By now you must be aware of the AI market being on a steady rise and heralding global news headlines.
AI chatbots have been the powerhouses uplifting the global business spectrum. It is expected to reach a whopping $267 billion by 2027.
That’s not all — AI is expected to be a major contributor to the global economy, with estimates stating that it could add up to $15.7 trillion by 2030.
About 37% of companies and organizations are already using it in some capacity. Even the top dogs are jumping on board, with nine out of ten leading businesses investing in AI technologies.
AI chatbot race
ChatGPT operates in two main phases, similar to how Google search works. While Google has a crawling and data collection phase before responding to user queries, ChatGPT has a pre-training phase for gathering data, followed by the inference phase for user and/or Web3 developer interactions. The scalability of the pre-training phase is how ChatGPT redefines the Web3 development landscape.
Now coming to Google BARD, it’s a significant step forward in making information universally accessible and useful. This cutting-edge tool leverages Google’s advanced Language Model for Dialogue Applications (LaMDA).
LaMDA is based on Google’s innovative Transformer neural network architecture. By harnessing the power of LaMDA, BARD enables users to create sophisticated and conversational agents that can understand and respond to human and developer language in a more natural and nuanced way.
Speaking of Ernie, it is a new AI model developed by Chinese tech giant Baidu. It integrates external data sources like different websites to enhance its understanding of language.
Ernie’s deep neural network architecture, combined with a knowledge graph containing a vast amount of structured data, enables it to make inferences about language. This has significant implications for the Web3 space, where natural language processing (NLP) is becoming increasingly important for decentralized applications and smart contracts.
One important thing to note is that Ernie AI is trained on Chinese data, so it might not perform as well in other languages. If you’re working on non-Chinese natural language processing tasks, you might want to use a different pre-trained language model.
Each of these three chatbots are pre-trained language models that can be utilized for creating AI chatbots with natural language processing capabilities. These models employ deep learning algorithms to analyze and process large amounts of natural language data.
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While each of these models has its strengths and weaknesses, Ernie is a bit behind the other two in the AI chatbot race as of now. It is majorly adept at processing Chinese language data, while ChatGPT and Google BARD are fundamentally trained at generating coherent and natural-sounding responses in familiar languages like English.
How AI chatbots are redefining the Web3 landscape?
In the realm of Web3 development, these language models are particularly useful for developing dApps with natural language interfaces. Additionally, NLP techniques can be employed to extract insights from unstructured data on the blockchain, such as transactional data and smart contract code.
One of the best real-life instances where an AI chatbot is redefining the Web3 landscape is Alexa. It is capable of engaging in conversations with users on a wide range of topics, and it represents an excellent example of Web3 and AI-powered chatbots.
These chatbots are programmed to simulate human conversation, and Amazon is currently working to enhance the intelligence and behavioral aspects of the Alexa chatbot to make them more human-like.
Another notable example of an AI chatbot in Web3 is the Hubspot chatbot builder (for customer support services). When people use your live chat widget to ask questions about your product or reach out for customer support, they want to feel like they’re talking to a real human.
With HubSpot’s chatbot builder, all you have to do is create personalized welcome messages that match your brand and set up branches that can direct sales questions or service requests to the appropriate team.
Plus, the Hubspot chat widget can connect seamlessly with your customer relationship management system (CRM), so you can customize your chat flows based on your contact’s information and keep your CRM data updated with every conversation.
Conclusion
In the futuristic space of Web3 development, ChatGPT and Google BARD have been quite robust. Ernie is still on the periphery of exploring Web3 development.
One area of improvement is Ernie’s ability to comprehend the unique language and coding terminologies for dApp and other Web3 software development. Another aspect of improvement is Ernie’s ability to handle multilingual interactions as it is trained significantly in the Chinese language.
For Web3 development companies, it’s important to be adept at natural language processing and AI chatbot development. Ongoing research and development can power the attributes of AI chatbots with different Web3 database training models.
This can make chatbots useful for Web3 development (such as with text to SQL in dApp coding), and drive increased adoption and engagement in the Web3 space.
Vinita Rathi is the Founder and Chief Executive Officer of Systango, specialising in Web3, Generative AI, Data and Blockchain.
This article was published through Cointelegraph Innovation Circle, a vetted organization of senior executives and experts in the blockchain technology industry who are building the future through the power of connections, collaboration and thought leadership. Opinions expressed do not necessarily reflect those of Cointelegraph.
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- Source: https://cointelegraph.com/innovation-circle/google-bard-vs-chatgpt-vs-ernie-the-ai-chatbot-race-and-web3
