Examining ChatGPT’s drawbacks and alternatives

For your friends’ enjoyment, you are preparing a smoothie. Your friend Ruchir comes over with a delicious apple and provides it to you to finish your refreshing masterpiece after it has already been blended with various fruit and yogurt. The drink is practically finished now, yet you can almost still smell the hints of apple. Ruchir announces, “I’ve changed my mind, I need to leave and would like my apple back,” before you take a taste. You respond, “Ah, excuse me, but that’s just not possible.” In a moment, we’ll return to this incident and discuss how it pertains to ChatGPT and reliable AI.

New technologies like OpenAI’s ChatGPT have attracted attention for their conversational capabilities as the field of artificial intelligence (AI) develops. But I also see how important it is to consider the hazards involved before implementing it directly in our organizations. I examine the risks and difficulties that come with using ChatGPT in a corporate setting in this conversation, which calls for a cautious implementation strategy. I’ll also stress how important it is to use IBM Watsonx to guarantee reliable AI solutions. And if you’re unsure, I suggest using the same common sense you typically have while using new Internet services.

Tools for AI are evolving

The enormous power of GPT-3 and GPT-4, two members of a new class of “gargantuan” and well-liked big language models employed in numerous AI applications, is harnessed by ChatGPT. Users can ask questions, write text, create email drafts, discuss code in various programming languages, convert spoken language to code, and more with ChatGPT. It stands out as a top-notch conversational chatbot that seeks to offer thoughtful and sensitive responses.

An wonderful resource for exploring creative writing, coming up with ideas, and communicating with AI is ChatGPT. Everyone is welcome to use it for free, and subscribers to ChatGPT Plus have access to a more sophisticated version. The chatbot’s capacity to recall prior exchanges enhances its dynamic and captivating experience. 

Natural language processing (NLP) systems and other AI-powered chatbots compete with ChatGPT despite the fact that it has attracted considerable attention and popularity. For instance, Google has created Bard, an AI chatbot that uses PaLM 2, its own language engine. Similar to this, Meta just unveiled its stunning LLaMA2 model. There will undoubtedly be more rivalry as the field of AI chatbots develops, as well as the entry of new players. To find the greatest solutions for business needs, it’s critical to be informed about the latest developments in this field.

Why not use ChatGPT in a corporate setting directly?

Using ChatGPT directly in an organization comes with dangers and difficulties. These include restrictions on the development of AI, security and data leakage, confidentiality and liability issues, intellectual property challenges, adherence to open-source licenses, ambiguous privacy, and international legal compliance. Here, I discuss these dangers and provide instances to show how they could materialize in your regular business operations.

I’ll begin by looking at alternatives to ChatGPT that are meant to reduce the hazards associated with using it directly. One such alternative is IBM Watsonx, which I do suggest for enterprise use because it rigorously handles data ownership and privacy concerns through curation and governance. I’ll end this dialogue by bringing you back to the smoothie tale, but feel free to use “your apple” in place of “your data” in the sections that follow.

It is critical for businesses to be aware of the potential hazards and difficulties associated with directly employing ChatGPT before looking into alternate alternatives. The evolution of new services over the course of the internet’s history (such as Google search, social media platforms, etc.) serves as a practical reminder of the value of data privacy and ownership in the organization. Keeping this in mind, the following important considerations are listed:

Privacy concerns and data leaks

Inputting private data into ChatGPT makes it part of the chatbot’s data model and makes it possible for it to be shared with others who ask pertinent inquiries. Data leakage could result from this, which would go against the security guidelines of the company.

Example: To reduce the danger of data leakage and potential security breaches, plans for a new product that your team is assisting a customer launch, including private specs and marketing tactics, should not be disclosed with ChatGPT.

Discretion and privacy

Similar to the previous point, revealing confidential customer or partner information may be against the terms of contracts and laws requiring such information to be protected. Confidential information may leak if ChatGPT’s security is breached, thereby harming the organization’s reputation and putting it at risk of lawsuit.

