Have you heard of “Generative AI”? Since the rise of OpenAI’s ChatGPT in early 2023, discussions surrounding Generative AI have been on the rise. What once started as a novelty has gradually transformed into a commercial application and an indispensable tool in daily life. Recently, AWS has also launched the all-new “Amazon BedRock Generative AI Model” and “Amazon Titan Large Language Model” to compete in the Generative AI market! In this article, Nextlink Technology will take you through what Generative AI is all about and how AWS’s two new services will revolutionize cloud-based AI applications.
| What You Need to Know About the Future Trends of Generative AI: 📍 What is Generative AI, and where is it applied? 📍 How does Amazon BedRock empower businesses to create their own generative models? 📍 How does Amazon Titan Large Language Model facilitate the development of large language models (LLMs)? |
Table of Contents
Table of Contents
What is Generative AI?
Generative AI is a type of artificial intelligence technology that can learn from existing data and generate new data that conforms to specific data standards. It can process different types of data, such as text, image recognition, audio files, or videos. To achieve this, Generative AI utilizes deep learning techniques, such as neural networks, by training on large datasets, enabling it to apply learned patterns and structures to new data generation tasks.
Generative AI finds applications in various domains, including natural language generation, image generation, audio generation, virtual character generation, and more. In everyday life, the applications of Generative AI are ubiquitous. For example, Tesla’s autonomous driving system uses deep learning technology to automatically recognize roads, lane lines, traffic signals, obstacles, and more by analyzing images and sensor data, enabling autonomous driving. In Tesla’s autonomous driving system, Generative AI is primarily used for predicting driving scenarios, determining the movement trajectories of other vehicles and pedestrians, as well as road conditions. Tesla’s autonomous driving system continues to learn and improve through vehicle driving records, enhancing safety and performance.
Apart from Tesla’s autonomous driving, Generative AI is also applied in website development to enhance marketing efforts, as well as in medical image recognition to aid doctors in precise disease localization. With the wave of Generative AI, AWS has recently introduced Amazon BedRock Generative AI Model and Amazon Titan Large Language Model, officially joining the Generative AI battle!
What is Amazon BedRock?
In mid-April 2023, Swami Sivasubramanian, Vice President of AWS for Databases, Data Analytics, and Machine Learning, announced the launch of “Amazon BedRock,” a new service that allows businesses to access their own foundation models (FMs) from AI21 Labs, Anthropic, Stability AI, AWS, and more via APIs to build generative AI models. Amazon BedRock follows AWS’s tradition of emphasizing a “fully managed” solution, enabling enterprises to customize foundation models using their extensive internal data through Amazon BedRock’s serverless service. This can be combined with other AWS cloud services, such as Amazon SageMaker machine learning solutions, to track, analyze, and test model parameters using the Experiments feature or manage a large number of foundation models with the Pipelines feature to create complete generative AI applications for enterprises.
Amazon Titan Large Language Model Sets the Future Trend!
Simultaneously with the announcement of Amazon BedRock, AWS introduced Amazon Titan, their own large language foundational model (FMs). Titan emphasizes three major functions. First, Titan automates tasks such as text summarization and text generation, making it capable of simplifying complex meeting records or writing blog articles, among other tasks. Second, Titan Embeddings, an embedded large language model, can transform text into corresponding numerical forms, improving search result accuracy and personalized recommendations. Lastly, “Responsible AI Models” is an important consideration in light of the recent proliferation of harmful, fraudulent, and inappropriate content on the internet. AWS Titan supports operations related to “Responsible AI,” automatically removing and filtering inappropriate content.
While both new services are currently in the testing phase for enterprise users and pricing has not been disclosed, it is evident that the Generative AI trend is continuing to gain momentum. Leveraging Generative AI-related services, combined with AWS’s machine learning solutions, not only saves time and costs but also opens up new business opportunities, allowing enterprises to establish a strong presence in the future market with Generative AI.
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