ChatGPT: The Future is Here

Has ChatGPT propelled AI to mainstream adoption?

The recent breakthrough in Natural Language Processing and Natural Language Understanding has significant implications for the future of digital experiences and search.

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ChatGPT is a conversational agent that utilizes a Large Language Model (LLM) - a neural network capable of comprehending and generating human language with remarkable accuracy. The broad scope of knowledge in language enables the LLMs to be applied across a wide range of applications.

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We have all undoubtedly experimented with ChatGPT and it has become the buzz word all over the internet. Despite witnessing various hype cycles over Machine Learning over the decade, this development is out of the box. Microsoft's quick integration of this technology into their product line illustrates the potential for disruption this can bring.

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Our conviction that Large Language Models (LLMs) are essential for revolutionizing digital experiences is reinforced by the buzz surrounding ChatGPT and Google's BARD. SolvFore's objective is to democratize AI, empowering enterprises to modernize their digital service and retail offerings. Additionally, we prioritize accuracy, trust, confidentiality, and security for our clients.

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ChatGPT- Is the hype justified or is it just a passing trend?

Despite the widespread attention ChatGPT has received, its limitations are significant.

Firstly, understanding language does not equate to intelligence. Language serves as a medium for conveying information, ideas, and emotions, rather than being the defining factor for our intelligence.

Large Language Models(LLMs) create vast inherent knowledge bases as they process training data, butthey lack an explicit concept of factual accuracy, truth, or physical realities outside of language comprehension. This is why there is ongoing research into "grounding" these models in hard rules, concepts, and other forms of expression.

ChatGPT's training data is finite, with the training horizon currently limited to October 2021. It cannot in dependently acquire new knowledge. While additional context and information can be provided during conversations, this is not equivalent to updating its internal knowledge through retraining.

The most noticeable manifestation of these limitations is the phenomenon of "hallucinations" - generated text that is grammatically and syntactically correct but contradicts common sense or contains factual in accuracies. The potential for creating believable but challenging-to-verify content can put enterprises at risk of falsely manipulated information or that lacks credibility.

"The primary issue is that while ChatGPT's responses have a high likelihood of being incorrect, they often appear to be plausible, and generating them is quite effortless."

This occurrence is frequent and can be particularly deceptive since the overall output seems to be of high quality and coherence, tricking anyone who isn't paying close attention.

How can Human involvement help with ChatGPT

While ChatGPT has its limitations, it also has huge potential to revolutionize various industries. Generative AI capabilities have reached a tipping point where their advantages seem to outweigh the disadvantages in many scenarios.

To address the above-mentioned challenges, human-guided enhancements and a learning layer is crucial to teach the model appropriate behavior. This layer, referred to as the Alignment Problem, involves ensuring that models comply with business rules and objectives. However, this issue is far from being resolved.

What are some potential applications of conversational and generative models in the enterprise, and what are the key considerations that need to be taken into account when implementing these technologies?

It is quite evident that relying solely on ChatGPT as the primary source of enterprise knowledge is impractical due to its limitations and the recurring costs of retraining it to maintain its accuracy. Nevertheless, there are also several ways in which generative AI can provide tangible benefits in the enterprise.

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For starters, one is to use it in situations where inaccuracies or fabrications can be overlooked.

Another way is to harness its linguistic capabilities without relying on its internal knowledge. For instance, a simple use case could be - in customer service, ChatGPT can assist in reformulating and summarizing support cases to facilitate agent onboarding and handoff. However, it's important to note that even in such scenarios, ChatGPT can produce hallucinations, making it crucial for a human to be in the loop if factual accuracy is a critical component of the solution.

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On similar lines, one of SolvFore’s recent ChatGPT implementation projects was for a food & beverage client. We designed and implemented a chatbot that helped the client to serve its customers and improve customer experience by helping them with personalized choices of drinks and answering customer queries in real-time that reduced their response time to queries.

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Ensuring Enterprises are Generative AI Safe

The potential use cases for the new breed of LLMs in the enterprise are endless, but there are also inherent risks that must be addressed.

• If an enterprise were to create a customer service version of ChatGPT, it would need to address several issues, including data availability and freshness. ChatGPT lacks the latest information and needs to tap into more data sources frequently to provide an accurate answer. However, retraining ChatGPT daily is currently not technologically or economically viable, and it needs access to the most up-to-date information across the entire enterprise.

• Additionally, privacy and security issues arise when different sources of content with varying levels of confidentiality are involved.

• Moreover, using an LLM's internal knowledge to search and answer questions can lead to wrong or dangerous answers due to conflicting information, hallucinations, biases, and more.

• Lastly, LLMs lack personalization and do not improve their recommendations over time unless explicitly prompted with preferences every time.

SolvFore has been successful in assisting enterprises to harness the power of ChatGPT integration while reducing risks and costs.

Our goal is to bring enterprise relevant experiences up to date with generative and conversational AI powered by LLMs at the core of this transformation.