The trends of semantic technology for 2024 will be based on a range of technological improvements. The introduction of ChatGPT has produced a variety of primary and secondary effects on semantic technology and the industries using it. ChatGPT significantly improves AI’s use and understanding of language.
Semantic technology uses “formal semantics” to provide meaning to the differing types of data we work with. Semantics is the formal study of language and its underlying structure. Semantics deals with words and phrases, and their relationships with one another during communications. Meaning and context are applied to the different types of data being used.
The semantic web and natural language processing are both forms of semantic technology, but each has different supportive roles in the Data Management process.
Natural language processing (NLP) is a branch of artificial intelligence, and uses machine learning to understand and process human language (text and spoken) and data. With natural language processing applications, businesses can analyze data and extract information about news, people, places, and events. NLP is currently used in a variety of everyday services and products, such as translation apps for foreign languages, voice-activated digital assistants, and scanning-for-spam email programs.
The semantic web is a framework that is applied to the world wide web using standards developed by the World Wide Web Consortium. The goal of using this semantic web framework is to make the internet data readable by computers. The semantic web relies heavily on metadata.
Semantic technology can help businesses locate and discover smarter data, extract information from large amounts of raw data, and discover relationships. Semantic graph databases can make it easier and more efficient for computers to integrate, process, and retrieve data.
ChatGPT and Semantic Technology
ChatGPT has become quite popular. It is based on chatbots, and uses a large language model that was created by OpenAI in 2022. It uses neural networks to process and generate responses to any sequence of characters that is recognizable, including different spoken languages, mathematical equations, and programming languages.
This new form of chatbot is capable of communicating in basic, human-like English, and has shown itself to be of use for a broad range of tasks, such as developing new software or writing a speech. ChatGPT can do the research for a report, and then create a document that is written in excellent English, or Japanese, or German. These new, smarter chatbots can also be used to create music, visual art, videos, and functional computer codes.
ChatGPT attempts to understand the user’s prompts, and then provides a response that it believes is best.
Large Language Models
A large language model (LLM) is a deep learning algorithm that acts as a neural network and can perform several natural language processing tasks. Large language models are based on transformer models and trained with massive datasets. These algorithms, or neural networks, use a network of nodes, similar to the human brain’s neurons.
Some examples of popular large language models are BARD, Cohere, PaLM, and GPT-4. They are extremely useful for tasks such as developing creative content, code generation, and language translation. A modern LLM is remarkably flexible and can perform a variety of different tasks, ranging from answering questions to translating languages. ChatGPT, using LLMs, has the potential to replace human writers and alter how people use virtual assistants and search engines.
There is a significant concern that large language models can present content that is outrageous or misleading.
Knowledge Graphs
Knowledge graphs have become useful tools for organizing and presenting structured information using a machine-readable format. A knowledge graph, also referred to as a semantic network, is used to represent real-world entities – objects, concepts, events, situations – and shows the relationships between them. This data is normally stored within a graph database and is presented as a graph structure.
The merging of large language models and knowledge graphs dramatically improved the abilities and understanding of artificial intelligence systems. Knowledge graphs provide the structured framework needed for identifying and connecting entities, their relationships, and their attributes. The combination of large language models and the structured presentation supported by knowledge graphs has been used to build more context-aware AI systems that are revolutionizing how we interact with computers and access information.
Knowledge graphs have become powerful tools used for organizing and presenting structured information, in turn promoting efficient data retrieval and inferences about relationships.
Web 3.0
The semantic web, which works with semantic technology, is a key concept in the development of Web 3.0. The semantic web is built on the concept the internet should be both structured and labeled, allowing computers to understand and search it more easily.
For example, if the word “novel” is searched for on Google, results for the audio and ebook supplier, a type of book, or the concept of “new” would be shown. However, with Web 3.0 (also referred to as Web3), the search engine would evaluate the meaning and context of your query, using previously acquired information. The search engine would determine which “apple” is being looked for, and present more refined results.
Web 3.0 also uses the concepts and technologies that support decentralization and blockchain.
Virtual Assistants
OpenAI ChatGPT can be transformed into a virtual assistant, with the appropriate modifications. Brian X. Chen, writing for The New York Times, compared ChatGPT with Google’s “Bard,” and, and found ChatGPT to be clearly the better choice. In the article “How to turn ChatGPT into your own personal assistant,” Aaron Heienickle describes how to make the appropriate modifications.
Older versions of virtual assistants, such as Google’s Bard, Amazon’s Alexa, and Apple’s Siri, had over a decade to improve, but ended up stagnating, and are currently used for such basic tasks as playing music and acting as timers.
A ChatGPT virtual assistant can perform a variety of tasks, including generating social media reports, marketing analytics, and suggesting best practices. Its abilities as a virtual assistant, when integrated with the appropriate tools, make it useful for improving productivity. Real-world examples have shown ChatGPT can streamline workflows and enhance efficiency.
Expect Improved Customer Service
Chatbots have always attempted to imitate human conversations, but their limited understanding and limited responses often result in the customer being referred to a human, who may also be of no help. The new customer service chatbots will use a large language library, and will be able to answer a much broader range of questions. For questions they haven’t experienced before, they should be able research the answers, though at present, the researched responses may not be trustworthy.
The GPT version of chatbots is much better at imitating human conversations and can respond with more humanlike questions and answers. They will be able to engage in more complex two-way conversations, and reduce the frustration many customers feel when dealing with less-advanced chatbots.
Increased Use and Development of Generative AI
As a result of generative AI being combined with large library models to form ChatGPT, generative AI is being used more frequently, receiving more attention, and being experimented with more. It is a reasonably safe prediction that generative AI will be used more and more, and continue to evolve.
Gartner predicts generative AI will become a general-purpose technology and improve human lives similar to the way the internet, electricity, and the steam engine did. Generative AI’s impact will continue to grow as people and organizations discover new and more innovative applications.
Summary
The introduction of ChatGPT is having a major impact on the world of artificial intelligence. OpenAI presented a powerful new chatbot near the end of 2022 that can communicate in common, human-like English (or Italian, or Dutch). It is extremely useful for accomplishing a broad range of tasks, ranging from answering customer service questions to researching and writing a report.
Businesses now have a path to providing more natural conversations with clients and customers through the use of ChatGPT.
Large language models are a primary building block in the development of ChatGPT, and have promoted a significant leap in the abilities of artificial intelligence. Knowledge graphs will become a more useful and recognizable tool. We can expect improvements in search engines and research in general as Web 3.0 becomes fully integrated into the internet. We can also expect to see significant improvements in virtual assistants and customer service chatbots.
Generative AI can be expected to become much more common in our day-to-day lives.
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