The future of artificial intelligence is evolving to provide customizable solutions for companies, including optimized chat experiences and simplified AI application development. These advancements could potentially replace the need for extensive coding and introduce new plugins and extensions.
ChatGPT and Midjourney are tools that use public data and consistent developer coding to create an end product. Amazon Web Services (AWS) aims to make generative AI that is productive, easy to navigate, and data secure for the companies that use its tools.
The brand is utilizing Amazon Bedrock to establish a distinct presence in the emerging AI market. The flagship hub, launched in April, contains multiple Foundation Models (FMs). AWS has trained these basic level APIs to provide organizations with desired AI features. Organizations can customize their selection of FMs and further develop applications by incorporating their own proprietary data for specific requirements.
Amazon Bedrock Product and Engineering General Manager, Atul Deo, explained that as a provider, they train models on a large corpus of data. However, there is a cutoff point, such as January 2023, where the model doesn’t have any information beyond that date. Companies still want private data, though.
The models used by each company and foundation will differ, resulting in unique applications based on the data provided. Base templates already exist for these models. However, using open-source information to populate the models can lead to repetitive applications across companies. AWS offers a strategy that allows companies to introduce their own data, making their apps distinct.
The ability to ask questions and receive real-time answers from the model is important. It is not helpful if the model can only answer questions based on outdated public data. The model solves the problem of passing relevant information and receiving relevant answers in real time. Deo emphasized the significance of this core problem.
Foundation models
Amazon Bedrock supports several foundation models, including Amazon Titan, as well as models from providers such as Anthropic, AI21 Labs, and StabilityAI. These models address important functions in the field of AI, such as text analysis, image generation, and multilingual generation, among others. Bedrock is an extension of the pre-trained models developed by AWS on its Stagemaker Jumpstart platform. These models have been utilized by various public FM providers, including Meta AI, Hugging Face, LightOn, Databricks, and Alexa.
AWS announced new Bedrock models from the brand Cohere at its AWS Summit in late July in New York City. These models include Command, which can execute various tasks for business applications, and Embed, which can perform cluster searches and classify tasks in over 100 languages.
According to Swami Sivasubramanian, the vice president of AWS machine learning, FMs are designed to be low cost and low latency. They are meant to be privately customized, with encrypted data, and are not utilized for training the original base model developed by AWS.
The brand collaborates with several companies using Amazon Bedrock, including Chegg, Lonely Planet, Cimpress, Philips, IBM, Nexxiot, Neiman Marcus, Ryanair, Hellmann, WPS Office, Twilio, Bridgewater & Associates, Showpad, Coda, and Booking.com.
Agents for Amazon Bedrock
At its summit, AWS introduced Agents for Amazon Bedrock, an auxiliary tool that enhances Foundational Models. Agents is designed for various use cases and provides an augmented chatting experience. It goes beyond basic question and answer functionality and can proactively perform tasks based on fine-tuned information.
AWS provided an example of how their system works in a commercial setting. Let’s say a customer at a retail store wants to exchange a pair of shoes. Using the Agent interface, the customer can specify that they want to exchange their size 8 shoes for a size 9. The Agent will then ask for the customer’s order ID. Once the ID is provided, the Agent will check the retail inventory in the background and inform the customer that the requested size is available. The Agent will then ask if the customer wants to proceed with the exchange. If the customer agrees, the Agent will confirm that the order has been updated accordingly.
In the past, it was difficult to achieve this. Older chatbots were inflexible, requiring you to switch to a human agent if they couldn’t understand you. However, with the advancements in large-scale models, they now have a better understanding of human conversation. This allows them to take actions and utilize a company’s exclusive data.
The brand provided examples of how an insurance company can utilize Agents to handle and manage insurance claims. Agents can also support corporate staff by assisting with tasks like referencing the company policy on PTO or scheduling time off, using a well-known AI prompt format, such as “Can you file PTO for me?”
Agents demonstrates the benefits of foundational models by allowing users to prioritize the aspects of AI that matter most to them. Instead of investing excessive time in developing and training individual language models, companies can allocate more time to refining and updating information that is crucial to their organizations within Agents. Boost your Amazon sales with SwiftStart AI-powered marketing tools
Deo stated that it is possible to adjust a model using proprietary data in order to have the most up-to-date and best results.
Many companies are shifting towards a business-centered strategy for AI, and AWS aims to assist brands and organizations in quickly launching their AI-integrated apps and services. This could result in a surge of new AI apps on the market, while also providing necessary updates to commonly used tools.
