There is a lot of hype around ChatGPT and for good reason. It’s conversational nature, ease of use, human-like performance make it feel like magic. There are significant drawbacks though that can make it unsuitable for some consumer interactions.
One of the primary issues with ChatGPT is hallucinations. Hallucinations refer to situations where the model generates responses that are factually incorrect, nonsensical, or misleading. These hallucinations can lead to a range of issues, including misinformation, misunderstanding, and potential harm. Here are some reasons why hallucinations are problematic:
Misinformation: Hallucinations can lead to the dissemination of incorrect information, which can be misleading or harmful to users. Users may unknowingly accept and share false information generated by the model.
Trust and Reliability: Hallucinations can erode trust in AI systems. Users may become skeptical of the information provided by AI models if they frequently encounter responses that are clearly inaccurate.
Ethical Concerns: In some cases, hallucinations may lead to the generation of biased or inappropriate content, which can have ethical and social implications. This includes generating offensive or harmful statements.
Educational and Research Use: In educational and research contexts, hallucinations can be problematic as they may lead to incorrect explanations or answers. Students and researchers relying on AI-generated content may be misled.
Lack of Common Sense: ChatGPT may occasionally generate responses that lack common sense, which can be confusing and frustrating for users.
Efforts are made to address these issues through model development and fine-tuning, but they remain challenges due to the complexity of language and the potential for the model to generate responses based on patterns in the training data, even when those patterns are incorrect.
In situations where your agents or customers need the exact answer every time cognitive search may be the better option. Cognitive search is a technology that aims to improve the process of searching and retrieving information from large and diverse datasets, including both structured and unstructured data. It leverages natural language processing (NLP) and other advanced techniques to enhance the search experience.
Here's an explanation of how cognitive search works:
Data Ingestion and Indexing: The first step in cognitive search involves collecting and ingesting data from various sources. These sources can include databases, content management systems, file repositories, websites, and more.
Once the data is collected, it needs to be indexed. Indexing is the process of creating a structured representation of the data, which allows for efficient and fast retrieval. This includes creating an index that maps keywords, phrases, and metadata to the corresponding documents or data items.
Natural Language Processing (NLP): NLP is a fundamental component of cognitive search. It enables the system to understand and process human language. NLP techniques are used to: break down text into individual words or phrases, remove common words like "the," "and," "in" that don't carry significant meaning, reduce words to their base or dictionary form (e.g., "running" to "run"), identify and categorize named entities like people, places, organizations, and dates, assess the sentiment or emotional tone of text.
Query Processing: When a user submits a search query, the cognitive search system processes it using NLP techniques. This involves breaking down the query, understanding the user's intent and identifying important keywords or concepts. Query expansion may be employed to broaden the scope of the search, taking into account synonyms, related terms, and contextual information.
Relevance Ranking: After processing the query, the system ranks the indexed documents or data items based on their relevance to the query. Relevance ranking is typically determined using a combination of factors, such as keyword match, metadata, document quality, and user behavior (click-through rates, previous searches, etc.).
Faceted Navigation: Cognitive search often provides faceted navigation, which allows users to filter and refine search results using various attributes or metadata associated with the documents. This helps users narrow down their search results efficiently.
User Interaction and Feedback: Cognitive search systems may incorporate user feedback and behavior to improve future search results. For example, if users frequently click on specific results, those results may be ranked higher in subsequent searches.
Machine Learning and AI: Some cognitive search systems may use machine learning and artificial intelligence to continually improve search results. This could include personalized recommendations and adaptive ranking based on user behavior.
Scalability and Performance: Cognitive search systems need to be highly scalable and performant, especially when dealing with large datasets. They should provide fast responses to user queries, even as the data grows.
Security and Access Control: Security is crucial in cognitive search, especially in enterprise environments. Access controls and permissions ensure that users only see information they are authorized to access.
In summary, cognitive search combines data indexing, natural language processing, relevance ranking, and user interaction to provide more accurate and efficient search results. It is a valuable tool for organizations looking to harness the full potential of their data and enable users to find the information they need quickly and effectively.
Cognitive search by design doesn’t “create” any content it surfaces the most relevant content from your data sources to answer a query or question and therefore isn’t subject to hallucinations. Users may need to work through more than one result to get the information they seek but it makes this easier by highlighting the information from a particular document, knowledgebase article, etc. that is most likely to answer a user’s question.
If your customers or agents need an answer that is they can rely on for 100% accuracy, cognitive search may be a better option than ChatGPT but there is always more to the story. We advise our customers when evaluating tools that help their employees and customers find relevant information to seek out tools that offer a full suite of solutions that include keyword-based searches, cognitive search and generative AI like ChatGPT. Each has a place in your organization and a specific job to do.
For example, cognitive search could not generate an after-call summary to write to your CRM. This is why it's important to not fall in love with any one AI "technique" but to consider an approach that matches the technology to the desired outcomes.
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