AWS simplifies AI app development for businesses with ENBLE.

AWS simplifies AI app development for businesses with ENBLE.

The Future of Artificial Intelligence: Customization and Accessibility

AWS Summit Image Source: Fionna Agomuoh / ENBLE

Artificial intelligence is rapidly evolving into an accessible and customizable experience for companies. The days of limited question-and-answer chatbots are long gone, as new AI tools offer expanded functionality and easier development processes. Companies no longer need months of coding to create AI applications. Instead, they can leverage optimized chat experiences and tools like Amazon Web Services (AWS) to tailor AI to their unique needs.

Traditionally, AI applications relied on public data and consistent developer coding. However, AWS is taking a different approach. They are committed to making generative AI that is not only more productive and user-friendly but also data secure for the companies that use their tools. By using platforms such as Amazon Bedrock, AWS is carving out a unique space in the AI market. Bedrock, launched in April, offers a variety of Foundation Models (FMs) that serve as base-level APIs for organizations to build upon. These FMs provide standard AI features, while companies have the flexibility to add their own proprietary data, making their applications stand out.

As Atul Deo, Amazon Bedrock Product and Engineering General Manager, explains, “Once the model is trained, there’s a cutoff point. For example, January of 2023, then the model doesn’t have any information after that point, but companies want data, which is private.” With Bedrock, companies can mix and match FMs and add their own data, ensuring their applications remain unique and tailored to their specific needs.

The choice of foundation models used by each company will vary, resulting in a wide range of unique applications. This is crucial since using open-source information alone can lead to repetitive applications across different companies. AWS’ strategy allows companies to differentiate themselves by incorporating their proprietary data into the AI models. Deo adds, “You want to be able to pass the relevant information to the model and get the relevant answers in real time. That is one of the core problems that it solves.”

Foundation Models: Powering Customization

AWS Summit Keynote Image Source: AWS / AWS

Amazon Bedrock supports several foundation models, including Amazon Titan and models from providers like Anthropic, AI21Labs, and StabilityAI. These models cover a range of important functions within the AI space, such as text analysis, image generation, and multilingual generation.

Bedrock is an extension of the pre-trained models developed by AWS on its Stagemaker Jumpstart platform. It has been instrumental in the creation of many public FMs, including Meta AI, Hugging Face, LightOn, Databricks, and Alexa. At the AWS Summit in New York City, AWS also announced new Bedrock models from Cohere, including Command and Embed. Command enables business applications to perform summarization, copywriting, dialog, text extraction, and question-answering. Embed facilitates cluster searches and classification tasks in over 100 languages.

According to Swami Sivasubramanian, Vice President of Machine Learning at AWS, these FMs are designed to be customizable, low-cost, low-latency, data encrypted, and secure. Companies across various industries, such as Chegg, Lonely Planet, IBM, and Booking.com, already collaborate with AWS using Amazon Bedrock.

Agents for Amazon Bedrock: Enhancing User Experience

In addition to Foundation Models, AWS unveiled Agents for Amazon Bedrock at their summit. This auxiliary tool expands the functionality of FMs, taking augmented chat experiences to the next level. Agents go beyond the typical question-and-answer chatbot experience, proactively executing tasks based on the information they are fine-tuned for.

To illustrate this, consider a retail customer who wants to exchange a pair of shoes. Using Agents, they can provide details about the desired exchange, such as switching from size 8 to size 9. Agents will then ask for their order ID, enabling them to access the retail inventory and confirm whether the requested size is in stock. If the customer approves, Agents will update the order accordingly. This entire process, which would traditionally require significant effort, is now streamlined thanks to advanced language models and proprietary data utilization.

Agents can be utilized in various industries, enabling insurance companies to file and organize claims or assist corporate staff with tasks like checking company policies or scheduling time off. By leveraging foundational models, users can focus on the aspects of AI that are most important to their organizations, saving valuable development time. “[You] can fine-tune a model with your proprietary data. As the request is being made, you want the latest and greatest,” explains Atul Deo.

Democratizing AI Application Development

AWS’ aim is to help brands and organizations quickly integrate AI into their apps and services. By reducing app development time, we can anticipate a surge of new AI apps on the market, along with much-needed updates to commonly used tools. This shift towards a business-centered AI strategy allows companies to allocate more time towards fine-tuning information critical to their operations.

The future of AI is bright, with increased customization and accessibility paving the way for innovative applications across industries. Thanks to companies like AWS, the complexities of AI development are being simplified, enabling organizations to unleash the power of AI without the need for extensive coding. As AI continues to evolve, we can expect to see even more exciting possibilities and improvements in the not-too-distant future.