The evolution of Web 3.0 is fundamentally to create a more transparent, intelligent, and open internet for creators and users to share value, bringing back control of the internet from big technology players into the palm of the users.

Gavin Wood coined the term “Web 3.0” in 2014, laying out his vision of the future of the internet. Web 3.0 is underpinned by blockchain technology– to decentralize data and distribute it across devices- while reducing risks of massive data leaks by eliminating a central point of failure. 

By implementing artificial intelligence (AI) coupled with blockchain technology, Web 3.0 aims to redefine the web experience with structural changes for decentralization, democratization, and transparency in all facets of the internet.

Features of Web 3.0 include: 

The Semantic Web: A web of linked data, combining semantic capabilities with NLP to bring “smartness” to the web for computers to understand information much like humans, interpreting data by identifying it, linking it to other data, and relating it to ideas. The user can leverage all kinds of available data that allow them to experience a new level of connectivity.

Customization: Web personalization refers to creating a dynamic, relevant website experience for users based on behavior, location, profile, and other attributes.Web 3.0 is all about providing users with a more personalized experience within a secure and transparent environment.

Trust: Web 3.0’s decentralization promotes more transactions and engagement between peers. Users can trust the technology(blockchain) to perform many tasks in lieu of trusting humans for services such as contracts and transfer of ownership. Trust is implicit and automatic — leading to the inevitable demise of the middleman.

Ubiquity: IoT is adding billions of devices to the Web. That means billions of smart, sensor driven devices, being used by billions of users, by billions of app instances. These devices and apps consistently talk to each other, exchanging valuable data.

Voice Interface: A voice interface is expected to be a key element of Web 3.0, driving interactions between humans to devices to apps. One of the pivotal changes underway in technology today is the shift from user-generated text inputs to voice recognition and voice-activated functions. 

Some of the technologies used in creating voice interfaces include:

Automatic Speech Recognition (ASR) technology transcribes user speech at the system’s front end. By tracking audio signals, spoken words convert to text.  

Text to speech (TTS). A voice-enabled device will translate a spoken command into text, execute the command, and prepare a text reply. A TTS engine translates the text into synthetic speech to complete the interaction loop with the user.

Natural Language Understanding (NLU) determines user intent at the back end.

Both ASR and NLU are used in tandem since they typically complement each other well for all text chat bots but not for voice interfaces. Voice has a lot of noice, accents and highly contextual on what we see at the moment and here Alan AI has developed a Global Spoken Language Understanding Model for Apps for Spoken Language Understanding (SLU)

Spoken Language Understanding (SLU) technology understands and learns the nuances of spoken language in context, to deliver superior responses to questions, commands, and requests. It is also a discovery tool that can help and guide users with human-like conversational voice through any workflow process. When taking the needed leap to classify and categorize queries, SLU systems collect better data and personalize voice experiences. Products then become smarter and channel more empathy, empowered to anticipate user needs and solve problems quickly. Exactly in tune with the intent of Web 3.0.

The Alan AI platform is a SLU-based B2B Voice AI platform for developers to deploy and manage Voice Interfaces for Enterprise Apps- deployment is a matter of days, for any application. 

Alan’s voice interface leverage the user context and existing UI of applications, a key to understanding responses for next-gen human voice conversations. 

Alan has patent protections for its unique contextual Spoken Language Understanding (SLU) technology to accurately recognize and understand human voice, within a given context. Alan’s SLU transcoder leverages the context to convert voice directly to meaning by using raw input from speech recognition services, imparting the accuracy required for mission-critical enterprise deployments and enabling human-like conversations, rather than robotic ones. Voice based interactions, coupled with the ability to allow users to verify the entered details without having the system to reiterate inputs, provides an unmatched end-user experience.