Generative AI Tutorials

Generative AI - New Innovations and Technologies

New Innovations and Technologies

Generative AI is quickly growing, with key advancements and upcoming technologies poised to change numerous sectors. The major themes for the upcoming ten years are outlined below, along with some predictions on how generative AI will affect AI advancements in the future.

Multimodal AI

The creation of multimodal models—which are capable of processing and producing many sorts of data—such as text, pictures, audio, and so on—is one of the most eagerly awaited developments in AI. AI systems become more flexible and intuitive with the help of these models, which improve interaction capabilities. To enhance the user experience, future AI assistants will include the ability to see, hear, and react.

Retrieval-Augmented Generation (RAG)

RAG generates more precise and verified results by fusing generative models with conventional search algorithms. This method produces grounded, empirically supported outputs, which helps minimize the "hallucinations" sometimes observed in big language models. It is anticipated that this technology will proliferate, particularly in business applications.

Open-Source Models

It is anticipated that more compact, effective open-source models would function similarly to more sizable private models like GPT-4. These models will become more widely available and deployable on local devices thanks to advancements in fine-tuning methods and reinforcement learning from human input, encouraging wider adoption and innovation.

Wearable AI

An emerging concept is the incorporation of generative AI into wearables. Though user experience and security issues may arise in the beginning, if successful iterations are made, personal computing and technology interaction might be completely transformed.

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Generative AI

Beginner 5 Hours

New Innovations and Technologies

Generative AI is quickly growing, with key advancements and upcoming technologies poised to change numerous sectors. The major themes for the upcoming ten years are outlined below, along with some predictions on how generative AI will affect AI advancements in the future.

Multimodal AI

The creation of multimodal models—which are capable of processing and producing many sorts of data—such as text, pictures, audio, and so on—is one of the most eagerly awaited developments in AI. AI systems become more flexible and intuitive with the help of these models, which improve interaction capabilities. To enhance the user experience, future AI assistants will include the ability to see, hear, and react.

Retrieval-Augmented Generation (RAG)

RAG generates more precise and verified results by fusing generative models with conventional search algorithms. This method produces grounded, empirically supported outputs, which helps minimize the "hallucinations" sometimes observed in big language models. It is anticipated that this technology will proliferate, particularly in business applications.

Open-Source Models

It is anticipated that more compact, effective open-source models would function similarly to more sizable private models like GPT-4. These models will become more widely available and deployable on local devices thanks to advancements in fine-tuning methods and reinforcement learning from human input, encouraging wider adoption and innovation.

Wearable AI

An emerging concept is the incorporation of generative AI into wearables. Though user experience and security issues may arise in the beginning, if successful iterations are made, personal computing and technology interaction might be completely transformed.

Related Tutorials

Frequently Asked Questions for Generative AI

Sequence of prompts stored as linked records or documents.

It helps with filtering, categorization, and evaluating generated outputs.



As text fields, often with associated metadata and response outputs.

Combines keyword and vector-based search for improved result relevance.

Yes, for storing structured prompt-response pairs or evaluation data.

Combines database search with generation to improve accuracy and grounding.

Using encryption, anonymization, and role-based access control.

Using tools like DVC or MLflow with database or cloud storage.

Databases optimized to store and search high-dimensional embeddings efficiently.

They enable semantic search and similarity-based retrieval for better context.

They provide organized and labeled datasets for supervised trainining.



Track usage patterns, feedback, and model behavior over time.

Enhancing model responses by referencing external, trustworthy data sources.

They store training data and generated outputs for model development and evaluation.

Removing repeated data to reduce bias and improve model generalization.

Yes, using BLOB fields or linking to external model repositories.

With user IDs, timestamps, and quality scores in relational or NoSQL databases.

Using distributed databases, replication, and sharding.

NoSQL or vector databases like Pinecone, Weaviate, or Elasticsearch.

With indexing, metadata tagging, and structured formats for efficient access.

Text, images, audio, and structured data from diverse databases.

Yes, for representing relationships between entities in generated content.

Yes, using structured or document databases with timestamps and session data.

They store synthetic data alongside real data with clear metadata separation.



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