Consider a healthcare facility that employs ChatGPT to help with patient inquiries. A potential legal responsibility and patient privacy rights guaranteed by legislation like HIPAA (Health Insurance Portability and Accountability Act) in the United States could be violated if confidential patient information, such as medical records or personal health information, is provided using ChatGPT.

Concerns about intellectual property

It can be difficult to determine who owns the code or text that ChatGPT generates. The output is supposed to belong to the person who provided the input, but when the result contains legally protected data that came from other inputs, problems may occur. Copyright issues might potentially surface if ChatGPT is utilized to produce written content based on protected intellectual property.

For instance, if written content is produced for marketing objectives and it contains copyrighted information from other sources without proper acknowledgement or permission, that content producers’ intellectual property rights may have been violated. Legal repercussions and harm to the company’s reputation may follow from this.

Adherence to open source licensing

If ChatGPT makes use of open-source libraries and incorporates that code into products, it may infringe on OSS licenses (such as the GPL) and cause legal issues for the company.

Example: If a business uses ChatGPT to produce code for a software product and the source of the training data that was used to train GPT is unknown, there is a chance that the open-source licenses governing that code may have been broken. The open-source community may take legal action as a result of this, as well as accusations of license infringement.

Limits to the growth of AI

It is forbidden to use ChatGPT for the creation of further AI systems, according to the terms of service. If the business is involved in AI development, using ChatGPT in this manner can be detrimental.

Example: A speech recognition technology company intends to integrate ChatGPT’s natural language processing capabilities to improve their current system. However, ChatGPT’s terms of service make it clear that it cannot be used to create other AI systems.

Improved reliability with IBM Watsonx

In keeping with the smoothie analogy, public ChatGPT makes use of your prompt data to strengthen its neural network, just to how the apple gives the smoothie flavor. Like the mixed apple, once your data reaches ChatGPT, you lose all control and knowledge of its usage. As a result, one must make confident that they have all necessary rights to include their apple and that it doesn’t, so to speak, include sensitive data.

To allay these worries, IBM Watsonx offers curated and transparent data and models, giving you more control and assurance over how your smoothie is made and used. Simply worded, watsonx could comply with Ruchir’s request if he requested for his apple back. The analogy and story are now complete.

Watsonx.data, watsonx.ai, and watsonx.governance are three crucial aspects introduced by IBM Watson that work together to develop trustworthy AI in a way that is not currently available in OpenAI models. These attributes categorize and classify data and AI models, ensuring ownership and provenance information is transparent. Additionally, they control the models and data, addressing persistent concerns about drift and bias. This methodical approach successfully allays the data ownership and privacy issues raised in this paper.

Hugging Face, an open-source business, and IBM have teamed up to develop a model ecosystem. Both businesses are using the watsonx capabilities to select and support models based on their usefulness and reliability.

Moving AI ahead

Risks relating to security, data leakage, confidentiality, liability, intellectual property, compliance, and privacy are associated with the direct use of AI chatbots like ChatGPT within a company. These risks may have negative effects on organizations, such as harm to their reputations and expensive legal issues.

IBM Watsonx is suggested as a remedy to reduce these dangers and create reliable AI. It provides curated and labeled data as well as AI models, providing ownership and origin transparency. By addressing issues with bias and drift, it adds another degree of credibility. Innovation and appropriate AI use are balanced in IBM Watsonx. Additionally, the IBM and Hugging Face partnership enhances the ecosystem of models.

Few models can currently match the wide range of general-purpose usage shown with ChatGPT and the GPT family of models, even though watsonx offers improved trust and rigor. The field of AI models is still developing, therefore further advancements are to be anticipated. It is critical to comprehend how models are graded and developed in order to assure the best outcomes. With the use of this information, companies can choose models that most closely match their requirements and standards for quality.

Organizations may use the power of AI while keeping control over their data and guaranteeing compliance with moral and legal standards by implementing watsonx. They may profit from curated models and increased transparency while also protecting their data, securing their intellectual property, and building stakeholder confidence. Enterprises must move cautiously, investigate options, and give reliable AI top priority as they traverse the world of AI.