How deep learning will fuel the metaverse in 2023 and beyond

The metaverse is becoming one of the hottest topics, not just in technology, but socially and economically as well. Both tech giants and startups are already working to create services for this new digital reality.

The metaverse is slowly evolving into a mainstream virtual world where you can work, learn, shop, be entertained and interact with others in ways never before possible. Gartner recently enumerated the metaverse as one of the key strategic technology trends for 2023, predicting that by 2026, 25% of the population will spend at least one hour a day there for work, shopping, education, social activities and/or entertainment. That means organizations that use the metaverse effectively will be able to engage with both human and machine customers and create new revenue streams and markets.

However, most of these metaverse experiences will only continue to progress with the help of deep learning (DL), as artificial intelligence (AI) and data science will be at the forefront of advancing this technology. For example, deep learning algorithms are helping computers become better at recognizing gestures and eye tracking, thanks to the latest advances in computer vision that enable natural interactions and a better understanding of emotion and body language. Since such technologies are an essential aspect of the metaverse’s immersive interface, deep learning technologies are now focused on further enhancing real-world AI storytelling, creative collaboration, and machine insight.

Shaping a new reality with deep learning

Currently, the digital realities being developed by different companies have their own attributes and integrated functionalities and are at different levels of development. Many of these multiverse platforms are expected to converge, and at this intersection, AI and data science domains, such as deep learning, will be critical in taking users to a new stage in their metaverse journey. Success in these efforts will depend on understanding vital elements of the algorithmic models and their statistics.

Deep learning-based software is already being integrated into virtual worlds; some examples are autonomously driving chatbots and other forms of natural language processing to ensure seamless interactions. Another example: In AR technology, deep learning-enabled AI is used in camera position estimation, immersive rendering, real-world object detection and 3D object reconstruction, ensuring the variety and usability of AR applications.

In October, Meta announced the launch of its Universal Speech Translator (UST) project, which aims to create AI systems that enable real-time speech-to-speech translation in all languages, regardless of the user’s language. In addition, the company’s recent advances in unattended speech recognition (wav2vec-U) and unattended machine translation (mBART) will support future work of translating more spoken languages ​​within the metaverse.

All such implementations require massive training data and modeling, now enabled by deep learning methodologies. In addition, AI-based Web3 technologies are now being called upon to automate smart contracts and decentralized ledgers and create universal blockchain technologies to enable virtual transactions.

“Deep learning offers much higher accuracy [and] almost no false positives, and if implemented correctly eliminates data noise (corruption),” said Jerrod Piker, Competitive Intelligence Analyst at Deep instincttold VentureBeat.

New ways of interacting

Piker said such implementations could help improve the metaverse, as a deep learning model is trained on all available data, yielding incredible results in image recognition and natural language processing.

“Meta has applied this when translating code from one programming language to another. Since the metaverse is a wide and open world, machine code translation can have a huge impact on the seamless integration between different platforms within the metaverse,” he said.

Likewise Scott Stephenson, CEO and co-founder of Diepgrambelieves that deep neural networks are more capable and sophisticated than neural networks with fewer layers.

“Companies have an exciting opportunity for their customers and community to engage with their brand(s) in new and exciting ways, and deep learning-based artificial intelligence plays an important role in facilitating those experiences,” said Stephenson.

He explained that companies can now have AI brand representatives — trained in a company’s unique language style and product documentation — roam the metaverse and evangelize whatever product or service the company is trying to promote.

“Instead of giving them dozens or even hundreds of lines of pre-scripted dialogue (as you’d experience in most video games these days), there’s no reason why a metaverse platform shouldn’t have a generative text chatbot running in the background all about conversation and engagement boost,” he said.

Overcome challenges through adoption

Despite its promise and potential, the metaverse continues to face user-based risks, such as data security. Deep learning-based AI models can play an important role in overcoming those challenges when integrated with legacy tools.

“Securing sensitive data that is created, transmitted, and shared through the metaverse requires more sophisticated techniques than previous data security efforts. Deep learning can deliver excellent results in this area with its uncanny ability to accurately identify content,” said Piker. “Constant inspection of certain sensitive data to ensure it is not leaked outside its intended channel, for example, is extremely important, and deep learning is unparalleled in correctly and efficiently identifying all kinds of digital content, with a much higher rate of error- positives than other machine learning models.”

Scott Likens, innovation and trust technology leader at PwCsaid many brands are beginning to see the real business value of the metaverse as deep learning and AI converge with VR to provide a much deeper experience for the metaverse in the future.

“The generation of assets in the metaverse now benefits from AI as there is currently a lack of content and digital assets to populate the metaverse. Moreover, with the advancements in data collection over the internet, we can feed the data-hungry deep learning models to create real-life yet synthetic worlds that are used to drive business strategy and more at a rate that we cannot in today’s times. can match. workforce,” says Likens.

“Deep learning technologies will become very important in the field of automation,” said Patrik Wilkens, vice president of operations at The Soul Publishingwhose universe of well-known channels 5-Minute Crafts, Bright Side and 123 GO! includes.

“Progress that used to take hours and hours of human effort is now achievable with incredible efficiency. With technology companies and content creators using the best technology and integrating deep learning into their processes, the manpower previously used to make things work can now be used for other things. This is especially important for creative domains,” Wilkens told VentureBeat.

Wilkens further explained that his company, TheSoul, is currently using deep learning-based algorithms for various metaverse use cases.

“We are currently using deep learning based artificial intelligence in our content to proofread, translate, [perform] quality assurance (QA) and construction graphics. We’re also in the development stages of a number of initiatives, including the 5-Minute Crafts marketplace within the metaverse,” he said. “That could work if your avatar walks into TheSoul’s mall-style building, watches a crafting video and go to the AI ​​assistant to help you buy the materials needed to complete the project.”

What to expect from the metaverse in 2023

Adrian McDermott, CTO at Zendeskbelieves that by 2023, we can expect deep learning and AI technologies to power and scale customer self-service in the metaverse.

“Companies will expand the use of AI and automation to route and escalate urgent user issues in real time, keeping the experience seamless,” McDermott told VentureBeat. “Large Language Models (LLMs) will play a role in helping brands understand customer needs in these new spaces, and generate potential responses to service requests. Self-service powered by deep learning-based AI models can relieve human agents by helping customers answer simple questions more easily, giving agents more time to delve into the more difficult cases.”

McDermott said we would start industries outside of retail and gaming building or testing metaverse experiences to stay competitive. Brands will use the metaverse to not only engage with customers, but build loyalty through digital collectibles, and automation will play a part in that journey.

“Don’t be surprised to see not only an expansion of digital shopping and concert experiences, but also increased enterprise use for hosting meetings, training and upskilling employees on critical work skills,” he said.

Similarly, Wilkens predicts that by 2023, we can expect brands to start building communities around virtual influencers.

“Brands will focus on developing more ‘meaningful’ content to engage their virtual influencers’ communities in an effort to be more human and authentically connect with audiences,” he said. “Moreover, we expect an emergence of avatars. They will be everywhere – especially in the metaverse – and will evolve dramatically on platforms like Snapchat due to new features like avatar fashion and digital items coming in 2023.”