation.
Data security concerns
ChatGPT makes use of user input and interactions to train further and improve future outputs. It means that, in some contexts and situations, the system may re-use one’s answer when conversing with a different user. Samsung engineers learned about that the hard way when their code, reviewed by ChatGPT, was later leaked.
As mentioned above, AI outputs are generated either from data harvested from the Internet and other data repositories or from the users. The system basically uses the building blocks and patterns found in the available data to deliver answers, and it may “reuse” larger pieces of content when necessary. This may lead to copyright infringement, and the issue is so serious that Valve, the company behind Steam, banned all games where AI-generated content was used.
No access to fresh data
The data used to train the system ended in September 2021. This limits the use of the tool when dealing with matters requiring access to the latest news or fresh information.
Why go for an alternative?
All Large Language Models (LLMs) and systems have their limitations and downsides. Yet knowing them and getting familiar with the ways they work brings various benefits:
- Limiting vendor locking – first and foremost, every company (including OpenAI) may reshape and change their policy, making all products based on their services prone to risk. Having backup support is always a good idea.
- Better control (sometimes) – ChatGPT has shown that there is a real risk of leakage. Choosing another vendor can limit this risk if their policy and contracts are carefully reviewed.
- Better fit to the ecosystem – last but not least – some LLM-based assistants have been delivered by the biggest players around, including Microsoft and Google. These companies have wide business offers composed of tools and suites. As such, they are actively making their solutions a fitting part of the rest of the ecosystem.
Alternative vendors of Generative AI-based language solutions
ChatGPT is undoubtedly the first and arguably the most popular LLM-based conversational solution. But hardly the only one.
Google Bard is a direct competitor of ChatGPT, a multi-purpose system that responds to user prompts and delivers answers to questions. The system has access to the global web, so the responses can contain links and “further reading.”
Contrary to ChatGPT, Bard is constantly gathering information through the internet and can provide sources for the information it delivers. Also, contrary to ChatGPT, Google Bard delivers responses in a more fragmented manner, using sections, headings, and bullet points.
Microsoft Bing is the world’s second most popular search engine, with a 2.98% market share (compared to the 92.08% controlled by Google). Microsoft launched its system using OpenAI’s experience and expertise, enabling Bing users to access it directly from the search engine. As such, the results are more context-rich and tailored compared to the standalone service of ChatGPT.
Microsoft is doing its best to use the lever it gained through its partnership with OpenAI, the company behind ChatGPT, by incorporating genAI-based solutions into more and more of its products. Copilot for Viva, the HR-aimed employee engagement platform, is a great example of how integrating GenAI can work.
JasperAI is a much more narrow system than the three mentioned above. The solution has been designed and developed to support marketing teams. Jasper AI is also involved in actively supporting generative AI promotion, with GenAI conferences held to show the advantages of using these solutions in marketing. So if one needs an AI like chatGPT for writing and other marketing-related tasks – it can be a great pick.
JasperAI is a focused and narrowed system compared to ChatGPT or Bard, with all the advantages and disadvantages of such a specialization. In some cases, the solution can be better than ChatGPT, while in others, it can be insufficiently flexible.
Claude by Anthropic
Claude is a direct competitor of Bard and ChatGPT. These systems prioritize keeping focus – they can operate on up to 8k tokens. Claude AI can operate on up to 100k tokens, translating to 75k words. This makes a huge difference when the system is asked to perform more complex tasks that involve, for example, scanning large reports and articles for the sake of summarization and cherry-picking facts. With the larger token base to operate on, the system is also a good chatGPT alternative for coding tasks.
Anthropic also claims to be more secure than the competition, following the ideal of constitutional AI.
You.com is a search engine that competes with Google when it comes to providing users with the information they’re looking for, rather than the information the internet wants to deliver with SEO and semantic shenanigans. The company stands its ground and has infused its search experience with a chat interface.
When it comes to You.com and YouChat (a thing like chatGPT and Bing), the search engine is the center of the experience, with the user being able to dig deeper to get more accurate information and to enrich their results with deeper context.
The system can also generate text and images using LLM and Stable Diffusion within the system. The tool is focused on delivering content, yet can be combined with other systems using extensions.
All the systems described above are proprietary and closed-source, meaning that only their creators truly know what’s inside and how they work. If the use case requires a company to keep strict control over its data and the place it is processed, the open-source implementation may be the path to follow. Also, the open-source models trained and used in an owned sandbox environment can get access to classified or sensitive data that would be impossible to share with external parties any other way. This can include, for example, medical data that may be processed by ChatGPT-like tools.
LLaMA was released by Meta (Facebook owner). It was trained on publicly available data. One of the goals of their team of researchers was to show that there is no need to use restricted or otherwise unavailable data to train a Large Language Model.
Researchers and business people are mostly free to use LLaMA for any purpose, with some exceptions. This raises doubts if the model is truly open source and the Open Source community is actively discussing the matter.
Alpaca is a model based on LLaMA and was fine-tuned using 52 thousand examples of instruction following. The main goal of the model was to provide the academic community with a model that has performance comparable to ChatGPT yet can be used at will for research purposes and its effects may be replicated in a repeatable environment.
Despite the fact that the model is aimed toward academic users, it can be adjusted to serve business purposes.
Advantages of open source models:
Open-source software is oftentimes comparable and competitive with proprietary and closed source. Benefits include:
- Customizability: As open-source projects, these models can be modified and fine-tuned for specific tasks and domains.
- Research and Learning: Developers and researchers can study the models’ architectures and techniques to improve their understanding of NLP and transformers.
- Community Support: Open-source projects often have active communities that contribute to the models’ development, documentation, and issue resolution.
- Lower (or alternate) Cost: By using open-source models, organizations can avoid the high costs associated with commercial language models. On the other hand, the company needs to provide the model with hosting and computational power, which may not be cheap considering the power consumption and data-heaviness of the AI training process.
- Privacy and Control: Using open-source alternatives to ChatGPT gives users more control over their data and privacy, as they can be deployed locally without relying on external servers.
ChatGPT is a powerful business tool and can be easily transformed into a generative AI-powered assistant. On the other hand, despite its flexibility and power, it has its limitations. And sometimes, reaching for the alternative may be the best way to achieve your business goals.
If you wish to talk more about ways to infuse your business with generative AI capabilities (similar to ChatGPT or otherwise), don’t hesitate to contact us now!