Meta: From Metaverse Nightmare to AI Success

Meta: From Metaverse Nightmare to AI Success.


Author(s): Rafe Brena, Ph.D.

Originally published on Towards AI.

LLaMa is a key piece
Photo by Pierre Borthiry – Peiobty on Unsplash

Though the first part of the title speaks for itself, the second is less obvious. I think (and justify in the following) that the unorthodox path of Meta in AI is at least interesting and is potentially a success.

The “Meta” name is associated with the “Metaverse,” which has proven to be highly unsuccessful regarding financials, the company’s image, growth strategy, and innovation. A disaster, in short.

Currently, the Metaverse is more of a drag for the company than an asset. Sure, they continue to make tons of money through Facebook and Instagram advertisements, so… Read the full blog for free on Medium.

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Published via Towards AI


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.